Abstract unavailable.

Abstract unavailable.

This article was originally published in Vol. 6 of the Journal of Applied Remote Sensing on 12 December 2012 with an incorrect citation identifier (CID) of 063614 and on 17 December 2012 with an incorrect CID of 061706. The paper was removed and republished online with the correct CID of 061710 on 3 January 2013.

Comparing numerical simulation results with accelerograph readings is essential in earthquake investigations and discoveries. We provide a case study on the magnitude 7.6 Taiwan Chi-Chi earthquake in 1999. More than 400 seismic sensor stations recorded this event, and the readings from this event increased global strong-motion records fivefold so that the accuracy of the earthquake simulation was enhanced significantly. Direct volume rendering is used to depict the space-time relationships of numerical results and seismic readings. When earthquake simulation data are volume rendered, it reveals the sequence of seismic wave initiation, propagation, attenuation, and energy releasing events of fault ruptures so that the direction of seismic wave propagation can be observed. Both accelerograph readings and earthquake simulation data are used to generate a sequence of ground-motion maps. Stacking these maps up in sequence forms a volume data. Visual analysis of the time-varying component reveals hidden features for better comparison and evaluation. Earthquake scientists are able to obtain insights and evaluate their simulation criteria from volume rendering.

A typical LIDAR (light detection and ranging) scan contains hundreds of millions of points. As such, the visualization of LIDAR point clouds poses a significant challenge in data analysis. We propose to visualize and process LIDAR point clouds on a large display wall with an array of monitors. This provides researchers with a high-resolution display environment for looking at and studying large data sets. High-resolution large displays offer both global perspectives and local details of point clouds, which is essential in the process of data exploration. The ability to explore, conceptualize, and correlate spatial and temporal changes of topographical records is required for the development of new analytical models that capture the mechanisms contributing toward cliff erosion. Large displays driven by high-performance parallel visualization cluster allow researchers to fully interact with LIDAR point clouds of slopes in Houshanyue mountain and cliff failures observed in Solana Beach in California. In our study, cases studies of visualization based approaches were conducted using large displays in digital immersive environments. Visual analytics techniques such as delineation, segmentation, and classification of features, change detection, and annotation were used to perform erosion assessment. The results showed that the researchers can observe the temporal change of a failure mass effectively in high-resolution large display environments.

Anomaly detection is an important task for hyperspectral data exploitation. A standard approach for anomaly detection in the literature is the method developed by Reed and Yu, also called RX algorithm. It implements the Mahalanobis distance, which has been widely used in hyperspectral imaging applications. A variation of this algorithm, known as kernel RX (KRX), consists of applying the same concept to a sliding window centered around each image pixel. KRX is computationally very expensive because, for every image pixel, a covariance matrix and its inverse has to be calculated. We develop an efficient implementation of the kernel RX algorithm. Our proposed approach makes use of linear algebra libraries and further develops a parallel implementation optimized for multi-core platforms, which is a well known, inexpensive and widely available high performance computing technology. Experimental results for two hyperspectral data sets are provided. The first one was collected by NASA's airborne visible infra-red imaging spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the terrorist attacks, and the second one was collected by the hyperspectral digital image collection experiment (HYDICE). Our anomaly detection accuracy, evaluated using receiver operating characteristics (ROC) curves, indicates that KRX can significantly outperform the classic RX while achieving close to linear speedup in state-of-the-art multi-core platforms.

Band selection is a common technique to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, the reduction process can be achieved by selecting the bands that contain the most object information. It is expected that these selected bands can offer an overall satisfactory detection and classification performance. In this paper, we propose a new particle swarm optimization (PSO) based supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples. Thus, this method requires much less computing time than other traditional methods. However, the PSO process itself may introduce additional computation cost. To tackle this problem, we propose parallel implementations via emerging general-purpose graphics processing units that can provide satisfactory results in speedup when compared to the cluster-based parallel implementation.

In our previous work, we proposed a joint optical flow and principal component analysis (PCA) approach to improve the performance of optical flow based detection, where PCA is applied on the calculated two-dimensional optical flow image, and motion detection is accomplished by a metric derived from the two eigenvalues. To reduce the computational time when processing airborne videos, parallel computing using graphic processing unit (GPU) is implemented on NVIDIA GeForce GTX480. Experimental results demonstrate that our approach can efficiently improve detection performance even with dynamic background, and processing time can be greatly reduced with parallel computing on GPU.

Recent advances in hyperspectral imaging sensors allow the acquisition of images of a scene at hundreds of contiguous narrow spectral bands. Target detection algorithms try to exploit this high-resolution spectral information to detect target materials present in a scene, but this process may be computationally intensive due to the large data volumes generated by the hyperspectral sensors, typically hundreds of megabytes. Previous works have shown that hyperspectral data processing can significantly benefit from the parallel computing resources of graphics processing units (GPUs), due to their highly parallel structure and the high computational capabilities that can be achieved at relative low costs. We studied the parallel implementation of three target detection algorithms (RX algorithm, matched filter, and adaptive matched subspace detector) for hyperspectral images in order to identify the aspects in the structure of these algorithms that can exploit the CUDA™ architecture of NVIDIA® GPUs. A data set was generated using a SOC-700 hyperspectral imager to evaluate the performance and detection accuracy of the parallel implementations on a NVIDIA® Tesla™ C1060 graphics card, achieving real-time performance in the GPU implementations based on global statistics.

Hyperspectral image compression has received considerable interest in recent years due to the enormous data volumes collected by imaging spectrometers for Earth Observation. JPEG2000 is an important technique for data compression, which has been successfully used in the context of hyperspectral image compression, either in lossless and lossy fashion. Due to the increasing spatial, spectral, and temporal resolution of remotely sensed hyperspectral data sets, fast (on-board) compression of hyperspectral data is becoming an important and challenging objective, with the potential to reduce the limitations in the downlink connection between the Earth Observation platform and the receiving ground stations on Earth. For this purpose, implementation of hyperspectral image compression algorithms on specialized hardware devices are currently being investigated. We have developed an implementation of the JPEG2000 compression standard in commodity graphics processing units (GPUs). These hardware accelerators are characterized by their low cost and weight and can bridge the gap toward on-board processing of remotely sensed hyperspectral data. Specifically, we develop GPU implementations of the lossless and lossy modes of JPEG2000. For the lossy mode, we investigate the utility of the compressed hyperspectral images for different compression ratios, using a standard technique for hyperspectral data exploitation such as spectral unmixing. Our study reveals that GPUs represent a source of computational power that is both accessible and applicable to obtaining compression results in valid response times in information extraction applications from remotely sensed hyperspectral imagery.

With recent technical advances in graphic processing units (GPUs), GPUs have outperformed CPUs in terms of compute capability and memory bandwidth. Many successful GPU applications to high performance computing have been reported. JPEG-LS is an ISO/IEC standard for lossless image compression which utilizes adaptive context modeling and run-length coding to improve compression ratio. However, adaptive context modeling causes data dependency among adjacent pixels and the run-length coding has to be performed in a sequential way. Hence, using JPEG-LS to compress large-volume hyperspectral image data is quite time-consuming. We implement an efficient parallel JPEG-LS encoder for lossless hyperspectral compression on a NVIDIA GPU using the computer unified device architecture (CUDA) programming technology. We use the block parallel strategy, as well as such CUDA techniques as coalesced global memory access, parallel prefix sum, and asynchronous data transfer. We also show the relation between GPU speedup and AVIRIS block size, as well as the relation between compression ratio and AVIRIS block size. When AVIRIS images are divided into blocks, each with 64×64 pixels, we gain the best GPU performance with 26.3x speedup over its original CPU code.

Accurate estimation of evapotranspiration (ET) has long been an important issue in hydrology. Many experimental observations indicate advection has a great impact on ET in arid and semiarid areas. However, most of the remote sensing models only focus on the vertical energy balance and deviate from reality. A revised Penman equation has been derived to estimate actual ET under normal conditions in order to account for advection. The parameter of the water availability for ET is introduced and the T s /f (surface temperature and vegetation fraction) space is used to characterize this parameter in the revised formula. The estimates were validated using the observations measured with eddy covariance systems at the Yingke station. In 22 of all 24 days, the difference between the observations and the estimates was smaller than 70  W/m 2 . The correlation coefficient is 0.91 and the RMSE is 48.38  W/m 2 . This finding reveals that this approach is capable of providing reliable results. In addition, when considering advection, the potential ET can be 83.23  W/m 2 larger than the available energy. This finding indicates that the advection effect needs to be considered in remote sensing models in order to derive more reliable regional ET.

TerraSAR-X add-on for digital elevation measurement (TanDEM-X) is a German Earth observation mission collecting a total of two global coverages of very high resolution (VHR) synthetic aperture radar (SAR) X-band data with a spatial resolution of around three meters in the years 2011 and 2012. With these, the TanDEM-X mission (TDM) will provide a unique data set which is complementary to existing global coverages based on medium (MR) or high resolution (HR) optical imagery. The capabilities of the TDM in terms of supporting the analysis and monitoring of global human settlement patterns are explored and demonstrated. The basic methodology for a fully-operational detection and delineation of built-up areas from VHR SAR data is presented along with a description of the resulting geo-information product-the urban footprint (UF) mask-and the operational processing environment for the UF production. Moreover, potential follow-on analyses based on the intermediate products generated in the context of the UF analysis are introduced and discussed. The results of the study indicate the high potential of the TDM with respect to an analysis of urbanization patterns, peri-urbanization, spatio-temporal dynamics of settlement development as well as population estimation, vulnerability assessment and modeling of global change.

Geo-visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data are generated every day. On the other hand, these data are not well utilized due to the lack of free and easily used data-visualization tools. This becomes even worse when most of the spatial data remains in the form of plain text such as log files. This paper describes a way of visualizing massive plain-text spatial data at no cost by utilizing Google Earth and NASA World Wind. We illustrate our methods by visualizing over 170,000 global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS). Our visualization results identify the most popular satellite images around the world and discover the global user download patterns. The benefits of this research are: 1. assisting in improving the satellite image downloading services provided by USGS, and 2. providing a proxy for analyzing the "hot spot" areas of research. Most importantly, our methods demonstrate an easy way to geo-visualize massive textual spatial data, which is highly applicable to mining spatially referenced data and information on a wide variety of research domains (e.g., hydrology, agriculture, atmospheric science, natural hazard, and global climate change).

Knowledge of the air and land surface temperature and their temporal and spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions and to monitoring environmental changes due to urbanization. We present a number of air and land surface temperature products that have been produced, archived, evaluated, and analyzed for 10 European cities within the framework of the European Space Agency-funded "Urban Heat Islands and Urban Thermography" project. We evaluate in what way these products are suited to explore the urban thermal dynamics and how products with different temporal and spatial resolution can provide a complementary view, both for thermal patterns as well as heat waves. Level of confidence was evaluated through quantitative, qualitative, and user-based analyses.

Fuel moisture content (FMC), an important variable for predicting the occurrence and spread of wildfire, is the ratio of foliar water content and foliar dry matter content. One approach for the remote sensing of FMC has been to estimate the change in canopy water content over time by using a liquid-water spectral index. Recently, the normalized dry matter index (NDMI) was developed for the remote sensing of dry matter content using high-spectral-resolution data. The ratio of a spectral water index and a dry matter index corresponds to the ratio of foliar water and dry matter contents; therefore, we hypothesized that FMC may be remotely sensed with a spectral water index divided by NDMI. For leaf-scale simulations using the PROSPECT (leaf optical properties spectra) model, all water index/NDMI ratios were significantly related to FMC with a second-order polynomial regression. For canopy-scale simulations using the SAIL (scattering by arbitrarily inclined leaves) model, two water index/NDMI ratios, with numerators of the normalized difference infrared index (NDII) and the normalized difference water index (NDWI), predicted FMC with R2 values of 0.900 and 0.864, respectively. Leaves from three species were dried or stacked to vary FMC; measured NDII/NDMI was best related to FMC. Whereas the planned NASA mission Hyperspectral Infrared Imager (HyspIRI) will have high spectral resolution and very high signal-to-noise properties, the planned 19-day repeat frequency will not be sufficient for monitoring FMC with NDII/NDMI. Because increased fire frequency is expected with climatic change, operational assessment of FMC at large scales may require polar-orbiting environmental sensors with narrow bands to calculate NDMI.

A comparative analysis of land use/land cover (LULC) classification results in the Brazilian Amazon based on four classification algorithms and four remote sensing datasets was conducted in order to better understand the selection of a classification algorithm suitable for a specific remote sensing data. It is shown that maximum likelihood classifier (MLC) provided reasonably good classification accuracy when Landsat Thematic Mapper (TM) or the TM and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data-fusion images were used, but nonparametric algorithms such as classification tree analysis for TM multispectral bands and K-nearest neighbor for the combination of TM and PALSAR data provided better classification than MLC. Individual PALSAR dataset is not suitable for detailed LULC classification and has much poorer classification accuracy (47.6% to 59.4%) than Landsat TM image (79.7% to 84.9%). However, integration of TM and PALSAR data through the wavelet-merging technique improved classification accuracy. It is implied that the importance of selecting a suitable classification algorithm for a specific dataset by considering such factors as overall classification accuracy and time and labor involved in a classification procedure. Important information for guiding the selection of remote sensing dataset and associated classification algorithms for LULC classification in the moist tropical regions is also provided.

Applications of the intensity-hue-saturation (IHS) based image fusion techniques in resource inventory and environmental monitoring are usually hampered by considerable spectral distortion in the spatially enhanced image. The image transform model needs to be regulated via a proper design of the weight structure and controlling parameters to achieve a better spectral fidelity. Use of localized weight estimation and an output constraint to modify the generalized intensity-hue-saturation transform (GIHS) for the purpose of rectifying digital numbers of the fused image back to their original counterparts are proposed. The weight localization was achieved via land cover classification of the multispectral data, and the spectral constraint was constructed using a ratio between individual spectral bands stratified with each land cover type and the modified image intensity value. This method was compared both spatially and spectrally with the traditional IHS and the GIHS that has a weight structure induced from the sensor's spectral response characteristics. Experiments with WorldView-2 multispectral and panchromatic data indicated that the new image fusion approach achieved the highest level of spectral fidelity with enhancement of spatial details comparable to the other IHS-based methods.

The gradients of elevations and latitudes in the Appalachian Mountains provide a unique regional perspective on landscape variations in the eastern United States and southeastern Canada. We reveal patterns and trends of landscape dynamics, land surface phenology, and ecosystem production along the Appalachian Mountains using time series data from Global Inventory Modeling and Mapping Studies and Advanced Very High Resolution Radiometer Global Production Efficiency Model datasets. We analyze the spatial and temporal patterns of the normalized difference vegetation index (NDVI), length of growing season (LOS), and net primary production (NPP) of selected ecoregions along the Appalachian Mountains regions. We compare the results in different spatial contexts, including North America and the Appalachian Trail corridor area. To reveal latitudinal variations, we analyze data and compare the results between the 30°-to-40°N and the 40°-to-50°N latitudes. The result reveal significant decreases in annual peak NDVI in the Appalachian Mountains regions. The trend for the Appalachian Mountains regions was a −0.0018 (R2=0.55, P<0.0001) NDVI unit decrease per year during 25 years from 1982 to 2006. The LOS was prolonged by 0.3  days per year−1 during the 25-year percent. The NPP increased by 2.68  g Cm−2 yr−2 from 1981 to 2000.

Accelerated urbanization creates challenges of water shortages, air pollution, and reductions in green space. To address these issues, methods for assessing urban expansion with the goal of achieving reasonable urban growth should be explored. In this study, an improved slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) cellular automata model is developed and applied to the city of Tangshan, China, for urban expansion research. There are three modifications intended to improve SLEUTH: first, the utilization of ant colony optimization to calibrate SLEUTH to simplify the calibration procedures and improve their efficiency; second, the introduction of subregional calibration to replace calibration of the entire study area; and third, the incorporation of social and economic data to adjust the self-modification rule of SLEUTH. The first two modifications improve the calibration accuracy and efficiency compared with the original SLEUTH. The third modification fails to improve SLEUTH, and further experiments are needed. Using the improvements to the SLEUTH model, forecasts of urban growth are performed for every year up to 2020 for the city of Tangshan under two scenarios: an inertia trend scenario and a policy-adjusted scenario.

Satellite radar altimeter measurements of lake and reservoir water levels complement in situ observations by providing stage information for ungauged basins and by filling data gaps in existing gauge records. Such additional measurements assist both research and operational programs. However, for a particular lake or reservoir, altimetric products offered to end-users may differ due to choice of employed instrument, processing technique, and applied geophysical corrections. To explore these differences, particularly with their potential impact on climate-based research, an intercomparison of three web-based water-level products (produced by Laboratoire d’Etudes en Géophysique et Océanographie Spatiale, National Aeronautics and Space Administration/United States Department of Agriculture, and European Space Agency/De Montfort University) has been undertaken based on 18 lakes and reservoirs. The products are well correlated with each other (r=0.87 to 0.99) and where in situ data are available are quite well correlated with the gauge measurements (r=0.73 to 0.99). Despite variations in data processing, the poorest root-mean-square differences between any altimeter product and gauge data (∼0.20 to 1.41 m) occur for the narrow reservoirs and smaller lakes. The largest discrepancies between the altimeter products occur for the lakes that freeze (Lake Athabasca and Woods). The current altimeter products provide acceptable accuracy, long-term trends and seasonality for climate applications. We discuss the merits of each product system, but recommend further validations and the provision of ice-detection flags.

The 21st century will experience widespread global changes due to climatic variations, shifting demographics, population migration, and economic development. These environmental changes are expected to have significant impacts on the quantity and quality of water, air, and land resources across the globe. The complexity of global environmental changes will pose significant challenges to the scientific community. Remote sensing, with its capability for handling spatial and temporal information of the earth's systems, has been extensively used to understand the problems of global environmental changes and their potential solutions. This special section focuses on advances in remote sensing for monitoring global environmental changes, and is comprised of ten full-length papers selected from a number of areas of expertise. Such a collection improves our understanding of global environmental changes through various types of assessment in which remote sensing technologies have demonstrated their application potential in dealing with different sustainability issues. This special section elucidates three important themes related to remote sensing for addressing global environmental changes: (1) land use and land cover changes; (2) environmental monitoring to address physical and ecological variations; and (3) cyber-enabled assessment of environmental changes via data quality improvement and visualization.

The 21st century will experience widespread global changes due to climatic variations, shifting demographics, population migration, and economic development. These environmental changes are expected to have significant impacts on the quantity and quality of water, air, and land resources across the globe. The complexity of global environmental changes will pose significant challenges to the scientific community. Remote sensing, with its capability for handling spatial and temporal information of the earth’s systems, has been extensively used to understand the problems of global environmental changes and their potential solutions. This special section focuses on advances in remote sensing for monitoring global environmental changes, and is comprised of ten full-length papers selected from a number of areas of expertise. Such a collection improves our understanding of global environmental changes through various types of assessment in which remote sensing technologies have demonstrated their application potential in dealing with different sustainability issues. This special section elucidates three important themes related to remote sensing for addressing global environmental changes: (1) land use and land cover changes; (2) environmental monitoring to address physical and ecological variations; and (3) cyber-enabled assessment of environmental changes via data quality improvement and visualization.

We summarize the capacity of high spatial resolution (<1  m) digital aerial imagery to support forest health monitoring. We review the current use of digital aerial imagery in the context of the recent mountain pine beetle epidemic in western Canada. Supported by this review, we posit that high spatial resolution digital aerial imagery can play at least two critical roles in forest health monitoring. First, the capacity to characterize damage at the individual tree level directly supports a broad range of forest health information needs (e.g., tree-level attributes for estimating the population at risk and for inputs to models, estimates of mortality, rates of population growth). Second, the level of detail afforded by the digital high spatial resolution aerial imagery provides critical calibration and validation data for lower spatial resolution remotely sensed imagery (e.g., QuickBird, Landsat) for large-area detection and mapping of forest damage and can be used in a double sampling scheme as a bridge between detailed field measures and landscape-level estimates of mortality. In an era with increasing numbers of commercially deployed sensors capable of acquiring high spatial resolution satellite imagery, the flexibility and cost-effectiveness of aerial image options should not be disregarded. Moreover, experiences with airborne imagery can continue to inform applications using high spatial resolution satellite imagery for forest health information needs.

Mangrove forests are being removed or degraded at an alarming rate, even though they play a vital role in the sustainability of tropical coastal communities. Many of these forests are identified as degraded based on observable changes in their leaves (e.g., density, size, color, etc.). Of these, color can be considered one of the most important indicators of degradation because changes in the spectral response may be indicative of changes in the leaf pigment content. In this investigation, hyperspectral laboratory techniques were applied to examine potential relationships between the mangrove leaf spectral response and three leaf pigments: chlorophyll a, chlorophyll b, and total carotenoid content. Using an ASD spectroradiometer, the spectral reflectance of leaf samples were collected from poor condition, dwarf and healthy black (Avicennia germinans) and from healthy and poor condition red (Rhizophora mangle) mangroves located in a degraded mangrove system of the Mexican Pacific. A subset of 150 representational leaves was then used for pigment content analysis. The results indicate significant relationships between the spectral response and the levels of chlorophyll a, b, and total carotenoid content contained in the leaves. In particular, wavebands at the red edge position were shown to be the best predictors of the pigment contents. The results also indicate that vegetation indices do not necessarily improve the ability to predict these constituents. Finally, the red edge position was found to be significantly different between the healthy and poor condition mangroves (P = 0), with the healthy mangroves having longer wavelengths associated with the red edge position.

We describe crown-extraction (CE) filtering to accurately determine tree apex positions for various coniferous species using an airborne light detection and ranging-derived digital canopy height model (DCHM). This method uses a square mask, with a frame at the edges, that overlaps pixels within the DCHM image; when no pixels touch the frame, the pixel at the center is extracted as a tree-crown pixel. The apex of each tree is determined by choosing the pixel with maximum height from the pixels in the crown. We compared the performance of this method and of two other methods (local-maximum filtering and canopy-segmentation method) for several species. The CE filtering had the most accurate results for most tree species with appropriate mask size selection. The mean omission, commission, and total errors for all tree species were 8.1%, 1.6%, and 9.7%, respectively, for CE filtering. Comparing mask sizes and canopy diameters estimated from the DCHM for each species revealed that the smallest canopy diameter of each species was close to the most appropriate mask size for that species in CE filtering. We also confirmed that the smoothing process used in the DCHM has little effect on the accuracy of CE filtering.

Substantial uncertainties exist in the current knowledge of aerosol-cloud-precipitation relationships and stem from the complicated interactions among the atmospheric constituents. We use a straightforward statistical method, the regression analysis technique, to examine the aerosol-cloud-precipitation relationships from satellite observational data sets, including the aqua moderate resolution imaging spectroradiometer (MODIS) aerosol and cloud products and the tropical rainfall measuring mission (TRMM) precipitation rate. Furthermore, the conventional MODIS aerosol product is combined with the Deep Blue algorithm product to reconstruct a complete global map of aerosol optical depth. Numerical simulations using the latest version of the community earth system model (CESM) are also carried out. Globally, distinct statistically significant relationships between aerosol optical depth, cloud fraction, and precipitation rate are obtained over both land and ocean. Signals agreeing with the first and second indirect effects of aerosols are detected, but other factors are likely contributors. The modeling results are found to generally agree with satellite observations, but the model usually overestimates the aerosol-cloud-precipitation relationship. An increasing trend in cloud fraction with the increase of aerosol optical depth (AOD) over ocean regions is found in the observations, while the reverse is true in the model simulation. It is mostly consistent that the model and observation both show a negative relationship between AOD and precipitation rate over land and a positive relationship over ocean.

Forty-three digital elevation models (DEMs) of the Managesh oil field, Kuwait desert study area, are derived from 28 advanced synthetic aperture radar (ASAR) images using radar interferometry (InSAR) technique. Weighted-average technique is used to reduce the noise in the integrated DEM. The final DEM is compared with DEM of Shuttle Radar Topography Mission (SRTM) and found to have a general agreement. The standard deviation (STD) of individual DEMs with reference to DEM of SRTM is in the range 10 to 40 m. The results indicate that the 90 m spatial resolution DEM of SRTM is noisier over the Managesh oil field as it shows drastic changes in elevations of neighboring pixels which is not expected for a plain desert like the Managesh oil field. Using mean filter, the noise level is estimated as one meter. The reasons for high noise in the DEM of SRTM may be due to uneven distribution of soil moisture leading to uneven penetration of microwaves. The results are in confirmation with the earlier investigators as explained in the text.

Content security protection methods should be adopted to guarantee the security of highly sensitive remote sensing images during their transmission and usage. The joint fingerprinting and decryption (JFD) framework, which integrates encryption and fingerprinting, can provide comprehensive, effective content security protection for remote sensing information. However, several challenges need to be overcome. In order to solve the contradictory relationship between encryption security and fingerprinted image quality in JFD, a selective content encryption strategy is proposed to enhance encryption security and a new method for choosing fingerprint embedding area is proposed to reduce partial decryption's influence on image quality. Furthermore, a new method for generating and controlling fingerprints is introduced to JFD to eliminate its vulnerability to collusion attacks. The experiment results show that the proposed techniques are highly effective. Because of its high security, good fingerprinted image quality and high data processing and transmission speed, the new JFD scheme is an effective content security protection approach for remote sensing images, especially suitable for massive remote sensing images.

An object-oriented, multiscale feature extraction approach is proposed for the land-cover classification of high spatial resolution images. The approach provides more discriminative features by considering the spatial context information from different segmentation levels. It consists of three successive substeps: segmentation by mean-shift algorithm, an iteratively merging process controlled by merging cost function and range-of-scale parameter, and feature extraction from linked multilevel image partitions. The mean-shift method is to get boundary-preserved and spectrally homogeneous over-segmentation regions. Then, a family of nested image partitions is constructed by a merging procedure. Meanwhile, every region of the finest scale is linked to image objects of its superlevels. Finally, every region in the finest scale is treated as a basic analysis unit, and the feature vectors are created by stacking statistics from the region and their superlevels. A support vector machine is used as a classifier and the method on two widely used high spatial resolution data sets over Pavia City, Italy, are evaluated. Compared with results reported in many papers, the result indicates superior accuracy.

Due to cloud coverage and obstruction, it is difficult to obtain useful images during the critical periods of monitoring vegetation using medium-resolution spatial satellites such as Landsat and Satellite Pour l'Observation de la Terre (SPOT), especially in pluvial regions. Although high temporal resolution sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), can provide high-frequency data, the coarse ground resolutions of these sensors make them unsuitable to quantify the vegetation growth processes at fine scales. This paper introduces a new data fusion model for blending observations of high temporal resolution sensors (e.g., MODIS) and moderate spatial resolution satellites (e.g., Landsat) to produce synthetic imagery with both high-spatial and temporal resolutions. By detecting temporal change information from MODIS daily surface reflectance images, our algorithm produced high-resolution temporal synthetic Landsat data based on a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image at the beginning time (T₁). The algorithm was then tested over a 185×185 km² area located in East China. The results showed that the algorithm can produce high-resolution temporal synthetic Landsat data that were similar to the actual observations with a high correlation coefficient (r) of 0.98 between synthetic imageries and the actual observations.

We present two methods to improve three hyperspectral stochastic algorithms for target detection; the algorithms are the constrained energy minimization, the generalized likelihood ratio test, and the adaptive coherence estimator. The original algorithms rely solely on spectral information and do not use spatial information; this usage is normally justified in subpixel target detection, since the target size is smaller than the size of a pixel. However, we found that since the background (and the false alarms) may be spatially correlated and the point spread function can distribute the energy of a point target between several neighboring pixels, the implementation of spatial filtering algorithms considerably improved target detection. Our first improvement used the local spatial mean and covariance matrices, which take into account the spatial local mean instead of the global mean. While this concept has been found in the literature, the effect of its implementation in both the estimated mean and the covariance matrix is examined quantitatively here. The second was based on the fact that the effect of a target of physical subpixel size will extend to a cluster of pixels. We tested our algorithms by using the data set and scoring methodology of the Rochester Institute of Technology Target Detection Blind Test project. The results showed that both spatial methods independently improved the basic spectral algorithms mentioned above, and when the two methods were used together, the results were even better.

The HJ-1 A and HJ-1 B satellites were launched on September 6, 2008 from China. The radiometric normalization of charge coupled device (CCD) images is still challenging work. In this paper, an automatic algorithm for relative radiometric normalization between HJ-1A/B CCD images and Landsat TM is presented. This method directly normalizes the digital numbers (DN) of HJ-1A/B CCD images, band by band, to surface reflectance. A united linear relationship between the DN of the target images and the surface reflectance of the referenced images was derived, and the applicable conditions are described here. The iteratively reweighted modification of the multivariate alteration detection (IR-MAD) transformation was used to automatically select pseudoinvariant features (PIFs). This procedure is simple, fast, and completely automatic. The algorithm was applied to normalize three subregions of different HJ-1A/B CCD images. The results show that the retrieval quality of the surface reflectance does meet the requirements of quantitative remote sensing.

Here, we demonstrate and characterize a high-energy, eye-safe, spectrally narrow, and frequency-agile near-IR optical parametric oscillator (OPO). The injection-seeded, noncritical phase-matched (NCPM) potassium titanyle arsenate (KTA) and ring-cavity OPO was pumped in single longitudinal mode (SLM) at 7 ns full width at half maximum FWHM and 30 Hz, neodymium-doped yttrium aluminum garnet (Nd:YAG), generating 243 mJ per pulse OPO signal output with a conversion efficiency of 27%, spectral linewidth of 157 MHz, and approximately M² of 29. Also, we demonstrate a nonmechanical method to switch the frequency of the OPO at a rate of 2 Hz from 1535.036 to 1535.195 nm, which represents the on/off resonances of carbon dioxide, respectively. However, the switching rate can be extended into the MHz range and is limited by the electronics driving the diode laser. Given the performance results of our frequency-agile OPO, this transmitter has great potential as a source in DIAL applications.

The U.S. Agency for International Development (USAID)'s Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to food insecurity emergencies on three continents. FEWS NET uses satellite remote sensing and ground observations of rainfall and vegetation in order to provide information on drought, floods, and other extreme weather events to decision makers. Previous research has presented results from a professional review questionnaire with FEWS NET expert end-users whose focus was to elicit Earth observation requirements. The review provided FEWS NET operational requirements and assessed the usefulness of additional remote sensing data. We analyzed 1342 food security update reports from FEWS NET. The reports consider the biophysical, socioeconomic, and contextual influences on the food security in 17 countries in Africa from 2000 to 2009. The objective was to evaluate the use of remote sensing information in comparison with other important factors in the evaluation of food security crises. The results show that all 17 countries use rainfall information, agricultural production statistics, food prices, and food access parameters in their analysis of food security problems. The reports display large-scale patterns that are strongly related to history of the FEWS NET program in each country. We found that rainfall data were used 84% of the time, remote sensing of vegetation 28% of the time, and gridded crop models 10% of the time, reflecting the length of use of each product in the regions. More investment is needed in training personnel on remote sensing products to improve use of data products throughout the FEWS NET system.

The spatial resolution of Landsat imagery has proven to be well suited for the analysis of vegetation patterns and dynamics at regional scale; however, the low temporal frequency is often a limitation for the quantification of vegetation dynamics. The spatial and temporal adaptive reflectance fusion model (STARFM) combines moderate resolution imaging spectrometer (MODIS) and Landsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) imagery to a high spatiotemporal resolution dataset. A time series of 333 STARFM images was generated between February 2000 and September 2007 (8-day interval) at Landsat spatial and spectral resolution for a 12×10  km heterogeneous test area within the North Queensland Savannas. Time series of observed Landsat and predicted STARFM images correlated high for each spectral band (0.89 to 0.99). The STARFM algorithm was tested in a regionalization study where sudden change events were analyzed for a pallustrine wetland. A MODIS subpixel analysis showed a very close relationship between STARFM normalized difference vegetation index (NDVI) data and MODIS NDVI data (root mean square error of 0.027). A phenological description of the major vegetation classes within the region revealed distinct differences and lag times within the ecosystem. The 2004 dry season NDVI minimum-map correlated highly with the validated 2004 foliage projective cover product (r² = 0.92) from the Queensland Department of Environment and Resource Management.

The planetary boundary layer (PBL) structures on four light wind days at Suzhou city, China were analyzed with both micropulse lidar (MPL) and passive microwave radiometer (MWR). The planetary boundary layer height (PBLH) was estimated with the first-derivation method from the lidar observations. The lidar-observed PBLH and microwave radiometer observations were used to estimate the lapse rate in a simple encroachment model. Under light wind conditions in this area, the nocturnal PBLH is about 300 to 400 m. The maximum of PBLH was about 800 to 1000 m. The daytime PBL development could be divided into three periods: growing, maintenance, and decay. The PBLH is nearly linear in the growing period with a growth ratio of about 110 to 120  m/h. The encroachment model lapse rate was calculated from the MPL and MWR observations. The lapse rate γ is estimated in the four days and the average is about 0.0010  ?startKend?/m. The vertical structure of air temperature and humidity were also analyzed with the microwave radiometer observations. Diurnal variations of relative humidity in the boundary layer partly explain the light fog appearing at night during this period.

Hyperspectral imaging is broadly used in the identification of hydroxylated and hydrated silicate minerals, especially at the wavelengths of 1.0 to 2.5 µm. The vibrations of the hydroxyl (OH) molecule, water molecule (H−O−H), and metal−OH bonds, or their combinations, produce prominent absorption features at ∼ 1.40, ∼ 1.91, and 2.20 to 2.40 μm wavelengths. In remote sensing applications, minerals showing one or two of these absorption features are usually categorized as hydroxylated and/or hydrated, and minerals showing all three absorption features are usually categorized as hydrated phyllosilicates. We examine the USGS spectral library of common silicate minerals and find that some showing these absorption features are not hydroxylated and/or hydrated but with slight alteration or impurities. This implies that the identification of hydroxylated and/or hydrated silicate minerals may not be merely based on these absorption features. Rather, incorporation of geological and environmental background is also important. We further calculate the absorption depths and silicate mineral ratios in these absorption features as references for discrimination. This study provides a cautionary perspective on the identification of hydrated phyllosilicate minerals with respect to mineralogical interpretations of Mars and other planets.

An unsupervised neuro-fuzzy system, Gaussian fuzzy self-organizing map (GFSOM), is proposed for hyperspectral image classification. This algorithm operates by integrating an unsupervised neural network with a Gaussian function-based fuzzy system. We also explore the potential for hyperspectral image analysis of three other artificial intelligence (AI)-based unsupervised techniques popular for multispectral image analysis: self-organizing map (SOM), fuzzy c-mean (FCM), and descending fuzzy learning vector quantization (DFLVQ). To apply these methods effectively and efficiently to hyperspectral imagery, an optimal learning sample selection strategy and a prototype initialization system are developed. An experimental study on classifying an EO-1/Hyperion hyperspectral image illustrates that GFSOM achieves the best accuracy, since it can model both the central tendency characteristics of input samples and capture the dispersion characteristics of data within a cluster. By adopting the system initialization approach developed here, all the AI-based techniques have the capability to classify hyperspectral images and can deliver acceptable accuracy, which could consequently accelerate their transitions from the multispectral to the hyperspectral field.

The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear (R²<0.90) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good (R² = 0.93) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.

We develop a robust framework for the registration of light detection and ranging (LiDAR) images with 2-D visual images using a method based on intensity gradients. Our proposed algorithm consists of two steps. In the first step, we extract lines from the digital surface model (DSM) given by the LiDAR image, then we use intensity gradients to register the extracted lines from the LiDAR image onto the visual image to roughly estimate the extrinsic parameters of the calibrated camera. In our approach, we overcome some of the limitations of 3-D reconstruction methods based on the matching of features between the two images. Our algorithm achieves an accuracy for the camera pose recovery of about 98% for the synthetic images tested, and an accuracy of about 95% for the real-world images we tested, which were from the downtown New Orleans area.

The estimation of surface soil moisture status and evapotranspiration from optical remote sensing using the vegetation index-surface temperature (VI-T_S) relationship is severely hampered in regions with strong topography, due to the influence of altitude and terrain orientation on surface temperature. In our study, a new empirical approach to normalize surface temperature for terrain elevation-a stratified linear regression model-is presented and is applied on moderate-resolution imaging spectroradiometer (MODIS) data over Calabria, Italy. The method incorporates remotely sensed land surface temperature, a vegetation index, and a digital elevation model. The influence of the newly developed normalization on the (VI-T_S) relationship and on a soil dryness index is compared to the influence of two existing normalization methods: one using a standard lapse rate of 0.65 K per 100 m and one using a lapse rate derived through simple linear regression between elevation and surface temperature. Stratified linear regression adequately corrects surface temperature while the two other normalization techniques seem to overestimate the actual temperature lapse rate during certain periods of the year. Comparison of a soil dryness index derived using the three different normalization methods with limited in situ soil moisture data results in a slightly stronger correlation for the stratified linear regression model than for the two other normalization methods. (VI-T_S)-based soil wetness estimation in mountainous terrains remains, however, limited by other spatially varying factors, including terrain orientation and atmospheric conditions.

Accurate estimation of chlorophyll-a (chl-a), a proxy of the eutrophication risk, is necessary in coastal areas for the assessment of water quality in accordance with European Directives. Local parameterization of remote sensing algorithms is useful to cope with the variability and specificity of optically-active in-water constituents. Using the Bay of Biscay coastal waters, affected by Basque river runoffs, as a case study, the objectives of this investigation are to: 1. develop an empirical algorithm to estimate water surface chl-a for the optically-complex Basque coastal waters; 2. explore the influence of suspended matter, phytoplankton species, and pigment content on the algorithm developed for medium resolution imaging spectrometer instrument (MERIS) imagery; 3. compare the local algorithm to three ocean color algorithms (OC4v6, Gitelson's algorithm, and the OC5); and 4. apply the local algorithm to the MERIS images. For this purpose, two surveys were undertaken within the study area, the Batel-1 survey in 2007, and the Batel-2, in 2009. The empirical algorithm was developed with remote sensing reflectances (Rrs), undertaken with a TriOS field spectrometer, and chl-a measured in situ from the Batel-2 survey. The algorithm was not affected by different concentrations of suspended matter in surface waters, within the range from 0.0 to 6.6  g·m−3. There was no significant effect of 23 accessory pigments found in the area on the algorithm. Eighty-four Rrs and chl-a measurements from the Batel-1 survey were used to validate the local algorithm and to compare it with output of the other algorithms. The local algorithm provided the lowest root-mean-square difference (RMS = 1.7  mg·m−3), the best correlation with the observed data (R = 0.8), together with the best slope-intercept combination between predicted and observed chl-a (slope = 0.5, intercept = 0.6).

A generalized sensor reduction technique is developed for a sensor network used in surveillance and target tracking operations. Reducing the number of sensors in the network leads to addressing immediate threats more quickly and lowering costs for acquiring and processing data. The methods in this work use Bellman optimality principles to estimate possible paths of an agent given an assumed environment model. These paths are then used to determine causal relationships between states in a surveillance field. By using this approach, a capture set is defined where the final states of trajectories are known using information from sensors located in other states. Sensors can then be removed from the network based on this capture set. This method is applied to a crowded hallway surveillance scenario where an agent may choose between two possible exits. The sensor network in this scenario determines if a target deviates from the crowd and moves toward an alternate exit. A proximity sensor grid is placed above the crowd to record the number of people that pass through the hallway. Our result shows that the Bellman optimal approximation of the capture set for the alternate exit identifies the region of the surveillance field where sensors are needed, allowing the others to be removed. Using the reduced sensor network, results are given that show the probability of a deviating agent becoming more distinct with respect to normal motion of the crowd. Therefore, we conclude that by incorporating a dynamic model of the agents' motion into the sensor network, sensors can be reduced, and increased detectability is noticed when sensors are removed early in a trajectory of interest.

A novel shadow detection method for color remotely sensed images that satisfies requirements for both high accuracy and wide adaptability in applications is presented. This method builds on previously reported work investigating the shadow properties in both red/green/blue (RGB) and hue saturation value (HSV) color spaces. The method integrates several shadow features for modeling and uses a region growing (RG) algorithm and a perception machine (PM) of a neural network (NN) to identify shadows. To ensure efficiency of the parameters, first the proposed method uses a small number of shadow samples manually obtained from an input image to automatically estimate the necessary parameters. Then, the method uses the estimated threshold to binarize the hue map of the input image for obtaining possible shadow seeds and applies the RG algorithm to produce a candidate shadow map from the intensity channel. Subsequently, all of the hue, saturation, and intensity maps from the candidate shadow map are filtered with a corresponding band-pass filter, and the filtered results are input into the PM algorithm for the final shadow segmentation. Experiments indicate that the proposed algorithm has better performance in multiple cases, providing a new and practical shadow detection method.

Gross primary production (GPP) is a key indicator of ecosystem performance, and helps in many decision-making processes related to environment. We used the Eddy covariancelight use efficiency (EC-LUE) model for estimating GPP in the Great Plains, United States in order to evaluate the performance of this model. We developed a novel algorithm for computing the photosynthetically active radiation (PAR) based on net radiation. A strong correlation (R² = 0.94,N = 24) was found between daily PAR and Landsat-based mid-day instantaneous net radiation. Though the Moderate Resolution Spectroradiometer (MODIS) based instantaneous net radiation was in better agreement (R² = 0.98,N = 24) with the daily measured PAR, there was no statistical significant difference between Landsat based PAR and MODIS based PAR. The EC-LUE model validation also confirms the need to consider biological attributes (C₃ versus C₄ plants) for potential light use efficiency. A universal potential light use efficiency is unable to capture the spatial variation of GPP. It is necessary to use C₃ and C₄ based land use/land cover map for using EC-LUE model for estimating spatiotemporal distribution of GPP.

Information on the distribution and transport of upper tropospheric humidity helps our understanding of atmospheric circulation. However, efforts to develop a dataset that is not diurnally biased-that is, one that is based on geostationary data-have been hindered by a lack of intercalibration of the historical geostationary satellite water vapor channels. Toward this end, the high-resolution infrared radiation sounder (HIRS) intersatellite bias is determined by using collocated observations of geostationary and HIRS to compute monthly biases between HIRS instruments on different satellites. The resulting HIRS intersatellite bias then allows the construction of a temporally consistent HIRS record, from which geostationary satellite water vapor channels can be intercalibrated. This intercalibration will allow a uniform analysis of upper tropospheric water vapor.

In this study, new add-on bands in a multispectral dataset of WorldView-2, DigitalGlobe's second next-generation satellite, have been evaluated. For extraction of a specific agriculture crop at a time, WorldView-2 multispectral single, as well as two-date data sets, were used. For this purpose, a class-based sensor independent spectral band ratio normalized difference vegetation index (NDVI) (CBSI-NDVI) and its possibilistice fuzzy classification approach was used. Different agriculture crops selected for the study were sugarcane, late wheat, cauliflower, berseem (fodder), early wheat and ratoon. It is found that bands four and eight with temporal data are good for extracting sugarcane, while bands four, eight and five, seven with temporal data are suitable for late wheat and bands four and eight work well for cauliflower. Similarly, bands five, seven and five, eight with temporal data are good for extracting berseem (fodder), bands four, eight work for early wheat with temporal data and for ratoon four, six single date or four, six and four, eight temporal data. This suitability of bands has been observed with respect to a maximum membership value difference, as well as maximum entropy difference, between the two closest agriculture crops. Thus, it can be concluded that existing bands five, seven and new bands four, six, eight in WorldView-2 are important for identifying and mapping crops mentioned in this study. This indicates new bands, especially four, six, eight introduced in WorldView-2, are more effective than existing bands in QuickBird for mapping specific crops.

Backscattering characteristics of L-band synthetic aperture radar (SAR) polarization data over industrial plantation forests of the fast-growing Acacia mangium in Sumatra, Indonesia, were investigated by combined analysis of microwave and optical reflectance imagery acquired by the Advanced Land Observation Satellite (ALOS) and ground observation data of forest stand characteristics. Prior to the satellite data analysis, growth curves of the planted trees are analyzed using ground observation data. The results show that tree growth fits a sigmoidal curve with a maximum growth rate between 1 and 2 years of age, which gradually declines after that. Our statistical analyses using satellite and ground observation data provide the following findings: 1. The regression between SAR backscattering intensity and forest stand characteristics fits negative quadratic curves for the cross-polarization HV, which show higher correlation than those obtained for the co-polarizations (HH, VV); 2. A decreasing trend in optical normalized difference vegetation index (NDVI) values with age prevails for the trees 2 years and older; 3. A significant correlation is obtained between SAR backscattering and NDVI for the cross-polarization. These findings suggest that the L-band SAR cross-polarization is strongly affected by the acacia tree foliage.

Most paddy rice fields in Asia are comprised of small parcels of land, and the weather conditions during the growing season are usually cloudy. This study develops a geographic information system (GIS) object-based post classification (GOBPC) that combines low-cost remotely sensed and GIS data to precisely map paddy rice fields in the intensively cultivated but fragmented growing areas which are characteristic of Asia. FORMOSAT-2 multispectral images have an 8-meter resolution and a one-day recurrence, making them ideal for mapping such areas. Multitemporal images are examined to distinguish the different growth characteristics between paddy rice and other types of ground cover. The pixel-based hybrid classification technique is used with both the unsupervised and supervised approach to distinguish the paddy rice fields from their surroundings. In addition to the pixel-based approach, we also use GOBPC to deal with over-fragmented parcels of land and to reduce the incidence of misclassification caused by speckle or mixed pixels (mixels) in the images. A comparison is made with the pixel-based technique. The Kappa index of agreement obtained with the GOBPC reaches 0.095 to 0.291, and there is a statistically significant improvement in the user and producer accuracy for all the classes (z>1.96) with McNemar's test in the four study areas. The proposed GOBPC approach is shown to be useful in highly fragmented rice growing areas and may have the potential for other agricultural applications.

In the southwest of China, it is anticipated that synthetic aperture radar (SAR) will become an important tool for forest inventory because of its all-weather capabilities. The Zhazuo area in Guizhou Province of southwest China, with a typical Karst landform, was selected as the test site. Six RADARSAT-2 polarimetric images were acquired in order to analyze polarimetric backscattering behavior and temporal variation of forest and deforested area. Polarimetric decomposition was conducted, and Pauli and Freeman-Durden decomposition were demonstrated to be more suitable for identifying forest and deforestation respectively. Finally, a scheme for multitemporal polarimetric SAR data fusion was proposed, which could greatly improve image quality and make forest identification more efficient. Support vector machine classification showed that the overall accuracy for forest identification was 87.63%, and the accuracy could be enhanced to 91.49% after gamma filtering.

Leaf area index (LAI) is a key variable for modeling the interaction between vegetation and the atmosphere. We collected field LAI measurements over 12 sites in 2005 in the Lys valley, Northern Italy, to calibrate regressive models using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and wide dynamic range vegetation index (WDRVI) products derived from 250-m moderate resolution imaging spectroradiomete (MODIS) imagery. Field data were compared to the 1 km MODIS leaf area index-fraction of photosynthetically active radiation (LAI/fAPAR) product to show that regressive techniques are better suited for local applications. We investigated these LAI-vegetation index (VI) regressive models to 1. test the sensitivity of the model to forest type and phenology, 2. identify the most suitable VI for LAI retrieval, and 3. verify the feasibility of using a linear model. Results show that in our experimental conditions the LAI-VI relationship is primarily influenced by phenology and that the leaf constant period (maximum LAI) is significantly different compared to the other phenological phases. Among the indices, EVI yielded the poorest performance (R²<0.41) and the linear regressive models for NDVI and WDRVI derived by pooling together data from different phenological phases show a good correlation with field data (R²<0.65); the use of a logarithmic model does not improve the performance. The LAI-WDRVI and LAI-NDVI models were inverted and applied to 2004 MODIS data and model performance was assessed by comparing predicted and measured LAI. Results show that WDRVI performs best in a linear regressive model, yielding a relative root mean square error <23%.

The East China Sea (ECS) is threatened by frequent Skeletonema costatum (S. costatum)blooms every year, which can cause severe environmental harm, as well as considerableeconomic losses. Remote sensing is an efficient tool for monitoring these harmful algal blooms(HABs) and studying concerned marine conditions. This study investigated two intensiveS. costatum HABs in the ECS by analysis of water distribution and spatial-temporal patternof four oceanographic parameters derived from moderate resolution imaging spectroradiometer(MODIS) and QuickSCAT satellite data using multiple remote sensing approaches (compositeimagery interpretation, classification, and parameters retrieval). Results show that high chlorophyll-a (Chl-a) concentrations and net primary production (NPP) decrease from the HABareas toward the open sea. A peak of Chl-a (<10 mgm−3) and NPP (<5000 mg · C · m−2 · d−1)are considered indicators of large-scale S. costatum blooms in the ECS. Low sea surfacetemperature (SST; approximately 23°C) are observed in S. costatum HAB areas. In early stages,winds in terms of direction and speed can bring nutrients to facilitate the formation of S. costatumblooms, but then sharply change into unfavorable conditions to cause the final disappearanceof HABs. This study also explored multiple oceanographic explanations in the ECS frombiochemical, meteorological, physical, and geological perspectives for a better understanding ofsuch S. costatum HABs mechanisms.

The aim of this study was to evaluate the potential of MODIS normalized difference vegetation index hypertemporal data analysis for assessing Mediterranean pasture conditions in North Western Sardinia (Italy). During the seasons 2006 to 2007 and 2007 to 2008, field observations were carried out to classify 67 pasture sites in three condition classes based on expert knowledge. The local net primary productivity scaling (LNS) method was applied, and its potential for discriminating the pasture condition classes was evaluated by logistic regression models (LRM). Yearly and average LNS maps were generated for the period 2000 to 2008, and analyzed to identify areas that exhibited persistently low LNS values (hotspots). The LNS method proved useful to discriminate pastures in different conditions (LRM bootstrapped Nagelkerke pseudo R2 ¼ 0.52). The analysis of persistence of low LNS values allow identifying regional hotspots of degradation. A qualitative evaluation of the main hotspots on aerial photographs revealed that approximately 62% of the hotspots were clearly characterized by pasture degradation patterns, whereas the remaining were associated to highly fragmented landscapes or to errors in the land cover map. This result emphasizes the importance of using multiscale approaches by integrating the LNS regional assessment with high spatial resolution remote sensing data analysis.

A strong earthquake with magnitude 7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001 caused wide spread destruction and casualties. Earthquakeinduced ground failures, including liquefaction and lateral spreading, were observed in many areas. Optical remote sensing offers an excellent opportunity to understand the post-earthquake effects both qualitatively and quantitatively. The impact of using conventional indices from Landsat-7 temporal images for the liquefaction is empirically investigated and compared with class-based sensor independent (CBSI) indices, while applying possibilistic fuzzy classification as a soft computing approach via supervised classification. Five spectral indices, namely simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) are investigated to identify liquefaction using temporal multi-spectral images. A soft-computing based fuzzy algorithm, which is independent of statistical distribution data assumption, is used to extract a single land cover class from remote sensing multi-spectral images. The result indicates that appropriately used indices can incorporate temporal variations, while extracting liquefaction with soft computing techniques for coarser spatial resolution with temporal remote sensing data. It is found that CBSI-NDVI with temporal data was good for extraction liquefaction while CBSI-TNDVI with temporal data was good for extraction water bodies.

Observations from satellite remote sensing provide important tools for regional and global monitoring of terrestrial ecosystems. This article evaluated a recently developed vegetation index (VI) model that estimates gross primary production (GPP) in an open grassland using flux measurements and 250-m moderate-resolution imaging spectroradiometer (MODIS) images. Two VIs, the two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI), were tested as indicators of both fraction of absorbed photosynthetic active radiation (f APAR) and light use efficiency (LUE). The standard MODIS GPP product was moderately correlated with flux-measured GPP as indicated by the coefficients of determination (R2) of 0.56 and a root mean square error (RMSE) of 15.4 gCm−2 8d−1. In comparison, results suggested that the VI model can provide improved estimates of GPP, with R2 of 0.80, 0.82, and 0.81 for EVI2 × EVI2 × photosynthetically active radiation (PAR), NDVI × EVI2 × PAR, and NDVI × NDVI × PAR, respectively. The underlying reason was that both EVI2 and NDVI were potential proxies of f APAR (R2 of 0.43 and 0.53, respectively) and LUE (R2 of 0.52 and 0.46, respectively). The primary analysis will be helpful for spatial improvements of future GPP models.

The quantitative description of forest canopy structure is significant for the investigation of a forest, which serves as an important component of the terrestrial ecosystem. Light detection and ranging (LIDAR), as a new technical means that can acquire high-precision vertical information, plays a crucial role in forest monitoring and management. Choosing Dayekou forest experimental area in the Heihe watershed as a study area, we separated the ground points from the vegetation points using the skewness-change algorithm based on the intensity information from airborne LIDAR data. After that, digital terrain model (DTM) and digital surface model (DSM) were generated, respectively, based on which the canopy height model (CHM) was acquired. Finally, using the variational window, the local maximum filter method was used to extract individual tree heights and crown widths from CHM. The determination coefficients of tree heights and crown widths were 0.8568 and 0.3923, respectively. The validation results indicated that the tree heights could be effectively extracted from intensity information of airborne LIDAR, while the accuracy of extracted crown widths needed to be improved. In the future work, aerial photos and other high-resolution images would be combined to improve the accuracy.

Terrain slope of Greenland is usually calculated from surface elevation on a large scale. Novel physical models were established to estimate the terrain slope within laser footprints on a smaller scale. Based on the time-stamped waveforms of ice, clouds, and land elevation satellite/geoscience laser altimeter system (ICESat/GLAS), the physical models for calculating the slopes were established in two cases: zero attitude angles and non-zero attitude angles, respectively. The expressions of waveform slopes are related not only to the waveform width and the mean pulse delay of the time-stamped waveform, both in nanoseconds, but also to the attitude of the satellite and the laser divergence angle. In order to calculate the waveform width and the mean pulse delay, a non-Gaussian mathematical function was proposed to curve-fit the waveforms. The slopes estimated from the waveforms were compared with the slopes calculated from surface elevations. Results show that the two methods generate almost identical slope estimations for the same terrain. Calculation results also indicate that the method for slope estimation proposed in this paper performs better for extreme sloping terrain than for gentle sloping terrain.

Using remote-sensing technologies, this study sought to provide an up-to-date map of C3/C4 distribution representative of temperate grassland in northern China. Several studies focused on the central grasslands of North America have demonstrated that C4 species coverage can be discriminated from C3 species by using time-series vegetation index data based on their phenological differences. Considering that the hydrothermal patterns and C4 percentage of grass flora in the study area and North America are different, we first examined temporal features of C3/C4 communities by using multitemporal moderate resolution imaging spectroradiometer normalized difference vegetation index data throughout the 2010 growing season. It was found that the asynchronous seasonality exhibited by communities with varied C3/C4 compositions also existed in our study region. Based on this asynchrony and separation rate, a hierarchical decision tree was developed to classify four grassland types with varied C3/C4 compositions. As a result, a classification map of the mixed C3/C4 grassland was generated with an overall accuracy of 87.3% and a kappa coefficient of 0.83. The geographic distribution of C3 and C4 species showed that the study area was dominated by C3 grasses, but C4-dominated grasslands accounted for 39% of the land cover. Thus C4 species also made an important contribution to grassland biomass, especially in dry and low-lying saline-alkaline habitats. The results also indicated that the trajectory-based methods for C3/C4 mapping rooted in asynchronous seasonality worked effectively in the climate regimes of both northern China and North America.

High-density (approximately 40 points m−2) airborne light detection and ranging (LiDAR) data were exploited to improve the classification of coniferous and deciduous trees in uneven aged mixed wood forests. The investigation was conducted at the individual tree level. Several features were extracted from airborne laser scanning data to characterize structural properties of individual trees, such as crown shape and foliage distribution. A decision tree algorithm was used to perform the selection of significant features and construction of a classifier. The classification was tested within various sites in Canadian forests with different species. The results demonstrated that the LiDAR features describing the foliage distribution within a tree play a significant role in discriminating coniferous and deciduous species. In the classification, 193 reference trees sampled from the entire study area were used for training, and three representative forest sites within the study area were used for assessment of accuracy. The classification accuracy was 77.3%. This study also suggested that high-density LiDAR data were effective in discriminating individual mature coniferous and deciduous trees. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.JRS.6.063536]

The author presents a technique to calculate instrument stellar magnitudes for silicon photosensors used in modern star trackers as well as estimates the error of the said technique. The technique implies calculation of instrument stellar magnitudes as specifically selected functions of color indices B-V, V-J of the Tycho-2 and two micron all sky survey (2MASS) catalogs. This function is a sum of basis functions with coefficients determined based on star trackers' response. The coefficients are calculated individually for each star tracker response. Calculation of the coefficients of the function is done using the least squares method for color indices of artificial stars. Spectra of artificial stars are created on the basis of reddening of typical subclasses spectra without interstellar extinction, such spectra taken from the Pickles catalog. Thickness of interstellar medium for reddening is selected basing on random law. Validation of accuracy of the proposed methodology is performed by calculating star brightness for standard photometric bands R and I and subsequent comparison of the obtained results with actual data. Such check indicates that the root-mean-square deviation of error is not over 0.08 m. Due to usage of data related to star brightness in J band of 2MASS catalog, the number of stars in the guide star catalog is increased by 30% as compared with using data only from Tycho and HIPPARCOS catalogs.

The problem of atmospheric intervention has received considerable attention from researchers in remote sensing who have developed a range of methods, either simple or sophisticated. The sophisticated methods require auxiliary information about the state of the atmosphere which is obtained either from standard databases or from simultaneous in-situ field measurements or by iterative techniques. It has been found that the darkest pixel atmospheric correction (DP) is one of the most effective atmospheric correction methods especially for visible spectral bands. The DP is the simplest and fully image-based correction method. The integrated use of the DP basic theory and the radiative transfer equation is implemented in this study. Indeed, this leads to the development of the proposed 'image-based atmospheric correction algorithm.' The proposed algorithm retrieves the aerosol optical thickness (AOT) only for areas with urban and maritime aerosols. The effectiveness of this algorithm is assessed by comparing the AOT values retrieved from the proposed 'image-based atmospheric correction algorithm' after applied to Landsat TM/ETM+ images with those measured in-situ both from MICROTOPS II hand-held sun photometer and the CIMEL sun photometer (AERONET). It has been found that the AOT values retrieved from the proposed algorithm were very close with those measured from the CIMEL sun photometer for the Limassol area in Cyprus.

National Aeronautics and Space Administration (NASA) moderate resolution imaging spectroradiometer (MODIS) Aqua-retrieved chlorophyll concentration data sets have been analyzed and interpreted, as have changes in chlorophyll concentrations associated with the tsunami that occurred on March 11, 2011, in Sendai, Tohoku (latitude 38.3°N, longitude 142.4°E). The eight-day and three-day composite data sets were archived from the NASA MODIS website to generate chlorophyll images during March 2011, before, during, and after the tsunami. The chlorophyll concentration variability was studied with reference to satellite images, a transect line, eight points, and mean plots over two subsets in the coastal and offshore water. An increase in chlorophyll concentration of up to 5.0  mg/m³ along the transect line and in the range of 1.0-4.3 mg/m³ in the coastal water points and subset (10×10) was observed. The chlorophyll mean ranged from 0.289 to 1.237  mg/m³ and 0.123 to 0.271  mg/m³ for subsets 1 and 2, respectively. The offshore water subset was about 300 km from the tsunami site, and the chlorophyll range has not shown more gradients in post-tsunami images, as was evident from subset-2 mean data. Data from March 2010 and 2009 was processed for the same locations to comply with any seasonal trend. But the recent 2011 tsunami had a sudden episodic impact on the coastal water of Japan, enhancing chlorophyll concentration without being tallied with seasonal trends. So, the episodic effect seemed to be significant instead of being a seasonal trend.As observed from field photographs and previous tsunami events, water receded from the land, along with a tsunami return wave that probably carried nutrients and minerals, and even upwelling would have enriched the phytoplankton growth and sudden chlorophyll concentration increase in the post-tsunami period found convincing in satellite images. The anomaly of chlorophyll observed was significant, with p<0.02 during 2011.

The satellite-borne synthetic aperture radar (SAR) has been proven to be a valuable tool for high resolution ocean surface wind measurements. However, oceanic surface phenomena observed by SAR and oceanic processes which can cause the change of backscatter in SAR imagery will influence the SAR wind retrieval. Upwelling is one of the main factors, and it is prevalent in the East China Sea. It smooths the sea surface which results in a lower backscatter cross section in SAR imagery. In this study, using sea surface temperature and chlorophyll2-a data derived from Earth Observing System (EOS) MODIS, the low backscatter features in ENVISAT advanced synthetic aperture radar (ASAR) imagery are analyzed. A CMOD4 algorithm is adopted to retrieve the sea surface wind speed from SAR imagery. Results show that the wind speed is negatively biased due to the low normalized radar cross section associated with the upwelling. In order to resolve the impact of coastal upwelling on SAR wind retrieval, combined with high resolution numerical meteorological model wind field data, a wind speed correction method is proposed by using linear robust regression. To demonstrate the applicability of this method, underestimated wind speeds retrieved from ENVISAT ASAR images in the upwelling areas of Zhejiang coast and the sea area near northeast of Taiwan are corrected. Results show that the accuracy of upwelling region SAR wind retrieval data has been improved.

Global climate change studies require long-term, radiometrically accurate, and stable observations from a number of satellites. Spatially uniform and radiometrically/spectrally stable vicarious calibration sites can be used to quantify the sensor gain change over time, monitor the instrument performance, and compare measurements from multiple instruments to maintain consistent radiometric calibration. This study uses AQUA moderate resolution imaging spectroradiometer (MODIS), MetOp-A advanced very high resolution radiometer (AVHRR), and Earth Observing-1 Hyperion to analyze the radiometric and spectral characteristics of the Dome C and Sonoran Desert sites and perform intercomparisons. The radiometric stability of both sites over a period of eight years as evaluated using AQUA MODIS is found to be better than 2% in the visible (0.64 μm) and near-infrared regions (0.86  μm), assuming the MODIS calibration is stable. The bidirectional reflectance distribution (BRDF) effect over Dome C is large, with greater than 5% seasonal variation, compared to the Sonoran Desert with less than 2% variation. However, the BRDF impact can be reduced to less than 2% for Dome C after normalization by an appropriate BRDF model. For water vapor absorption channels, such as AVHRR channel 2 (0.86  μm), this study suggests that the Sonoran Desert is largely affected with greater than 6% absorption variability compared to that of Dome C with less than 2%. The study also reveals that the operationally calibrated AVHRR top-of-atmosphere reflectance is lower than that of MODIS by about 8%.

Airborne multispectral and hyperspectral imaging can be used to detect potentially diseased trees rapidly over a large area using unique spectral signatures. Ground inspection and management can be focused on these detected zones, rather than an entire grove, making it less labor-intensive and time-consuming. We propose a method to detect the areas of citrus groves infected with citrus greening disease [Huanglongbing (HLB)] using airborne hyperspectral and multispectral imaging. This would prevent further spread of infection with efficient management plans of infected areas. Two sets of hyperspectral images were acquired in 2007 and 2009, from different citrus groves in Florida. Multispectral images were acquired only in 2009. A comprehensive ground truthing based on ground measurements and visual check of the citrus trees was used for validating the results using 2007 images. In 2009, a more accurate polymerase chain reaction test for selected trees from ground truthing was carried out. With a handheld spectrometer, ground spectral measurements were obtained along with their degrees of infection. A hyperspectral imaging software (ENVI, ITT VIS) was used for the analysis. HLB infected areas were identified using image-derived spectral library, mixture tuned matched filtering (MTMF), spectral angle mapping (SAM), and linear spectral unmixing. The accuracy of the MTMF method was greater than the other methods. The accuracy of SAM using multispectral images (87%) was comparable to the results of the MTMF and also yielded higher accuracy when compared to SAM analysis on hyperspectral images. A possible inaccurate ground truthing for the grove in 2007 generated more false positives.

Rapid urbanization in Ho Chi Minh City (HCMC), Vietnam, is creating societal impacts on the environment attributed to the increasing population. Understanding spatio-temporal dimensions of land-use changes that shape the urbanization is thus critical to the process of urban planning. We explore the urban growth in HCMC through Landsat images for 1990, 2002, and 2010 using the linear mixture model (LMM). The data are processed through four steps: (1) data pre-processing, (2) image classification by LMM using endmembers extracted from the original image using minimum noise fraction, (3) accuracy assessment of the classification results using field verification data, and (4) urban growth analysis to understand the spatial changes of land cover. The results achieved by comparisons between the classification results and ground reference data indicate that the overall accuracy and Kappa coefficient obtained for 1990 were 87.1% and 0.83, respectively, while those for 2002 were 92.5% and 0.89, and those for 2010 were 89.6% and 0.86. The results of urban growth analysis indicate that high albedo class (i.e., built-up areas) expanded from 12.3% in 1990 to 27.2% in 2002 and to 31.1% in 2010. When investigating land-cover conversions to high albedo class from 1990 to 2002, the largest conversion is observed for soil class (9.2%), followed by vegetation class (7.2%), and low albedo class (2.2%). From 2002 to 2010, 4.5% area of soil class was converted to high albedo class, while conversions from vegetation and low albedo classes were 3.5% and 2.5%, respectively.

Shrub vegetation is a key element of Mediterranean forest areas and it is necessary to develop tools that allow a precise knowledge of this vegetation. This study aims to predict shrub volume and analyze the factors affecting the accuracy of these estimations in small stands using airborne discrete-return LiDAR data. The study was performed over 83 circular stands with 0.5 m radius located in Chiva (Spain) mainly occupied by Quercus coccifera. The vegetation inside each area was clear cut, and the height and the diameter of each plant was measured to compute the volume of shrub vegetation per stand. Volume values were related with maximum height values derived from LiDAR data reaching a coefficient of determination value R² = 0.26. Afterwards, factors affecting the quality of volume estimations were analyzed, i.e., vegetation type, LiDAR density, and accuracy of the digital terrain model (DTM). Significant accuracy improvements (R² = 0.71) were detected for stands with 0.5 m, LiDAR data density greater than 8  points/m², vegetation Q. coccifera, and error associated to the DTM less than 0.20 m. These results show the feasibility of using LiDAR data to predict shrub volume under certain conditions, which can contribute to improved forest management and characterization.

The work is concerned with assessing the health status of trees of the Norway spruce species using airborne hyperspectral (HS) data (HyMap). The study was conducted in the Sokolov basin in the western part of the Czech Republic. First, statistics were employed to assess and validate diverse empirical models based on spectral information using the ground truth data (biochemically determined chlorophyll content). The model attaining the greatest accuracy (D718/D704∶RMSE = 0.2055  mg/g, R² = 0.9370) was selected to produce a map of foliar chlorophyll concentrations (Cab). The Cab values retrieved from the HS data were tested together with other nonquantitative vegetation indicators derived from the HyMap image reflectance to create a statistical method allowing assessment of the condition of Norway spruce. As a result, we integrated the following HyMap derived parameters (Cab, REP, and SIPI) to assess the subtle changes in physiological status of the macroscopically undamaged foliage of Norway spruce within the four studied test sites. Our classification results and the previously published studies dealing with assessing the condition of Norway spruce using chlorophyll contents are in a good agreement and indicate that this method is potentially useful for general applicability after further testing and validation.

This paper presents parameter design methodology and related optomechanical engineering of a 905-nm diode-laser biaxial, eye-safe lidar ceilometer prototype for cloud-height monitoring. Starting with a brief review of the state-of-the-art ceilometer technology, acceptable parameter ranges are identified for the key system parts. Parameter tuning is achieved by imposing goal criteria on the simulated signal-to-noise ratio and laser-telescope overlap factor. The system is based on a low-cost pulsed semiconductor laser, low-cost Fresnel-lens telescope, a low-noise-equivalent power avalanche-photodiode optoelectronic receiver, and collimating/focusing adjustable parts. Finally, preliminary test measurements are presented.

A general synthetic aperture radar (SAR) signal model based on the Maxwell's equations is derived, and three approximations are discussed for engineering applications. Based on this signal model, a novel operation of SAR, called outer circular synthetic aperture radar (Outer-CSAR), is investigated for wide observation. The Outer-CSAR works similarly to the general circular SAR, but the beam of the SAR antenna points at the outer of the circle instead of the inner. The signal model and imaging algorithm are presented for the Outer-CSAR, and furthermore, simulation is given to validate the signal model and imaging algorithm.

Pan-sharpening of remote sensing multispectral imagery directly influences the accuracy of interpretation, classification, and other data mining methods. Different tasks of multispectral image analysis and processing require specific properties of input pan-sharpened multispectral data such as spectral and spatial consistency, complexity of the pan-sharpening method, and other properties. The quality of a pan-sharpened image is assessed using quantitative measures. Generally, the quantitative measures for pan-sharpening assessment are taken from other topics of image processing (e.g., image similarity indexes), but the applicability basis of these measures (i.e., whether a measure provides correct and undistorted assessment of pan-sharpened imagery) is not checked and proven. For example, should (or should not) a quantitative measure be used for pan-sharpening assessment is still an open research topic. Also, there is a chance that some measures can provide distorted results of the quality assessment and the suitability of these quantitative measures as well as the application for pan-sharpened imagery assessment is under question. The aim of the authors is to perform statistical analysis of widely employed measures for remote sensing imagery pan-sharpening assessment and to show which of the measures are the most suitable for use. To find and prove which measures are the most suitable, sets of multispectral images are processed by the general fusion framework method (GFF) with varying parameters. The GFF is a type of general image fusion method. Variation of the method parameter set values allows one to produce imagery data with predefined quality (i.e., spatial and spectral consistency) for further statistical analysis of the assessment measures. The use of several main multispectral sensors (Landsat 7ETM+, IKONOS, and WorldView-2) imagery allows one to assess and compare available quality assessment measures and illustrate which of them are most suitable for each satellite.

The authors present a study focused on the feasibility of using holographic polymer dispersed liquid crystal wavelength filters for hyperspectral imaging (HSI). For this study, stacks of these filters were fabricated in the visible wavelength range of 600 to 800 nm. These filters were demonstrated to have a number of properties useful for HSI applications, including uniform reflection efficiency of 80% across a 35 mm optical aperture, polarization insensitivity for normal incidence, spectral resolution of 10 nm, and fast switching times on the order of microseconds. In addition, the ability to modulate each filter in the stack at a different frequency allows for spectral multiplexing, thus enabling synchronous detection and demodulation of the image data. Although the filters in their current state show promise for HSI applications, techniques to further improve performance in terms of viewing angle range and transmission throughput are presented. Finally, a system level integration of such a stack into the prototype drive and detection unit is discussed.

Recently, support vector machines (SVMs) have been widely used with success for hyperspectral image classification. Indeed, a SVM-based classifier makes it possible to achieve more accurate classification under the condition that a small number of training samples are available. However, there are several issues that need to be considered and investigated before SVMs become operational in remote sensing applications. As the kernel functions are the backbone of SVMs, the most important problem is how to determine the kernel function and the corresponding parameters. A conventional method for setting the kernel parameters is the grid search method. Throughout the training process, classifiers are trained with different kernel parameters, and only one of the classifiers is required for the testing process. This makes the training process time-consuming. As a solution, we propose a new method using the intercluster distances in the feature spaces to set the kernel parameters. We propose a multistep algorithm based on the SVMs in a similarity space. These techniques define and use the spectral similarity measures. In this new space, the pixels corresponding to the class-of-interest will obtain similar values. This space can be seen as a new feature space with a relatively low dimension. For the first step, hyperspectral data are projected onto this similarity space. The second step concerns tuning the SVM parameters to separate the class-of-interest pixels from background pixels. The results demonstrate that the proposed algorithm is efficient and reliable for hyperspectral image classification purposes. Furthermore, the experiment's results showed that the intercluster distance could choose proper kernel parameters with which the testing accuracy of trained SVMs is determined by the standard ones, and the training time could be significantly shortened.

High-performance computing (HPC) is necessary for remote-sensing image compression to achieve real-time output. There are one panchromatic and four multispectrum bands with a total 970-Mbps data rate in the Formosat-5 remote-sensing instrument. Three Xilinx Virtex 5 field-programmable gate array parts with external memory are used to perform real-time image data compression based on Consultative Committee for Space Data Systems (CCSDS) 122.0-B-1 ["Image data compression," Recommendation for Space Data System Standards, CCSDS 122.0-B-1, CCSDS, Washington, DC (2005)]. Parallel and concurrent handling methodologies are used to achieve HPC in the image compression process with fewer multipliers, smaller memory buffers, and shorter process time.

Correctly determining a measurement range in light detection and ranging instruments, based on time-of-flight measurements on laser pulses, requires the association of each received echo pulse with its causative emitted laser pulse. Without further precautions, this definite association is only possible under specific conditions constraining the usability of range finders and laser scanners with very high measurement rates. We give an introduction to known techniques for avoiding and resolving range ambiguities. The specific disadvantages of these methods led to the development of a technique based on pulse-position modulation (PPM) of the laser pulse train using a pseudorandom noise signal and the subsequent analysis of the impact of the PPM on groups of consecutive range measurements. The error probability of our approach has been evaluated by simulations based on real scan data. The use of a discrete uniform distribution of amplitudes as a modulation signal shows high detection robustness even for difficult terrain like forest areas. The encouraging results of the simulations led to the practical implementation of PPM to our laser scanners and the development of the associated software RiMTA, which resolves range ambiguities without the necessity of a priori information on the target situation and without any user interaction required.

Geocoding is crucial for using remotely sensed data in almost all applications, especially when a combination of multiple data sources is required. However, geocoding a synthetic-aperture radar image with the standard range-Doppler method is a time-consuming process due to unavoidable iterations. Using a replacement sensor model, for example the rational polynomial camera (RPC) model, can significantly reduce the calculation time cost. Another way to improve the calculation efficiency is to use massively parallel processing techniques. Modern graphics processing units (GPU) can be used as parallel processing units for computationally intensive applications. Using NVIDIA's Compute Unified Device Architecture (CUDA) the implementation of the range-Doppler and RPC methods on GPUs is easy, because the existing C/C++ code can be reused. With further optimizations for GPU processing, tremendous improvements can be achieved. The CUDA implementations run about 10 to 30 times faster compared with similar implementations on the central processing unit (CPU), and almost 200 times faster if particularly optimized for GPU computing.

Leaf area index (LAI) is a key parameter in most land surface models. Models that operate at multiple spatial scales may require consistent LAI inputs at different spatial resolutions or from different sensors. For example, the atmosphere-land exchange inverse model and associated disaggregation algorithm (DisALEXI) use the moderate resolution imaging spectroradiometer (MODIS) LAI product to model fluxes at regional scales (1- to 10-km grid resolution), and Landsat-based LAI to disaggregate to field scale (30-m grid). In order to make a MODIS-consistent LAI product from Landsat imagery for this combined scheme, a simple reference-based regression tree approach was developed. This approach uses homogeneous and high-quality LAI retrievals from MODIS as references to develop a regression tree relating these MODIS LAI samples to Landsat surface reflectances. Results show that the approach can produce accurate estimates of LAI from Landsat, as evaluated using field measurements collected during the soil moisture experiment of 2002, conducted in central Iowa during a period of rapid vegetation growth. The coefficient of determination (r2) computed between Landsat retrievals and field measurements was 0.94 at the field scale, with an overall mean bias error (MBE) of -0.07 and mean absolute difference (MAD) of 0.23. MAD values of 0.17 and 0.32 were obtained for low to moderate LAI (0-3) and high LAI (>3), respectively, with some underestimation for the high LAI (MBE = -0.28). The LAI maps retrieved from Landsat were consistent with the MODIS estimates when aggregated to coarser scales. MAD computed between Landsat- and MODIS-derived LAI ranged from 0.07 to 0.83 for different Landsat dates, with no significant bias compared to MODIS high-quality retrievals. This approach demonstrates a simple framework for producing MODIS-consistent LAI from Landsat data for modeling the land surface at different spatial scales.

A novel height-retrieval method of individual urban structure from high-resolution (HR) synthetic aperture radar (SAR) images is presented based on the geometric property of the double-bounce contribution. The double-bounce contributions caused by the interaction of the visible walls and the surrounding ground are the key features of urban structure. Their scattering distributions and geometric properties are analyzed, a set of geometric models is built, and then a building-height retrieval approach based on these models is proposed. An analysis of the sensitiveness of the height retrieval to errors on the sensor system, building, and measured parameters is performed. The validity and superiority of the proposed approach are demonstrated by four simulation examples, two real very high-resolution MiniSAR examples, and four real HR TerraSAR-X examples.

Remote sensing is currently an indispensable tool for retrieving the leaf area index (LAI) of forests. However, the applicability of remote sensing in retrieving LAI of forests in urban areas has not been thoroughly investigated. The ability of spectral and spatial information from IKONOS-2 imagery to retrieve LAI of forests was studied through analyzing the correlations of four commonly used vegetation indices (VIs) and four texture measures (TEXs) with LAI measured at different types of plots in the urban area of Nanjing, China and comparing the ability of models based on these parameters to estimate LAI of forests. The results show that VIs and TEXs calculated from the high-resolution remote sensing data are both applicable in retrieving LAI of forests in urban areas. The relative advantages of VIs and TEXs are related to the density and spatial regularity of forests. TEX exceeds VI for regularly planted low broad-leaf forests with low density owing to the deterioration of the linkage of VIs with canopy LAI caused by strong soil noise. For forests with moderate and high density, VI exceeds TEX in the retrieval of LAI. As to natural broad-leaf forests with high density and spatial complexity, combining VI and TEX can improve the accuracy of the retrieved LAI by 8.9% to 27.0%. VIs and TEXs are exclusive in retrieving LAI due to the intrinsic linkages of these parameters. The atmospherically resistant vegetation index over-perform other VIs in retrieving LAI of forests owing to its ability to constrain atmospheric disturbance on remote sensing data, which is serious and exhibits great spatial variability in the study area.

Evaluation is an essential step of model development. However, there is a missing definition of appropriate validation strategies, needed to guarantee reproducibility and generalizability of modeling results. Also, there is a lack of a generally agreed set of 'optimal' statistical measure(s) to assess model accuracy. The objective of the present study is to provide for remote sensing practitioners (i.e., non-statisticians) guidance for model validation strategies and to propose an optimal set of statistical measures for the quantitative assessment of model performance in the context of vegetation biophysical variable retrieval from Earth observation (EO) data. For these purposes, main terms and concepts were reviewed. Then, validation strategies were tested on a polynomial regression model and discussed. Moreover, a literature review was carried out, summarizing the statistical measures used to evaluate model performances. Supported by some exemplary datasets, these measures were calculated and their meanings discussed in view of several model validation criteria. From the results, we recommend to further exploit cross-validation and bootstrapping strategies to guarantee the development/validation of reliable models. An 'optimal' statistic set is suggested, including root mean square error (RMSE), coefficient of determination (R²), slope and intercept of Theil-Sen regression, relative RMSE, and Nash-Sutcliffe efficiency index. A wide acceptance and use of these statistics should enable a better intercomparison of scientific results, urgently needed in times of increasing model development activities that are carried out with respect to upcoming EO missions.

The development of new multispectral sensors with unique band settings is critical for mapping the spatial distribution of increaser vegetation species in disturbed rangelands. The objective of this study was to evaluate the potential of WorldView-2 imagery for spectral classification of four increaser species, namely Hyparrhenia hirta, Eragrostis curvula, Sporobolus africanus, and Aristida diffusa, in the Okhombe communal rangelands of South Africa. The 8-bands were extracted from the WorldView-2 image, and 24 of the most widely used vegetation indices in estimating grassland biophysical parameters were calculated. The random forest algorithm and forward variable method were applied to identify the optimal variables (WorldView-2 spectral bands, vegetation indices, and a combination of bands and indices) for classifying the species. The random forest algorithm could classify species with an overall accuracy of 82.6% (<b<KHAT[an estimate of κ]=0.76</b< ) using six of the WorldView-2 spectral bands and an overall accuracy of 90% (<b<KHAT=0.87 </b<) using a subset of vegetation indices (=n<b<9</b< ). Three bands selected were located at the new WorldView-2 spectral regions of coastal blue, yellow, and the red-edge. There was no significant improvement in increaser species classification by using a combination of bands and indices. Overall, the study demonstrated the potential of the WorldView-2 data for improving increaser separability at species level.

In order to analyze the mechanism of the urban heat island, it is paramount and meaningful to estimate the anthropogenic heat flux in cities. A case study was carried out to study the energy balance process in Beijing, China, based on a canopy energy balance equation and to estimate the urban energy fluxes and anthropogenic heat discharge and their seasonal and spatial variations. Two ASTER images and meteorological observation data from the winter and summer seasons were used for the study. The results showed that: (1) in Beijing, the anthropogenic heat discharge flux reached a maximum of 163.76  W m −2 in winter and 288.26  W m −2 in summer. Spatially, the magnitude of the flux was significantly affected by urban land cover types. In winter, the highest value occurred at an urban commercial district with an average value of 47.60  W m −2 . In summer, the highest value occurred at the airport and the industrial areas with the regional average reaching 47.29  W m −2 ; the spatial pattern of the heat discharge appears to be clustered, with some, localized high-accumulation centers such as in industrial areas and commercial districts. (2) The anthropogenic discharge was one of the important contributors to the surface-atmosphere energy exchange in cities. The heat discharge had a positive effect on elevating the surface temperature and formation of the urban heat island, especially in the summer. The study confirms the importance to account for the impact of the anthropogenic heat flux on urban energy budget.

Hyperspectral remote-sensing approaches are suitable for detection of the differences in 3-carbon (C<sub>3 /sub> and four carbon (C₄) grass species phenology and composition. However, the application of hyperspectral sensors to vegetation has been hampered by high-dimensionality, spectral redundancy, and multicollinearity problems. In this experiment, resampling of hyperspectral data to wider wavelength intervals, around a few band-centers, sensitive to the biophysical and biochemical properties of C₃ or C₄ grass species is proposed. The approach accounts for an inherent property of vegetation spectral response: the asymmetrical nature of the inter-band correlations between a waveband and its shorter- and longer-wavelength neighbors. It involves constructing a curve of weighting threshold of correlation (Pearson's r ) between a chosen band-center and its neighbors, as a function of wavelength. In addition, data were resampled to some multispectral sensors-ASTER, GeoEye-1, IKONOS, QuickBird, RapidEye, SPOT 5, and WorldView-2 satellites-for comparative purposes, with the proposed method. The resulting datasets were analyzed, using the random forest algorithm. The proposed resampling method achieved improved classification accuracy (κ=0.82 ), compared to the resampled multispectral datasets (κ=0.78, 0.65, 0.62, 0.59, 0.65, 0.62, 0.76, respectively). Overall, results from this study demonstrated that spectral resolutions for C₃ and C₄ grasses can be optimized and controlled for high dimensionality and multicollinearity problems, yet yielding high classification accuracies. The findings also provide a sound basis for programming wavebands for future sensors.

We report on the development effort of a nanosecond-pulsed optical parametric amplifier (OPA) for remote trace gas measurements for Mars and Earth. The OPA output has ∼500  MHz linewidth and is widely tunable at both near-infrared and mid-infrared wavelengths, with an optical-optical conversion efficiency of up to ∼39% . Using this laser source, we demonstrated open-path measurements of CH 4 (3291 and 1652 nm), CO 2 (1573 nm), H 2 O (1652 nm), and CO (4764 nm) on the ground. The simplicity, tunability, and power scalability of the OPA make it a strong candidate for general planetary lidar instruments, which will offer important information on the origins of the planet's geology, atmosphere, and potential for biology.

A field demonstration was done from Virginia Beach, Virginia, to show the use of high-energy (250-mJ) eyesafe Doppler lidar for measurements of offshore wind. The lidar is located onshore and pointed near-horizontally to reach a target area many kilometers away. In sample measurements, the lidar scan's hypothetical turbine is located 6 km away. For one beam elevation of interest, the horizontal wind vector is measured by scanning the beam in azimuth. The elevation can then be changed to profile the wind at many altitudes. An example measurement is shown in which wind vector is determined at six altitudes covering the height of a supposed turbine and above. In addition to the wind vector, wind shear is measured across a turbine blade span width. Over a two-week period in October 2011, range capability was found to vary from 4.5 to 17 km depending on weather and aerosol backscatter conditions. A comparison was made with an anemometer to validate the lidar's measurements.

There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques provide an avenue to select robust settings capable of operating consistently across a large variety of image scenes. Many researchers in this area are faced with a paucity of data. Unfortunately, there are no data splitting methods for model validation of datasets with small sample sizes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research has developed a framework for optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD; these characteristics include the Fisher's score, ratio of target pixels and number of clusters. We have developed method for selecting hyperspectral image training and test subsets that yields consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. The small sample training and test selection method is contrasted with randomly selected training sets as well as training sets chosen from the CADEX and DUPLEX algorithms for the well known Reed-Xiaoli anomaly detector.

River discharge is an important parameter in understanding water cycles, and consistent long-term discharge records are necessary for related research. In practice, discharge records based on in situ measurement are often limited because of technological, economic, and institutional obstacles. Satellite remote sensing provides an attractive alternative way to measure river discharge by constructing an empirical rating curve between the parameter provided by remote sensing techniques and simultaneous ground discharge data. River width is a popular parameter for constructing the empirical curve, since change in river discharge can be represented by a change in river width. In some rectangular channels, however, river width does not change significantly with river discharge, so an alternative parameter is necessary. We analyze a novel technique using river island area as an indicator of discharge. A river island often has a flat terrain, and its area decreases with higher discharge. This technique is validated by three river islands in the Yangtze River in China. All 61 remotely sensed images acquired by the HuanJing (HJ) satellites from 2009 to 2010 were correlated with corresponding in situ discharge of the nearby Zhicheng hydrological station. The performance of fitted curves for inferring river discharge is validated using 36 HJ images taken in 2011, and the influence of remotely sensed imagery and river islands is discussed. All three river islands can be used as indicators of river discharge, although their performances are much different. For the river island with the best result, the mean accuracy of the estimates is less than 10% of the observed discharge, and all relative errors are within 20%, validating the effectiveness of the proposed method.

The estimation of chlorophyll concentration in marine waters is fundamental for a number of scientific and practical purposes. Standard ocean color algorithms applicable to moderate resolution imaging spectroradiometer (MODIS) imagery, such as OC3M and MedOC3, are known to overestimate chlorophyll concentration ([CHL]) in Mediterranean oligotrophic waters. The performances of these algorithms are currently evaluated together with two relatively new algorithms, OC5 and SAM_LT, which make use of more of the spectral information of MODIS data. This evaluation exercise has been carried out using in situ data collected in the North Tyrrhenian and Ligurian Seas during three recent oceanographic campaigns. The four algorithms perform differently in Case 1 and Case 2 waters defined following global and local classification criteria. In particular, the mentioned [CHL] overestimation of OC3M and MedOC3 is not evident for typical Case 1 waters; this overestimation is instead significant in intermediate and Case 2 waters. OC5 and SAM_LT are less sensitive to this problem, and are generally more accurate in Case 2 waters. These results are finally interpreted and discussed in light of a possible operational utilization of the [CHL] estimation methods.

Urban tree canopy is widely believed to have myriad environmental, social, and human-health benefits, but a lack of precise canopy estimates has hindered quantification of these benefits in many municipalities. This problem was addressed for New York City using object-based image analysis (OBIA) to develop a comprehensive land-cover map, including tree canopy to the scale of individual trees. Mapping was performed using a rule-based expert system that relied primarily on high-resolution LIDAR, specifically its capacity for evaluating the height and texture of aboveground features. Multispectral imagery was also used, but shadowing and varying temporal conditions limited its utility. Contextual analysis was a key part of classification, distinguishing trees according to their physical and spectral properties as well as their relationships to adjacent, nonvegetated features. The automated product was extensively reviewed and edited via manual interpretation, and overall per-pixel accuracy of the final map was 96%. Although manual editing had only a marginal effect on accuracy despite requiring a majority of project effort, it maximized aesthetic quality and ensured the capture of small, isolated trees. Converting high-resolution LIDAR and imagery into usable information is a nontrivial exercise, requiring significant processing time and labor, but an expert system–based combination of OBIA and manual review was an effective method for fine-scale canopy mapping in a complex urban environment. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.JRS.6.063567]

In this study bio-optical water quality indicators, chlorophyll a , colored dissolved organic matter (CDOM), and total suspended matter (TSM) were derived from the Envisat-MERIS satellite data and were compared with in situ measurements collected in the Lithuanian optically Case 2 coastal waters of the Baltic Sea. Eight MERIS full-resolution Level 1b images, acquired during late spring and summer 2010, were processed using five, neural network-based processors for optically Case 2 or coastal and inland waters: FUB, C2R, Eutrophic, Boreal, and standard MERIS Level 2. Results showed that the FUB processor provided the most accurate estimates of the concentration of chlorophyll a [R 2 =69% ; mean absolute error(MAE)=7.76  mg/m³ ] and TSM (R 2 =89% ; MAE=3.93  g/m³ ). In situ CDOM absorption was most accurately estimated using the Boreal processor (R 2 =69% ; MAE=0.20  1/m ). We analyzed the factors that were most influential in explaining the differences in the accuracy and found that the Secchi depth and the sampling time were the most important factors. The greatest differences between satellite-derived and in situ values of water quality indicators were in correspondence with the lowest Secchi depth, suggesting that the plume zone created by freshwater coming from the hyper-eutrophic lagoon was the most sensitive region for the validation. The evident match between in situ measurements and satellite-based estimates was observed when field measurements were acquired 1-2 h before to approximately 2-4 h after the satellite overpass. Results of this validation work confirmed that remote sensing techniques are suitable for monitoring the changes of optical constituents in Lithuanian coastal waters.

The current study investigates a method to provide land surface parameters [i.e., land surface temperature (LST) and normalized difference vegetation index (NDVI)] at a high spatial (∼30 and 60 m) and temporal (daily and 8-day) resolution by combining advantages from Landsat and moderate-resolution imaging spectroradiometer (MODIS) satellites. We adopt a previously developed subtraction method that merges the spatial detail of higher-resolution imagery (Landsat) with the temporal change observed in coarser or moderate-resolution imagery (MODIS). Applying the temporal difference between MODIS images observed at two different dates to a higher-resolution Landsat image allows prediction of a combined or fused image (Landsat+MODIS ) at a future date. Evaluation of the resultant merged products is undertaken within the Southeastern Arizona region where data is available from a range of flux tower sites. The Landsat+MODIS fused products capture the raw Landsat values and also reflect the MODIS temporal variation. The predicted Landsat+MODIS LST improves mean absolute error around 5°C at the more heterogeneous sites compared to the original satellite products. The fused Landsat+<b>MODIS NDVI product also shows good correlation to ground-based data and is relatively consistent except during the acute (monsoon) growing season. The sensitivity of the fused product relative to temporal gaps in Landsat data appears to be more affected by uncertainty associated with regional precipitation and green-up, than the length of the gap associated with Landsat viewing, suggesting the potential to use a minimal number of original Landsat images during relatively stable land surface and climate conditions. Our extensive validation yields insight on the ability of the proposed method to integrate multiscale platforms and the potential for reducing costs associated with high-resolution satellite systems (e.g., SPOT, QuickBird, IKONOS).

A new approach for measuring high-altitude winds and temperatures has been developed that employs gas filter correlation radiometry from low Earth orbit. This approach would provide unprecedented observations of atmospheric dynamics from the lower stratosphere into the middle thermosphere. Our objective was to test the Doppler wind and temperature sounder (DWTS) retrievals (including uncertainties) using realistic middle atmosphere virtual input. We present several sampling scenarios for DWTS, and demonstrate its capability to retrieve planetary-scale variations in global temperature and vector winds.

Microwave remote sensing can provide reliable measurements of surface soil moisture. However, some land surface conditions can have a perturbing influence on soil moisture retrievals. In the soil moisture experiments in 2005 (SMEX05), we attempted to contribute to the understanding of the effect of dew using concurrent ground and aircraft observations. Early morning flights were conducted with an airborne microwave radiometer from June 19 to July 2, 2005, in Iowa, USA over an agricultural domain. Results of the experiment indicated that dew had a small but measurable effect on the observed 10.7-GHz brightness temperatures. The results indicate that the H-pol emissivity increased 0.015 to 0.04 for the corn sites, 0.014 to 0.02 for soybean, and 0.01 for forest sites as dew evaporated. These results suggest that the presence of dew decreases X-band land surface emissivity slightly and the effect of dew varies with vegetation types. Our findings are consistent with other works in the literature that has found that the effect of dew depends on both the type of vegetation and the wavelength of observation, but further studies should be conducted to verify this hypothesis.

A method is presented for integrating the information available in a limited area (corresponding to Tuscany in Italy) coming from satellite sensors, point measurement stations and ground-based radars. The objective is the exploitation of the complementary information provided by the variety of methods and instruments nowadays existing for measuring precipitation or precipitation-related parameters, in order to upgrade the capability of reconstructing weather phenomena of main interest. Ground- and satellite-based measurements, working locally or remotely, are jointly analyzed to evaluate how heterogeneous data can amplify the effectiveness of the measurements, when synergically analyzed, and this holds also when some of the available instruments essentially give just qualitative information. A way to synthesize the different information provided by various instruments is presented, assessing the quality of all the available observations. Namely, steps are described for the achievement of a mosaic of qualitative weather radars, and it is shown how the joint analysis of satellite, rain gauge and lightning observations can support a correct interpretation of precipitation phenomena. Finally, a logical scheme for data integration is presented and discussed.

Persistent scatterer interferometry (PSI) techniques using amplitude analysis and considering a temporal deformation model for PS pixel selection are unable to identify PS pixels in rural areas lacking human-made structures. In contrast, high rates of land subsidence lead to significant phase-unwrapping errors in a recently developed PSI algorithm (StaMPS) that applies phase stability and amplitude analysis to select the PS pixels in rural areas. The objective of this paper is to present an enhanced algorithm based on PSI to estimate the deformation rate in rural areas undergoing high and nearly constant rates of deformation. The proposed approach integrates the strengths of all of the existing PSI algorithms in PS pixel selection and phase unwrapping. PS pixels are first selected based on the amplitude information and phase-stability estimation as performed in StaMPS. The phase-unwrapping step, including the deformation rate and phase-ambiguity estimation, is then performed using least-squares ambiguity decorrelation adjustment (LAMBDA). The atmospheric phase screen (APS) and nonlinear deformation contribution to the phase are estimated by applying a high-pass temporal filter to the residuals derived from the LAMBDA method. The final deformation rate and the ambiguity parameter are re-estimated after subtracting the APS and the nonlinear deformation from that of the initial phase. The proposed method is applied to 22 ENVISAT ASAR images of southwestern Tehran basin captured between 2003 and 2008. A quantitative comparison with the results obtained with leveling and GPS measurements demonstrates the significant improvement of the PSI technique.

Traditional change detection of land use and land cover using multitemporal satellite optical imaging sensors requires two essential steps: 1. normalization of images acquired on different dates by unchanged pixels and 2. detection of changed areas from normalized images by rapid change measurements. In image normalization, finding the unchanged pixels by extracting the pseudo-invariant features has been a widely adopted approach. Rapid change detection analyses are known as useful methods in a wide field of applications, such as disaster management and urban land management, in which multitemporal satellite imageries of the same area taken at two or more different time steps are compared in a rapid fashion over the time horizon to identify changes. The challenge, however, is to find a proper reference basis for detecting changes in a rapid fashion while maintaining a reasonable computational load. In response to the acute need for rapid change detection, this study proposes an innovative method to identify a set of pseudo-variant features (PVFs) corresponding to changed pixels via a fast search algorithm. Once PVFs are found, they may be employed as a reference basis to detect the changed objects at the ground level. The experimental results show that the proposed PVFs method can offer a quality reference basis for detecting changes based on multitemporal satellite imageries in a rapid fashion to meet various managerial goals in the field.

At-satellite reflectance based tasseled cap parameters were extracted from HJ-1A/B satellite imagery, the charge coupled device (CCD) data of the Chinese environmental satellites that were launched on September 6, 2008. Sixteen scenes selected from the four sensors (HJ 1A CCD1, HJ 1A CCD2, HJ 1B CCD1, HJ 1B CCD2), respectively were used. The objectives are evaluating the consistency of the tasseled cap parameters for four sensors and proposing combined tasseled cap parameters for all four HJ-1A/B CCD. The results indicated that the direction of corresponding tasseled cap vectors of four sensors were almost the same. Then a combined at-satellite reflectance-based tasseled cap transformation was developed based on eight HJ-1A/B CCD scenes representing a variety of landscapes of China in both leaf-on and leaf-off seasons. Extraction combines the principle component transform with Gram-Schmidt Orthogonalization (GSO) process. The accurate brightness was obtained with the help of the first principal component eigenvector, and then greenness and wetness. The fourth one was obtained by the GSO. The first two dimensions (the brightness and the greenness) typically capture over 98%, and the brightness, greenness, and wetness account for over 99.9% of the total spectral variance. The fourth one occupies a very low proportion.

Urban settlements in developing countries are, at present, growing five times as fast as those in developed countries. This paper presents the urban expansion and land use/land cover changes in the fast urbanizing coastal area of the Dakshina Kannada district in Karnataka state, South India, during the years 1983-2008 as a case study. Six Indian Remote Sensing (IRS) satellite images were used in the present work. Supervised classification was carried out using maximum likelihood algorithm. The overall accuracy of the classification varied from 79% to 86.6%, and the kappa statistics varied from 0.761 to 0.850. The results indicate that the urban/built-up area in the study area has almost tripled during the study period. During the same time, the population has increased by 215%. The major driving forces for the urbanization were the enhanced economic activity due to the port and industrialization in the area. The urban/built-up area is projected to increase to 381  km ² and the population in the study area is expected to reach 2.68 million by the year 2028. Urban growth prediction helps urban planners and policymakers provide better infrastructure services to a huge number of new urban residents.

Aircraft remote sensing of freshwater ecosystems offers federal and state monitoring agencies an ability to meet their assessment requirements by rapidly acquiring information on ecosystem responses to environmental change for water bodies that are below the resolution of space-based platforms. During this study, hyperspectral data were collected over a two-day period from glacial lakes, ponds, and man-made reservoirs in New Hampshire, Massachusetts, Connecticut, and Rhode Island. These lakes ranged from five to greater than 1600 hectares and oligotrophic-mesotrophic to eutrophic and hypereutrophic conditions. Water samples were collected by several New England state agencies coincident with the airborne remote-sensing flights to provide ground reference data for algorithm development and testing. Using an inverse modeling approach remotely sensed reflectances from the near-infrared to red portion of the spectrum were used to develop an empirical model to estimate chlorophyll a concentrations. The accuracy of the algorithm was assessed from the RSM error of predicted and measured chlorophyll values for all lakes sampled. Results showed a strong statistical relationship between measured and predicted values. The predicted chlorophyll concentrations were used to assess the biological condition, trophic status, and recreational risk to human health for the New England lakes and ponds surveyed.

Relative radiometric normalization (RRN) to remove sensor effects, solar and atmospheric variation from at-sensor radiance values is often necessary for effective detection of temporal change. Traditionally, pseudo-invariant features (PIFs) are chosen subjectively, where as an analyst manually chooses known objects, often man-made, that should not change over time. An alternative method of selecting PIFs uses a principal component analysis (PCA) to select the PIFs. We compare the two RRN methods using PIFs in multiple Landsat images of urban and rural areas in Australia. An assessment of RRN quality was conducted including measurements of slope, root mean square error, and normalized difference vegetation index. We found that in urban areas both methods performed similarly well. However, in the rural area the automated PIF selection method using a PCA performed better due to the rarity of built features that are required for the manual PIF selection. We also found that differences in performance of the manual and automated methods were dependent on the accuracy assessment method tested. We conclude with a discussion on the relative merits of different RRN methods and practical advice on how to apply the automated PIF selection method.

Wavelet parameters (e.g., wavelet type, level of decomposition) affect the performance of the wavelet denoising algorithm in hyperspectral applications. Current studies select the best wavelet parameters for a single spectral curve by comparing similarity criteria such as spectral angle (SA). However, the method to find the best parameters for a spectral library that contains multiple spectra has not been studied. In this paper, a criterion named normalized spectral angle (NSA) is proposed. By comparing NSA, the best combination of parameters for a spectral library can be selected. Moreover, a fast algorithm based on threshold constraint and machine learning is developed to reduce the time of a full search. After several iterations of learning, the combination of parameters that constantly surpasses a threshold is selected. The experiments proved that by using the NSA criterion, the SA values decreased significantly, and the fast algorithm could save 80% time consumption, while the denoising performance was not obviously impaired.

After six years in a polar Venus orbit, the visible and infrared thermal imaging spectrometer (VIRTIS) on ESA's Venus Express mission provided an enormous amount of new data, including a three-dimensional view of the atmosphere and information on global surface properties of the planet. An overview is given of selected scientific results achieved by use of VIRTIS data comprising atmospheric thermal structure, molecular and particulate composition, chemistry and dynamics, and surface features.

The possibilities for applying multiwavelength elastic lidar probing of the atmosphere to help monitor air-quality over large industrial and densely populated areas, based predominantly on the use and analysis of commonly obtainable backscatter-related lidar quantities, are examined. Presented are two-wavelength (1064/532  nm ) lidar observations on the spatial distribution, structure, composition, and temporal evolution of close-to-surface atmospheric aerosol fields over heterogeneous orographic areas (adjacent city, plain, and mountain) near Sofia, Bulgaria. Selected winter-time evening lidar measurements are described. Range profiles, histograms, and evolutional range-time diagrams of the aerosol backscatter coefficients, range-corrected lidar signals, normalized standard deviations, and backscatter-related Ångström exponents (BAE) are analyzed. Near-perfect correlation between the aerosol density distribution and orographic differentiation of the underlying terrain is established, finding expression in a sustained horizontal stratification of the probed atmospheric domains. Distinctive features in the spatial distribution and temporal evolution of both the fine- and coarse aerosol fractions are revealed in correlation with terrain's orography. Zonal aerosol particle size distributions are qualitatively characterized by using an approach based on BAE occurrence frequency distribution analysis. Assumptions are made about the aerosol particle type, origin, and dominating size as connected (by transport-modeling data) to local pollution sources.

Conventional raster-based cellular automata (CA) confront many difficulties because of cell size and neighborhood sensitivity. Alternatively, vector CA-based models are very complex and difficult to implement. We present a hybrid cellular automata (HCA) model as a combination of cellular structure and vector concept. The space is still defined by a set of cells, but rasterized spatial objects are also utilized in the structure of transition rules. Particle swarm optimization (PSO) is also used to calculate the urbanization probability of cells based on their distance from the development parameters. The proposed model is applied to Landsat satellite imagery of the city of Tehran, Iran with 28.5-m spatial resolution to simulate the urban growth from 1988 to 2010. Statistical comparison of the ground truth and the simulated image using a kappa coefficient shows an accuracy of 83.42% in comparison to the 81.13% accuracy for the conventional Geo-CA model. Moreover, decreasing the spatial resolution by a factor of one-fourth has reduced the accuracy of the HCA and Geo-CA models by 1.19% and 3.04%, respectively, which shows the lower scale sensitivity of the proposed model. The HCA model is developed to have the simplicity of cellular structure together with optimum features of vector models.

Using multiple satellite observations, Argo floats profiles, and one-dimensional (1-D) ocean mixed layer model, this study systematically investigated the impacts of the binary typhoons Hagibis and Mitag [which coexisted respectively in the South China Sea (SCS) and western North Pacific (WNP) during November, 22 to 26, 2007] on upper ocean environments. It was observed that intense Ekman pumping and two mesoscale cold, cyclonic eddies, which, induced by long forcing time of strong wind stress curls, appeared respectively in two certain areas instead of after the binary typhoons' trails. Both cyclonic eddies retained for ∼39 days, accompanied with maximum sea surface height anomaly (SSHA) reduction of ∼25  cm induced by Hagibis and of ∼44  cm induced by Mitag, respectively. The largest sea surface temperature (SST) drop of 7°C and 2°C, the maximum chlorophyll a (Chl-a ) enhancement respectively was >20 times and ∼3 times in these two eddies' regions induced by Typhoon Hagibis and Mitag, respectively. The results of the 1-D ocean mixed layer model showed that, given its 84 h forcing time, the simulated MLT cooling and mixed layer deepening induced by Hagibis were ∼-2.8°C and 45 m, respectively, ∼-0.5°C and 25 m for Mitag at its 66 h forcing time. This work provides convincing evidences that typhoons, which appear frequently in the SCS and the WNP, play a notable role in the activities of mesoscale eddies in these areas.

A hyperspectral chromotomosynthetic imaging (CTI) system is used to detect and classify a collection of 21 scattered, spectrally diverse point-like sources. The instrument operates in the visible to near IR (400 to 800 nm) and has the potential to collect spectral imagery at great than 10 Hz. A two-dimensional wideband spatial image of the target scene is used to detect and spatially characterize the targets leading to optimization of the three-dimensional (3-D) spatial/spectral reconstruction of the hyperspectral image cube. The instrument is assessed by directly comparing results to spatial data collected by a wideband image and hyperspectral data collected using a liquid crystal tunable filter (LCTF). Target classification using k-means clustering of observed spectra yielded 5 to 6 target classes for each methodology, indicating information obtained using CTI was similar to that collected by the LCTF. The wide-band spatial content of the scene reconstructed from the CTI data is of same or better quality in terms of background noise and target intensities as a single frame collected by the undispersed imaging system with projections taken at every 1 deg. Performance is dependent on the number of projections used, with projections at 5 deg producing adequate results in terms of target characterization. The CTI has 2 to 4 times the spectral resolution of the LCTF. The data collected by the CTI system can simultaneously provide spatial information of equal quality as a the bandpass imaging system, provide high-frame rate slitless one-dimensional spectra, and generate 3-D hyperspectral imagery which can be exploited to provide the same results as a traditional multiband spectral imaging system.

Area-averaged vegetation index (VI) depends on spatial resolution and the computational approach used to calculate the VI from the data. Certain data treatments can introduce scaling effects and a systematic bias into datasets gathered from different sensors. This study investigated the mechanisms underlying the scaling effects of a two-band spectral VI defined in terms of the ratio of two linear sums of the red and near-infrared reflectances (a general form of the two-band VI). The general form of the VI model was linearly transformed to yield a common functional VI form that elucidated the nature of the monotonic behavior. An analytic investigation was conducted in which a two-band linear mixture model was assumed. The trends (increasing or decreasing) in the area-averaged VIs could be explained in terms of a single scalar index, ην, which may be expressed in terms of the spectra of the vegetation and nonvegetation endmembers as well as the coefficients unique to each VI. The maximum error bounds on the scaling effects were derived as a function of the endmember spectra and the choice of VI. The validity of the expressions was explored by conducting a set of numerical experiments that focused on the monotonic behavior and trends in several VIs.

Due to its unique geographical location and regional climate, the Yellow River and its tributaries are prone to ice jams almost every spring. Ice jams can cause levees to burst, leading to severe flooding, property damage, and human casualties. Hence, there is an urgent need to carry out observations of ice conditions and make risk assessments of ice jam occurrence. Field observation is the most reliable technique, but it is usually too expensive and time-consuming, which has led to the evaluation of applied remote sensing for data capture and analysis. Owing to the factors of timeliness, image resolution, human safety, and cost, satellite or manned aerial remote sensing cannot fully meet the requirements of ice condition observation. An unmanned aerial vehicle (UAV) remote sensing system is proposed for the collection of river ice imagery, providing the benefits of low cost, flexible launch and landing logistics, safety, and appropriate hyperspatial image resolution. One Inner Mongolian segment of the Yellow River was chosen as a test area to demonstrate key technologies and specific procedures of observation and assessment of ice conditions using the UAV system. The specific UAV remote sensing system and its components are introduced along with the procedures of UAV operation and imagery acquisition. Image preprocessing techniques and ice information extraction are described in detail followed by analysis and risk assessment of the ice conditions based on the resulting panoramic imagery. Results prove the feasibility and effectiveness of applying the fixed-wing UAV system to rapid observation and risk assessment of ice jam formation over the Yellow River under harsh weather conditions including low temperatures and strong winds.

A series of environmental policies in Sichuan province was executed to restore the grassland and forestland on some degraded lands after 2000. But the effectiveness on land use and cover change (LUCC) has not yet been systematically investigated. We undertook a detailed analysis about land use and cover change between 2000 and 2005 in Sichuan province. Our study mainly utilized remotely sensed data of 2005 China-Brazil Earth Resources Satellite II (CBERS II) and 2000 Landsat 5 thematic mapper (TM) data. Land use and cover change between 2000 and 2005 was visually interpreted by CBERS II with ArcInfo Workstation based on land use and cover database interpreted from TM. Then LUCC was validated by ground truth with global positioning system receivers. Our analysis illustrates that the conservation policies to restore the grassland and forestland were successful to a lesser extent. But more measures to restore the grassland and forestland of Sichuan province have to be taken further in the future.

Retrieval of the aboveground forest biomass (AGB), especially in high biomass forests ( > 100 t/ha), remains a challenging task for the researchers worldwide. The retrieval of AGB over a tropical forest area in India using Envisat advanced synthetic aperture radar C-band backscatter, interferometric synthetic aperture radar (InSAR) coherence and semi-empirical models viz., water cloud model (WCM) and interferometric water cloud model (IWCM), is studied. In process, the model parameters, i.e., backscatter from vegetation and ground, two-way tree transmissivity, and coherence from vegetation and ground were retrieved. The model training procedure to retrieve the model parameters consisted of an iterative regression of WCM and IWCM. High AGB accuracy (R²= 0.73) with low root mean square error (RMSE=53.76 t/ha) was achieved through multidate weighted averaging using RMSE-based weighting coefficients and WCM. Multidate data and InSAR coherence images showed better results (R²=0.90, RMSE = 35.92 t/ha) compared to individual coherence images. The InSAR coherence was found to be better for AGB retrieval than SAR backscatter as the former did not saturate for high AGB values.

We present an algorithm for daytime radiation fog detection (ADRFD) to characterize the fog distribution over land areas through two main steps: (1) detection of areas with clouds and radiation fog based on edge pixels and (2) separation of radiation fog from clouds based on object properties. The algorithm is tested in southeast China over an area of 2,640,000 km² (longitude extent: 105°E to 122°E, latitude extent: 23°N to 40°N), with 1 km resolution moderate resolution imaging spectroradiometer/TERRA images, digital elevation model of China (grid size is 1×1 km² and accuracy of elevation is 25 m), and the fog detection accuracies are evaluated using the observation results from 3590 ground observation stations. Results show that ADRFD can detect areas with clouds and radiation fog and effectively differentiate radiation fog from clouds based on object properties with a Kappa coefficient of 0.89 and critical success index at 0.87. It is concluded that ADRFD is a promising approach for daytime radiation fog detection over large land surfaces based on the condition that fog objects do not intersect with cloud objects or are not located under cloud objects. Extensions of this current study will be on improving the parameters determination method of ADRFD. ADRFD can be used to detect other types of fog in different regions and different seasons theoretically, but it should be tested further. In addition, more improvements are needed to allow for the detection of smaller areas of fog and fog regions intersecting with or under clouds.

Crop rotation is one of the important decisions made independently by numerous farm managers, and is a critical variable in models of crop growth and soil carbon. In Iowa and much of the Midwestern United States (US), the typical management decision is to rotate corn and soybean crops for a single field; therefore, the land-cover changes each year even though the total area of agricultural land-use remains the same. The price for corn increased from 2001 to 2010, which increased corn production in Iowa. We tested the hypothesis that the production increase was the result of changes in crop rotation in Iowa using the annual remote sensing classification (the cropland data layer) produced by the United States Department of Agriculture, National Agricultural Statistics Service. It was found that the area planted in corn increased from 4.7 million hectares in 2001 to 5.7 million hectares in 2007, which was correlated with the market price for corn. At the county level, there were differences in how the increase in corn production was accomplished. Northern and central counties had little land to expand cultivation and generally increased corn production by converting to a corn-corn rotation from the standard corn-soybean rotation. Southern counties in Iowa increased corn production by expanding into land that was not under recent cultivation. These changes affect the amount of soil carbon sequestration.

Based on the strong correlation between nondimensional wave height and wave age, we propose a quartic polynomial model by piecewise method according to significant wave height. In order to validate our new model, a validation dataset was created by interpolating the mean wave period data of ERA-40 to 23 cycles counterpart measuring points of Jason-1 altimeter with bilinear interpolation. We found that (1) the previous inversion models based on altimeter measurements cannot provide enough accuracy with our global validation dataset; (2) compared with H98 model, the RMS error and bias were reduced by 0.8949 s and 0.3759 s, respectively, by piecewise quartic polynomial model. Furthermore, the bias of the new model is −0.0197  s, which indicates that it offers enough accuracy in the global range.

Most traditional pixel-based analyses are based on the digital number of each pixel. Whereas images can provide more details such as color, size, shape, and texture, object-oriented processing is more advantageous. Multiresolution segmentation, which was proposed by Baatz and Schäpe, is one of the most powerful segmentation algorithms. On the other hand, meaningful segmentation is the most important issue in object-oriented processing. Currently, meaningful segmentation, which is recommended by Baatz's multiresolution segmentation approach, is a trial-and-error task that is very tedious and time consuming. Therefore, a genetic algorithm (GA) is used for finding optimal parameters of Baatz's multiresolution segmentation approach for three building groups' meaningful segmentation. The optimal parameters are found by GA and its generality has been evaluated on a simulated image as well as some IKONOS and GeoEye image patches. The evaluations show the efficiency of GA for finding optimal multiresolution segmentation parameters for meaningful segmentation of the simulated image and the three groups of building images.

This paper presents absolute radiometric calibration coefficients (gains) that explain the relationship between the digital number (DN) and at-sensor radiance for the multispectral camera (MSC) on Korea's first high-resolution satellite (KOMPSAT-2). Absolute radiometric calibration was performed using a reflectance-based method. In addition, the suitability of vicarious results from radiance- and reflectance-based validations was analyzed with reference to IKONOS and QuickBird images. The latter are spectrally similar to KOMPSAT-2 images and have been validated in a large number of studies. For all bands, the R² values of fitted lines for the gain ranged from 0.82 to 0.94, representing an improvement compared to previous findings for the KOMPSAT-2 MSC. To analyze the suitability of the vicarious results, same-pixel at-sensor radiances across different spectral bands were compared. In all bands, except the red band of QuickBird, the at-sensor radiances of KOMPSAT-2 MSC were highly correlated with those of IKONOS and QuickBird. In addition, same-pixel comparisons of reflectance across different spectral bands showed that the slopes of the least-squares lines for each band were similar to the results of the radiance comparison. The standard deviation among top of atmosphere (TOA) reflectances was within 0.019 for all bands. To calculate the tasseled cap transformation (TCT) coefficients for the KOMPSAT-2 MSC, an empirical method was applied using radiometric normalization. The results were similar to those obtained using the TCT coefficients for IKONOS and QuickBird in the brightness, greenness, and wetness components. The TCT images showed similar patterns. The absolute radiometric calibration coefficients presented here appear to be a good standard for maintaining the optical quality of the KOMPSAT-2 MSC, for which prelaunch, on-board, and vicarious calibration data are lacking.

Land-use and land-cover (LULC) classification accuracy in different seasons is not constant due to seasonal variations in spectral characteristics of different land-cover classes. This study addresses the problem of selecting a suitable season for mapping land-cover and identifying changes between seasons of midlatitude (29 deg 30′; to 31 deg 0′S) region of distinctive summer and winter rainfall, a broad altitudinal range, a temperate to subtropical climate and diverse land uses (e.g., summer and winter crops and nature conservation). Six landsat thematic mapper (TM) images from 2007 to 2009 were used taking three sequential three-date composites for seasonal change detection. January (midsummer) was the most suitable season in providing high spectral separability between most classes. The study demonstrates the means for improving LULC classification accuracy through the selection of optimal season for individual LULC class mapping and also provides a method of combining two or more classifications using referential refinement technique to generate aggregate LULC map of the region.

We present a novel systematic method for change detection in dual polarimetric (Dual-pol) synthetic aperture radar (SAR) images based on swarm intelligence techniques and fractal geometry. As the two main algorithms of swarm intelligence, ant colony optimization (ACO) and particle swarm optimization (PSO) have great potential in change detection. Additionally, fractal geometry appears to be a highly effective means of characterizing textural features in Dual-pol SAR images. The proposed method exploits fractal images to form a new difference image. Fractal images are computed based on wavelet multiresolution analysis. Moreover, by minimizing an optimal function value in the iteration process, the changes are detected by applying ACO and PSO to the difference image. Experimental results of detecting changes in Dual-pol SAR images reveal that the proposed method is a highly effective and efficient means of change detection in Dual-pol SAR images.

Supervised machine learning algorithms are used to classify pixels of a multi-sensor remote sensing dataset comprising RADAR and optical measurements for central Sudan. A total of 19 layers were used, 16 RADAR bands from RADARSAT DN, and texture bands acquired on 13 December 2008 (dry season) and on 2 June 2009 (wet season), and three optical bands acquired by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on 25 February 2009. Three different machine learning supervised classification algorithms were used to test the advantage of combining RADAR and optical data: a decision rule, a decision tree, and a naive Bayesian. In all the experiments performed, a combination of RADAR and optical bands leads to higher predictive accuracy and better land cover classification than either sensor used independently. The decision rule classifier performed best among the three methods used.

With the launch of GOES-R expected in 2015, research is currently under way to fully understand the characteristics of every channel on its Advanced Baseline Imager (ABI). The ABI will have two infrared (IR) window bands centered near 10.35 and 11.2 μm. Since no broad-band space-borne sensor has a channel near 10.35 μm, radiative transfer model simulations are used to study the clear-sky gaseous absorption properties in this wavelength range. It is shown that water vapor preferentially absorbs radiation at 11.2 μm compared to 10.35 μm, making the 10.35 μm a "cleaner" window IR band.

The application of synthetic aperture radar (SAR) in urban areas is far from resolved with an increased spatial resolution, so building extraction from SAR images remains a difficult task. According to SAR imaging principles, the outline of a building is usually incomplete in a single-aspect SAR image, and microwave interactions between adjacent targets further complicate this phenomenon in urban areas. Thus, in this study, dual-aspect high-resolution SAR images obtained, respectively from ascending and descending orbits are introduced to extract the building footprints in urban areas, and a method for building footprint extraction based on the fusion of dual-aspect SAR images is proposed. First these dual-aspect SAR images are co-registered, and then the preliminary positions of each potential building are determined using Markov random field models and Hough transform. Next the test images are partitioned into several subimages that contain only one building target. Then the edge of a building is extracted within the subimage of each aspect using a region-growing method and gradient algorithm, and then detection results obtained from each aspect are fused to produce the ultimate outline of the buildings based on Dempster-Shafer evidence theory. Experiments using TerraSAR-X images demonstrate that this method can extract the complete footprint of buildings in urban areas and can also improve the accuracy when estimating the dimensions of buildings.

Hyperspectral data classification using supervised approaches, in general, and the statistical algorithms, in particular, need high quantity and quality training data. However, these limitations, and the high dimensionality of these data, are the most important problems for using the supervised algorithms. As a solution, unsupervised or clustering algorithms can be considered to overcome these problems. One of the emerging clustering algorithms that can be used for this purpose is the kernel-based fuzzy c-means (KFCM), which has been developed by kernelizing the FCM algorithm. Nevertheless, there are some parameters that affect the efficiency of KFCM clustering of hyperspectral data. These parameters include kernel parameters, initial cluster centers, and the number of spectral bands. To address these problems, two new algorithms are developed. In these algorithms, the particle swarm optimization method is employed to optimize the KFCM with respect to these parameters. The first algorithm is designed to optimize the KFCM with respect to kernel parameters and initial cluster centers, while the second one selects the optimum discriminative subset of bands and the former parameters as well. The evaluations of the results of experiments show that the proposed algorithms are more efficient than the standard k-means and FCM algorithms for clustering hyperspectral remotely sensed data.

Light detection and ranging (LiDAR) point cloud data can contain millions of point returns from a diverse range of surface features, and directly reconstructing buildings from these data is challenging. Trees and other vegetation pose a particular problem in many built environments. This paper investigates several efficient procedures for detecting buildings and excluding vegetation using LiDAR and imagery data. Two general approaches for identifying and filtering out returns from vegetation are investigated: the first uses a normalized difference vegetation index (NDVI) image, while the second uses height differences. The utility of an entropy filter for improving NDVI filter performance as well as two distinct approaches for height-difference modeling are also evaluated. All methods use efficient raster-based algorithms for filtering while retaining the high spatial precision of the vector LiDAR point returns. Following removal of nonbuilding points, remaining points are segmented into distinct building features. In addition, we place particular emphasis on the analysis of processing challenges and special cases as well as the accuracy of these different methods on a large-volume LiDAR dataset covering a challenging build environment.

This study highlights the need for digital mapping of urban sprawl phenomenon in catchment areas with the use of both satellite and ground remote-sensing techniques. The Yialias River, located in the central part of Cyprus, was selected as a case study area. In this catchment, devastating flash flood events took place in both 2003 and 2009, with catastrophic results. Initially, ground spectroradiometric measurements were applied to investigate the spectral similarity of different classes such as those of "urban fabric" and "marl/chalk origin formations" within the watershed. Temporal land cover changes were analyzed by using multitemporal satellite imagery (ASTER) and by incorporating both pixel- and object-oriented classification techniques. To create effective land use and land cover maps, a classification model was proposed based on spectral, texture, and shape characteristics. The pixel-based classification results were compared and evaluated with the object-based classification products. The optimum classification products were imported to geographical information systems and FRAGSTATS software and were used to visually and statistically detect landscape identifying trends based on spatial landscape metrics. The final results indicated considerable urban expansion within the study area during the last 10 years.

Using historical satellite surface soil moisture products, the Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the submonthly scale accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain gauge observations. In order to adapt to the irregular retrieval frequency of heritage soil moisture products, a new variable correction window method is developed that allows for better efficiency in leveraging temporally sparse satellite soil moisture retrievals. Results confirm the advantage of using this variable window method relative to an existing fixed-window version of SMART over a range of one- to 30-day accumulation periods. Using this modified version of SMART and heritage satellite surface soil moisture products, a 1.0-deg, 20-year (1979 to 1998) global rainfall dataset over land is corrected and validated. Relative to the original precipitation product, the corrected dataset demonstrates improved correlation with a global gauge-based daily rainfall product, lower root-mean-square-error (-13%) on a 10-day scale and provides a higher probability of detection (+5%) and lower false alarm rates (-3.4%) for five-day rainfall accumulation estimates. This corrected rainfall dataset is expected to provide improved rainfall forcing data for the land surface modeling community.

Individual crown condition of Eucalyptus gomphocephala was assessed using two classification models to understand changes in forest health through space and time. Using high resolution (0.5 m) digital multispectral imagery, predictor variables were derived from textural and spectral variance of all pixels inside the crown area. The results estimate crown condition as a surrogate for tree health against the total crown health index. Crown condition is derived from combining ground-based crown assessment techniques of density, transparency, dieback, and the regrowth of foliage. This object-based approach summarizes the pixel data into mean crown indices assigned to crown objects which became the carrier of information. Models performed above expectations, with a significant weighted Cohen's kappa (κ > 0.60 and p < 0.001) using 70% of available data. Using in situ data for model development, crown condition was predicted forwards (2010) and backwards (2007) in time, capturing trends in crown condition and identifying decline in the healthiest between 2008 and 2010. The results confirm that combining spectral and textural information increased model sensitivity to small variations in crown condition. The methodology provides a cost-effective means for monitoring crown condition of this or other eucalypt species in native and plantation forests.

We evaluate the land surface temperature (LST) generated from the spinning enhanced visible and infrared imager (SEVIRI) onboard the MSG-2 satellite, which was retrieved using the split-window method where the land surface emissivity (LSE) was estimated from the day/night temperature-independent spectral indices-based method. The SEVIRI-derived LST was compared with the MODIS-derived LST extracted from the MOD11B1 V5 product during 7 clear-sky days. The results show that (1) discrepancies exist between the two LST products, with a maximum average difference of 4.9 K; (2) these differences are considered to be time-dependent, since higher discrepancies are observed during the daytime; (3) these differences are land-cover dependent, e.g., bare areas generally present larger differences than vegetated areas; and (4) these differences are inversely proportional to view zenith angle differences. Finally, the main sources of LST differences are investigated and identified in terms of LSE, instrumental noise equivalent temperature difference (NEΔT), and misregistration of the two LST products. The LST differences arising from NEΔT and misregistration are within 0.4 K. Therefore, these discrepancies may mainly result from errors in LSE, which are caused primarily by the atmospheric correction error for the SEVIRI-derived LST.

To investigate discrimination of firing signatures in the battlespace, blast, and flash signatures were collected from two large-caliber guns. Signatures included blast wave trajectories from high-speed (1600 Hz) imagery, visible and near-infrared (450 to 850 nm) spectra, and mid-wave infrared (1800 to 6000  cm−1) spectra. Thirty empirical and 12 phenomenological features were extracted from the signatures over 25 observations. Multiple discriminant analysis was used for feature selection and dimensionality reduction, and discrimination was based on Bayesian probability theory. When discriminating between three munitions configurations fired from a 152 mm howitzer, leave-one-out testing with the empirical features demonstrated classification accuracies of 92% to 96%. Phenomenological features (for example, blast energy, combustion and excitation temperatures, and species concentrations) accurately classified munitions less consistently (72% to 96%). The two most salient features for differentiating between two weapons (a single 120 mm tank firing and all 152 mm howitzer firings) were soot emissivity and H2O concentration. Empirical features consistently discriminated the weapons best when more than two features were used. Moderate to strong correlations (r2=0.5 to 1.0) were observed amongst blast, visible, and infrared features. A limited examination indicated that strongly correlated features may be substituted for one another.

Land cover change is a significant issue for environmental managers for sustainable management. Remote sensing techniques have been shown to have a high probability of recognizing land cover patterns and change detection due to periodic coverage, data integrity, and provision of data in a broad range of the electromagnetic spectrum. We evaluate the applicability of remote sensing techniques for land cover pattern recognition, as well as land cover change detection of the Hatiya Island, Bangladesh, and quantify land cover changes from 1977 to 1999. A supervised classification approach was used to classify Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM), and Multispectral Scanner (MSS) images into eight major land cover categories. We detected major land cover changes over the 22-year study period. During this period, marshy land, mud, mud with small grass, and bare soil had decreased by 85%, 46%, 44%, and 24%, respectively, while agricultural land, medium forest, forest, and settlement had positive changes of 26%, 45%, 363%, and 59%, respectively. The primary drivers of such landscape change were erosion and accretion processes, human pressure, and the reforestation and land reclamation programs of the Bangladesh Government.

The Qingjiang River Basin, which is 423 km long in the Hubei province, China, is the first large tributary of the Yangtze River below the Three Gorges. The Qingjiang River Basin surface water area monitoring plays an important role in the water resource management strategy and regular monitoring management of the Yangtze River watershed. Hydropower cascade exploitation, which started in 1987, has formed three reservoirs including the Geheyan reservoir, the Gaobazhou reservoir, and the Shuibuya reservoir in the midstream and downstream of the Qingjiang River Basin. They have made a great impact on surface water area changes of the Qingjiang River Basin and need to be taken into account. We monitor the Qingjiang River Basin surface water area changes from 1973 to 2010. Ten scenes from the Multispectral Scanner System (MSS), seven scenes from the Thematic Mapper (TM), and two scenes from the Enhanced Thematic Mapper Plus (ETM+) remote sensing data of Landsat satellites, the normalized different water index (NDWI), the modified NDWI (MNDWI), and Otsu image segmentation method were employed to quantitatively estimate the Qingjiang River Basin surface water area in the 1970s, 1980s, 1990s, and 2000s, respectively. The results indicate that the surface water area of the Qingjiang River Basin shows a growing trend with the hydropower cascade development from the 1980s to the first decade of the 21st century. The study concluded the significance of human activities impact on surface water spatiotemporal distribution. Surface water accretion is significant in most parts of the Qingjiang River Basin and might be related to the constructed cascade hydropower dams.

An automatic road extraction method for vague aerial images is proposed in this paper. First, a high-resolution but low-contrast image is enhanced by using a Retinex-based algorithm. Then, the enhanced image is segmented with an improved Canny edge detection operator that can automatically threshold the image into a binary edge image. Subsequently, the linear and curved road segments are regulated by the Hough line transform and extracted based on several thresholds of road size and shapes, in which a number of morphological operators are used such as thinning (skeleton), junction detection, and endpoint detection. In experiments, a number of vague aerial images with bad uniformity are selected for testing. Similarity and discontinuation-based algorithms, such as Otsu thresholding, merge and split, edge detection-based algorithms, and the graph-based algorithm are compared with the new method. The experiment and comparison results show that the studied method can enhance vague, low-contrast, and unevenly illuminated color aerial road images; it can detect most road edges with fewer disturb elements and trace roads with good quality. The method in this study is promising.

A dual-polarization lidar and photography are used to sense internal waves in West Sound, Orcas Island, Washington, from a small aircraft. The airborne lidar detected a thin plankton layer at the bottom of the upper layer of the water, and this signal provides the depth of the upper layer, amplitude of the internal waves, and the propagation speed. The lidar is most effective when the polarization filter on the receiver is orthogonal to the transmitted light, but this does not depend significantly on whether the transmitted light is linearly or circularly polarized. The depolarization is greater with circular polarization, and our results are consistent with a single parameter Mueller scattering matrix. Photographs of the surface manifestation of the internal waves clearly show the propagation direction and width of the phase fronts of the internal waves, even though the contrast is low (2%). Combined with the lidar profile, the total energy of the internal wave packet was estimated to be 9 MJ.

The commercial development of uncooled-microbolometer, long-wave infrared (LWIR) imagers, combined with advanced radiometric calibration methods developed at Montana State University, has led to new uses of thermal imagery in remote sensing applications. One specific novel use of these calibrated imagers is imaging of vegetation for CO₂ gas leak detection. During a four-week period in the summer of 2011, a CO₂ leak was simulated in a test field run by the Zero Emissions Research and Technology Center in Bozeman, Montana. An LWIR imager was deployed on a scaffold before and during the CO ₂ release, viewing a vegetation test area that included regions of high and low CO₂ flux. Increased root-level CO₂ concentration caused plant stress that led to reduced thermal regulation of the vegetation, which was consistent with increased diurnal variation of IR emission observed in this study. In a linear regression, the IR data were found to have a strong relationship to the CO₂ emission and to be consistent with the location of leaking CO₂ gas. Reducing the continuous data set to one image per day weakened the regression fit, but maintained sufficient significance to indicate that this method could be implemented with once-daily airborne images.

A set of low-cost, compact multispectral imaging systems have been developed for deployment on tethered balloons for education and outreach based on basic principles of optical remote sensing. They have proven to be sufficiently capable, and they are now being used in research as well. The imagers use tiny complementary metal-oxide semiconductor cameras with low-cost optical filters to obtain images in red and near-infrared bands, and a more recent version includes a blue band. The red and near-infrared bands are used primarily for identifying and monitoring vegetation through the normalized difference vegetation index (NDVI), while the blue band can be used for studying water turbidity and so forth. The imagers are designed to be carried by tethered balloons to altitudes currently up to approximately 50 m. These undergraduate-student-built imaging systems are being used by university and college students for a broad range of applications in multispectral imaging, remote sensing, and environmental science.

Satellite radar altimeter measurements of lake and reservoir water levels complement in situ observations by providing stage information for ungauged basins and by filling data gaps in existing gauge records. Such additional measurements assist both research and operational programs. However, for a particular lake or reservoir, altimetric products offered to end-users may differ due to choice of employed instrument, processing technique, and applied geophysical corrections. To explore these differences, particularly with their potential impact on climate-based research, an intercomparison of three web-based water-level products (produced by Laboratoire d'Etudes en Géophysique et Océanographie Spatiale, National Aeronautics and Space Administration/United States Department of Agriculture, and European Space Agency/De Montfort University) has been undertaken based on 18 lakes and reservoirs. The products are well correlated with each other (r=0.87 to 0.99) and where in situ data are available are quite well correlated with the gauge measurements (r=0.73 to 0.99). Despite variations in data processing, the poorest root-mean-square differences between any altimeter product and gauge data (∼0.20 to 1.41 m) occur for the narrow reservoirs and smaller lakes. The largest discrepancies between the altimeter products occur for the lakes that freeze (Lake Athabasca and Woods). The current altimeter products provide acceptable accuracy, long-term trends and seasonality for climate applications. We discuss the merits of each product system, but recommend further validations and the provision of ice-detection flags.

Optical close-range instruments can be applied to derive water quality parameters for monitoring purposes and for validation of optical satellite data. In situ radiometers are often difficult to deploy, especially from a small boat or a remote location. The water insight spectrometer (WISP-3) is a new hand-held radiometer for monitoring water quality, which automatically performs measurements with three radiometers (L_sky , L_u , E_d ) and does not need to be connected with cables and electrical power during measurements. The instrument is described and its performance is assessed by an intercomparison to well-known radiometers, under real fieldwork conditions using a small boat and with sometimes windy and cloudy weather. Root mean squared percentage errors relative to those of the TriOS system were generally between 20% and 30% for remote sensing reflection, which was comparable to those of the other instruments included in this study. From this assessment, it can be stated that for the tested conditions, the WISP-3 can be used to obtain reflection spectra with accuracies in the same range as well-known instruments. When tuned with suitable regional algorithms, it can be used for quick scans for water quality monitoring of Chl, SPM, and aCDOM.

We propose a novel mixture model that combines two special cases of heavy-tailed Rayleigh distribution. These two special families possess the only analytical forms of heavy-tailed Rayleigh distribution. As a consequence, the mixture model has an analytical form. Because heavy-tailed Rayleigh distribution is a member of spherically invariant random process, one can obtain the parameter estimation by the method-of-moments technique. Finally, the mixture model has been tested on various synthetic aperture radar images, and the performance of this model is strong compared with other models such as K distribution, G0 distribution, and heavy-tailed Rayleigh models.