The Arctic region and the Antarctic region, as the two-polar regions of the earth, are sensitive to the global change to be the research focus. However, the existing earth observing system satellite data in the two-polar regions of the earth is not enough. The Moon is the unique natural satellite of the earth, which has advantages of global-scale coverage and long observation time. Therefore, the moon-based platform turns out to be a potential platform to comprehensively and continuously observe the Earth on a global scale, especially for the contrastive study of the Earth two-polar regions. Moreover, comparing to the limited life of the current satellites, the longevity of the moon is helpful to collect long-term time series data, which makes it possible to research long-term earth science phenomena in the two-polar regions. This paper comparatively analyzes the angular coverage performance of the two-polar regions of the earth observed from the moon-based platform. The observation angles of long-period 40 years through the geometry model of the moon-based platform from different sensor locations on the moon are calculated. The sensors are set on four potential sites on the moon--- the North Pole, the South Pole, the Sinus Iridum area and the Mare Nectaris area. When the two-polar regions of the earth are observed from four different locations on the moon, the different observation angular characteristics are obtained. This is helpful for the site selection of the moon-based platform.
Natural tropical rainforests in China’s Xishuangbanna region have undergone dramatic conversion to rubber plantations in recent decades, resulting in altering the region’s environment and ecological systems. Therefore, it is of great importance for local environmental and ecological protection agencies to research the distribution and expansion of rubber plantations. The objective of this paper is to monitor dynamic changes of rubber plantations in China’s Xishuangbanna region based on multitemporal Landsat images (acquired in 1989, 2000, and 2013) using a C5.0-based decision-tree method. A practical and semiautomatic data processing procedure for mapping rubber plantations was proposed. Especially, haze removal and deshadowing were proposed to perform atmospheric and topographic correction and reduce the effects of haze, shadow, and terrain. Our results showed that the atmospheric and topographic correction could improve the extraction accuracy of rubber plantations, especially in mountainous areas. The overall classification accuracies were 84.2%, 83.9%, and 86.5% for the Landsat images acquired in 1989, 2000, and 2013, respectively. This study also found that the Landsat-8 images could provide significant improvement in the ability to identify rubber plantations. The extracted maps showed the selected study area underwent rapid conversion of natural and seminatural forest to a rubber plantations from 1989 to 2013. The rubber plantation area increased from 2.8% in 1989 to 17.8% in 2013, while the forest/woodland area decreased from 75.6% in 1989 to 44.8% in 2013. The proposed data processing procedure is a promising approach to mapping the spatial distribution and temporal dynamics of rubber plantations on a regional scale.
Airborne light detection and ranging (LiDAR) system calibration is a crucial procedure for ensuring the accuracy of point data. A common practice is to use conjugate planar patches to recover systematic parameters based on coplanar constraints and to use planes with different orientations to decrease the correlations between the systematic errors. When there are not sufficient planar patches and the configuration of planar patches is not optimal, it is difficult to guarantee the reliability of the estimated system parameters. Based on the analyses of the bore-sight angle effects, we find that not only the orientations but also the distribution of planar patches play an important role in the calibration procedure. We propose an improved method for bore-sight calibration based on the principles of symmetry of coordinate offsets and low correlations between bore-sight angles. Comparisons of the experimental results of bore-sight angle calibration suggest that the proposed configuration of conjugate planar patches can decrease the correlations between bore-sight angles and improve the reliability of calibration results. The optical results obtained from four gable-roof buildings are very close to the results calculated by the RiProcess software with a deviation of about 0.001 deg.
The three-dimensional (3-D) structure of forests, especially the vertical structure, is an important parameter of forest ecosystem modeling for monitoring ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of super-resolution imaging and a small number of measurements, distribution compressive sensing (DCS) inversion techniques for polarimetric SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of DCS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at the P-band. A new DCS-based FP TomoSAR method is proposed: a new wavelet-based distributed compressive sensing FP TomoSAR method (FP-WDCS TomoSAR method). The method takes advantage of the joint sparsity between polarimetric channel signals in the wavelet domain to jointly inverse the reflectivity profiles in each channel. The method not only allows high accuracy and super-resolution imaging with a low number of acquisitions, but can also obtain the polarization information of the vertical structure of forested areas. The effectiveness of the techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne datasets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana.
In this study, an analytical methodology based on four typical statistical analysis methods was proposed to understand soil moisture (SM) dynamics and their response to climate change in Central Asia and Xinjiang over 30 years using the essential climate variable-soil moisture dataset and the Climate Research Unit (CRU) dataset. The results are as follows: (1) In general, the SM of the study area decreased significantly over the last 30 years. The significant warming trend dominated the soil desiccation. (2) The soil desiccation trend is more severe in Central Asia than in Xinjiang, while the SM in Xinjiang has increased gradually since 2004. The trends of soil desiccation in Central Asia and Xinjiang consistently and negatively feedback to the significant warming trend. (3) The SM for all five countries of Central Asia distinctly decreased. The significant increase in temperature dominated the soil desiccation in the other four countries of Central Asia except for Kyrgyzstan, while precipitation had no significant impact on SM. (4) The regions with drying and warming trends and with drying and cooling trends (∼89% of the total area) were largely distributed in major agricultural areas; these trends are unfavorable to the sustainable development of agriculture.
The Amazon Basin experienced an abrupt transition from extreme drought to flood during 2010–2012, causing significant loss and damage to the property of thousands of families. We used datasets derived from the latest products of the Gravity Recovery and Climate Experiment (GRACE), Tropical Rainfall Measuring Mission (TRMM), and the Global Land Data Assimilation System (GLDAS) to assess the extent, intensity, and dynamics of the 2010–2012 abrupt transition from extreme drought to flood in the Amazon. The monthly developing processes during the abrupt transition from extreme drought to flood between 2010 and 2012 were reproduced and examined by comparisons between GRACE terrestrial water storage anomaly and the precipitation derived from TRMM satellite estimates and GLDAS datasets. Accumulated precipitation during the peak of 2010 drought and 2012 flood looks very much similar to terrestrial water storage deficit and surplus, both at the temporal and spatial scales. Furthermore, strong correlations between the 2010 and 2012 extreme drought/flood events over the Amazon and El Niño-Southern Oscillation were also detected. This study can be helpful for archiving historical information on disasters that can contribute to the elaboration of regional scale drought/flood disaster prevention and mitigation strategies in the Amazon.
Poyang Lake is the largest freshwater lake in China and one of the most important wetlands in the world. Vegetation, an important component of wetland ecosystems, is one of the main sources of the carbon in the atmosphere. Biomass can quantify the contribution of wetland vegetation to carbon sinks and carbon sources. Synthetic aperture radar (SAR), which can operate in all day and weather conditions and penetrate vegetation to some extent, can be used to retrieve information about vegetation structure and the aboveground biomass. In this study, RADARSAT-2 polarimetric SAR data were used to retrieve aboveground vegetation biomass in the Poyang Lake wetland. Based on the canopy backscatter model, the vegetation backscatter characteristics in the C-band were studied, and a good relation between simulated backscatter and backscatter in the RADARSAT-2 imagery was achieved. Using the backscatter model, pairs of training data were built and used to train the back propagation artificial neural network. The biomass was retrieved using this ANN and compared with the field survey results. The root-mean-square error in the biomass estimation was 45.57 g/m2. This shows that the combination of the model and polarimetric decomposition components can efficiently improve the inversion precision.
Forests are one of the most important sinks for carbon. Estimating the amount of carbon stored in forests is a major task for understanding the global carbon cycle. From local to global scales, remote sensing has been extensively used for forest biomass estimation. With the availability of multisensor image data, fusion has become a valuable method in remote sensing applications. Light detection and ranging (LiDAR) can provide information on the vertical structure of forests, whereas hyperspectral images can provide detailed spectral information of forests. Effective fusion of LiDAR and hyperspectral data is expected to help extract important biophysical parameters of forests. However, it is still unclear as to how forest biophysical and biochemical attributes derived from hyperspectral data relate to structural attributes derived from LiDAR data. A summary of previous research on LiDAR-hyperspectral fusion for forest biomass estimation is valuable for further improvement of biomass estimation methods. A review on the status of hyperspectral data, LiDAR data, and the fusion of these two data sources for forest biomass estimation in the last decade is provided. Some future research topics and major challenges are also discussed.
Vegetation phenology reveals the response of vegetation to global climate change. The time series of remote sensing data
have been applied to generate land surface pheology and vegetation seasonality information. In this study, land surface
phenology was detected from time series of radar backscatter data from 2003 to 2007 and compared with phenological
metrics derived from SPOT VEGETATION NDVI and MODIS land cover dynamic product across Australia. An
asymmetric Gaussian method was used to extract phenological metrics, the start of season (SOS) and the end of season
(EOS) from the time series. Comparing the spatial pattern of average SOS and EOS from the three datasets, similar
spatial pattern are mapped across western and southeastern Australia. However, different phenological patterns are
captured in the tropical ecosystems of northern and eastern Australia. These results showed the potential of microwave
data in monitoring vegetation dynamics as complementary phenological information.
Tuukka Petäjä, Gerrit de Leeuw, Hanna Lappalainen, Dmitri Moisseev, Ewan O'Connor, Valery Bondur, Nikolai Kasimov, Vladimir Kotlyakov, Huadong Guo, Jiahua Zhang, Gennadii Matvienko, Veli-Matti Kerminen, Alexander Baklanov, Sergej Zilitinkevich, Markku Kulmala
Human activities put an increasing stress on the Earth’ environment and push the safe and sustainable boundaries of the vulnerable eco-system. It is of utmost importance to gauge with a comprehensive research program the current status of the environment, particularly in the most vulnerable locations. The Pan-Eurasian Experiment (PEEX) is a new multidisciplinary research program aiming at resolving the major uncertainties in the Earth system science and global sustainability questions in the Arctic and boreal Pan-Eurasian regions. The PEEX program aims to (i) understand the Earth system and the influence of environmental and societal changes in both pristine and industrialized Pan-Eurasian environments, (ii) establish and sustain long-term, continuous and comprehensive ground-based airborne and seaborne research infrastructures, and utilize satellite data and multi-scale model frameworks filling the gaps of the insitu observational network, (iii) contribute to regional climate scenarios in the northern Pan-Eurasia and determine the relevant factors and interactions influencing human and societal wellbeing (iv) promote the dissemination of PEEX scientific results and strategies in scientific and stake-holder communities and policy making, (v) educate the next generation of multidisciplinary global change experts and scientists, and (vi) increase the public awareness of climate change impacts in the Pan- Eurasian region. In this contribution, we underline general features of the satellite observations relevant to the PEEX research program and how satellite observations connect to the ground-based observations.
Global change now poses a severe threat to the survival and development of humankind. It is increasingly drawing the attention of every country in the world. Global change refers to changes in the biophysical environment resulting from natural factors or human activities, as well as changes in society and human well-being. These changes are either at a global scale or at a local scale that has expanded to a global phenomenon. Meanwhile, global environmental problems are becoming more and more serious, threatening our lifestyle and survival.
Global environmental change has gained widespread global attention. It is a complex system with special spatial and temporal evolutionary characteristics. Sensitive factors are indicators of global environmental change, and some can be observed with Earth observation technology. RADARSAT-2 is capable of polarimetric and interferometric observations, which can provide an effective way to document some sensitive factors of global environmental change. This study focuses on the usage of RADARSAT-2 data for observing sensitive factors of environmental change and building highly accurate application models that connect synthetic aperture radar data and observable sensitive factors. These include (1) extracting spatiotemporal distribution of large-scale alluvial fan, (2) extracting vegetation vertical structure, (3) detecting urban land cover change, and (4) monitoring seasonal floods. From this study, RADARSAT-2 data have been demonstrated to have excellent capabilities in documenting several sensitive factors related to global environmental change.
As an important urban agglomeration of China, the Jing-Jin-Tang area has experienced intense urbanization since the 1980s. This study explores the spatiotemporal dynamics of urban areas in this region using multitemporal Landsat images. An enhanced built-up (BU) index method was applied to extract BU areas with an overall accuracy ranging from 75% to 91.35%. Seven spatial metrics were used to discern urban growth patterns at city and county levels. The results indicate that all cities witnessed a rapid growth of BU areas with different spatial patterns. Beijing has been aggregating since the 1990s and a large homogeneous urban patch has formed. The construction and development of metropolitan Beijing and Tianjin started in the early 1980s and became almost fully developed by the end of 1990. Tangshan, like many medium-sized cities in China, is still enduring a development process with an accelerating pace. The metropolitan areas of Beijing and Tianjin have been greatly developed with BU densities exceeding 90% since 2000, compared with Tangshan’s 55% in 2010. These results provide spatial information on the evolution of urban extent in the period of 1990s to 2010s in this region.
Glacier movement is closely related to changes in climatic, hydrological, and geological factors. However, detecting glacier surface flow velocity with conventional ground surveys is challenging. Remote sensing techniques, especially synthetic aperture radar (SAR), provide regular observations covering larger-scale glacier regions. Glacier surface flow velocity in the West Kunlun Mountains using modified offset-tracking techniques based on ALOS/PALSAR images is estimated. Three maps of glacier flow velocity for the period 2007 to 2010 are derived from procedures of offset detection using cross correlation in the Fourier domain and global offset elimination of thin plate smooth splines. Our results indicate that, on average, winter glacier motion on the North Slope is 1 cm/day faster than on the South Slope—a result which corresponds well with the local topography. The performance of our method as regards the reliability of extracted displacements and the robustness of this algorithm are discussed. The SAR-based offset tracking is proven to be reliable and robust, making it possible to investigate comprehensive glacier movement and its response mechanism to environmental change.
This study explores a spatiotemporal comparative analysis of urban agglomeration, comparing the Greater Toronto and Hamilton Area (GTHA) of Canada and the city of Tianjin in China. The vegetation–impervious surface–soil (V–I–S) model is used to quantify the ecological composition of urban/peri-urban environments with multitemporal Landsat images (3 stages, 18 scenes) and LULC data from 1985 to 2005. The support vector machine algorithm and several knowledge-based methods are applied to get the V–I–S component fractions at high accuracies. The statistical results show that the urban expansion in the GTHA occurred mainly between 1985 and 1999, and only two districts revealed increasing trends for impervious surfaces for the period from 1999 to 2005. In contrast, Tianjin has been experiencing rapid urban sprawl at all stages and this has been accelerating since 1999. The urban growth patterns in the GTHA evolved from a monocentric and dispersed pattern to a polycentric and aggregated pattern, while in Tianjin it changed from monocentric to polycentric. Central Tianjin has become more centralized, while most other municipal areas have developed dispersed patterns. The GTHA also has a higher level of greenery and a more balanced ecological environment than Tianjin. These differences in the two areas may play an important role in urban planning and decision-making in developing countries.
The Qinghai-Tibetan Plateau has been experiencing a distinct warming trend, and climate warming has a direct and quick impact on the alpine grassland ecosystem. We detected the greenness trend of the grasslands in the plateau using Moderate Resolution Imaging Spectroradiometer data from 2000 to 2009. Weather station data were used to explore the climatic drivers for vegetation greenness variations. The results demonstrated that the region-wide averaged normalized difference vegetation index (NDVI) increased at a rate of 0.036 yr −1 . Approximately 20% of the vegetation areas, which were primarily located in the northeastern plateau, exhibited significant NDVI increase trend (p -value <0.05 ). Only 4% of the vegetated area showed significant decrease trends, which were mostly in the central and southwestern plateau. A strong positive relationship between NDVI and precipitation, especially in the northeastern plateau, suggested that precipitation was a favorable factor for the grassland NDVI. Negative correlations between NDVI and temperature, especially in the southern plateau, indicated that higher temperature adversely affected the grassland growth. Although a warming climate was expected to be beneficial to the vegetation growth in cold regions, the grasslands in the central and southwestern plateau showed a decrease in trends influenced by increased temperature coupled with decreased precipitation.
In recent years, the urban impervious surface has been recognized as a key quantifiable indicator in assessing urbanization impacts on environmental and ecological conditions. A surge of research interests has resulted in the estimation of urban impervious surface using remote sensing studies. The objective of this paper is to examine and compare the effectiveness of two algorithms for extracting impervious surfaces from Landsat TM imagery; the multilayer perceptron neural network (MLPNN) and the support vector machine (SVM). An accuracy assessment was performed using the high-resolution WorldView images. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2) were calculated to validate the classification performance and accuracies of MLPNN and SVM. For the MLPNN model, the RMSE, MAE, and R2 were 17.18%, 11.10%, and 0.8474, respectively. The SVM yielded a result with an RMSE of 13.75%, an MAE of 8.92%, and an R2 of 0.9032. The results indicated that SVM performance was superior to that of MLPNN in impervious surface classification. To further evaluate the performance of MLPNN and SVM in handling the mixed-pixels, an accuracy assessment was also conducted for the selected test areas, including commercial, residential, and rural areas. Our results suggested that SVM had better capability in handling the mixed-pixel problem than MLPNN. The superior performance of SVM over MLPNN is mainly attributed to the SVM's capability of deriving the global optimum and handling the over-fitting problem by suitable parameter selection. Overall, SVM provides an efficient and useful method for estimating the impervious surface.
The differential interferometric synthetic aperture radar (SAR)(DInSAR) technique has been applied to the earth surface deformation monitoring in many areas. In this paper, the DInSAR technique is used to process the spaceborne SAR data including C band ENVISAT ASAR, L band JERS SAR, and ALOS PALSAR data to derive the temporal land subsidence information in the Fengfeng coal mine area, Hebei province in China. Since JERS and ALOS do not have precise orbit, an orbit adjustment must be accomplished before the DInSAR interferogram was formed. Twenty-three differential interferograms are derived to show the temporal change of the land subsidence range and position. At the acquisition time of ENVISAT ASAR, the leveling in the Dashucun coal mine in Fengfeng area was carried, the historical excavation data in 8 coal mines in Fengfeng area from 1992 to 2007 were collected as well. In our analysis, the DInSAR results are compared with leveling data and historical excavation data. The comparison results show the DInSAR subsidence results are consistent with the leveling results and the historical excavation data, and the L band DInSAR shows more advantages than C band in the coal mining induced subsidence monitoring in a rural area. The feasibility and limitations in coal mining induced subsidence monitoring with DInSAR are analyzed, and the possibility of underground mining activity monitoring by spaceborne InSAR data is evaluated.The experimental results show that both C and L band can accomplish monitoring mining area subsidence, but C band has more restricted conditions of its perpendicular baseline. In order to get a satisfactory outcome in mining area subsidence by the DInSAR method, the time series of SAR images of every visit and SAR deformation interferograms should be archived.
The advances in polarimetric synthetic aperture radar (SAR) interferometry techniques provide a promising way to extract sub-canopy surface parameters using processed SAR images. In this paper, we evaluate the fully maximum likelihood decomposition model of polarimetric SAR interferometry for sub-canopy soil moisture estimation. We further propose a methodology for sub-canopy soil estimation using repeat pass space-borne SIR-C (Shuttle Imaging Radar C) L-band polarimetric SAR interferometric data. The comparison of the inversion results with the field measurements and the climate data of Hotan region from 1951 to 2006 suggests good inversion potential of the proposed method.
The large 8.0 scale earthquake that occurred in Wenchuan, Sichuan province, China on May 12, 2008 caused huge damage to people's lives and property. Airborne and spaceborne remote sensing can be used accurately and effectively in almost real-time to monitor and assess earthquake disasters, providing an important scientific basis and decision-making support for government emergency command and post-disaster reconstruction. The high resolution, multi-band, multi-polarization, and full-polarization synthetic aperture radar (SAR) system and theories developed in recent years provide important data resources and the basic methodology for post-earthquake monitoring and evaluation. In this paper, the cities of Beichuan and Dujiangyan in the Wenchuan earthquake region are chosen as study sites. Using advanced high-resolution, multi-band, multi-polarization, and full-polarization SAR data, and applying urban building backscattering models and target backscattering and polarimetric target decomposition theory, the backscattering characteristics, polarimetric characteristics and texture features between collapsed and intact buildings post-earthquake are extensively compared and analyzed. Subsequently, a new SAR detection method for collapsed urban buildings is proposed from these characterizations. Preliminary results from comparisons between this method and high-resolution optical data show that the proposed method is effective and powerful in detecting collapsed urban buildings devastated by an earthquake.
Remote Sensing is the acquisition of information about an object without touching it. Remote sensing data and image analysis are used as major tools in investigating natural formations and man-made structures. Remote sensing techniques have proven to be very useful in the search for archaeological sites. Techniques such as aerial photography, colorinfrared photography, thermal infrared multi-spectral scanning, and radar imaging have successfully been used to locate potential archaeological sites and add questions to known sites. Image fusion, defined by Franklin and Blodgett (1933) as
the computation of three new values for a pixel based on the known relationship between the input data for the location in the image, has been advocated in a large number of papers as a suitable technique to improve the spatial appraisal of an image produced by merging low spatial resolution data with high spatial resolution data. The different images to be fused can come from different sensors of the same basic type or they may come from different types of sensors. The composite image should contain a more useful description of the scene than provided by any of the individual source
images. In our work, the simultaneously acquired SPOT5 multi-spectral images and SPOT5 panchromatic images are collected. First of all, the geometric correction is conducted to all the images with the error less than 0.5 pixels to make sure the high quality of image fusion. Then image fusion in pixel lever is performed and the image fusion quality is assessed by different criteria.
Digital Earth (DE) is a virtual presentation of the planet based on geographic coordinate, and is an information system
with tremendous amount of multiple resolutions and multiple scales data as shown in multiple dimensions. Since the
exact description about DE has not completed, most experts have their own understanding of DE, so there are a lot of
various digital earth prototype system was developed, such as the Alexandria digital earth modeling system developed by
the UCSB, digital earth prototype developed by the NASA, and earth simulator developed by the Japan and so on. Each
of them has their own infrastructure and characteristics in developing process. Besides, there are still many commercial
digital earth software popularly, such as the famous Google earth, word wind, skyline, and blue link and so on. They
have the one biggest common that is all of them were based on the vast remote sensing image and represented by virtual
reality technology. But when we reviewed the current situation of digital earth research presented from the outcomes of
the International Symposium on Digital Earth four times, and investigated most of these digital earth system and
software, we found the studies on digital earth system have some shortcomings. Therefore, facing this situation, in this
paper, firstly, we will review the situation and the development of the Digital Earth research. Then, we will emphasize
on how to construct the Digital Earth Prototype and develop its system from some new perspectives by the most
prevalent techniques.
With the all-weather and day/night imaging capability, synthetic aperture radar (SAR) plays an important role in inundation extent detection. The inundation area detection using SAR will be easy as a result of the dark image tones yielded by specular reflection to the radar wave. Object oriented method (OOM) was applied to detect inundation extent using multi-polarized ENVISAT ASAR data. The traditional pixel-based methods used in information extraction and classification focus on the single pixel, so when they are applied in SAR imagery no perfect results can be achieved because of the speckle of SAR imagery. On the other hand, the pixel-based methods have limitations for detecting inundation extent and flood monitoring because of the neglect of the information of the adjacent pixels. The OOM, which no longer looks at individual pixels, but rather homogeneity areas (image objects), would be much more effective. In this paper, the OOM is applied in the ENVISAT ASAR alternative polarized (VV/VH) images using the software eCognition. The study site is located in Poyang Lake wetland, which has different inundation extent at different time. The images were segmented firstly, then the standard nearest neighbor classifier and the membership function classifier were used to classify the image objects, finally the different inundation areas were detected. The classification accuracies for two classifiers from the OOM are 95.78% and 92.24%, which are higher than that of the maximum likelihood classifier, 86.02%.
With the all-weather and day-night imaging capability, synthetic aperture radar (SAR) plays an important role in
inundation extent change detection. Inundation extent change detection using SAR will be easy as a result of the dark
image tones yielded by specular reflection. Change vector analysis (CVA) method, an effective change detection method,
is also a valuable inundation extent change detection method. In CVA method, change magnitude and change direction
can be generated separately, which can be used to determine change areas and change types. CVA method also has the
ability to process any number of spectral bands and to produce detailed change information. In this paper, CVA method
was applied to inundation extent change detection using multi-temporal multi-polarization ENVISAT ASAR alternative
polarization images acquired on 2004-08-29, 2004-12-12 and 2005-03-27. The test site is located in Poyang Lake
wetland, where land surface had different inundation extent when images were acquired. Firstly these 3 phases of images
were registered together. Then the change vectors were calculated using these images. After that change magnitude and
direction cosine images were produced. At last the change areas and the corresponding change type were extracted
separately using decision tree method. The result indicates that CVA method has potential utility in inundation extent
change detection.
The backscattering and emission measured respectively by scatterometer and radiometer show promise for the estimation of surface soil moisture and vegetation characteristics. In this paper, the 13.4GHz scatterometer of QuikSCAT and the 6.9GHz radiometer of AMSR-E are simultaneously used for the estimation of the near-surface soil moisture and vegetation water content. An algorithm using synthetic passive and active microwave data is proposed to estimation land surface parameters. At last, the retrieval algorithm was applied on AMSR and QUIKSCAT observations which have been carried out for the SMEX02 (Soil Moisture Experiment 2002) region in Ames, Iowa for the time period June 25 to July 13, 2002. The result shows a consistent performance.
The tropical and subtropical regions are characterized by their extraordinary resource environment, weather and climate. So the spaceborne synthetic aperture radar (SAR) with all day and all weather imaging capability has particular function to detect these regions. The available spaceborne radar systems take into consideration observing the tropical and subtropical regions, however, until recently, there has not been the professional radar satellite for observing these regions. This paper proposed the concept of TSARSAT (Tropical SAR Satellite), and analyzed its technological characteristics. The radar satellite will play an important role in rice growth monitoring and yield estimation, tropical rain forest monitoring, disaster monitoring and warning, ocean detection, coast belt surveying and topography mapping.
China has conducted radar remote sensing technology and application for more than 20 years. In 1979, the first synthetic aperture radar (SAR) sample system in China was successfully developed by the Chinese Academy of Sciences. Since then a single channel with single polarization SAR and a multi-channel with multi-polarization SAR systems were produced in 1983 and 1987 respectively. Since 1988 the Hi- Tech Research and Development Program of China has organized and conducted a research titled 'Spaceborne SAR and Image Processing Technique', and has accomplished a real time digital SAR imaging processor.An airborne SAR with L-band and HH polarization was flown experimentally in 1997, acquiring imagery with 3 X 3 m spatial resolution. All these progresses have laid good foundations for the development of spaceborne SAR in China. At the same time, China has carried out extensive international cooperation in radar remote sensing, and has participated in SIR-C/X-SAR, Radarsat, ERS-1/2, JERS-2 SAR and GlobeSAR research programs for earth observations. The remote sensing satellite ground station in China can receive Radarsat and other spaceborne SAR data. Using these SAR data, significant application results have been achieved in the detection of ecology, hydrology, geology and oceanography, and monitoring of natural hazards.
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