Estimation of surface net radiation (SNR) is essential for understanding the land surface energy transformation, snow melting calculations, modeling crop growth, and addressing water resource management. In this study, two sets of experiments were performed to identify, respectively, the impacts of MODIS land surface temperature (LST) products, ground-based incoming shortwave and long-wave radiation and albedo measurements, as well as the performance of CoLM with respect to modeling SNR in the Tibetan Plateau at three timescales (half-hourly, hourly, daily, and monthly). The results show that the two experiments provide nearly the similar results and are obvious higher than ground measured SNR validations at three different timescales. SNRs obtained at half-hourly and hourly timescales closely match the real data fluctuations, while daily timescale is too large to catch the short-term fluctuations according to the peak values at the three timescales. Moreover, compared with Method 2, Method 1 is more accurate at different timescales.
The body length and weight are critical physiological parameters for fishes, especially eel-like fishes like swamp eel(Monopterusalbus).Fast and accurate measuring of body length is significant for swamp eel culturing as well as its resource investigation and protection. This paper presents an Android smart phone-based photogrammetry technology for measuring and estimating the length and weight of swamp eel. This method utilizes the feature that the ratio of lengths of two objects within an image is equal to that of in reality to measure the length of swamp eels. And then, it estimates the weight via a pre-built length-weight regression model. Analysis and experimental results have indicated that this method is a fast and accurate method for length and weight measurements of swamp eel. The cross-validation results shows that the RMSE (root-mean-square error) of total length measurement of swamp eel is0.4 cm, and the RMSE of weight estimation is 11 grams.
During recent years, cluster systems have played a more important role in the architecture design of high-performance computing area which is cost-effective and efficient parallel computing system able to satisfy specific computational requirements in the earth and space sciences communities. This paper presents a powerful cluster system built by Satellite Environment Center, Ministry of Environment Protection of China that is designed to process massive remote sensing data of HJ-1 satellites automatically everyday. The architecture of this cluster system including hardware device layer, network layer, OS/FS layer, middleware layer and application layer have been given. To verify the performance of our cluster system, image registration has been chose to experiment with one scene of HJ-1 CCD sensor. The experiments of imagery registration shows that it is an effective system to improve the efficiency of data processing, which could provide a response rapidly in applications that certainly demand, such as wild land fire monitoring and tracking, oil spill monitoring, military target detection, etc. Further work would focus on the comprehensive parallel design and implementations of remote sensing data processing.
The increasing volume of industrial solid wastes presents a critical problem for the global environment. In the detection and monitoring of these industrial solid wastes, the traditional field methods are generally expensive and time consuming. With the advantages of quick observations taken at a large area, remote sensing provides an effective means for detecting and monitoring the industrial solid wastes in a large scale. In this paper, we employ an object-oriented method for detecting the industrial solid waste from HJ satellite imagery. We select phosphogypsum which is a typical industrial solid waste as our target. Our study area is located in Fuquan in Guizhou province of China. The object oriented method we adopted consists of the following steps: 1) Multiresolution segmentation method is adopted to segment the remote sensing images for obtaining the object-based images. 2) Build the feature knowledge set of the object types. 3) Detect the industrial solid wastes based on the object-oriented decision tree rule set. We analyze the heterogeneity in features of different objects. According to the feature heterogeneity, an object-oriented decision tree rule set is then built for aiding the identification of industrial solid waste. Then, based on this decision tree rule set, the industrial solid waste can be identified automatically from remote sensing images. Finally, the identified results are validated using ground survey data. Experiments and results indicate that the object-oriented method provides an effective method for detecting industrial solid wastes.
As a kind of huge environmental risk source, tailings pond could cause a huge environmental disaster to the downstream area once an accident happened on it. Therefore it has become one key target of the environmental regulation in china. Especially, recently environmental emergencies caused by tailings pond are growing rapidly in China, the environmental emergency management of the tailings pond has been confronting with a severe situation. However, the regulatory agency is badly weak in the environmental regulation of tailings pond, due to the using of ground surveys and statistics which is costly, laborious and time consuming, and the lacking of strong technical and information support. Therefore, in this paper, according to the actual needs of the environmental emergency management of tailings pond, we firstly make a brief analysis of the characteristics of the tailings pond and the advantages and capability of remote sensing technology, and then proposed a comprehensive and systematic indexes system and the method of environmental risk monitoring of tailings pond based on remote sensing and GIS. The indexes system not only considers factors from the upstream area, the pond area and the downstream area in a perspective of the risk space theory, but also considers factors from risk source, risk receptor and risk control mechanism in a perspective of risk systems theory. Given that Zhangjiakou city has up to 580 tailings pond and is nearly located upstream of the water source of Beijing, so finally we apply the proposed indexes system and method in Zhangjiakou area in China to help collect environmental risk data of tailings pond in that area and find out it works well. Through the use case in Zhajiakou, the technique of using remote sensing to monitor environmental risk of tailings pond is feasible and effective, and would contribute to the establishment of ‘Space-Ground’ monitoring network of tailings pond in future.
A new approach for determining the forest leaf area index (LAI) from a geometric-optical model inversion using multisensor observations is developed. For improving the LAI estimate for the forested area on rugged terrain, a priori information on tree height and the spectra of four scene components of a geometric-optical mutual shadowing (GOMS) model are extracted from airborne light-detection and ranging (LiDAR) data and optical remote sensing data with high spatial resolution, respectively. The slope and aspect of the study area are derived from digital elevation model data. These extracted parameters are applied in an inversion to improve the estimates of forest canopy structural parameters in a GOMS model. For the field investigation, a bidirectional reflectance factor data set of needle forest pixels is collected by combining moderate-resolution-imaging-spectroradiometer (MODIS) and multiangle-imaging-spectroradiometer (MISR) multiangular remote sensing observations. Then, forest canopy parameters are inverted based on the GOMS model. Finally, the LAI of the forest canopy of each pixel is estimated from the retrieved structural parameters and validated by field measurements. The results indicate that the accuracy of forest canopy LAI estimates can be improved by combining observations of passive multiangle and active remote sensors.
Leaf Area Index (LAI) is a key vegetation structural parameter in ecosystem. Our new approach is on forest LAI
retrieval by GOMS model (Geometrical-Optical model considering the effect of crown shape and Mutual Shadowing)
inversion using multi-sensor observations. The mountainous terrain forest area in Dayekou in Gansu province of China
is selected as our study area. The model inversion method by integrating MODIS, MISR and LIDAR data for forest
canopy LAI retrieval is proposed. In the MODIS sub-pixel scale, four scene components' spectrum (sunlit canopy, sunlit
background, shaded canopy and shaded background) of GOMS model are extracted from SPOT data. And tree heights
are extracted from airborne LIDAR data. The extracted four scene components and tree heights are taken as the a priori
knowledge applied in GOMS model inversion for improving forest canopy structural parameters estimation accuracy.
According to the field investigation, BRDF data set of needle forest pixels is collected by combining MODIS BRDF
product and MISR BRF product. Then forest canopy parameters are retrieved based on GOMS. Finally, LAI of forest
canopy is estimated by the retrieved structural parameters and it is compared with ground measurement. Results indicate
that it is possible to improve the forest canopy structural parameters estimation accuracy by combining observations of
passive and active remote sensors.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.