Remote sensing data from the Terra Moderate-Resolution Imaging Spectroradiometer (MODIS) and geospatial data were used to estimate grass yield and livestock carrying capacity in the Tibetan Autonomous Prefecture of Go...Remote sensing data from the Terra Moderate-Resolution Imaging Spectroradiometer (MODIS) and geospatial data were used to estimate grass yield and livestock carrying capacity in the Tibetan Autonomous Prefecture of Golog, Qing-hai, China. The MODIS-derived normalized difference vegetation index (MODIS-NDVI) data were correlated with the aboveground green biomass (AGGB) data from the aboveground harvest method. Regional regression model between the MODIS-NDVI and the common logarithm (LOG10) of the AGGB was significant (r2 = 0.51, P < 0.001), it was, there-fore, used to calculate the maximum carrying capacity in sheep-unit year per hectare. The maximum livestock carrying capacity was then adjusted to the theoretical livestock carrying capacity by the reduction factors (slope, distance to water, and soil erosion). Results indicated that the grassland conditions became worse, with lower aboveground palatable grass yield, plant height, and cover compared with the results obtained in 1981. At the same time, although the actual livestock numbers decreased, they still exceeded the proper theoretical livestock carrying capacity, and overgrazing rates ranged from 27.27% in Darlag County to 293.99% in Baima County. Integrating remote sensing and geographical information system technologies, the spatial and temporal conditions of the alpine grassland, trend, and projected stocking rates could be forecasted for decision making.展开更多
ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, th...ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.展开更多
Offshore oil slicks are significant for both the monitoring of marine spill accidents and the detection of marine oil resources.The use of remote sensing technology to detect the thickness of oil slicks is a major are...Offshore oil slicks are significant for both the monitoring of marine spill accidents and the detection of marine oil resources.The use of remote sensing technology to detect the thickness of oil slicks is a major area of research.The reflected light from oil slicks changes with the thickness of the oil.This is the theoretical basis of research on optical remote sensing of offshore oil slicks.A two-beam interference model that considers the offshore oil slick as a flat plate has been developed in this study.A quantitative remote sensing model which describes a series of processes that use oil slick thickness and reflectance as variables is established.The use of the Fresnel equation to analyze parameters in the model indicated that the key property of the quantitative relationship between the oil slick thickness and reflectance was ultimately the disappearance or extinction of the oil slick.This model has been tested and verified by data from offshore oil slick spectral response experiments.Results showed that the oil slick thickness remote sensing model can be theoretically analyzed and is efficient.The research indicated that the major cause of variations in the spectral response as a function of oil slick thickness was the different light-scattering characteristics.These characteristics can be used in remote sensing applications to identify the different types of offshore oil slicks.The theoretical interpretation of each parameter in this model led to the development of a look-up table of the model parameters which will improve the efficiency of future offshore oil slick remote sensing.展开更多
基金Supported by the National Basic Research Program (973 Program) of China (Nos.2009CB421102 and 2005CB422005-01)the Second Scheme of CAS Action Plan for the Development of Western China (No.KZCX2-XB2-06-02)the National Key Technology R&D Program of China (No.2006BAC01A02-01)
文摘Remote sensing data from the Terra Moderate-Resolution Imaging Spectroradiometer (MODIS) and geospatial data were used to estimate grass yield and livestock carrying capacity in the Tibetan Autonomous Prefecture of Golog, Qing-hai, China. The MODIS-derived normalized difference vegetation index (MODIS-NDVI) data were correlated with the aboveground green biomass (AGGB) data from the aboveground harvest method. Regional regression model between the MODIS-NDVI and the common logarithm (LOG10) of the AGGB was significant (r2 = 0.51, P < 0.001), it was, there-fore, used to calculate the maximum carrying capacity in sheep-unit year per hectare. The maximum livestock carrying capacity was then adjusted to the theoretical livestock carrying capacity by the reduction factors (slope, distance to water, and soil erosion). Results indicated that the grassland conditions became worse, with lower aboveground palatable grass yield, plant height, and cover compared with the results obtained in 1981. At the same time, although the actual livestock numbers decreased, they still exceeded the proper theoretical livestock carrying capacity, and overgrazing rates ranged from 27.27% in Darlag County to 293.99% in Baima County. Integrating remote sensing and geographical information system technologies, the spatial and temporal conditions of the alpine grassland, trend, and projected stocking rates could be forecasted for decision making.
文摘ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.
基金supported by National Natural Science Foundation of China (Grant Nos. 40971186 and 41001196 )the Open Research Fund of Key Laboratory of Digital Earth,Center for Earth Observation and Digital Earth,Chinese Academy of Sciences (Grant No. 2010LDE007)
文摘Offshore oil slicks are significant for both the monitoring of marine spill accidents and the detection of marine oil resources.The use of remote sensing technology to detect the thickness of oil slicks is a major area of research.The reflected light from oil slicks changes with the thickness of the oil.This is the theoretical basis of research on optical remote sensing of offshore oil slicks.A two-beam interference model that considers the offshore oil slick as a flat plate has been developed in this study.A quantitative remote sensing model which describes a series of processes that use oil slick thickness and reflectance as variables is established.The use of the Fresnel equation to analyze parameters in the model indicated that the key property of the quantitative relationship between the oil slick thickness and reflectance was ultimately the disappearance or extinction of the oil slick.This model has been tested and verified by data from offshore oil slick spectral response experiments.Results showed that the oil slick thickness remote sensing model can be theoretically analyzed and is efficient.The research indicated that the major cause of variations in the spectral response as a function of oil slick thickness was the different light-scattering characteristics.These characteristics can be used in remote sensing applications to identify the different types of offshore oil slicks.The theoretical interpretation of each parameter in this model led to the development of a look-up table of the model parameters which will improve the efficiency of future offshore oil slick remote sensing.
