Vegetation indices (VI) are one of the standard science products available from the Moderate Resolution Imaging Spectroradiometer (MODIS), Validation of MODIS-VI products was an important prerequisite to using these v...Vegetation indices (VI) are one of the standard science products available from the Moderate Resolution Imaging Spectroradiometer (MODIS), Validation of MODIS-VI products was an important prerequisite to using these variables for global modeling. In this study, validation of the MODIS-VI products including single-day MODIS, level 2 (gridded) daily MODIS surface reflectance (MOD09), 16-day composited MODIS (MOD13) was performed utilizing multisensor data from MODIS, Thematic Mapper (TM), and field radiometer, for a rice-planting region in southern China. The validation approach involved scaling up independent fine-grained datasets, including ground measurement and high spatial resolution imagery, to the coarser MODIS spatial resolutions. The 16-day composited MODIS reflectance and VI matched well with the ground measurement reflectance and VI. The VI of TM and MODIS were lower than the ground VI. The results demonstrated the accuracy, reliability, and utility of the MODIS-VI products for the study region.展开更多
The classification of thematic mapper imagery in areas with strong topographic variations has proven problematic in the past using a single classifier, due to the changing sun illumination geometry. This often results...The classification of thematic mapper imagery in areas with strong topographic variations has proven problematic in the past using a single classifier, due to the changing sun illumination geometry. This often results in the phenomena of identical object with dissimilar spectrum and different objects with similar spectrum. In this paper, an integrated classification method that combines a decision tree with slope data, tasseled cap transformation indices and maximum likelihood classifier is introduced, to find an optimal classification method for thematic mapper imagery of plain and highland terrains. A Landsat 7 ETM+ image acquired over Hangzhou Bay, in eastern China was used to test the method. The results indicate that the performance of the inte- grated classifier is acceptably good in comparison with that of the existing most widely used maximum likelihood classifier. The integrated classifier depends on hypsography (variation in topography) and the characteristics of ground truth objects (plant and soil). It can greatly reduce the influence of the homogeneous spectrum caused by topographic variation. This integrated classifier might potentially be one of the most accurate classifiers and valuable tool for land cover and land use mapping of plain and highland terrains.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 40171065)the National High Technology Research and Development Program (863 Program) of China (No. 2002AA243011)
文摘Vegetation indices (VI) are one of the standard science products available from the Moderate Resolution Imaging Spectroradiometer (MODIS), Validation of MODIS-VI products was an important prerequisite to using these variables for global modeling. In this study, validation of the MODIS-VI products including single-day MODIS, level 2 (gridded) daily MODIS surface reflectance (MOD09), 16-day composited MODIS (MOD13) was performed utilizing multisensor data from MODIS, Thematic Mapper (TM), and field radiometer, for a rice-planting region in southern China. The validation approach involved scaling up independent fine-grained datasets, including ground measurement and high spatial resolution imagery, to the coarser MODIS spatial resolutions. The 16-day composited MODIS reflectance and VI matched well with the ground measurement reflectance and VI. The VI of TM and MODIS were lower than the ground VI. The results demonstrated the accuracy, reliability, and utility of the MODIS-VI products for the study region.
文摘The classification of thematic mapper imagery in areas with strong topographic variations has proven problematic in the past using a single classifier, due to the changing sun illumination geometry. This often results in the phenomena of identical object with dissimilar spectrum and different objects with similar spectrum. In this paper, an integrated classification method that combines a decision tree with slope data, tasseled cap transformation indices and maximum likelihood classifier is introduced, to find an optimal classification method for thematic mapper imagery of plain and highland terrains. A Landsat 7 ETM+ image acquired over Hangzhou Bay, in eastern China was used to test the method. The results indicate that the performance of the inte- grated classifier is acceptably good in comparison with that of the existing most widely used maximum likelihood classifier. The integrated classifier depends on hypsography (variation in topography) and the characteristics of ground truth objects (plant and soil). It can greatly reduce the influence of the homogeneous spectrum caused by topographic variation. This integrated classifier might potentially be one of the most accurate classifiers and valuable tool for land cover and land use mapping of plain and highland terrains.
