In order to study landscape pattern of Hainan natural meadow, the interpretation has been conducted on TM of natural meadow by using ArcGIS 9.3 and the vector data of natural meadow types in the test site have been ob...In order to study landscape pattern of Hainan natural meadow, the interpretation has been conducted on TM of natural meadow by using ArcGIS 9.3 and the vector data of natural meadow types in the test site have been obtained, which have been transformed into grid data in ArcView 3.3. Nine indexes have been selected for calculation based on Fragstats 3.3 and Excel 2007, including largest patch index, mean patch size, edge density, shape index, Simpson's diversity index, Simpson's evenness index, fragmentation index, dominance index and separation index. The results show that landscape patch shape of natural meadow is simple, fragmentation index and landscape diversity are high and landscape indexes are highly correlated.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
文摘In order to study landscape pattern of Hainan natural meadow, the interpretation has been conducted on TM of natural meadow by using ArcGIS 9.3 and the vector data of natural meadow types in the test site have been obtained, which have been transformed into grid data in ArcView 3.3. Nine indexes have been selected for calculation based on Fragstats 3.3 and Excel 2007, including largest patch index, mean patch size, edge density, shape index, Simpson's diversity index, Simpson's evenness index, fragmentation index, dominance index and separation index. The results show that landscape patch shape of natural meadow is simple, fragmentation index and landscape diversity are high and landscape indexes are highly correlated.
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.
