This paper introduces briefly two remote sensing case studies on land use in the subtropic region of China. One is on slope land use in the Yangtze River Three Gorges area. This is a large area of 60497 km2.First of a...This paper introduces briefly two remote sensing case studies on land use in the subtropic region of China. One is on slope land use in the Yangtze River Three Gorges area. This is a large area of 60497 km2.First of all, geometric correction and supervised classification were conducted for ten scenes of Landsat-5 TM or MSS images. The resolution of the processed images is 50 m ×50 m on ground. By the classification the land use/cover categories in this area were discriminated. Then the croplands including rice fields and upland fields were extracted from the land use/cover maps. Simultaneously the slope grade maps were prepared based on the topographic maps. Overlaying the slope grade maps and the cropland maps, the area and percentage of the croplands in different slope grades were determined. This case study indicated that 71.5% of the uplands was situated on the slope above 15° and 25% on the slope above 25° in this area. It is dangerous, and urgent cultivation or engineering measures should be taken. Another case study is on soil erosion in Linshan County of Guangxi Province. Airphoto interpretation and supervised classification of a Landsat TM image were carried out for discriminating land cover/use categories in an area of 3557.8 km2.And the soil erosion intensity grades were determined according to the land cover/use maps and slope maps.It wed discovered that the land suffering soil erosion accounted for 2404.0 km2, 67.6% of the total area of the county. Necessary measures to control soil erosion should be taken also.展开更多
Hyper-spectral data is widely used to determine soil properties. However, few studies have explored the soil spectral characteristics as response to soil erosion. This study analysed the spectral response of different...Hyper-spectral data is widely used to determine soil properties. However, few studies have explored the soil spectral characteristics as response to soil erosion. This study analysed the spectral response of different eroded soils in subtropical China, and then identify the spectral characteristics and soil properties that better discriminate softs with different erosion degrees. Two methods were compared： direct identification by inherent spectral characteristics and indirect identification by predictions of critical soft properties. Results showed that the spectral curves for different degrees of erosion were similar in morphology, while overall reflectance and characteristics of specific absorption peaks were different. When the first method is applied, some differences among different eroded groups were found by integration of associated indicators. However, the index of such indicators showed apparent mixing and crossover among different groups, which reduced the accuracy of identification. For the second method, the correlation between critical soil properties, such as soil organic matter （SOM）, iron and aluminium oxides and reflectance spectra, was analysed. The correlation coefficients for the moderate eroded group were primarily between -0.3 to -0.5, which were worse than the other twogroups. However, the maximum value of R2 was obtained as 0.86 and 0.94 for the non-apparent eroded and the severe group. Furthermore, these two groups also showed some differences in the spectral response of iron complex state （Fep）, Aluminium amorphous state （Alo） and the modelling results for soil organic matter （SOM）. The study proved that it is feasible to identify different degrees of soil erosion by hyperspectral data, and that indirect identification by modelling critical soil properties and reflectance spectra is much better than direct identification. These results indicate that hyper-spectral data may represent a promising tool in monitoring and modelling soil erosion.展开更多
文摘This paper introduces briefly two remote sensing case studies on land use in the subtropic region of China. One is on slope land use in the Yangtze River Three Gorges area. This is a large area of 60497 km2.First of all, geometric correction and supervised classification were conducted for ten scenes of Landsat-5 TM or MSS images. The resolution of the processed images is 50 m ×50 m on ground. By the classification the land use/cover categories in this area were discriminated. Then the croplands including rice fields and upland fields were extracted from the land use/cover maps. Simultaneously the slope grade maps were prepared based on the topographic maps. Overlaying the slope grade maps and the cropland maps, the area and percentage of the croplands in different slope grades were determined. This case study indicated that 71.5% of the uplands was situated on the slope above 15° and 25% on the slope above 25° in this area. It is dangerous, and urgent cultivation or engineering measures should be taken. Another case study is on soil erosion in Linshan County of Guangxi Province. Airphoto interpretation and supervised classification of a Landsat TM image were carried out for discriminating land cover/use categories in an area of 3557.8 km2.And the soil erosion intensity grades were determined according to the land cover/use maps and slope maps.It wed discovered that the land suffering soil erosion accounted for 2404.0 km2, 67.6% of the total area of the county. Necessary measures to control soil erosion should be taken also.
文摘Hyper-spectral data is widely used to determine soil properties. However, few studies have explored the soil spectral characteristics as response to soil erosion. This study analysed the spectral response of different eroded soils in subtropical China, and then identify the spectral characteristics and soil properties that better discriminate softs with different erosion degrees. Two methods were compared： direct identification by inherent spectral characteristics and indirect identification by predictions of critical soft properties. Results showed that the spectral curves for different degrees of erosion were similar in morphology, while overall reflectance and characteristics of specific absorption peaks were different. When the first method is applied, some differences among different eroded groups were found by integration of associated indicators. However, the index of such indicators showed apparent mixing and crossover among different groups, which reduced the accuracy of identification. For the second method, the correlation between critical soil properties, such as soil organic matter （SOM）, iron and aluminium oxides and reflectance spectra, was analysed. The correlation coefficients for the moderate eroded group were primarily between -0.3 to -0.5, which were worse than the other twogroups. However, the maximum value of R2 was obtained as 0.86 and 0.94 for the non-apparent eroded and the severe group. Furthermore, these two groups also showed some differences in the spectral response of iron complex state （Fep）, Aluminium amorphous state （Alo） and the modelling results for soil organic matter （SOM）. The study proved that it is feasible to identify different degrees of soil erosion by hyperspectral data, and that indirect identification by modelling critical soil properties and reflectance spectra is much better than direct identification. These results indicate that hyper-spectral data may represent a promising tool in monitoring and modelling soil erosion.
