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为实现水土流失区植被遥感信息的准确提取,本文采用2007年ALOS 10 m多光谱影像,利用土壤调节植被指数SAVI和MSAVI,对福建长汀水土流失区马尾松林不同植被覆盖密度的3个实验区进行植被提取,并选用不同的土壤调节因子(L=0.25,0.5,0.75,1)做实验,将结果和以NDVI植被指数提取的结果进行对比,分析了提取效果及受土壤噪音的影响程度。实验表明,SAVI指数能提高水土流失区的植被提取精度。在中、低植被覆盖区,其提取的总精度比NDVI高出2%~7%,Kappa系数高出7%~18%;而土壤调节因子L的取值对植被信息的提取也呈现出一定的规律性,即:随着L从0向1递增,SAVI提取稀疏植被的能力上升而探测阴坡植被的能力下降。总体来看,对于低植被覆盖和中等植被覆盖地区,可分别用SAVI(L取0.75)和SAVI(L取0.5)来提取植被信息,对于高植被覆盖区,仍可直接用NDVI进行植被信息提取;研究发现MSAVI在植被信息提取中并不具有特别的优势。
In order to achieve the accurate extraction of vegetation remote sensing information in soil and water and soil loss area, this paper uses the 10 m multispectral image of ALOS in 2007 and three experimental zones of different vegetation cover density in Pinus massoniana plantation in Changting, Fujian Province, using SAVI and MSAVI. Vegetation was extracted and different soil adjustment factors (L = 0.25,0.5,0.75,1) were chosen to test. The results were compared with the results of NDVI vegetation index. The effects of extraction and soil noise were analyzed. Experiments show that the SAVI index can improve the extraction accuracy of vegetation in the soil erosion area. In middle and low vegetation coverage areas, the total accuracy of extraction was 2% -7% higher than that of NDVI and Kappa coefficient was 7% ~ 18% higher than that of NDVI. The value of soil adjustment factor L also showed a certain amount of vegetation information extraction Regularity, that is, as L increases from 0 to 1, the ability of SAVI to extract sparse vegetation increases and its ability to detect shady vegetation decreases. In general, vegetation information can be extracted using SAVI (L = 0.75) and SAVI (L = 0.5) for low vegetation cover and middle vegetation cover, respectively. Vegetation information can still be extracted directly by NDVI for high vegetation coverage The study found that MSAVI does not have special advantages in vegetation information extraction.