根据结构化植被因子指数的概念，以TM影像为信息源，探讨了利用遥感技术提取陕北黄土区结构化植被因子指数（Cs）的途径与方法.结果表明：在陕北黄土区，Cs能更好地描述植被群落的水土保持效益，其与绿度植被指数（归一化植被指数NDVI、修正土壤调整植被指数MSAVI）和黄度植被指（归一化差异衰败指数NDSVI、归一化耕作指数NDTI）等单一的遥感植被指数虽然均存在良好的相关关系，但用绿度与黄度植被指数相结合可综合反映植被的水土保持功能，能较好地克服单一指数在描述植被控制水土流失中的不足；MSAVI、NDTI分别是基于遥感影像提取Cs较为理想的绿度和黄度植被指数；根据群落结构化植被因子指数与遥感植被指数的关系推算区域尺度上的结构化植被因子指数是可行的，但由于不同地区植物物候期的差异，要使该方法在其他地区适用，仍需开展相应的率定和验证工作.
Based on the concept of structured vegetation cover index (Cs) and by using TM images as the information source, the extraction way of Cs for Loess
Area in North Shaanxi by using remote sensing techniques was explored. In study area, Cs was better than the traditional projected vegetation overage index in expressing the relationships between vegetation structure and soil erosion. The Cs was closely related to the remote sensing vegetation indices, such as green indices NDVI (Normalized Difference Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index), and yellow indices NDSVI (Difference Senescent Vegetation Index) and NDTI (Normalized Difference Tillage Index). The combination of the green and yellow indices could better express the effects of vegetation on soil erosion, compared with the single index. Among these remote sensing vegetation indices, the MSAVI and NDTI could be the ideal green and yellow vegetation indices for the extraction of Cs from TM images. It was possible to extract the Cs from remote sensing data through the regression analysis of Cs and remote sensing vegetation indices. However, this method was just valid
ated and applied to the study area. Whether it could be applied to other regions was needed to be further validated due to the phonological differences from one region to another.