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RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments
Institution:1. Departamento de Geografía, Universidad Alberto Hurtado, Cienfuegos 41, Santiago, Chile;2. Department of Geography, Friedrich Schiller University, Jena, Löbdergraben 32, 07737 Jena, Germany;1. Department of Geomatics, Taiyuan University of Technology, Taiyuan 030024, China;2. Chinese Academy of Meteorological Sciences, Beijing 100081, China;3. Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;1. Institute of Remote Sensing and Geographic Information System, Peking University, 100871 Beijing, China;2. Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, France;1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No. 18 Shuangqing Road, Beijing 100085, China;2. University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
Abstract:Understanding land use land cover change (LULCC) is a prerequisite for urban planning and environment management. For LULCC studies in urban/suburban environments, the abundance and spatial distributions of bare soil are essential due to its biophysically different properties when compared to anthropologic materials. Soil, however, is very difficult to be identified using remote sensing technologies majorly due to its complex physical and chemical compositions, as well as the lack of a direct relationship between soil abundance and its spectral signatures. This paper presents an empirical approach to enhance soil information through developing the ratio normalized difference soil index (RNDSI). The first step involves the generation of random samples of three major land cover types, namely soil, impervious surface areas (ISAs), and vegetation. With spectral signatures of these samples, a normalized difference soil index (NDSI) was proposed using the combination of bands 7 and 2 of Landsat Thematic Mapper Image. Finally, a ratio index was developed to further highlight soil covers through dividing the NDSI by the first component of tasseled cap transformation (TC1). Qualitative (e.g., frequency histogram and box charts) and quantitative analyses (e.g., spectral discrimination index and classification accuracy) were adopted to examine the performance of the developed RNDSI. Analyses of results and comparative analyses with two other relevant indices, biophysical composition index (BCI) and enhanced built-up and bareness Index (EBBI), indicate that RNDSI is promising in separating soil from ISAs and vegetation, and can serve as an input to LULCC models.
Keywords:Soil index  Ratio normalized difference soil index (RNDSI)  Biophysical composition index (BCI)  Enhanced built-up and bareness index (EBBI)  Land use land cover change (LUCC)
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