RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments

被引:107
作者
Deng, Yingbin [1 ]
Wu, Changshan [1 ,2 ]
Li, Miao [3 ]
Chen, Renrong [4 ]
机构
[1] Univ Wisconsin, Dept Geog, Milwaukee, WI 53201 USA
[2] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China
[3] Harbin Normal Univ, Key Lab Remote Sensing Monitoring Geog Environm, Harbin 150025, Heilongjiang, Peoples R China
[4] S China Normal Univ, Sch Geog Sci, Guangzhou 510631, Peoples R China
来源
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION | 2015年 / 39卷
关键词
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); SPECTRAL MIXTURE ANALYSIS; IMPERVIOUS SURFACE; BUILT-UP; VEGETATION; LAND; IMPACT; WATER; AREA;
D O I
10.1016/j.jag.2015.02.010
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
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. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:40 / 48
页数:9
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