Title :
Spectral indices for estimating the fractional cover of non-vegetation land-cover types in karst environment
Author :
Ding, Ling ; Zhang, Xia ; Ji, Min ; Shuai, Tong ; Qin, Huanhu ; Zhang, Xuewen
Author_Institution :
Geomatics Coll., Shandong Univ. of Sci. & Technol., Qingdao, China
Abstract :
Karst rocky desertification is a typical type of land desertification. It is associated with human disturbance and fragile eco-geological setting with high complexity and heterogeneity in karst regions. For the particularity of karst ecosystems and complexity of rocky desertification, Fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (Soil), and exposed bedrock (Rock) were selected as the main symptoms of the ground desertification and key ecological evaluation indicators. In this study, field spectral reflectance measurements were used to develop several karst spectral indices (KSI) based on unique spectral features of non-vegetation land-cover types. The relationship of the mixed spectra of main land cover types and their responding fraction cover is analyzed preliminarily. The study indicates that the proposed karst spectral indices (KSI) have potential ability to estimate fractions of non-photosynthetic vegetation (NPV) and bare soil (Soil), with higher coefficient of determination (R2 of 0.73 and 0.70, respectively), while the karst spectral indices(KSI) can provide some information for fractions of exposed bedrock (Rock), with the maximum R2 of 0.58.
Keywords :
ecology; environmental degradation; optical variables measurement; vegetation; bare soil; ecological evaluation indicator; exposed bedrock; fractional cover estimation; karst ecosystem; karst rocky desertification; karst spectral indices; land desertification; nonphotosynthetic vegetation; nonvegetation land-cover type; photosynthetic vegetation; spectral reflectance measurement; Earth; Ecosystems; Reflectivity; Remote sensing; Rocks; Soil; Vegetation; Fractional cover; Karst rocky desertification; Spectral features; Spectral indices;
Conference_Titel :
Remote Sensing, Environment and Transportation Engineering (RSETE), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9172-8
DOI :
10.1109/RSETE.2011.5964821