Assessment of grassland degradation near Lake Qinghai, West China, using Landsat TM and in situ reflectance spectra data
Abstract:The severity of grassland degradation near Lake Qinghai, West China was assessed from a Landsat Thematic Mapper (TM) image in conjunction with in situ samples of per cent grass cover and proportion (by weight) of unpalatable grasses (PUG) collected over 1 m2 sampling plots. Spectral reflectance at each sampling plot was measured with a spectrometer and its location determined with a Global Positioning System (GPS) receiver. After radiometric calibration, the TM image was geometrically rectified. Ten vegetation indices were derived from TM bands 3 and 4, and from the spectral reflectance data at wavelengths corresponding most closely to those of TM3 and TM4. Regression analyses showed that NDVI and SAVI are the most reliable indicators of grass cover and PUG, respectively. Significant relationships between TM bands-derived indices and in situ sampled grass parameters were established only after the former had been calibrated with in situ reflectance spectra data. Through the established regression models the TM image was converted into maps of grass cover parameters. These maps were merged to form a degradation map at an accuracy of 91.7%. It was concluded that TM imagery, in conjunction with in situ grass samples and reflectance spectra data, enabled the efficient and accurate assessment of grassland degradation inside the study area.
Document Type: Research Article
Affiliations: 1: Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Science Beijing 100101 China, Email: firstname.lastname@example.org 2: College of Geographic Science Nanjing Normal University Nanjing 210097 China, Email: email@example.com 3: School of Geography and Environmental Science University of Auckland Private Bag 92019 Auckland New Zealand, Email: firstname.lastname@example.org 4: College of Geographic Science Nanjing Normal University Nanjing 210097 China
Publication date: 2004-10-01