Abstract
Monitoring and assessing karst rock resulting from desertification is important to local sustainable development in the karst mountain regions of Southwest China. A new derived index, the Normalized Difference Rock Index (NDRI), is proposed to map karst rock. It takes advantage of the unique spectral response of karst rock and other land cover forms. Supervised maximum likelihood classification trials using different input bands (Landsat TM band 3–5, NDVI and NDRI derived from TM imagery) were assessed for accuracy of karst rock mapping in Bijie County, Southwest China. Results were compared with land use maps for 2000, with the best result obtained using a combination of the NDVI and NDRI indices. This paper offers a tool for land cover mapping.
Highlights
The karst mountain region of Southwest China is one of the largest karst areas in the world
Trial 5 was used with binary judging, and the land cover types were classified into vegetation, water, and karst rock
The new combination of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Rock Index (NDRI) exemplified in this paper can map karst rock at an accuracy level of 80% using supervised classification, and the method is greatly superior to that with Thematic Mapper (TM) band supervised classification or the binary judging method
Summary
The karst mountain region of Southwest China is one of the largest karst areas in the world This karst geomorphology covers about 620,000 km, and the ecological environment is extremely fragile (Wang and Liu 2004; Zhang et al 2006). This has led to serious land degradation in the form of desertification: soil is severely or even completely eroded, so that bedrock is exposed over large areas, the carrying capacity of land declines severely, and the landscape resembles a rocky desert because of severe human impacts on the vulnerable ecogeo-environment (La Moreaux et al 1997). Traditional methods of mapping karst rock, such as manual interpretation and computer-assisted digital processing of aerial photographs and satellite images, have limitations. With computer-assisted digital processing, results vary depending on the characteristics of the training samples selected by the analyst, because different analysts do not interpret images in the same manner
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