A knowledge-based approach to mapping degraded meadows on the Qinghai-Tibet Plateau, China

摘要

The spatially unique properties of degraded meadows (heitutan) on the Qinghai-Tibet Plateau offer an excellent opportunity in assessing the utility and effectiveness of various knowledge in their automatic and accurate mapping from satellite imagery. After a Landsat Operational Land Imager image was K-means clustered to produce a degradation severity map, it was also used to generate a normalized difference vegetation index (NDVI) map that was subsequently converted to a degradation severity map, as well. Both maps were further refined with spatial knowledge derived from topographic data and the image via onscreen digitization. It is found that elevation is a more useful knowledge than channel in refining the image-derived results. It can reduce the area of K-means clustering results by 37% through exclusion of non-genuine heitutan at a very high elevation. This knowledge is especially beneficial to severe and slight heitutan. Channel knowledge is less effective by reducing the mapped heitutan by 15%, with a similar pace of reduction across all three classes of degradation severity. However, it is more useful in refining the NDVI-derived results than with the K-means results as all sparsely vegetated areas were indiscriminately lumped together. Both K-means clustering and NDVI produced drastically different results, but they converge closely with each other with a disparity of only 6% between them after the application of the spatial knowledge. Both methods achieved a similar overall mapping accuracy around 70%. Slope gradient and aspect are of limited use to the mapping due to lack of distinction between degraded heitutan and intact meadows. More research should focus on the universality of the knowledge and the impact of scale on the findings.