期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2000
卷号：XXXIII Part B7(/1-4)
页码：955-961
出版社：Copernicus Publications
摘要：Current research suggests that metrics of landscape pattern can act as indicators of ecological processes and biodiversity maintenance. Land cover maps, created through remote sensing, enable the evaluation of such pattern. This paper compares supervised and unsupervised classification techniques for the estimation of landscape pattern, in tropical landscapes that are often inaccessible and difficult to classify. In the mountains of the Western Ghats of India, thirteen landscapes ranging from 9-54 km 2 were delineated for mapping. Supervised classification accuracy ranged from 70- 92%. Unsupervised classification accuracy was uniformly worse, ranging from 31-75%. Misclassification errors have been previously believed not to bias metrics of landscape pattern. This paper reports a significant bias in patch and landscape metrics estimates concomitant with misclassification resulting from unsupervised classification. For all landscapes, patch size, shape and nearest neighbor distance metrics derived from unsupervised classification were significantly greater than those derived from supervised classification. Landscape metrics of mean patch size, mean patch shape, mean nearest neighbor distance and the Shannon index of landscape diversity determined from unsupervised classification were also significantly greater than those from supervised. Metrics of interspersion- juxtaposition and contagion do not however demonstrate significant differences. Possible explanations for the observed bias are discussed. Whether the bias noticed extends to other methods of unsupervised classification, requires examination. This exercise has strong implications for the development of country level methodology for monitoring biodiversity in India
