Effects of land use classification on landscape metrics based on remote sensing and GIS

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	Effects of land use classification on landscape metrics based on remote sensing and GIS
	Liu Delin ; Hao Shilong ; Liu Xianzhao ; Li Bicheng ; He Shanfeng ; Warrington D. N.
	2012
关键词	Land use Principal component analysis Remote sensing Watersheds
英文摘要	Landscape metrics are important when studying landscape structure and function. This study investigated the sensitivity of two core sets of landscape metrics to different land use classification systems by analyzing two typical landscapes at a small watershed scale and a county scale on the Loess Plateau, China. The results showed that (1) eight landscape metrics, selected using Spearman's correlation coefficient method and principle component analysis, adequately reflected the major landscape properties at both scales. (2) Land use classification had a significant influence on most of the selected landscape metrics, affecting their magnitude and the way in which they changed with increasing classification details; four general patterns were exhibited, i.e., increasing; decreasing; increasing and then decreasing; and relatively constant at the small watershed scale, while for the county scale, they were increasing; decreasing; increasing and then decreasing; and remaining relatively constant after initially increasing. (3) The general response patterns of most of the selected landscape metrics to the changing land use classification were similar among different years at both scales, which implied that the age of the data source used to generate the land use maps since 1982 had little effect on the landscape metrics, and that the underlying landscape pattern described by the metrics had not notably changed. 2012 Springer-Verlag.
出处	Environmental Earth Sciences
页	1-9
收录类别	EI
语种	英语
内容类型	EI期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/27639]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Liu Delin,Hao Shilong,Liu Xianzhao,et al. Effects of land use classification on landscape metrics based on remote sensing and GIS. 2012.