Evaluating the effectiveness of landscape configuration metrics from landscape composition metrics |
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Authors: | Xiaojian Wei Zhanchun Xiao Qingling Li Peiling Li Chao Xiang |
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Affiliation: | 1.School of Geomatics,East China University of Technology,Nanchang,China;2.Key Laboratory for Digital Land and Resources of Jiangxi Province,East China University of Technology,Nanchang,China;3.Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG,Nanchang,China;4.Journal of Natural Science of Wuhan University,Wuhan,China;5.School of Resource and Environment Science,Wuhan University,Wuhan,China;6.Organizational Department of the CPC Jinniu District Committe,Chengdu,China;7.Jiangxia Land Resources and Planning Bureau,Wuhan,China |
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Abstract: | Although landscape configuration and landscape composition metrics are correlated theoretically and empirically, the effectiveness of configuration metrics from composition metrics has not been explicitly investigated. This study explored to what extent substantial information of configuration metrics increases from certain easily calculated and extensively used composition metrics and how strongly the effectiveness is influenced by different factors. The effectiveness of 12 landscape configuration metrics from the percentage of landscape (PLAND) of each land-use class and patch density (PD) was evaluated through the coefficient of determination (R 2) of multivariate stepwise linear regression analysis of 150 town-based landscape samples from three regions. The different landscape configuration metrics from PLAND and PD presented significantly different performances in terms of effectiveness [the contagion index and aggregation index possess minimal information, and the effective mesh size (MESH) and area-weighted mean patch fractal dimension possess abundant information]. Furthermore, the effectiveness of configuration metrics showed different responses to changing cell sizes and different land-use categorization in different regions (interspersion and juxtaposition index, patch cohesion index, and MESH exhibited large variations in R 2 among the different regions). No single, uniform, consistent characteristic of effectiveness was determined across different factors. This new approach to understanding the effectiveness of configuration metrics helps clarify landscape metrics and is fundamental to landscape metric assessment. |
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