关键词: 景观动态, 主成分分析, 驱动力贡献率, CLUE-S模型模拟

Abstract: Based on the TM images, social-economic data, and field investigation data in 1990, 2000, and 2009, and by adopting redundancy analysis (RDA) and principal component analysis (PCA), this paper analyzed the contribution rates of landscape driving forces in coastal area of Fengxian reclamation zone at Yangtze Estuary. Kappa index was used to validate the simulation effectiveness of CLUE-S model. In the study area, anthropogenic landscape driving factors had a higher contribution rate than natural landscape driving factors (57.1% vs. 42.9%). The prediction accuracy rate of CLUE-S model was above 82%, the Kappa index of the main landscape types (farm land, unutilized land, and breeding ponds) was greater than 0.75, the simulation results were acceptable, and the landscape driving forces selected could better simulate the spatial distribution patterns of the abrupt transformation of the main landscape types.

Key words: landscape dynamics, principal component analysis (PCA), contribution rate of driving force, CLUE-S model simulation