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Chinese Journal of Ecology ›› 2021, Vol. 40 ›› Issue (9): 2927-2935.doi: 10.13292/j.1000-4890.202109.022

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Ecological sensitivity evaluation of Longnan County based on AHP and MSE weighting method.

HE Su-ling1,2,3, ZOU Feng-qiong4, WANG Jin-liang1,2,3*   

  1. (1Faculty of Geography, Yunnan Normal University, Kunming 650500, China; 2Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China; 3Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China; 4Schoolof Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China).
  • Online:2021-09-10 Published:2021-09-17

Abstract: Ecological sensitivity is one of the important indicators for regional ecological fragility, the evaluation results from which play an important guiding role in regional ecological protection and development planning. Seven sensitive factors were selected from two aspects of soil erosion and acid rain erosion. With the support of GIS, we evaluated the spatial and temporal variations of ecological sensitivity in Longnan County, Jiangxi Province from 2003 to 2018 by combining analytic hierarchy process (AHP) and mean square deviation (MSE) decision-making method. The results showed that: (1) From 2003 to 2018, the ecological sensitivity of Longnan County always showed a spatial pattern of high in the south and low in the north, and gradually decreased over time. (2) The ecological sensitivity had a positive spatial correlation, which showed temporal fluctuation. (3) Construction land and arable land were the land types with low ecological sensitivity, while woodland showed high ecological sensitivity. The degree of ecological sensitivity in the study area increased with increasing slope. Our results can provide scientific reference for ecological protection and territorial planning of Longnan County.

Key words: ecological sensitivity, analytic hierarchy process, mean square deviation decision method, GIS, spatial autocorrelation.