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张掖市山丹县生态脆弱性时空动态分析

Spatiao-temporal characteristic of eco-environment vulnerability in Shandan County,Gansu Province
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摘要 以张掖市山丹县为例,基于敏感性-恢复力-压力度(Sensitivity-Resilience-Pressure,SRP)的概念模型,从自然和人文因素两方面选取气象因子、地形因子、环境状况因子等11个评价指标,利用遥感和地理信息技术,采用空间主成分分析法,对山丹县2010年和2020年的生态环境脆弱性进行综合定量评价,并根据生态环境脆弱性指数(EVI)进行脆弱性分区.结果表明:山丹县生态环境整体状况较好,以轻度脆弱为主,2010年和2020年轻度脆弱区占比分别为40.61%和44.85%,主要分布于山丹县南部区域;时间变化上,2010—2020年山丹县生态环境有明显改善,微度和轻度脆弱性区域面积比例有所增大,中度和重度脆弱性区域面积在不断减少. Ecological vulnerability of various regions has become hotspot research in sustainable development and global environmental change.The Shandan County in Gansu Province was taken as a case study,according to the conceptual model of“ecological sensitivity-resilience-pressure”,11 evaluation indicators were chosen from natural and anthropological aspects including meteorological factors,topographical factors and environmental factors.Combining with remote sensing and ArcGIS technology,spatial principal component analysis was utilized to quantitatively evaluate the ecological vulnerability of Shandan County in 2010 and 2020,then the study area was classified into five levels according to the ecological vulnerability index(EVI).The results showed that:the overall ecological environment of ShandanCounty was in good condition,mainly dominated by light vulnerability.The light vulnerability occupied by 40.61%and 44.85%in 2010 and 2020 respectively,concentrated in southern part of study area;from the time perspective,the environment expressed a significant improvement from 2010 to 2020,the proportion of areas with slight and light vulnerability increased,while the proportion of moderate and heavy vulnerability decreased.
作者 邹桃红 罗蕾 刘家福 ZOU Tao-Hong;LUO Lei;LIU Jia-fu(College of Geographic Science and Tourism,Jilin Normal University,Siping 136000,China)
出处 《吉林师范大学学报(自然科学版)》 2024年第3期132-140,共9页 Journal of Jilin Normal University:Natural Science Edition
基金 国家自然科学基金项目(41977411) 四平市哲学社会科学规划项目(SPSK22120)。
关键词 生态环境 脆弱性 SRP模型 空间主成分分析 山丹县 eco-environment vulnerability SRP conceptual model spatial principal component analysis Shandan County
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