西南林业大学学报2026，Vol.46Issue(3)：100-107,8.DOI:10.11929/j.swfu.202503033
基于RSEI模型的泰顺县生态环境质量评价及驱动力分析
Research on the Assessment and Motivating Factors of Ecological Environment Quality in Taishun County Based on the RSEI Model
摘要
Abstract
Based on Landsat 8 remote sensing imagery,Digital Elevation Model(DEM),and Integrated Forest Resources Map data from 2016 to 2020,this study constructed the Remote Sensing Ecological Index(RSEI).Combining Principal Component Analysis and regression modeling,we quantitatively assessed the spati-otemporal dynamics of eco-environmental quality and its driving mechanisms.The results indicate that the RSEI exhibited a trend of increasing initially and then decreasing,peaking at 0.711 in 2018 before declining to 0.697 in 2020,with an overall increase of 1.2％.Eco-environmental quality grades were predominantly"Excellent"and"Good"(annual average proportion:71％).However,the area proportion of"Poor"and"Very Poor"grades in-creased from 11.5％to 13.1％,indicating significant degradation risks.Changes in eco-environmental quality were concentrated in areas with slopes of 5°-30° and elevations of 500-1000 m.Degraded areas were significantly more extensive on south-facing slopes than on north-facing slopes.Expansion of construction land and a regional drying-warming climate trend were identified as the primary negative driving factors,while vegetation coverage and humidity acted as positive regulators.This study concludes that Taishun County exhibits a pattern of"overall stability with localized degradation"in eco-environmental quality.To address this,enhanced management of con-struction land and implementation of ecological restoration projects are recommended,with particular focus on south-facing slopes and high-elevation areas.关键词
生态环境/遥感生态指数/时空分异/地形效应/驱动因素Key words
ecological environment/remote sensing ecological index/spatiotemporal differentiation/terrain effect/motivating factor分类
资源环境引用本文复制引用
邬枭楠,方婷轩,孟森,伊力塔,王剑武..基于RSEI模型的泰顺县生态环境质量评价及驱动力分析[J].西南林业大学学报,2026,46(3):100-107,8.基金项目
浙江省"尖兵""领雁"研发攻关计划项目(2022C02019)资助. （2022C02019）
