基于GIS的生态脆弱移民区生态环境质量评估--以宁夏红寺堡区为例
Evaluation of Ecological Environment in Ecologically Vulnerable Immigrating Area Based on GIS-A Case of Hongsipu District in Ningxia
李群 1郭慧秀 1贾科利1
作者信息
- 1. 宁夏大学资源环境学院，宁夏银川750021
- 折叠
摘要
参考国家最新颁布的《生态环境评价规范》,从5个方面构建生态环境评价模型,利用GIS技术进行数据获取、运算及评价单元细化,以监测局部区域的具体生态环境问题并分析空间变化趋势,最后以宁夏红寺堡区为例探讨生态脆弱移民区的生态环境质量评价。结果表明：红寺堡区各乡镇生态环境状况一般,EI值存在明显区域差异和地带性,东南部优于中西部。太阳山镇生态环境状况最好,镇东南存在土地胁迫度和污染指数高的生态环境问题；南川乡和红寺堡镇次之,分别存在乡东南部水网密度低和镇中南土地胁迫高的问题；大河乡生态环境最差,西南地区存在植被覆盖度低、荒漠化等生态环境问题。
Abstract
The model of ecological environmental evaluation consisting of five aspects was built based on the latest national specification of eco-logical environmental evaluation and the data acquisition, calculation and evaluation refined into evaluation unit were done with the GIS tech-nique. Through the monitoring of specific problems of ecological environment in partial local area and the analyse of spatial variation trend, the evaluation of environmental quality in the vulnerable migration area of Hongsipu district was explored. The results showed that the ecological and environmental quality in the townships of Hongsipu district was in normal condition;the EI value significantly existed regional and zonal differ-ences, which in the southeast area was better than the mid-west area. The ecological environment in Taiyangshan Town was best. In the southeast of the town there were the stress of land and high index of ecological and environmental pollution; next, in Nanchuan Country and Hongsipu Town, where there were low density and rural water networks in the southeastern of the township and high stress of land in central-south of the township. The worst ecological environmental condition was in the Dahe Country, in which there were low vegetation coverage, desertification and other ecological and environmental problems in its southwest region.
关键词
生态脆弱移民区/GIS/生态环境评价/红寺堡区Key words
Ecologically vulnerable area/GIS/Ecological environment evaluation/Hongsipu district引用本文复制引用
基金项目
教育部人文社会科学研究青年基金(13YJCZH059)
宁夏大学研究生创新项目(GIP2015004)
出版年
2016
国家科技期刊平台
NSTL
万方数据