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基于改进遥感生态指数的城市环境质量评估

Urban Environmental Quality Assessment Based on Reconstructed Remote Sensing Ecological Index
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摘要 针对基于RSEI指数评估城市环境质量以第一主成分或全部主成分构建生态指数信息利用不补充和第四主成分多为噪声的问题,利用改进遥感生态指数的方法对城市环境质量评估。研究选择上海市嘉定区2007、2013和2020年Landsat5/8遥感影像,通过主成分分析方法得到绿度、湿度、干度和温度四个生态因子的特征值贡献率,总体上评价上海市嘉定区近14年的生态环境质量。结果表明,改进遥感生态指数不但能集成所有生态指标95%以上的信息而且还避免噪声信息的干扰,因此能更好的定量评价城市生态环境质量。 In response to the problems that the information utilization of the ecological index is not complemented by the first principal component or all principal components to construct the ecological index based on the RSEI index to assess the urban environmental quality and the fourth principal component is mostly noise.This paper uses the method of improving the remote sensing ecological index to assess the urban environmental quality.The study selects Landsat 5/8 remote sensing images of 2007,2013 and 2020 in Jiading District,Shanghai,and obtains the contribution rates of the eigenvalues of four ecological factors,namely,greenness,humidity,dryness and temperature,through the method of principal component analysis,and reconstructs the remote sensing ecological index by using the contribution rates of the first three principal components as weights to evaluate the ecological environment quality of Jiading District,Shanghai,in general for the past 14 years.The results show that the improved remote sensing ecological index can not only integrate more than 95%of the information of all ecological indicators but also avoid the interference of noise information,so it can better quantitatively evaluate the quality of urban ecological environment.
作者 刘德胜 连帅帅 孙悦 王柳艳 LIU Desheng;LIAN Shuaishuai;SUN Yue;WANG Liuyan(College of Information and Electronic Technology,Jiamusi University,Jiamusi Heilongjiang 154007,China)
出处 《佳木斯大学学报(自然科学版)》 CAS 2022年第5期128-131,143,共5页 Journal of Jiamusi University:Natural Science Edition
基金 黑龙江省重点研发计划项目(GA21A302) 黑龙江省高等学校优秀创新团队项目(2019-KYYWF-1335) 黑龙江省省属高校基本科研业务费(2020-KYYWF-0225)。。
关键词 生态指数 相关性分析 多元线性回归 环境质量 ecological index correlation analysis multiple linear regression environmental quality
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