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Comparison between modified remote sensing ecological index and RSEI

  • role： First author第一作者
  • Affiliation：

    College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

  • Email：liuying712100@163.com
  • Introduction：刘英，研究方向为环境遥感。E-mail：liuying712100@163.com
LIU Ying1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

  • Email：chaoyadang99@163.com
  • Introduction：党超亚，研究方向为遥感技术应用。E-mail：chaoyadang99@163.com
DANG Chaoya1*,  
  • Affiliation：

    College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

YUE Hui1,  
  • Affiliation：

    Shandong Provincial Key Laboratory of Water and Soil Conservation and Environment Protection, College of Resources and Environment, Linyi University, Linyi 276005, China

LYU Chunguang2,  
  • Affiliation：

    College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

QIAN Jiaxin1,  
  • Affiliation：

    College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

ZHU Rong1

résumé

Establishing a more accurate remote sensing ecological index is necessary to evaluate urban ecological quality and provide timely warnings. Taking the Beijing city as the study area, this paper used five indices (vegetation index, humidity, Land Surface Temperature (LST), Normalized Difference Build-up and bare Soil Index (NDBSI) and air quality) through the Principal Component Analysis (PCA) method to construct a Modified Remote Sensing Ecological Index (MRSEI). The Eco-environment Index (EI) was derived from the Pressure-State-Response model (PSR) combined with the entropy weight method to compare with MRSEI and RSEI. Moreover, the nuclear principal component analysis (KPCA) was applied to establish the Nonlinear Remote Sensing Ecological Index (NRSEI), which was integrated vegetation index, humidity, LST, and NDBSI. Finally, MRSEI and NRSEI were separately compared with the remote sensing ecological index (RSEI). The results showed that MRSEI could reflect the spatial distribution of air quality, and the correlation coefficients between MRSEI and EI were 0.829 in 2014 and 0.857 in 2017 (P<0.01), which were improved by 0.035 and 0.055 over that of RSEI, respectively. Compared with EI, the average absolute error, root mean square error, and average relative error of MRSEI in the main districts were all lower than that of RSEI. These results indicated that the MRSEI in evaluating urban ecological quality was better than RSEI and the air quality indicator was feasible to monitor the ecological environment of Beijing. The contribution rate of the first principal component from NRSEI was increased by 11.94%—21.45% than that of RSEI in the experiment areas. Compared with RSEI, the correlation coefficients between each indicator and NRSEI increased by 0.128—0.198. NRSEI could demonstrate the transition of different ecological levels. RSEI sometimes underestimated the areas with poor ecological environments, and it sometimes overestimated the areas with excellent ecological environments. NRSEI was more consistent with the ecological conditions reflected by remotely sensed images. MRSEI is more suitable than RSEI for monitoring the ecological quality of Beijing. NRSEI, taking into account the weak linear or nonlinear correlations of various indicators, is better than RSEI in assessing the ecological environment quality.

mots-clés

remote sensing;modified remote sensing ecological index;nonlinear remote sensing ecological index;air quality index;kernel principal component analysis;Pressure-State-Response model

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