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The improvement of ecological environment index model RSEI

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Abstract

Protecting the ecological environment is an important goal of the world sustainable development. Rapid and quantitative evaluation of regional ecological environment is the technical support and necessary condition for this goal. The ecological environment index model (RSEI) which used to assess ecological environment is the most popular now. But it changed into two completely opposite models in the application. Most researchers choose which model to use based on the desired results. This article concludes the reason by studying the operating mechanism of the model and finds that it is the eigenvector direction in the principal component analysis causes this to happen. Taking Pingyu County as an example, this article calculates RSEI with Landsat 8 images in different periods in Google Earth Engine using the two existing models respectively and finds that two models show two opposite result trends in spatial distribution. Using any model to calculate the same image, the results are also opposite if changing the input order of the indicators. It is the eigenvector direction determines the spatial distribution by comparing and analyzing the eigenvector of each image and its corresponding RSEI. Then, this paper improves the model by fixing the eigenvector direction based on the actual effects on ecological environment of the four indicators, taking absolute values of the eigenvectors of NDVI and Wet which have a positive effect on the ecological environment and the opposite of absolute values of the eigenvectors of LST and NDSI which have a negative effect on the ecological environment, in order to improve the RSEI model. Using the improved model calculate each image, the results are consistently accurate. Furthermore, this paper also proposed a model for users who calculating the principal components through software where the eigenvector direction cannot be altered artificially. This paper proposes the improved model which is suitable for all users whether using software or conducting programming. The improved model is suitable for all images of any input order of the indicators. It provides the possibility of applying remote sensing big data to the ecological environment. At the same time, the study of the mechanism of the model provides a scientific basis for future scholars to calculate in batches.

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Acknowledgments

Special thanks to The Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions and National Science & Technology Infrastructure of China, Data Center of Lower Yellow River Regions, National Earth System Science Data Sharing Infrastructure, and National Science & Technology Infrastructure of China (http://henu.geodata.cn).

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Authors and Affiliations

  1. College of Environment and Planning, Henan University, Kaifeng, 475004, China

    Li Ning, Wang Jiayao & Qin Fen

  2. Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions(Henan University), Ministry of Education, Henan University, Kaifeng, 475004, China

    Li Ning, Wang Jiayao & Qin Fen

  3. Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng, 475004, China

    Li Ning, Wang Jiayao & Qin Fen

Authors
  1. Li Ning
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  2. Wang Jiayao
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  3. Qin Fen
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Correspondence to Qin Fen.

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Responsible Editor: Biswajeet Pradhan

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Ning, L., Jiayao, W. & Fen, Q. The improvement of ecological environment index model RSEI. Arab J Geosci 13, 403 (2020). https://doi.org/10.1007/s12517-020-05414-7

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  • DOI: https://doi.org/10.1007/s12517-020-05414-7

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