Interval association of remote sensing ecological index in China based on concept lattice

The correlation coefficient can calculate paired correlations among different ecological indicators as a whole, but it cannot calculate the specific interval association and the correlation among multiple indicators. This paper proposed an interval association (IA) method of the remote sensing ecolo...

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Bibliographic Details
Published in:Environmental science and pollution research international 2022-05, Vol.29 (23), p.34194-34208
Main Authors: Liao, Weihua, Nie, Xin, Zhang, Zhiheng
Format: Article
Language:English
Subjects:
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Clustering
Complex systems
Correlation coefficient
Correlation coefficients
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Chemistry
Environmental conditions
Environmental Health
Environmental science
Granulation
Indicators
Mathematical analysis
Remote sensing
Research Article
Spatial distribution
Structural hierarchy
Waste Water Technology
Water Management
Water Pollution Control
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Summary:The correlation coefficient can calculate paired correlations among different ecological indicators as a whole, but it cannot calculate the specific interval association and the correlation among multiple indicators. This paper proposed an interval association (IA) method of the remote sensing ecological index (RSEI), based on the concept lattice and frequent closed itemset. In the IA method, the ecosystem was viewed as a complex system with a hierarchical structure, and the association among multiple indicators was calculated using the information granulation of RSEI. The interval association support degree (IASD) could measure the association clustering strength of these IA concepts. Calculation of MODIS data compiled by Google Earth Engine (GEE) showed that the IA concepts of RSEI in China were primarily composed of selected middle indicator intervals in 2017. The overall eco-environmental condition in China was general when assessed through IA. The spatial distribution of the remote sensing eco-environment in China displayed strong spatial association clustering. Furthermore, the IA of RSEI focused on the first few concepts with high IASD values.
ISSN:0944-1344
1614-7499
DOI:10.1007/s11356-021-17588-y