Comprehensive Eco-Environment Quality Index Model with Spatiotemporal Characteristics

Area of Science:

• Environmental Science
• Remote Sensing
• Geographic Information Systems (GIS)

Background:

• Ecological environment assessment is vital for sustainable development.
• Existing quantitative models (EI, EQI, RSEI) lack dynamic capabilities for large-scale, long-term, and high-frequency research.
• There is a need for advanced models to address these limitations.

Purpose of the Study:

• To construct a comprehensive ecological environment quality index model (S) using remote sensing big data.
• To address the limitations of current models in dynamic research across large areas and long time series.
• To provide a more accurate and efficient tool for ecological assessment.

Main Methods:

• Utilized remote sensing big data as the primary data source.
• Developed the comprehensive ecological environment quality index model (S).
• Aggregated ecological indices (NDVI, NDBSI, Lst, Wet) using full-sequence dynamic dimensionless, automated principal component analysis, and multi-temporal averaging.
• Verified the model's spatial and temporal accuracy using Henan Province as a case study.

Main Results:

• The developed model (S) demonstrated superior performance in both temporal and spatial accuracy compared to EI, EQI, and RSEI.
• Validation through profiles, samples, and cluster analysis confirmed the model's advantages in time, space, and precision.
• The model effectively integrates multiple ecological indices for a holistic assessment.

Conclusions:

• The comprehensive ecological environment quality index model (S) is a significant advancement for ecological assessment.
• The model's ability to handle dynamic, large-scale, and long-term data makes it suitable for sustainable development research.
• Remote sensing big data combined with the novel model offers a powerful approach to understanding and managing ecological environments.