Beyond the Remote Sensing Ecological Index: A Comprehensive Ecological Quality Evaluation Using a Deep-Learning-Based Remote Sensing Ecological Index

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 3; p. 558
• Main Authors: Gong, Xi, Li, Tianqi, Wang, Run, Hu, Sheng, Yuan, Shuai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Biosphere; China; Correlation coefficient; Correlation coefficients; Datasets; Deep learning; Depth indicators; Earth resources technology satellites; Ecological conditions; ecological index; ecological quality evaluation; Economic development; Ecosystem integrity; Environmental economics; Environmental quality; Geospatial data; Geosphere; Image retrieval; Land use; Landsat; Long short-term memory; Machine learning; Methods; Natural resources; Neural networks; Nonlinear systems; Population growth; Quality assessment; Remote sensing; Risk management; Rivers; Socioeconomic factors; Socioeconomics; Sustainable development; Terrestrial ecosystems; Urban areas; Urbanization; China; Hubei China; Yangtze River

Ecological integrity is fundamental to human survival and development. However, rapid urbanization and population growth have significantly disrupted ecosystems. Despite the focus of the International Geosphere-Biosphere Programme (IGBP) on terrestrial ecosystems and land use/cover changes, existing...