Biometric Technology Today, Vol. 2025 No. 1 (0): Theme: The Adaptive Biometric Technology: Innovations in AI, Security, Data Mining, and Network Optimization, Theme: Adaptive Biometric Technology: Innovations in AI, Security, Data Mining, and Network Optimization

Spatiotemporal Characteristics and Trend Analysis of Remote Sensing Ecological Quality in Liaoning Province

Authors

  • Linlin Li , Liaoning Provincial Ecological Meteorology and Satellite Remote Sensing Center, Shenyang 110166, China Panjin National Climate Observatory, Panjin 124000, China
  • Qiang Gong , Liaoning Provincial Ecological Meteorology and Satellite Remote Sensing Center, Shenyang 110166, China Panjin National Climate Observatory, Panjin 124000, China
  • Ruipeng Ji , Liaoning Provincial Ecological Meteorology and Satellite Remote Sensing Center, Shenyang 110166, China Panjin National Climate Observatory, Panjin 124000, China
  • Xiaoyue Zhang , Liaoning Provincial Ecological Meteorology and Satellite Remote Sensing Center, Shenyang 110166, China Panjin National Climate Observatory, Panjin 124000, China
  • Ying Wang , Liaoning Provincial Ecological Meteorology and Satellite Remote Sensing Center, Shenyang 110166, China Panjin National Climate Observatory, Panjin 124000, China
  • Yudi Shi , Liaoning Provincial Ecological Meteorology and Satellite Remote Sensing Center, Shenyang 110166, China Panjin National Climate Observatory, Panjin 124000, China
  • Bin Zhou Liaoning Provincial Ecological Meteorology and Satellite Remote Sensing Center, Shenyang 110166, China Panjin National Climate Observatory, Panjin 124000, China

Keywords:

GEE;RSEI, Spatiotemporal Analysis, Trend Analysis, Liaoning Province

Abstract

Liaoning Province's economy is highly dependent on heavy industry, thus facing severe challenges between industrial development and ecological balance. Evaluating ecological changes is crucial for formulating effective protection policies. Based on the GEE platform, the Remote Sensing Ecological Index (RSEI) was calculated from 2000 to 2023. The ecological changes were assessed using Theil-Sen and Mann-Kendall analysis, trend stability was evaluated using the coefficient of variation, and the mutation years were identified using Pettitt and Mann-Kendall tests to reveal spatiotemporal distribution patterns. Partial correlation analysis was used to identify significant influencing factors, and the Hurst index was employed to predict future trends in ecological quality. The study shows that:①The overall ecological environment quality in Liaoning Province is on the rise, with an average annual RSEI of 0.53, fluctuating between 0.48 and 0.58, peaking in 2017, and 2009 being a significant mutation year.②The spatial difference in the average ecological environment quality over the years in Liaoning Province is significant, with a polarization phenomenon, deteriorating gradually from southeast to northwest.③Vegetation coverage and cumulative precipitation are important factors affecting remote sensing ecological quality.④It is expected that about 63.98% of Liaoning Province's area will face the risk of ecological degradation in the future. The results provide important data support for Liaoning Province to achieve sustainable development and ecological civilization construction.

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Published

2025-06-11

Issue

Vol. 2025 No. 1 (0): Theme: The Adaptive Biometric Technology: Innovations in AI, Security, Data Mining, and Network Optimization

Section

Theme: Adaptive Biometric Technology: Innovations in AI, Security, Data Mining, and Network Optimization