Research on the Spatiotemporal Changes and Driving Forces of Ecological Quality in Inner Mongolia Based on Long-Term Time Series

Gang Ji, Zilong Liao, Kaixuan Li, Tiejun Liu (), Yaru Feng and Zhenhua Han
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Gang Ji: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research (MWR), Beijing 100038, China
Zilong Liao: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research (MWR), Beijing 100038, China
Kaixuan Li: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research (MWR), Beijing 100038, China
Tiejun Liu: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research (MWR), Beijing 100038, China
Yaru Feng: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research (MWR), Beijing 100038, China
Zhenhua Han: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research (MWR), Beijing 100038, China

Sustainability, 2025, vol. 17, issue 13, 1-22

Abstract: The ecological environment of Inner Mongolia constitutes a critical component of China’s ecological civilization construction. To comprehensively assess and monitor ecological quality dynamics in this region, this study employed MODIS remote sensing data products (2000–2020) and derived four key indicators, —vegetation index (NDVI), wetness index (WET), build-up and soil index (NDBSI), and land surface temperature (LST)—via the Google Earth Engine (GEE) platform. A Remote Sensing-based Ecological Index (RSEI) was constructed using principal component analysis (PCA) to establish an annual long-term time series, thereby eliminating subjective bias from artificial weight assignment. Integrated methodologies—including Theil–Sen Median and Mann–Kendall trend analysis, Hurst exponent, and geographical detector—were applied to investigate the spatiotemporal evolution of ecological quality in Inner Mongolia and its responses to climatic and anthropogenic drivers. This study proposes a novel framework for large-scale ecological quality assessment using remote sensing. Key findings include the following: The mean RSEI value of 0.41 (2000–2020) indicates an overall improving trend in ecological quality. Areas with ecological improvement and degradation accounted for 76.06% and 23.84% of the region, respectively, exhibiting a spatial pattern of “northwestern improvement versus southeastern degradation.” Pronounced regional disparities were observed: optimal ecological conditions prevailed in the Greater Khingan Range (northeast), while the Alxa League (southwest) exhibited the poorest conditions. Northwestern improvement was primarily driven by increased precipitation, rising temperatures, and conservation policies, whereas southeastern degradation correlated with rapid urbanization and intensified socioeconomic activities. Our results demonstrate that MODIS-derived RSEI effectively enables large-scale ecological monitoring, providing a scientific basis for regional green development strategies.

Keywords: habitat quality; remote sensing ecological index (RSEI); driving factors; Inner Mongolia; China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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