Seasonal variations in ecological environment quality across different geomorphological regions and their response mechanisms to climate change

Understanding seasonal variations in ecological environment quality (EEQ) across diverse geomorphological regions is essential for accurately assessing ecosystem health and resilience amid climate change. However, systematic quantitative studies examining these fluctuations and their underlying climate response mechanisms remain scarce, particularly in regions with complex terrain. This study focused on Shaanxi Province, known for its diverse landforms and complex climate. Using the Google Earth Engine (GEE) platform, we systematically analyzed the spatiotemporal variations in EEQ across six geomorphic regions from 2002 to 2022. This analysis integrated the Remote Sensing Ecological Index (RSEI) with climatic factors to explore the region's response mechanisms to climate change. The results revealed the following: (1) EEQ exhibited significant regional variation, with the Daba and Qinling Mountain regions recording the highest annual average RSEI (> 0.6) and the wind-sand transition zone the lowest (< 0.25). The Guanzhong Plain, marked by intense human activity, uniquely displayed fluctuating degradation. (2) Single-season RSEI evaluations (e.g., summer) tended to overestimate EEQ in arid regions and underestimate degradation risks in humid regions. Averaging RSEI across all four seasons provided a more accurate depiction of interannual ecosystem stability. (3) EEQ responses to climate change vary significantly across regions due to the constraints imposed by water-heat balance and geomorphological characteristics. Precipitation plays a dominant role in influencing EEQ in arid regions such as the Loess Plateau, whereas temperature exerts a more significant effect in humid regions like the Daba Mountains. Human activities, particularly in the Guanzhong Plain, markedly reduce climate sensitivity, highlighting the complex interplay between natural and anthropogenic drivers. Employing an innovative "seasonal fluctuation-geomorphological heterogeneity-climate response" framework, this study reveals biases in traditional single-season assessments and delivers scientific evidence for tailored ecological restoration and climate adaptation strategies.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-07-21
DOI
https://doi.org/10.1038/s41598-025-11442-8
Citations
3
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
5.15

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Seasonal variations in ecological environment quality across different geomorphological regions and their response mechanisms to climate change

Y Zhang, Zhibin Shan, Jianfeng Li, Liangyan Yang
3 citations
Scientific Reports
Remote Sensing in Agriculture
5.15
article

Seasonal variations in ecological environment quality across different geomorphological regions and their response mechanisms to climate change

Y Zhang, Zhibin Shan, Jianfeng Li, Liangyan Yang
article en
3 citations

Abstract

Understanding seasonal variations in ecological environment quality (EEQ) across diverse geomorphological regions is essential for accurately assessing ecosystem health and resilience amid climate change. However, systematic quantitative studies examining these fluctuations and their underlying climate response mechanisms remain scarce, particularly in regions with complex terrain. This study focused on Shaanxi Province, known for its diverse landforms and complex climate. Using the Google Earth Engine (GEE) platform, we systematically analyzed the spatiotemporal variations in EEQ across six geomorphic regions from 2002 to 2022. This analysis integrated the Remote Sensing Ecological Index (RSEI) with climatic factors to explore the region's response mechanisms to climate change. The results revealed the following: (1) EEQ exhibited significant regional variation, with the Daba and Qinling Mountain regions recording the highest annual average RSEI (> 0.6) and the wind-sand transition zone the lowest (< 0.25). The Guanzhong Plain, marked by intense human activity, uniquely displayed fluctuating degradation. (2) Single-season RSEI evaluations (e.g., summer) tended to overestimate EEQ in arid regions and underestimate degradation risks in humid regions. Averaging RSEI across all four seasons provided a more accurate depiction of interannual ecosystem stability. (3) EEQ responses to climate change vary significantly across regions due to the constraints imposed by water-heat balance and geomorphological characteristics. Precipitation plays a dominant role in influencing EEQ in arid regions such as the Loess Plateau, whereas temperature exerts a more significant effect in humid regions like the Daba Mountains. Human activities, particularly in the Guanzhong Plain, markedly reduce climate sensitivity, highlighting the complex interplay between natural and anthropogenic drivers. Employing an innovative "seasonal fluctuation-geomorphological heterogeneity-climate response" framework, this study reveals biases in traditional single-season assessments and delivers scientific evidence for tailored ecological restoration and climate adaptation strategies.

Scientific ReportsVol. 15(1)
Ankang University (CN), China Land Surveying and Planning Institute (CN), Yangtze River Pharmaceutical Group (China) (CN), Shaanxi Provincial Land Engineering Construction Group
Education Department of Shaanxi Province
Climate action
Openalex Percentile: Top 5%
Remote Sensing in Agriculture
5.15
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.