Evidence map›Paper›PMID 40691700›Full record

ArticleScientific reports2025

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

Jianfeng Li, Yang Zhang, Liangyan Yang, Zhibin Shan

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jianfeng LiTechnology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co., Ltd, and Xi'an Jiaotong University, Xi'an, 712046, China. ljf_sxdj@126.com.
Yang ZhangTechnology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co., Ltd, and Xi'an Jiaotong University, Xi'an, 712046, China.
Liangyan YangTechnology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co., Ltd, and Xi'an Jiaotong University, Xi'an, 712046, China.
Zhibin ShanSchool of Economics and Management, Ankang University, Ankang, 725000, China.

Funding

Education Department of Shaanxi Provincial Government 23JK0273Enterprise Innovation and Youth Talent Support Program of Shaanxi Association for Science and Technology 20230517Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements 2023KFKTB008Technology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co., Ltd. and Xi'an Jiaotong University 2024WHZ0238
6 · The paper itself

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.

Identifiers

PMID40691700
PMCPMC12279952

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.