Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, China
Ecological transition zones spanning semi-humid to semi-arid regions pose distinctive monitoring challenges owing to their climatic vulnerability and geomorphic diversity. This study focuses on Shanxi Province, a typical ecologically fragile area in the Loess Plateau of China. Based on the Google Ea...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/14/5/952 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849327231287951360 |
|---|---|
| author | Chi Cheng Yanqiang Wang |
| author_facet | Chi Cheng Yanqiang Wang |
| author_sort | Chi Cheng |
| collection | DOAJ |
| description | Ecological transition zones spanning semi-humid to semi-arid regions pose distinctive monitoring challenges owing to their climatic vulnerability and geomorphic diversity. This study focuses on Shanxi Province, a typical ecologically fragile area in the Loess Plateau of China. Based on the Google Earth Engine (GEE) platform and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we established the Remote Sensing Ecological Index (RSEI) series from 2000 to 2024 for Shanxi Province. The Theil–Sen Median, Mann–Kendall, and Hurst indices were comprehensively applied to systematically analyze the spatiotemporal differentiation patterns of ecological environmental quality. Furthermore, geodetector-based quantification elucidated the synergistic interactions among topographic, climatic, and anthropogenic drivers. The results indicate the following: (1) From 2000 to 2024, ecological restoration initiatives have shaped an “aggregate improvement-localized degradation” paradigm, with medium-quality territories persistently accounting for 30–40% of the total land area. (2) Significant spatial heterogeneity exists, with the Lüliang Mountain area in the west and the Datong Basin in the north being core degradation zones, while the Taihang Mountain area in the east shows remarkable improvement. However, Theil–Sen Median–Hurst index predictions reveal that 60.07% of the improved areas face potential trend reversal risks. (3) The driving mechanisms exhibit spatial heterogeneity, where land use type, temperature, precipitation, elevation, and slope serve as global dominant factors. This research provides scientific support for formulating differentiated ecological restoration strategies, establishing ecological compensation mechanisms, and optimizing territorial spatial planning in Shanxi Province, contributing to the achievement of sustainable development goals. |
| format | Article |
| id | doaj-art-35bcb64c63cd470097c8d92d02e6938f |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-35bcb64c63cd470097c8d92d02e6938f2025-08-20T03:47:57ZengMDPI AGLand2073-445X2025-04-0114595210.3390/land14050952Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, ChinaChi Cheng0Yanqiang Wang1College of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, ChinaEcological transition zones spanning semi-humid to semi-arid regions pose distinctive monitoring challenges owing to their climatic vulnerability and geomorphic diversity. This study focuses on Shanxi Province, a typical ecologically fragile area in the Loess Plateau of China. Based on the Google Earth Engine (GEE) platform and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we established the Remote Sensing Ecological Index (RSEI) series from 2000 to 2024 for Shanxi Province. The Theil–Sen Median, Mann–Kendall, and Hurst indices were comprehensively applied to systematically analyze the spatiotemporal differentiation patterns of ecological environmental quality. Furthermore, geodetector-based quantification elucidated the synergistic interactions among topographic, climatic, and anthropogenic drivers. The results indicate the following: (1) From 2000 to 2024, ecological restoration initiatives have shaped an “aggregate improvement-localized degradation” paradigm, with medium-quality territories persistently accounting for 30–40% of the total land area. (2) Significant spatial heterogeneity exists, with the Lüliang Mountain area in the west and the Datong Basin in the north being core degradation zones, while the Taihang Mountain area in the east shows remarkable improvement. However, Theil–Sen Median–Hurst index predictions reveal that 60.07% of the improved areas face potential trend reversal risks. (3) The driving mechanisms exhibit spatial heterogeneity, where land use type, temperature, precipitation, elevation, and slope serve as global dominant factors. This research provides scientific support for formulating differentiated ecological restoration strategies, establishing ecological compensation mechanisms, and optimizing territorial spatial planning in Shanxi Province, contributing to the achievement of sustainable development goals.https://www.mdpi.com/2073-445X/14/5/952Remote Sensing Ecological Index (RSEI)ecological environmental qualityspatiotemporal changesgeodetectorGoogle Earth Engine (GEE) |
| spellingShingle | Chi Cheng Yanqiang Wang Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, China Land Remote Sensing Ecological Index (RSEI) ecological environmental quality spatiotemporal changes geodetector Google Earth Engine (GEE) |
| title | Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, China |
| title_full | Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, China |
| title_fullStr | Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, China |
| title_full_unstemmed | Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, China |
| title_short | Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, China |
| title_sort | spatiotemporal changes and the drivers of ecological environmental quality based on the remote sensing ecological index a case study of shanxi province china |
| topic | Remote Sensing Ecological Index (RSEI) ecological environmental quality spatiotemporal changes geodetector Google Earth Engine (GEE) |
| url | https://www.mdpi.com/2073-445X/14/5/952 |
| work_keys_str_mv | AT chicheng spatiotemporalchangesandthedriversofecologicalenvironmentalqualitybasedontheremotesensingecologicalindexacasestudyofshanxiprovincechina AT yanqiangwang spatiotemporalchangesandthedriversofecologicalenvironmentalqualitybasedontheremotesensingecologicalindexacasestudyofshanxiprovincechina |