Spatiotemporal Variations and Driving Forces Analysis of Ecosystem Health in the Bosten Lake Wetland in China

ABSTRACT Wetlands play a crucial role in maintaining local ecosystems, offering significant ecological and economic value, especially in water conservation and climate regulation. However, due to the limitations of field observation data in large‐scale spatiotemporal analyses, current health assessment frameworks for inland wetlands in arid regions remain underdeveloped. To address this gap, we propose a wetland ecosystem health assessment system based on remote sensing data. This system enables the evaluation of complex and interrelated characteristics such as spatial structure changes, landscape patterns, and spatial connectivity in inland wetlands in arid areas. By overcoming the spatiotemporal constraints of field data, our approach provides theoretical support for wetland protection and management, as well as for the rational development and sustainable utilization of wetland resources, thereby offering effective strategies to tackle the challenges facing wetland ecosystems. This study focuses on the Bosten Lake wetland as the research area, constructing a wetland ecosystem health assessment system based on the Land Use Land Cover (LULC) dataset, anthropogenic factors, ecological elements, and topography. Using the pressure‐state‐response (PSR) model, landscape indices, analytic hierarchy process (AHP), wetland ecosystem health index (WEHI), Theil–Sen slope estimation, Mann–Kendall (MK) trend analysis, and geographical detector methods, we investigated the spatiotemporal characteristics of ecosystem health, transitions in health levels, trends in health changes, and their driving forces. The findings are as follows: (1) From 2000 to 2020, the health level of the Bosten Lake wetland exhibited a wave‐like pattern of decline, growth, decline, and growth. The WEHI in the lake region was higher compared to the lakeshore and marginal areas, while the small lake areas tend to have relatively higher WEHI levels. The area of unhealthy regions decreased while the area of healthy regions expanded. (2) Significant improvement in the wetland ecosystem health during 2000–2020 was observed mainly in the western part of the large lake regions and the small lake regions, while slight deterioration was noted around the lake periphery. (3) The key factors influencing the health of the Bosten Lake wetland included ecosystem service value (ESV), gross domestic product (GDP), largest patch index (LPI), and population (POP). In contrast, slope and nighttime light (NTL) had minimal impact. Interactions between ESV and LSI, GDP, or Shannon diversity index (SHDI) strongly influenced the wetland ecosystem.

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Publication Details

Journal
Land Degradation and Development
Published
2025-06-13
DOI
https://doi.org/10.1002/ldr.5686
Citations
3
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
3.32

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article

Spatiotemporal Variations and Driving Forces Analysis of Ecosystem Health in the Bosten Lake Wetland in China

Yusufujiang Rusuli, Yimuran Kuluwan, Kamuran Maimaitiaili, Nazhakaiti Nijiati
3 citations
Land Degradation and Development
Land Use and Ecosystem Services
3.32
article

Spatiotemporal Variations and Driving Forces Analysis of Ecosystem Health in the Bosten Lake Wetland in China

Yusufujiang Rusuli, Yimuran Kuluwan, Kamuran Maimaitiaili, Nazhakaiti Nijiati
article en
3 citations

Abstract

ABSTRACT Wetlands play a crucial role in maintaining local ecosystems, offering significant ecological and economic value, especially in water conservation and climate regulation. However, due to the limitations of field observation data in large‐scale spatiotemporal analyses, current health assessment frameworks for inland wetlands in arid regions remain underdeveloped. To address this gap, we propose a wetland ecosystem health assessment system based on remote sensing data. This system enables the evaluation of complex and interrelated characteristics such as spatial structure changes, landscape patterns, and spatial connectivity in inland wetlands in arid areas. By overcoming the spatiotemporal constraints of field data, our approach provides theoretical support for wetland protection and management, as well as for the rational development and sustainable utilization of wetland resources, thereby offering effective strategies to tackle the challenges facing wetland ecosystems. This study focuses on the Bosten Lake wetland as the research area, constructing a wetland ecosystem health assessment system based on the Land Use Land Cover (LULC) dataset, anthropogenic factors, ecological elements, and topography. Using the pressure‐state‐response (PSR) model, landscape indices, analytic hierarchy process (AHP), wetland ecosystem health index (WEHI), Theil–Sen slope estimation, Mann–Kendall (MK) trend analysis, and geographical detector methods, we investigated the spatiotemporal characteristics of ecosystem health, transitions in health levels, trends in health changes, and their driving forces. The findings are as follows: (1) From 2000 to 2020, the health level of the Bosten Lake wetland exhibited a wave‐like pattern of decline, growth, decline, and growth. The WEHI in the lake region was higher compared to the lakeshore and marginal areas, while the small lake areas tend to have relatively higher WEHI levels. The area of unhealthy regions decreased while the area of healthy regions expanded. (2) Significant improvement in the wetland ecosystem health during 2000–2020 was observed mainly in the western part of the large lake regions and the small lake regions, while slight deterioration was noted around the lake periphery. (3) The key factors influencing the health of the Bosten Lake wetland included ecosystem service value (ESV), gross domestic product (GDP), largest patch index (LPI), and population (POP). In contrast, slope and nighttime light (NTL) had minimal impact. Interactions between ESV and LSI, GDP, or Shannon diversity index (SHDI) strongly influenced the wetland ecosystem.

Land Degradation and DevelopmentVol. 36(14)
Xinjiang Normal University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 8%
Land Use and Ecosystem Services
3.32
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