Simulation and Prediction of the East Dongting Lake Wetland Landscape Based on the PLUS Model

The East Dongting Lake Wetland, an internationally vital reserve, faces growing ecological threats, necessitating enhanced predictive research on its landscape dynamics. Using the PLUS model and Markov chain method, this study analyzes landscape changes (2010–2022) and simulates 2030 patterns under two scenarios. The key findings reveal the following: (1) poplar plantations plummeted from 28.65% to 2.79% due to restoration policies (e.g., tree removal), while grasslands surged from 21.43% to 59.64%; mudflats and water bodies fluctuated naturally. (2) Natural drivers dominated changes—precipitation and elevation influenced water bodies and grasslands the most, whereas road proximity primarily affected poplar plantations. (3) The PLUS model proved effective for small-scale wetland predictions. (4) Simulations showed divergent 2030 outcomes: under natural development, poplar plantations would rebound to 57.86 km2, whereas ecological regulation—restricting plantations and expanding grasslands to 882.70 km2—better supported biodiversity. This study underscores policy-driven restoration success and the PLUS model’s utility for local-scale simulations, offering actionable insights for Dongting Lake’s management and a methodological framework for wetland conservation.

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Publication Details

Journal
Applied Sciences
Published
2025-09-03
DOI
https://doi.org/10.3390/app15179699
Citations
1
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
1.11
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Simulation and Prediction of the East Dongting Lake Wetland Landscape Based on the PLUS Model

C. Zhang, Ting Miao, Zhiqiang Wang, Ruojun Yang
1 citations
Applied Sciences
Land Use and Ecosystem Services
1.11
article

Simulation and Prediction of the East Dongting Lake Wetland Landscape Based on the PLUS Model

C. Zhang, Ting Miao, Zhiqiang Wang, Ruojun Yang
article en
1 citations

Abstract

The East Dongting Lake Wetland, an internationally vital reserve, faces growing ecological threats, necessitating enhanced predictive research on its landscape dynamics. Using the PLUS model and Markov chain method, this study analyzes landscape changes (2010–2022) and simulates 2030 patterns under two scenarios. The key findings reveal the following: (1) poplar plantations plummeted from 28.65% to 2.79% due to restoration policies (e.g., tree removal), while grasslands surged from 21.43% to 59.64%; mudflats and water bodies fluctuated naturally. (2) Natural drivers dominated changes—precipitation and elevation influenced water bodies and grasslands the most, whereas road proximity primarily affected poplar plantations. (3) The PLUS model proved effective for small-scale wetland predictions. (4) Simulations showed divergent 2030 outcomes: under natural development, poplar plantations would rebound to 57.86 km2, whereas ecological regulation—restricting plantations and expanding grasslands to 882.70 km2—better supported biodiversity. This study underscores policy-driven restoration success and the PLUS model’s utility for local-scale simulations, offering actionable insights for Dongting Lake’s management and a methodological framework for wetland conservation.

Applied SciencesVol. 15(17)
Hunan University of Science and Technology (CN)
Openalex Percentile: Top 20%
Land Use and Ecosystem Services
1.11
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