Evaluation and Optimization Strategies for Forest Landscape Stability in Different Landform Types of the Loess Plateau DOI Creative Commons
Mei Zhang, Peng Liu,

Zhong Zhao

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(6), С. 1105 - 1105

Опубликована: Март 20, 2025

This study aims to develop a forest landscape stability assessment framework that integrates structure, function, and resilience assess under different landform types on the Loess Plateau, propose differentiated optimization strategies. Remote sensing images ground survey data were combined compare effectiveness of machine learning models in aboveground biomass (AGB) inversion. Meanwhile, fragmentation multifunctionality assessed, Landscape Stability Index (LSI) was proposed quantify regional stability. The main findings are as follows: (1) between 2000 2022, degree hilly gully region improved significantly, Simpson’s Diversity (SDI) value showed an increasing trend; plateau decreasing trend SDI value. higher significant changes, while more stable, with “Interior” “Dominant” dominating. (2) eXtreme Gradient Boosting model outperformed other AGB estimation, R2 = 0.81 RMSE 24.67 ton ha−1. (3) LSI generally increased, especially Yanchang, showing increase ecological stability; decreased, Baishui, weakening Based results, strategies for stabilities proposed, including hierarchical management fragmentation, multi-objective improve SDI, adaptive AGB. this can effectively landscapes, reveal differences restoration regions, provide new perspectives Plateau.

Язык: Английский

Evaluation and Optimization Strategies for Forest Landscape Stability in Different Landform Types of the Loess Plateau DOI Creative Commons
Mei Zhang, Peng Liu,

Zhong Zhao

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(6), С. 1105 - 1105

Опубликована: Март 20, 2025

This study aims to develop a forest landscape stability assessment framework that integrates structure, function, and resilience assess under different landform types on the Loess Plateau, propose differentiated optimization strategies. Remote sensing images ground survey data were combined compare effectiveness of machine learning models in aboveground biomass (AGB) inversion. Meanwhile, fragmentation multifunctionality assessed, Landscape Stability Index (LSI) was proposed quantify regional stability. The main findings are as follows: (1) between 2000 2022, degree hilly gully region improved significantly, Simpson’s Diversity (SDI) value showed an increasing trend; plateau decreasing trend SDI value. higher significant changes, while more stable, with “Interior” “Dominant” dominating. (2) eXtreme Gradient Boosting model outperformed other AGB estimation, R2 = 0.81 RMSE 24.67 ton ha−1. (3) LSI generally increased, especially Yanchang, showing increase ecological stability; decreased, Baishui, weakening Based results, strategies for stabilities proposed, including hierarchical management fragmentation, multi-objective improve SDI, adaptive AGB. this can effectively landscapes, reveal differences restoration regions, provide new perspectives Plateau.

Язык: Английский

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