Potential analysis of the comprehensive land consolidation for entire karst region based on improved random forest method: a case study in the Lijiang River Basin

Comprehensive land consolidation is essential for safeguarding arable land, promoting efficient land use, and enhancing regional ecological environments. Accurate land use classification provides the fundamental basis for reliable potential assessment. In karst regions such as the Lijiang River Basin, complex topography and geomorphological heterogeneity present significant challenges for traditional land cover classification, often resulting in greater uncertainty and reduced reliability in consolidation potential analyses. To address the challenges of heterogeneous karst geomorphology and fragmented land use, we propose an enhanced Random Forest (RF) classification algorithm that integrates recursive feature elimination, sequential backward selection, and a weighted voting mechanism. This approach enables optimal multi-scale feature selection and significantly improves classification performance. Building on this foundation, we developed a comprehensive evaluation model for land consolidation potential that simultaneously considers agricultural, construction, and ecological land types. We systematically introduced complex network analysis to quantify spatial associations among these land types, thereby enabling optimized spatial zoning of consolidation potential. The results indicate that: (1) The enhanced RF algorithm achieved the highest classification accuracy in the Lijiang River Basin, with overall accuracy and Kappa coefficient improved by 3% and 4%, respectively, compared to the conventional RF. This method effectively addresses misclassification issues related to small and fragmented land parcels. (2) The spatial distribution of consolidation potential grades for various land types in the Lijiang River Basin shows clear differentiation, ranking from highest to lowest as ecological land, agricultural land, and construction land. The proposed model increased the coverage of comprehensive consolidation areas for multiple land types to 94.97% of the basin area. The proposed method provides a theoretical foundation and technical pathway for holistic, full-cycle, and spatially optimized land consolidation management in karst and other complex ecological regions, demonstrating strong scalability and practical value.

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Publication Details

Journal
Geo-spatial Information Science
Published
2025-11-14
DOI
https://doi.org/10.1080/10095020.2025.2583814
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
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article

Potential analysis of the comprehensive land consolidation for entire karst region based on improved random forest method: a case study in the Lijiang River Basin

Weiping Jiang, Guiwen Lan, Zhao Li, Jian Wang et al.
Geo-spatial Information Science
Land Use and Ecosystem Services
article

Potential analysis of the comprehensive land consolidation for entire karst region based on improved random forest method: a case study in the Lijiang River Basin

Weiping Jiang, Guiwen Lan, Zhao Li, Jian Wang, Qian Tang
article en

Abstract

Comprehensive land consolidation is essential for safeguarding arable land, promoting efficient land use, and enhancing regional ecological environments. Accurate land use classification provides the fundamental basis for reliable potential assessment. In karst regions such as the Lijiang River Basin, complex topography and geomorphological heterogeneity present significant challenges for traditional land cover classification, often resulting in greater uncertainty and reduced reliability in consolidation potential analyses. To address the challenges of heterogeneous karst geomorphology and fragmented land use, we propose an enhanced Random Forest (RF) classification algorithm that integrates recursive feature elimination, sequential backward selection, and a weighted voting mechanism. This approach enables optimal multi-scale feature selection and significantly improves classification performance. Building on this foundation, we developed a comprehensive evaluation model for land consolidation potential that simultaneously considers agricultural, construction, and ecological land types. We systematically introduced complex network analysis to quantify spatial associations among these land types, thereby enabling optimized spatial zoning of consolidation potential. The results indicate that: (1) The enhanced RF algorithm achieved the highest classification accuracy in the Lijiang River Basin, with overall accuracy and Kappa coefficient improved by 3% and 4%, respectively, compared to the conventional RF. This method effectively addresses misclassification issues related to small and fragmented land parcels. (2) The spatial distribution of consolidation potential grades for various land types in the Lijiang River Basin shows clear differentiation, ranking from highest to lowest as ecological land, agricultural land, and construction land. The proposed model increased the coverage of comprehensive consolidation areas for multiple land types to 94.97% of the basin area. The proposed method provides a theoretical foundation and technical pathway for holistic, full-cycle, and spatially optimized land consolidation management in karst and other complex ecological regions, demonstrating strong scalability and practical value.

Geo-spatial Information Science
Wuhan University (CN), Guilin University of Technology (CN)
Openalex Percentile: Top 68%
Land Use and Ecosystem Services
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