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Vegetation Classification in a Mountain–Plain Transition Zone in the Sichuan Basin, China

Author

Listed:
  • Wenqian Bai

    (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
    College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Zhengwei He

    (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
    College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Yan Tan

    (Department of Geography, Environment and Population, The University of Adelaide, Adelaide 5000, Australia)

  • Guy M. Robinson

    (Department of Geography, Environment and Population, The University of Adelaide, Adelaide 5000, Australia
    Laboratory for Interdisciplinary Spatial Analysis (LISA), Department of Land Economy, University of Cambridge, Cambridge CB3 9EP, UK)

  • Tingyu Zhang

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Xueman Wang

    (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
    College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Li He

    (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
    College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Linlong Li

    (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
    College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Shuang Wu

    (School of Land Resources and Surveying and Mapping Engineering, Shandong Agricultural and Engineering University, Jinan 250100, China)

Abstract

Developing an effective vegetation classification method for mountain–plain transition zones is critical for understanding ecological patterns, evaluating ecosystem services, and guiding conservation efforts. Existing methods perform well in mountainous and plain areas but lack verification in mountain–plain transition zones. This study utilized terrain data and Sentinel-1 and Sentinel-2 imagery to extract topographic, spectral, texture, and SAR features as well as the vegetation index. By combining feature sets and applying feature elimination algorithms, the classification performance of one-dimensional convolutional neural networks (1D-CNNs), Random Forest (RF), and Multilayer Perceptron (MLP) was evaluated to determine the optimal feature combinations and methods. The results show the following: (1) multi-feature combinations, especially spectral and topographic features, significantly improved classification accuracy; (2) Recursive Feature Elimination based on Random Forest (RF-RFE) outperformed ReliefF in feature selection, identifying more representative features; (3) all three algorithms performed well, with consistent spatial results. The MLP algorithm achieved the best overall accuracy (OA: 81.65%, Kappa: 77.75%), demonstrating robustness and lower dependence on feature quantity. This study presents an efficient and robust vegetation classification workflow, verifies its applicability in mountain–plain transition zones, and provides valuable insights for small-region vegetation classification under similar topographic conditions globally.

Suggested Citation

  • Wenqian Bai & Zhengwei He & Yan Tan & Guy M. Robinson & Tingyu Zhang & Xueman Wang & Li He & Linlong Li & Shuang Wu, 2025. "Vegetation Classification in a Mountain–Plain Transition Zone in the Sichuan Basin, China," Land, MDPI, vol. 14(1), pages 1-25, January.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:1:p:184-:d:1569114
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    References listed on IDEAS

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    1. Bao She & Jiating Hu & Linsheng Huang & Mengqi Zhu & Qishuo Yin, 2024. "Mapping Soybean Planting Areas in Regions with Complex Planting Structures Using Machine Learning Models and Chinese GF-6 WFV Data," Agriculture, MDPI, vol. 14(2), pages 1-25, January.
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