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

Wenqian Bai, Zhengwei He (), Yan Tan, Guy M. Robinson (), Tingyu Zhang, Xueman Wang, Li He, Linlong Li and Shuang Wu
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Wenqian Bai: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, 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
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
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
Li He: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, 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
Shuang Wu: School of Land Resources and Surveying and Mapping Engineering, Shandong Agricultural and Engineering University, Jinan 250100, China

Land, 2025, vol. 14, issue 1, 1-25

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.

Keywords: mountain–plain vegetation classification; machine learning; feature optimization; Sentinel-1; Sentinel-2 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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