Identifying the Restoration Stages of Degraded Alpine Meadow Patches Using Hyperspectral Imaging and Machine Learning Techniques

Wei Luo, Lu Wang (), Lulu Cui, Min Zheng, Xilai Li and Chengyi Li
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Wei Luo: College of Computer Technology and Applications, Qinghai University, Ningzhang Road, Xining 810016, China
Lu Wang: College of Computer Technology and Applications, Qinghai University, Ningzhang Road, Xining 810016, China
Lulu Cui: College of Computer Technology and Applications, Qinghai University, Ningzhang Road, Xining 810016, China
Min Zheng: College of Agriculture and Animal Husbandry, Qinghai University, Ningzhang Road, Xining 810016, China
Xilai Li: College of Agriculture and Animal Husbandry, Qinghai University, Ningzhang Road, Xining 810016, China
Chengyi Li: College of Agriculture and Animal Husbandry, Qinghai University, Ningzhang Road, Xining 810016, China

Agriculture, 2024, vol. 14, issue 7, 1-22

Abstract: The accurate identification of different restoration stages of degraded alpine meadow patches is essential to effectively curb the deterioration trend of ‘Heitutan’ (areas of severely degraded alpine meadows in western China). In this study, hyperspectral imaging (HSI) and machine learning techniques were used to develop a method for accurately distinguishing the different restoration stages of alpine meadow patches. First, hyperspectral images representing the four restoration stages of degraded alpine meadow patches were collected, and spectral reflectance, vegetation indexes (VIs), color features (CFs), and texture features (TFs) were extracted. Secondly, valid features were selected by competitive adaptive reweighted sampling (CARS), ReliefF, recursive feature elimination (RFE), and F-test algorithms. Finally, four machine learning models, including the support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and extreme gradient boosting (XGBoost), were constructed. The results demonstrated that the SVM model based on the optimal wavelengths (OWs) and prominent VIs achieved the best value of accuracy (0.9320), precision (0.9369), recall (0.9308), and F1 score (0.9299). In addition, the models that combine multiple sets of preferred features showed a significant performance improvement over the models that relied only on a single set of preferred features. Overall, the method combined with HSI and machine learning technology showed excellent reliability and effectiveness in identifying the restoration stages of meadow patches, and provided an effective reference for the formulation of grassland degradation management measures.

Keywords: degraded alpine meadow patches; hyperspectral imaging; machine learning; feature fusion (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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