A Study on the Classification of Shrubs and Grasses on the Tibetan Plateau Based on Unmanned Aerial Vehicle Multispectral Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 21; p. 4106
• Main Authors: Chen, Xiaoqiang, Deng, Hui, Zhang, Wenjiang, Zhang, Houxi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Accuracy; Algorithms; Classification; Climate change; Datasets; Decision trees; Drone aircraft; Ecological balance; Ecological monitoring; Ecology; Ecosystems; Environmental monitoring; feature optimization; Geographical distribution; Grasses; Imagery; Infrared spectra; Kobresia; Kobresia pygmaea; Learning algorithms; Machine learning; Machinery condition monitoring; Near infrared radiation; OBIA; Populus simonii; Salix psammophila; Shrubs; Support vector machines; Tibetan Plateau; UAV remote sensing; Unmanned aerial vehicles; Vegetation; vegetation classification; Willow; China; Tibetan Plateau
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16214106

The ecosystem of the Qinghai–Tibet Plateau is highly fragile due to its unique geographical conditions, with vegetation playing a crucial role in maintaining ecological balance. Thus, accurately monitoring the distribution of vegetation in the plateau region is of paramount importance. This study employs UAV multispectral imagery in combination with four machine-learning models—Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Random Forest (RF)—to investigate the impact of different features and their combinations on the fine classification of shrubs and grasses on the Qinghai–Tibet Plateau, including Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea. The results indicate that near-infrared spectral information can improve classification accuracy, with improvements of 5.21%, 1.65%, 6.64%, and 5.03% for Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea, respectively. Feature selection effectively reduces redundant information and enhances model classification accuracy, with all four machine-learning models achieving the best performance on the optimized feature set. Furthermore, the RF model performs best on the optimized feature set, achieving an overall accuracy (OA) of 95.32% and a kappa coefficient of 0.94. This study provides important scientific support for the fine classification and ecological monitoring of plateau vegetation.