Recognition of thaw slumps based on machine learning and UAVs: A case study in the Qilian Mountains, northeastern Qinghai-Tibet Plateau

The thawing of permafrost on the Qinghai-Tibet Plateau (QTP) leads to more frequent occurrences of thaw slump (TS), which have significant impacts on local ecosystems, carbon cycles, and infrastructure development. Accurate recognition of TS would help in understanding its occurrence and evolution. Machine learning capabilities for TS recognition are still not fully exploited. We systematically evaluate the performance of machine learning models for TS recognition from unmanned aerial vehicle (UAV) and propose an ensemble learning object-based model for TS recognition (EOTSR). The EOTSR has the following advantages: 1) pioneering the introduction of spatial information to assist in recognition; 2) the misclassification of recognition models is improved by object-based technology; and 3) attempting to integrate the strengths of different machine learning models to obtain a recognition accuracy no less than that of commonly used deep learning models. The results show that object-based technology is more suitable for TS recognition than pixel-based technology. Recursive feature elimination (RFE)-based feature selection proves that texture and geometry are effective complements to TS recognition. Among the improved object-based machine learning models, support vector machine (SVM) has the highest recognition accuracy, with an overall accuracy of 93.06 %. McNemar's test proves that EOTSR significantly improves TS recognition compared to a single model and achieves an overall accuracy of 97.32 %. The EOTSR model provides an effective recognition method for the increasingly frequent TS events in the permafrost regions of the QTP, and can produce label data for deep learning models based on satellite imagery.