Classification and identification of degraded alpine m eadows based on machine learning techniques

Aim to address the intelligent recognition problem of different types of degraded grasslands in the alpine region of t he Qinghai-Tibet Plateau. The study takes the grassland sample p lots in Henan County, Qinghai Province as the research object an d constructs three machine learning models for classifying and id entifying degraded alpine meadows using the random forest, mult ilayer perceptron neural network, and support vector machine m ethods. The performance and effectiveness of the models are eval uated from five different perspectives, namely classification accur acy, F-measure, precision, recall, and the time taken to identify ea ch image. The experimental results demonstrate that all three alg orithms can efficiently identify different types of degraded alpine meadows, with the support vector machine algorithm being the o ptimal model, achieving the best performance across all evaluatio n metrics. This study provides a theoretical foundation and techn ical support for monitoring and restoring degraded grasslands in high-altitude regions.