基于梯度提升回归树的三江源地区植被指数的预测方法研究

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中图分类号：S19 文献标识码：A 文章编号：1007-0435（2025）05-1655-14

Abstract：To reveal the spatio-temporal patternand influencing factors of vegetation changes in the Sanji angyuanregion from 2OOO to 2O23，and to forecast the possible change trend of vegetation under climate change， in this study NDVI data and climte data including temperature，precipitation，wind speed and barometric pressure from four regions within Sanjiangyuan-Dari，Mado，Yushu，and Qumalai were utilized to analyze the normalized Diference Vegetation Index at the same period. The NDVI prediction model was established by using machine learning algorithms-Gradient Boosting Regressor，AdaBoost Regressor，Random Forest，and Neural Networks.On this bisis，allmodels were fine-tuned and validated to enhance performance and reliability. Finally，an optimal model of simulation accuracy was selected to simulate vegetation change under multiple scenarios.The results showed that temperature was the most significant meteorological factor influencing NDVI， explaining up to 67.29% of the variability. The Gradient Boosting Regressor showed better performance than other models in all the study areas. This model achieved a Mean Squared Error（MSE）ranging from O.000 45 to 0.001 04 and an R2 value exceeding O.9O. It showed strong fiting ability. The Gradient Boosting Regressor proved to be highly accurate and stable in predicting NDVI， which provides a robust approach for forecasting vegetation changes and is instrumental for early warning of vegetation degradation in response to climate change. Theresearch findings provide a robust scientific basis for ecological conservation initiatives，facilitating the for mulation of strategies to alleviate the efects ofclimatechange on the vegetation within the Sanjiangyuan area.

Key words：NDVI;Machine learning;Gradient boosted regression tree;Sanjiangyuan region

在过去的几十年中，为了有效监测陆地植被活动的变化，归一化植被指数（Nomalized difference veg-etationindex，NDVI）和叶面积指数（Leafareaindex，LAI）被相继提出[1-2]。（剩余16642字）