编号
010036501
推送时间
20221017
研究领域
森林生态
年份
2022
类型
期刊
语种
英语
标题
Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data
来源期刊 forest
期
第365期
发表时间
20220915
关键词
forest growing stock;
CatBoost;
category features;
Lasso;
Sentinel;
摘要
The forest growing stock is one of the key indicators in monitoring forest resources, and its quantitative estimation is of great significance. Based on multi-source data, including Sentinel-1 radar remote sensing data, Sentinel-2 optical remote sensing data, digital elevation model (DEM), and inventory data for forest management planning and design, the Lasso feature selection method was used to remove the non-significant indicators, and three machine learning algorithms, GBDT, XGBoost, and CatBoost, were used to estimate forest growing stock. In addition, four category features, forest population, dominant tree species, humus thickness, and slope direction, were involved in estimating forest growing stock. The results showed that the addition of category features significantly improved the performance of the models. To a certain extent, radar remote sensing data also could improve estimating accuracy. Among the three models, the CatBoost model (R2 = 0.78, MSE = 0.62, MAE = 0.59, MAPE = 16.20%) had the highest estimating accuracy, followed by XGBoost (R2 = 0.75, MSE = 0.71, MAE = 0.62, MAPE = 18.28%) and GBDT (R2 = 0.72, MSE = 0.78, MAE = 0.68, MAPE = 20.28%). View Full-Text
服务人员
王璐
服务院士
蒋有绪
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