西北林学院学报2024，Vol.39Issue(3)：162-170,265,10.DOI:10.3969/j.issn.1001-7461.2024.03.21
基于遥感的北京市森林地上碳储量监测
Remote Sensing Based Monitoring of Forest Aboveground Carbon Storage in Beijing
摘要
Abstract
Cities are the main regions for CO2 emissions,and promoting urban carbon reduction and low-car-bon development is of great help in achieving"the dual carbon strategy"(carbon peaking and carbon neu-trality)as soon as possible.Urban forest carbon storage is an important indicator reflecting urban CO2 ab-sorption capacity and evaluating ecosystem quality.This study focused on the forests in Beijing,using Landsat8OLI remote sensing images,digital elevation,and secondary resource survey data as data sources.Stepwise regression analysis,recursive elimination algorithm,and Boruta algorithm were used for feature selection.Then,multiple linear regression models,BP neural network,random forest algorithm,and ex-treme gradient boosting algorithm models were used to construct the Beijing Forest AGC(above ground carbon)model.Finally,the most effective model was selected to invert and estimate the overall forest AGC in Beijing.The results showed that 1)when selecting feature sets based on the Boruta algorithm for con-structing four AGC models,its R2 was the best,superior to the two feature selection methods of SRA and RFE.2)The forest AGC model constructed by XGBoost algorithm had the highest accuracy.The values of R2,RMSE,and RRMSE of training and testing sets obtained by selecting feature sets based on Boruta algo-rithm were 0.95 and 0.69,3.16 and 5.18,17.70％and 21.49％,respectively.3)In 2014,the total forest AGC in Beijing was 8931820.34 tons,which is relatively small compared to the actual value.The spatial distribution showed a phenomenon of high in the northwest,low in the middle and southeast.Miyun Dis-trict,Huairou District,and Yanqing District had more forest AGC,while Chaoyang District,Fengtai Dis-trict,and Shijingshan District had fewer.Overall,the feature selection based on Boruta and the modern in-tegrated XGBoost forest AGC model has good estimation performance.This study provides technical sup-port for precise monitoring of forest AGC in mega cities.关键词
城市森林/碳储量/XGBoost模型/Boruta算法/北京市Key words
urban forest/carbon storage/XGBoost model/Boruta algorithm/Beijing分类
农业科技引用本文复制引用
贺晨瑞,庞丽峰,谭炳香,黄逸飞,孙学霞..基于遥感的北京市森林地上碳储量监测[J].西北林学院学报,2024,39(3):162-170,265,10.基金项目
"十四五"重点研发计划项目(2022YFD2200505-03). （2022YFD2200505-03）
