西北林学院学报2025，Vol.40Issue(2)：207-219,13.DOI:10.3969/j.issn.1001-7461.2025.02.21
基于遥感数据和优化Blending算法的人工林地上生物量估算研究
Estimation of Above-Ground Biomass of Planted Forest Based on Remote Sensing Data and Optimized Blending Algorithm
摘要
Abstract
Forest biomass is an important index for understanding forest carbon cycle and evaluating forest carbon storage and health status.Accurately estimating the biomass of the dominant tree species in the Saihanba Forest Farm is of great guiding significance for assessing regional forest carbon cycle and realizing sustainable forest development.This study took the Saihanba Forest Farm as the study area and used Sen-tinel-1 radar remote sensing data,Sentinel-2 optical remote sensing data,and DEM data to extract radar and texture features of radar data,spectrum,texture,vegetation index,tasseled cap features of optical data,and elevation,slope,slope aspect topographic features of DEM data,respectively.Combined with the forest management inventory data of the farm,the planted forest biomass of the main dominant tree species in the forest farm was estimated by using the HPOPCA-Blending integrated estimation model.The results showed that 1)234 parameter features were extracted from Sentinel-1,Sentinel-2,and DEM multi-source data.2)The forest biomass of Larix gmelinii var.principis-rupprechtii,Betula platy phylla,Pinus syl-vestris var.mongholica,Quercus mongolica,and Picea asperata in the grid center was in approximately normal distribution.3)Compared with ANN,MLR,XGB,LSTM,and KNN estimation models,the HPO-PCA-Blending integrated estimation model greatly improved the estimation accuracy of the planted forest biomass of the main dominant tree species,and the R increased by more than 0.3.The combination of the multi-source data parameter features and the HPO-PCA-Blending integrated estimation model can achieve accurate estimation of the planted forest biomass in the Saihanba Forest Farm as well as some other re-gions,which provides an important method and theoretical basis for forest biomass estimation and forest resource management.关键词
塞罕坝林场/人工林/森林生物量/机器学习/多源数据Key words
Saihanba Forest Farm/planted forest/forest biomass/machine learning/multi-source data分类
农业科技引用本文复制引用
辛守英,王晓红,焦琳琳..基于遥感数据和优化Blending算法的人工林地上生物量估算研究[J].西北林学院学报,2025,40(2):207-219,13.基金项目
中央引导地方科技发展资金项目(246Z5901G) （246Z5901G）
唐山市科技局应用基础研究计划项目(20130202b) （20130202b）
华北理工大学博士专项(BS201818) （BS201818）
河北省自然科学基金青年基金项目(D2019209322,D2019209317). （D2019209322,D2019209317）
