[1]黄天宝,欧光龙,吴勇,等. 基于机器学习算法的森林生物量多源遥感估测[J].西北林学院学报,2024,39(1):10-18.[doi:10.3969/j.issn.1001-7461.2024.01.02]
 HUANG Tian-bao,OU Guang-long,WU Yong,et al. Multi-source Remote Sensing Estimation of Forest Biomass Based on Machine Learning Algorithm[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2024,39(1):10-18.[doi:10.3969/j.issn.1001-7461.2024.01.02]
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 基于机器学习算法的森林生物量多源遥感估测()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第39卷
期数:
2024年第1期
页码:
10-18
栏目:
出版日期:
2024-01-31

文章信息/Info

Title:
 Multi-source Remote Sensing Estimation of Forest Biomass Based on Machine Learning Algorithm
文章编号:
1001-7461(2024)01-0010-09
作者:
 黄天宝1欧光龙1吴勇1徐熊伟23王振会23蔺如喜23徐灿23*
 (1.西南林业大学,云南 昆明 650244;2.中国地质调查局 昆明自然资源调查中心,云南 昆明 650111;3.自然资源部 自然生态系统碳汇工程技术创新中心,云南 昆明 650111)
Author(s):
 HUANG Tian-bao1OU Guang-long1WU Yong1XU Xiong-wei23WANG Zhen-hui23LIN Ru-xi23XU Can23*
 (1.Southwest Forestry University,Kunming,Kunming 650244,Yunnan,China; 2.Kunming Comprehensive Center of Natural Resources Survey,China Geological Survey,Kunming 650111,Yunna,China; 3.Technology Innovation Center for Natural Ecosystem Carbon Sink,Ministry of Natural Resources,Kunming 650100,Yunnan,China)
关键词:
 森林生物量遥感估测多源遥感机器学习算法集成元谋
Keywords:
 forest biomass remote sensing estimation multi-source remote sensing machine learning algorithm integration Yuanmou
分类号:
S771.8
DOI:
10.3969/j.issn.1001-7461.2024.01.02
文献标志码:
A
摘要:
 为进一步探索不同空间分辨率影像在10 m×10 m样地尺度下森林生物量估测性能及协同机器学习算法(RF、SVM、DT、GBM、k-NN、Stacking)的估测效果,利用光学遥感GF2(高分二号卫星)、sentinel 2A、Landsat 8 OLI、SUM(整合3种遥感数据源)影像及辅助变量DEM高程数据、环境因子、林分因子(森林类型、优势树种),在Boruta算法变量选择下用机器学习算法对元谋地区乔木林森林生物量(地上+地下)进行遥感估测,并比较4种影像下的估测精度。研究表明:1)基于Boruta算法分别对3种影像及整合3种影像条件下进行变量选择,单一影像中sentinel 2A的植被指数PEIP、Landsat 8 OLI的纹理因子b2_ME_9×9、GF2的GNDVI分别为3种影像下的最高得分变量,多源融合估测森林生物量中GF2的GNDVI为最佳得分变量;2)基于Boruta算法选择的变量构建RF、SVM、DT、GBM、k-NN算法以及对5个模型的Stacking集成算法,SUM的Stacking集成算法的估测效果最优,模型决定系数(R2)为0.73,均方根误差(RMSE)为28.46 t·hm-2,集成算法下的SUM的估测性能优于sentinel 2A、Landsat 8 OLI,GF2优于sentinel 2A,sentinel 2A的估测性能优于Landsat 8 OLI。研究结果说明在生物量遥感估测中高分辨率影像具有较好的估测效果,同时多源遥感协同估测、集成算法均可提高森林生物量遥感估测精度,可为森林生物量遥感估测提供参考和借鉴。
Abstract:
 In order to further explore the estimation performance of forest biomass at 10 m×10 m geoscale and the estimation effect of collaborative machine learning algorithms (RF,SVM,DT,GBM,k-NN,Stacking) of the images with different spatial resolutions,this study used optical remote sensing GF2 (Gaofen-2 satellite),sentinel 2A,Landsat 8 OLI,SUM (integration of three remote sensing data sources) images and auxiliary variables DEM elevation data,environmental factors,forest stand factors (forest type,dominant tree species).Under the variable selection of Boruta algorithm,the machine learning algorithm was used to estimate the forest biomass (above ground+under ground) in Yuanmou area,and the estimation accuracy under the four images was compared.The results showed that 1) based on the Boruta algorithm,the vegetation index PEIP of sentinel2A,the texture factor of Landsat 8 OLI b2_ME_9×9 and the GNDVI of GF2 in a single image were the highest score variables under the three images,and the GNDVI of GF2 in forest biomass was estimated by multi-source fusion as the best score variable.2) Based on the variables selected by the Boruta algorithm,RF,SVM,DT,GBM,k-NN algorithm and stacking integration algorithm of 5 models,SUM’s Stacking integration algorithm had the best estimation effect,model R2 was 0.73,RMSE was 28.46 t·hm-2,the estimation performance of SUM under the ensemble algorithm was better than sentinel2A and Landsat 8 OLI,GF2 was better than sentinel2A,sentinel2A outperformed Landsat 8 OLI,indicating that high-resolution images have good estimation effects in biomass remote sensing estimation,and multi-source remote sensing collaborative estimation and integrated algorithms can improve the accuracy of forest biomass remote sensing estimation,which can provide references for forest biomass remote sensing estimation.

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备注/Memo

备注/Memo:
 收稿日期:2023-01-03修回日期:2023-03-20
基金项目:中国地质调查局昆明自然资源调查中心全国典型地区碳汇综合调查研究(ZD20220133);国家自然科学基金(31770677、31760206、31660202);云南省王广兴专家工作站( 2018IC100);云南省万人计划青年拔尖人才专项( YNWR-QNBJ-2018-184)。
第一作者:黄天宝。研究方向:森林生物量遥感估测。E-mail:liuzhi@swfu.edu.cn
*通信作者:徐灿,工程师。研究方向:景观生态学。E-mail:xucan@mail.cgs.gov.cn
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