| 基于星载ICESat-2/ATLAS数据的森林地上生物量估测 |
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| 引用本文： | 宋涵玥,舒清态,席磊,邱霜,魏治越,杨泽至.基于星载ICESat-2/ATLAS数据的森林地上生物量估测[J].农业工程学报,2022,38(10):191-199. |
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| 作者姓名： | 宋涵玥 舒清态 席磊 邱霜 魏治越 杨泽至 |
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| 作者单位： | 西南林业大学林学院，昆明 650224 |
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| 基金项目： | 国家自然科学基金项目（31860205，31460194） |
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| 摘 要： | 为探讨星载激光雷达数据ICESat-2（Ice，Cloud，and land Elevation Satellite-2）在山地森林地上生物量（Aboveground Biomass，AGB）的估测可行性和方法。以ATLAS（Advanced Terrain Laser Altimeter System）光子点云数据为主要信息源，以滇西北典型山地香格里拉为研究区，结合地面54块实测生物量遥感样地，在前期进行点云数据去噪、分类预处理基础上，对研究区74 873个林地光斑进行冠层参数及地形因子的提取（共计53个变量），采用非参数模型随机森林回归和超参数优化后的随机森林进行建模，以均方根误差（Root Mean Square Error，RMSE）、决定系数（R2）、总体估测精度（P1）作为模型的评价指标，建立研究区AGB模型。研究结果表明：1）分析以ICESat-2/ATLAS提取的冠层参数、地形因子与生物量的相关性可知，冠层光子总数与生物量具有极显著相关性（P<0.01），基于陆地卫星的乔木冠层百分比、冠层光子比率、坡度、光子总数、表观反射率与生物量具有显著相关性（0.01
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| 关 键 词： | 森林 生物量 算法 ICESat-2 激光雷达 随机森林 超参数优化 |
| 收稿时间： | 2022/3/8 0:00:00 |
| 修稿时间： | 2022/5/9 0:00:00 |
Remote sensing estimation of forest above-ground biomass based on spaceborne lidar ICESat-2/ATLAS data |
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| Song Hanyue,Shu Qingtai,Xi Lei,Qiu Shuang,Wei Zhiyue,Yang Zezhi.Remote sensing estimation of forest above-ground biomass based on spaceborne lidar ICESat-2/ATLAS data[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(10):191-199. |
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| Authors: | Song Hanyue Shu Qingtai Xi Lei Qiu Shuang Wei Zhiyue Yang Zezhi |
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| Institution: | College of Forestry, Southwest Forestry University, Kunming 650224, China |
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| Abstract: | Abstract: In order to evaluate the feasibility of the remote sensing estimation using spaceborne Lidar ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) data for the forest Aboveground Biomass (AGB) in the mountains region, the Random Forest Regression (RFR) model was conducted by combining the Advanced Terrain Laser Altimeter System (ATLAS) photon point cloud data and 54 sample plots in Shangri-La, a typical mountain area in northwest Yunnan, Southwest China. On the basis of the data denoising and classification, the 50 canopy parameters and 3 topographic factors of 74 873 footprints were extracted. A biomass model was established with 53 parameters as the independent variables after the hyper-parametric optimizing RF, and the biomass data was collected from 54 remote sensing plots to serve as dependent variables. The Root Mean Square Error (RMSE), coefficient of determination (R2) and overall estimation accuracy (P1) were used to evaluate the model accuracy. The results showed that: 1) There was the highest significant correlation of the number canopy photons parameters with the forests aboveground biomass (P<0.01) by a correlation analyzing between the 53 footprint parameters and footprints biomass. The other parameters, landsat percentage canopy, canopy photon rate, slope, number of photons and apparent surface reflectance were significantly correlated with biomass (0.01
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| Keywords: | forest biomass algorithms ICESat-2 laser radar random forest hyperparameter optimization |
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