| 协同多源遥感数据的北亚热带森林蓄积量贝叶斯分层估测 |
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| 引用本文： | 林文科,陆亚刚,蒋先蝶,李桂英,李登秋,陆灯盛.协同多源遥感数据的北亚热带森林蓄积量贝叶斯分层估测[J].遥感学报,2022,26(3):468-479. |
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| 作者姓名： | 林文科 陆亚刚 蒋先蝶 李桂英 李登秋 陆灯盛 |
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| 作者单位： | 1.福建师范大学 湿润亚热带山地生态国家重点实验室培育基地, 福州 350007;2.福建师范大学 地理研究所, 福州 350007;3.国家林业和草原局华东调查规划设计院, 杭州 310000;4.福建省减灾中心, 福州 350001 |
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| 基金项目： | 国家自然科学基金（编号：32171787）；国家重点研发计划（编号：2017YFD0600900） |
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| 摘 要： | 精确估算森林蓄积量是国家实现2060年前碳中和目标的迫切需求,而基于遥感的森林蓄积量定量反演是当前遥感应用领域面临的重要挑战和研究热点.光学遥感数据由于无法获取森林高度信息并存在信号饱和问题,反演森林蓄积量的精度较低,而机载Lidar数据能获取高度信息,但成本高、观测范围有限.本研究利用Sentinel-2多光谱、资源...
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| 关 键 词： | 遥感 森林蓄积量 贝叶斯分层模型 Sentinel-2 资源三号 机载Lidar 多源数据 |
| 收稿时间： | 2021/8/9 0:00:00 |
Modeling forest growing stock volume in a north subtropical region using the hierarchical Bayesian approach based on multi-source data |
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| LIN Wenke,LU Yagang,JIANG Xiandie,LI Guiying,LI Dengqiu,LU Dengsheng.Modeling forest growing stock volume in a north subtropical region using the hierarchical Bayesian approach based on multi-source data[J].Journal of Remote Sensing,2022,26(3):468-479. |
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| Authors: | LIN Wenke LU Yagang JIANG Xiandie LI Guiying LI Dengqiu LU Dengsheng |
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| Institution: | 1.State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou 350007, China;2.Institute of Geography, Fujian Normal University, Fuzhou 350007, China;3.Institute of East China Inventory and Planning, National Forestry and Grassland Administration, Hangzhou 310000, China;4.Disaster Reduction Center of Fujian Province, Fuzhou 350001, China |
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| Abstract: | Accurate estimation of Forest Growing Stock Volume (FGSV) is needed to achieve the goal of carbon neutral. Quantitative inversion of FGSV using remote sensing technologies is still a research challenge. Optical remote sensing technology is one of the most important means for FGSV estimation, but cannot provide sufficiently accurate estimates due to lack of canopy structure features and data saturation problem. Although airborne Lidar can overcome the shortcoming of optical sensor data, its high cost in data collection and limited observation area constrain its extensive application. This research employs integration of Sentinel-2, ZY-3 stereo, and airborne Lidar data to explore the performance of FGSV estimation in north subtropical regions, and examines the advantages of using the hierarchical Bayesian approach to develop FGSV estimation models under the condition of small population of sample plots. The objective is to solve low modeling accuracy caused by the single sensor data and insufficient number of sample plots. The results indicate that the hierarchical Bayesian approach based on combination of Sentinel-2 and Canopy Height Model (CHM) data (subtraction of Lidar-derived digital elevation model data from ZY-3 stereo-derived digital surface model data) provides the best estimation results with relative Root Mean Square Error (rRMSE) of 27.6%. The Root Mean Square Error (RMSE) using this approach reduced by 13.6 m3/ha comparing with the RMSE based on Sentinel-2 data alone, and reduced by 7.4 m3/ha based on CHM data alone. The research shows that use of multi-source data can effectively improve the problems of overestimation when FGSV is small and of underestimation when FGSV is relatively high, that is, use of multi-source data can reduce the overestimation by one forth and the underestimation by one third comparing with use of single data source alone. Comparing with traditional modeling approaches such as linear regression and random forest, the hierarchical Bayesian approach can effectively reduce the requirement of number of samples due to use of stratification strategy and reduce the impacts of forest types and terrain differences on FGSV estimation accuracy. This research provides new insights of using integration of different data sources to develop FGSV estimation models to achieve accurate estimates, and provides key technology for FGSV mapping in subtropical regions. |
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| Keywords: | remote sensing forest growing stock volume hierarchical Bayesian approach Sentinel-2 ZY-3 airborne Lidar multi-source data |
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