A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data
文献类型：期刊论文
| 作者 | Guo, Linghui1; Zhang, Yang1; Xu, Muchao1; Yan, Jingjing1; Zhang, Hebing1; Zou, Youfeng1; Gao, Jiangbo2 |
| 刊名 | FORESTS
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 15期号:12页码:2139 |
| 关键词 | forest canopy height ICESat-2 ATLAS forest species map random forest |
| DOI | 10.3390/f15122139 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Precise information on forest canopy height (FCH) is critical for forest carbon stocks estimation and management, but mapping continuous FCH with satellite data at regional scale is still a challenge. By fusing ICESat-2, Sentinel-1/2 images and ancillary data, this study aimed to develop a workflow to obtain an FCH map using a machine learning algorithm over large areas. The vegetation-type map was initially produced by a phenology-based spectral feature selection method. A forest characteristic-based model was then proposed to map spatially continuous FCH after a multivariate quality control. Our results show that the overall accuracy (OA) and average F1 Score (F1) for eight main vegetation types were more than 90% and 89%, respectively, and the vegetation-type map agreed well with the census areas. The forest characteristic-based model demonstrated a greater potential in FCH prediction, with an R-value 60.47% greater than the traditional single model, suggesting that the addition of the multivariate quality control and forest structure characteristics could positively contribute to the prediction of FCH. We generated a 30 m continuous FCH map by the forest characteristic-based model and evaluated the product with about 35 km2 of airborne laser scanning (ALS) validation data (R = 0.73, RMSE = 2.99 m), which were 45.34% more precise than the China FCH, 2019. These findings demonstrate the potential of our proposed workflow for monitoring regional continuous FCH, and will greatly benefit accurate forest resources assessment. |
| URL标识 | 查看原文 |
| WOS关键词 | FEATURE-SELECTION ; LAND ; CORN |
| WOS研究方向 | Forestry |
| 语种 | 英语 |
| WOS记录号 | WOS:001384332700001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211286]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Gao, Jiangbo |
| 作者单位 | 1.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Peoples R China; 2.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Linghui,Zhang, Yang,Xu, Muchao,et al. A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data[J]. FORESTS,2024,15(12):2139. |
| APA | Guo, Linghui.,Zhang, Yang.,Xu, Muchao.,Yan, Jingjing.,Zhang, Hebing.,...&Gao, Jiangbo.(2024).A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data.FORESTS,15(12),2139. |
| MLA | Guo, Linghui,et al."A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data".FORESTS 15.12(2024):2139. |
入库方式： OAI收割
来源：地理科学与资源研究所
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