High-resolution forest age mapping based on forest height maps derived from GEDI and ICESat-2 space-borne lidar data
文献类型：期刊论文
| 作者 | Lin, Xudong; Shang, Rong; Chen, Jing M.; Zhao, Guoshuai5; Zhang, Xiaoping5; Huang, Yiping5; Yu, Guirui4; He, Nianpeng4; Xu, Li4; Jiao, Wenzhe3 |
| 刊名 | AGRICULTURAL AND FOREST METEOROLOGY
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 339页码:109592 |
| 关键词 | Forest age High resolution GEDI Stand growth equation Machine learning Lidar |
| DOI | 10.1016/j.agrformet.2023.109592 |
| 文献子类 | Article |
| 英文摘要 | Forest age is a key parameter for estimating forest growth and carbon uptake and for forest management. Remote sensing provides indirect but useful information for mapping forest age at large scales. However, existing regional and global forest age products were generated at low spatial resolutions (often 1000 m) and are not useful for most forest stands in China that are smaller than 1000 m. This study aims to map forest age at the 30 m resolution based on forest height maps mainly derived from the Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data, and analyze the roles of auxiliary data including temperature, precipitation, slope, and aspect in forest age mapping. Forest age is defined as the average age of dominant tree species within a pixel. Five commonly-used stand growth equations and twelve machine learning methods were tested for their suitability for mapping forest age of different tree species. We found that the Logistic equation performed the best among the tested stand growth equations and the Random Forest (RF) was the best among the tested machine learning methods. According to RF, forest height contributed predominantly to the variance in forest age mapping, while temperature, precipitation, slope, and aspect also had an overall non-negligible and variable contribution among different tree species. By integrating the climate and topo-graphical variables, RF was applicable for forest age mapping without classifying the tree species. These results show that forest height maps derived from space-borne lidar data such as GEDI and ICESat-2 data are highly useful for mapping forest stand age, and the methodology developed in this study highlights a perspective for generating national and global forest age products at a high spatial resolution. |
| WOS关键词 | STAND AGE ; SPATIAL-DISTRIBUTION ; REGRESSION TREES ; CARBON FLUXES ; NORWAY SPRUCE ; PINUS-RADIATA ; SITE INDEX ; GROWTH ; BIOMASS ; MODELS |
| WOS研究方向 | Agriculture ; Forestry ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001037770300001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200886]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 作者单位 | 1.[Lin, Xudong; Shang, Rong; Chen, Jing M.] Fujian Normal Univ, Sch Geog Sci, Key Lab Humid Subtrop Ecogeog Proc, Minist Educ, Fuzhou 350007, Peoples R China 2.MIT, Dept Civil & Environm Engn, Cambridge, MA 02139 USA 3.Chinese Acad Sci, Inst Geog Sci & Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 4.Fujian Forestry Survey & Planning Inst, Fuzhou 350003, Peoples R China 5.Lin, Xudong; Shang, Rong; Chen, Jing M.] Fujian Normal Univ, Acad Carbon Neutral, Fuzhou 350007, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lin, Xudong,Shang, Rong,Chen, Jing M.,et al. High-resolution forest age mapping based on forest height maps derived from GEDI and ICESat-2 space-borne lidar data[J]. AGRICULTURAL AND FOREST METEOROLOGY,2023,339:109592. |
| APA | Lin, Xudong.,Shang, Rong.,Chen, Jing M..,Zhao, Guoshuai.,Zhang, Xiaoping.,...&Jiao, Wenzhe.(2023).High-resolution forest age mapping based on forest height maps derived from GEDI and ICESat-2 space-borne lidar data.AGRICULTURAL AND FOREST METEOROLOGY,339,109592. |
| MLA | Lin, Xudong,et al."High-resolution forest age mapping based on forest height maps derived from GEDI and ICESat-2 space-borne lidar data".AGRICULTURAL AND FOREST METEOROLOGY 339(2023):109592. |
入库方式： OAI收割
来源：地理科学与资源研究所
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