Knowledge Management System Of Institute Of Botany,CAS
| Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data | |
Liu, Xiaoqiang1; Su, Yanjun1; Hu, Tianyu1; Yang, Qiuli1; Liu, Bingbing2,3; Deng, Yufei2,3; Tang, Hao4; Tang, Zhiyao5; Fang, Jingyun5 ; Guo, Qinghua5
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| 2022 | |
| 发表期刊 | REMOTE SENSING OF ENVIRONMENT
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| ISSN | 0034-4257 |
| 卷号 | 269 |
| 摘要 | Spatially continuous estimates of forest canopy height at national to global scales are critical for quantifying forest carbon storage, understanding forest ecosystem processes, and developing forest management and restoration policies to mitigate global climate change. Spaceborne light detection and ranging (lidar) platforms, especially the Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) Advanced Topographic Laser Altimeter System (ATLAS), can measure forest canopy height in discrete footprints globally. Their coverage provides a promising data source for national to global-scale forest canopy height estimates. However, previous studies usually used a regression-based approach to develop spatially continuous forest canopy height distribution through the aid of optical images, which cannot take full advantage of the dense spaceborne lidar footprints and may still suffer from the saturation effect of optical images. In this study, we developed a novel neural network guided interpolation (NNGI) method to map forest canopy height by fusing GEDI, ICESat-2 ATLAS, and Sentinel-2 images. To evaluate the performance of the proposed NNGI method, we generated a 30-m forest canopy height product of China for the year 2019. More than 140 km2 drone-lidar data were collected across the country to train and validate the NNGI method. The results showed that the average forest canopy height of China is 15.90 m with a standard deviation of 5.77 m. We evaluated the interpolated forest canopy height product of China by over 1,100,000 GEDI validation footprints (R2 = 0.55, RMSE = 5.32 m), about 33 km2 drone-lidar validation data (R2 = 0.58, RMSE = 4.93 m), and over 59,000 field plot measurements (R2 = 0.60, RMSE = 4.88 m). Benefiting from the interpolation-based mapping strategy, the resulting product had almost no saturation effect in areas with tall forest canopies. The high mapping accuracy demonstrates the feasibility of the proposed NNGI method for monitoring spatially continuous forest canopy height at national to global scales by integrating multi-platform spaceborne lidar data and optical images, enabling opportunities to provide more accurate quantification of terrestrial carbon storage and better understanding of forest ecosystem processes. |
| 关键词 | Forest canopy height GEDI ICESat-2 ATLAS Lidar Spatial interpolation Deep neural network |
| 学科领域 | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
| DOI | 10.1016/j.rse.2021.112844 |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS关键词 | AIRBORNE LIDAR ; TREE HEIGHT ; SPATIAL PREDICTION ; LAND ; SRTM ; ALGORITHM ; DENSITY ; PRODUCT ; SCIENCE ; MISSION |
| WOS研究方向 | Science Citation Index Expanded (SCI-EXPANDED) |
| WOS记录号 | WOS:000759689300002 |
| 出版者 | ELSEVIER SCIENCE INC |
| 文献子类 | Article |
| 出版地 | NEW YORK |
| EISSN | 1879-0704 |
| 资助机构 | Strategic Priority Research Program of Chinese Academy of Sciences [XDA19050401] ; National Natural Science Foundation of China [41871332, 31971575, 41901358] |
| 作者邮箱 | ysu@ibcas.ac.cn |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ibcas.ac.cn/handle/2S10CLM1/28813 |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830002, Peoples R China 4.Xinjiang Lidar Appl Engn Technol Res Ctr, Urumqi 830002, Peoples R China 5.Natl Univ Singapore, Fac Arts & Social Sci, Dept Geog, Singapore 117570, Singapore 6.Peking Univ, Coll Urban & Environm Sci, Inst Ecol, Beijing 100871, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Xiaoqiang,Su, Yanjun,Hu, Tianyu,et al. Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data[J]. REMOTE SENSING OF ENVIRONMENT,2022,269. |
| APA | Liu, Xiaoqiang.,Su, Yanjun.,Hu, Tianyu.,Yang, Qiuli.,Liu, Bingbing.,...&Guo, Qinghua.(2022).Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data.REMOTE SENSING OF ENVIRONMENT,269. |
| MLA | Liu, Xiaoqiang,et al."Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data".REMOTE SENSING OF ENVIRONMENT 269(2022). |
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| Neural_network_guide（6152KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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