中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 植物研究所
  3. 中国科学院植物研究所
  4. 植被与环境变化国家重点实验室
Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data

文献类型：期刊论文

作者Liu, Xiaoqiang1; Su, Yanjun1; Hu, Tianyu1; Yang, Qiuli1; Liu, Bingbing2,3; Deng, Yufei2,3; Tang, Hao6; Tang, Zhiyao5; Fang, Jingyun5; Guo, Qinghua5
刊名REMOTE SENSING OF ENVIRONMENT
出版日期2022
卷号269
ISSN号0034-4257
关键词Forest canopy height GEDI ICESat-2 ATLAS Lidar Spatial interpolation Deep neural network
DOI10.1016/j.rse.2021.112844
文献子类Article
英文摘要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.
学科主题Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号1879-0704
出版地NEW YORK
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)
语种英语
出版者ELSEVIER SCIENCE INC
WOS记录号WOS:000759689300002
资助机构Strategic Priority Research Program of Chinese Academy of Sciences [XDA19050401] ; National Natural Science Foundation of China [41871332, 31971575, 41901358]
源URL[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.Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830002, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Peking Univ, Coll Urban & Environm Sci, Inst Ecol, Beijing 100871, Peoples R China
5.Natl Univ Singapore, Fac Arts & Social Sci, Dept Geog, Singapore 117570, Singapore
6.Xinjiang Lidar Appl Engn Technol Res Ctr, Urumqi 830002, 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).

入库方式： OAI收割

来源：植物研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。