气候变化领域集成服务门户(CCPortal): Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data

CCPortal

DOI	10.1016/j.rse.2021.112844
	Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data
	Liu, Xiaoqiang; Su, Yanjun; Hu, Tianyu; Yang, Qiuli; Liu, Bingbing; Deng, Yufei; Tang, Hao; Tang, Zhiyao; Fang, Jingyun; Guo, Qinghua
发表日期	2022
ISSN	0034-4257
EISSN	1879-0704
卷号	269
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.
英文关键词	Forest canopy height; GEDI; ICESat-2 ATLAS; Lidar; Spatial interpolation; Deep neural network
语种	英语
WOS研究方向	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000759689300002
来源期刊	REMOTE SENSING OF ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281595
作者单位	Chinese Academy of Sciences; Institute of Botany, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Xinjiang University; National University of Singapore; Peking University
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],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).