气候变化领域集成服务门户(CCPortal): Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China

CCPortal

DOI	10.3390/rs16091481
	Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China
	Lyu, Guanting; Wang, Xiaoyi; Huang, Xieqin; Xu, Jinfeng; Li, Siyu; Cui, Guishan; Huang, Huabing
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	9
卷号	16 期号:9
英文摘要	Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their complex spatial variation. Here, we proposed a deep learning-based method that combines Residual convolutional neural networks (ResNet) with in situ measurements, microwave (Sentinel-1 and VOD), and optical data (Sentinel-2 and Landsat) to estimate forest biomass and track its change over the mountainous regions. Our approach, integrating in situ measurements across representative elevations with multi-source remote sensing images, significantly improves the accuracy of biomass estimation in Tibet's complex mountainous forests (R2 = 0.80, root mean squared error = 15.8 MgC ha-1). Moreover, ResNet, which addresses the vanishing gradient problem in deep neural networks by introducing skip connections, enables the extraction of complex spatial patterns from limited datasets, outperforming traditional optical-based or pixel-based methods. The mean value of forest biomass was estimated as 162.8 +/- 21.3 MgC ha-1, notably higher than that of forests at comparable latitudes or flat regions in China. Additionally, our findings revealed a substantial forest biomass carbon sink of 3.35 TgC year-1 during 2015-2020, which is largely underestimated by previous estimates, mainly due to the underestimation of mountainous carbon stock. The significant carbon density, combined with the underestimated carbon sink in mountainous regions, emphasizes the urgent need to reassess mountain forests to better approximate the global carbon budget.
英文关键词	mountain forest; Sentinel-1; VOD; landsat; forest biomass; carbon sink; deep learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001220103200001
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288937
作者单位	Chinese Academy of Sciences; Institute of Tibetan Plateau Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Tsinghua University; Lanzhou University; Yanbian University; Sun Yat Sen University
推荐引用方式 GB/T 7714	Lyu, Guanting,Wang, Xiaoyi,Huang, Xieqin,et al. Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China[J]. 中国科学院青藏高原研究所,2024,16(9).
APA	Lyu, Guanting.,Wang, Xiaoyi.,Huang, Xieqin.,Xu, Jinfeng.,Li, Siyu.,...&Huang, Huabing.(2024).Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China.REMOTE SENSING,16(9).
MLA	Lyu, Guanting,et al."Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China".REMOTE SENSING 16.9(2024).