气候变化领域集成服务门户(CCPortal): Mapping of Forest Structural Parameters in Tianshan Mountain Using Bayesian-Random Forest Model, Synthetic Aperture Radar Sentinel-1A, and Sentinel-2 Imagery

CCPortal

DOI	10.3390/rs16071268
	Mapping of Forest Structural Parameters in Tianshan Mountain Using Bayesian-Random Forest Model, Synthetic Aperture Radar Sentinel-1A, and Sentinel-2 Imagery
	Wang, Ting; Xu, Wenqiang; Bao, Anming; Yuan, Ye; Zheng, Guoxiong; Naibi, Sulei; Huang, Xiaoran; Wang, Zhengyu; Zheng, Xueting; Bao, Jiayu; Gao, Xuemei; Wang, Di; Wusiman, Saimire; Nzabarinda, Vincent; De Wulf, Alain
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	7
卷号	16 期号:7
英文摘要	The assessment of forest structural parameters is crucial for understanding carbon storage, habitat suitability, and timber stock. However, the labor-intensive and expensive nature of field measurements, coupled with inadequate sample sizes for large-scale modeling, poses challenges. To address the forest structure parameters in the Western Tianshan Mountains, this study used UAV-LiDAR to gather extensive sample data. This approach was enhanced by integrating Sentinel satellite and topographic data and using a Bayesian-Random Forest model to estimate forest canopy height, average height, density, and aboveground biomass (AGB). Validation against independent LiDAR-derived samples confirmed the model's high accuracy, with coefficients of determination (R2) and root mean square errors (RMSE) indicating strong predictive performance (R2 = 0.63, RMSE = 5.06 m for canopy height; R2 = 0.64, RMSE = 2.88 m for average height; R2 = 0.68, RMSE = 62.84 for density; and R2 = 0.59, RMSE = 29.71 Mg/ha for AGB). Notably, the crucial factors include DEM, Sentinel-1 (VH and VV backscatter in dB), and Sentinel-2 (B6, B8A, and B11 bands). These factors contribute significantly to the modeling of forest structure. This technology aims to expedite and economize forest surveys while augmenting the range of forest parameters, especially in remote and rugged terrains. Using a wealth of UAV-LiDAR data, this outcome surpasses its counterparts' by providing essential insights for exploring climate change effects on Central Asian forests, facilitating precise carbon stock quantification, and enhancing knowledge of forest ecosystems.
英文关键词	forest height; forest density; forest aboveground biomass; Bayesian-Random Forest model; Central Asian
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:001200805900001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299661
作者单位	Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Ghent University; Lanzhou University; Nanjing University; Kunming University of Science & Technology; Xinjiang Normal University; Ghent University
推荐引用方式 GB/T 7714	Wang, Ting,Xu, Wenqiang,Bao, Anming,et al. Mapping of Forest Structural Parameters in Tianshan Mountain Using Bayesian-Random Forest Model, Synthetic Aperture Radar Sentinel-1A, and Sentinel-2 Imagery[J],2024,16(7).
APA	Wang, Ting.,Xu, Wenqiang.,Bao, Anming.,Yuan, Ye.,Zheng, Guoxiong.,...&De Wulf, Alain.(2024).Mapping of Forest Structural Parameters in Tianshan Mountain Using Bayesian-Random Forest Model, Synthetic Aperture Radar Sentinel-1A, and Sentinel-2 Imagery.REMOTE SENSING,16(7).
MLA	Wang, Ting,et al."Mapping of Forest Structural Parameters in Tianshan Mountain Using Bayesian-Random Forest Model, Synthetic Aperture Radar Sentinel-1A, and Sentinel-2 Imagery".REMOTE SENSING 16.7(2024).