气候变化领域集成服务门户(CCPortal): Forest Aboveground Biomass and Forest Height Estimation Over a Sub-tropical Forest Using Machine Learning Algorithm and Synthetic Aperture Radar Data

CCPortal

DOI	10.1007/s12524-024-01821-5
	Forest Aboveground Biomass and Forest Height Estimation Over a Sub-tropical Forest Using Machine Learning Algorithm and Synthetic Aperture Radar Data
	Ali, Noman; Khati, Unmesh
发表日期	2024
ISSN	0255-660X
EISSN	0974-3006
起始页码	52
结束页码	4
卷号	52 期号:4
英文摘要	Forest aboveground biomass (AGB) is a key measurement in studying terrestrial carbon storage, carbon cycle, and climate change. Machine learning based algorithms can be applied to estimate forest AGB using remote sensing-based data. Our study utilized L-band ALOS-2/PALSAR-2 Synthetic Aperture Radar (SAR) data in combination with multi-parameter linear regression (LR) and Random forest regression (RF) for forest carbon estimation. Six L-band fully polarimetric acquisitions are used in this study. The input parameters to the RF algorithm are the backscatter, decomposition powers and species information. The multi-temporal backscatter (HH1 to HH6, HV1 to HV6, VV1 to VV6) and the temporal average are used. Furthermore, average decomposi-tion parameters from G4U decomposition-Double bounce (Dbl), Odd bounce (Odd), Volume scattering (Vol), and Helix scattering (Hlx) for all six dates. In the first case (1), the model is trained to estimate only the AGB. In the second case (2), the model is trained for forest height estimation. In the third case (3), the model is trained to predict both the AGB and height of the forest. In contrast to the LR method, there is a significant improvement in AGB estimation achieved with the RF algorithms. This study shows the potential of combined retrieval of AGB and forest height using time-series L-band backscatter data.
英文关键词	L-Band ALOS-2/PALSAR-2 SAR data; Aboveground biomass model; Height of forest model; AGB and height of forest model
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing
WOS类目	Environmental Sciences ; Remote Sensing
WOS记录号	WOS:001155008100002
来源期刊	JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298123
作者单位	Panjab University; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Indore
推荐引用方式 GB/T 7714	Ali, Noman,Khati, Unmesh. Forest Aboveground Biomass and Forest Height Estimation Over a Sub-tropical Forest Using Machine Learning Algorithm and Synthetic Aperture Radar Data[J],2024,52(4).
APA	Ali, Noman,&Khati, Unmesh.(2024).Forest Aboveground Biomass and Forest Height Estimation Over a Sub-tropical Forest Using Machine Learning Algorithm and Synthetic Aperture Radar Data.JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING,52(4).
MLA	Ali, Noman,et al."Forest Aboveground Biomass and Forest Height Estimation Over a Sub-tropical Forest Using Machine Learning Algorithm and Synthetic Aperture Radar Data".JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 52.4(2024).