Harmonizing remote sensing and ground data for forest aboveground biomass estimation

Bibliographic Details
Title:	Harmonizing remote sensing and ground data for forest aboveground biomass estimation
Authors:	Ying Su, Zhifeng Wu, Xiaoman Zheng, Yue Qiu, Zhuo Ma, Yin Ren, Yanfeng Bai
Source:	Ecological Informatics, Vol 86, Iss , Pp 103002- (2025)
Publisher Information:	Elsevier, 2025.
Publication Year:	2025
Collection:	LCC:Information technology LCC:Ecology
Subject Terms:	Forests, Aboveground biomass, Remote sensing data, Machine learning, Information technology, T58.5-58.64, Ecology, QH540-549.5
More Details:	Accurate aboveground biomass (AGB) estimation is crucial for evaluating management and conservation policy of forests. However, the complexity of forest ecosystems and the diversity of geography bring great challenges to traditional biomass estimation methods. This study aims to develop an optimized AGB estimation framework that integrates heterogeneous data sources (i.e., ground survey data, National Forest Continuous Inventory (NFCI) data, and both active and passive remote sensing data) to enhance estimation accuracy and address the needs of future satellite missions and forest monitoring efforts. Using Longyan City, Fujian Province, China, as a case study, we construct a machine learning-based AGB estimation framework and generate high-resolution AGB spatial distribution maps through stepwise variable selection, hyperparameter optimization, and incremental integration of data sources. The effectiveness of this approach was demonstrated by a 0.67 increase in the correlation coefficient R2, a 43.57 % reduction in the root mean square error (RMSE), and a 68.00 % reduction in the mean square error (MSE) achieved through the optimal combination of data sources. The optimization framework not only significantly improves AGB estimation accuracy but also facilitates the identification of key areas for afforestation through the generated spatial distribution map, offering a scientific foundation for targeted forest management and ecological restoration. This study highlights the potential of combining heterogeneous data sources with machine learning techniques, providing a scalable solution for future forest monitoring tasks.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	1574-9541
Relation:	http://www.sciencedirect.com/science/article/pii/S1574954125000111; https://doaj.org/toc/1574-9541
DOI:	10.1016/j.ecoinf.2025.103002
Access URL:	https://doaj.org/article/578e58573ffd454e984d5a58c0154afa
Accession Number:	edsdoj.578e58573ffd454e984d5a58c0154afa
Database:	Directory of Open Access Journals

More Details
ISSN:	15749541
DOI:	10.1016/j.ecoinf.2025.103002
Published in:	Ecological Informatics
Language:	English