Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data

Ariful Khan; Md Shawkat Islam Sohel; Md. Shamim Reza Saimun; Mohammad A S Arfin Khan; Mohammed Salim Uddin; Melanie L. Harris; Parvez  Rana

doi:10.1007/s44274-025-00256-0

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Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data

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Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data

Ariful Khan, Md Shawkat Islam Sohel, Md. Shamim Reza Saimun, Mohammad A S Arfin Khan, Mohammed Salim Uddin, Melanie L. Harris and Parvez Rana

Discover Environment, Vol.3, pp.1-13

2025

DOI: https://doi.org/10.1007/s44274-025-00256-0

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Abstract

forest structure

basal area

tree density

volume

random forest algorithm

Estimating forest attributes is crucial for understanding forest performance. While forest protection and tree plantations can serve as cost-effective mitigation strategies to address climate change challenges, monitoring natural forests and plantations remains expensive and challenging for a developing nation like Bangladesh, which is highly donor-dependent and lacks advanced remote sensing research facilities such as LiDAR or drone technology. In this context, open-source remote sensing data can serve as an effective tool for monitoring forest structure. In this study, we evaluated the ability of Landsat-8 and Sentinel-1 data to predict forest attributes using ground-measured tree data from 110 plots (each 400 m2 in size). We applied the random forest algorithm to predict tree height, density, basal area, and volume in two forest-protected areas of Bangladesh. For tree height and tree density, Sentinel-1 showed slightly higher prediction accuracy (RMSE = 7% and 46%, respectively) compared to Landsat-8 and combined data (Landsat-8 and Sentinel-1). Landsat-8 data had a higher prediction accuracy (RMSE = 23%) for basal area compared to Sentinel-1 and combined data. For volume, the combined dataset outperformed Sentinel-1 and Landsat-8; however, prediction accuracy was low. Our results indicate that height and basal area can be well predicted by combining Sentinel and Landsat data. The results underscore the value of open-source remote sensing tools as cost-effective alternatives for forest monitoring, offering critical insights for forest management and climate change mitigation strategies in developing nations.

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Title: Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data
Authors: Ariful Khan - Shahjalal University of Science and Technology
Md Shawkat Islam Sohel - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Md. Shamim Reza Saimun - Shahjalal University of Science and Technology
Mohammad A S Arfin Khan - Shahjalal University of Science and Technology
Mohammed Salim Uddin - Shahjalal University of Science and Technology
Melanie L. Harris - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Parvez Rana (Corresponding Author) - Natural Resources Institute Finland
Publication details: Discover Environment, Vol.3, pp.1-13
Publisher: Springer Cham
Date published: 2025
DOI: 10.1007/s44274-025-00256-0
ISSN: 2731-9431
Copyright note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Data Availability: Data are available from the corresponding author.
Grant note: The first author Ariful Khan was supported by the National Science and Technology (NST) fellowship 2020/21.
Organisation Unit: Australian Centre for Pacific Islands Research; National Centre for Timber Durability and Design Life; School of Science, Technology and Engineering; Sustainability Research Cluster
Language: English
Record Identifier: 991128900702621
Output Type: Journal article

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