Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data
Abstract 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...
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| Format: | Article |
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Springer
2025-06-01
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| Series: | Discover Environment |
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| Online Access: | https://doi.org/10.1007/s44274-025-00256-0 |
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| author | Ariful Khan Md. Shawkat Islam Sohel Md. Shamim Reza Saimun Mohammed Abu Sayed Arfin Khan M. Salim Uddin Melanie L. Harris Parvez Rana |
| author_facet | Ariful Khan Md. Shawkat Islam Sohel Md. Shamim Reza Saimun Mohammed Abu Sayed Arfin Khan M. Salim Uddin Melanie L. Harris Parvez Rana |
| author_sort | Ariful Khan |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-b669eaabdda74cb8aeb615bf4f3246e6 |
| institution | OA Journals |
| issn | 2731-9431 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Environment |
| spelling | doaj-art-b669eaabdda74cb8aeb615bf4f3246e62025-08-20T02:05:49ZengSpringerDiscover Environment2731-94312025-06-013111310.1007/s44274-025-00256-0Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 dataAriful Khan0Md. Shawkat Islam Sohel1Md. Shamim Reza Saimun2Mohammed Abu Sayed Arfin Khan3M. Salim Uddin4Melanie L. Harris5Parvez Rana6Department of Forestry and Environmental Science, Shahjalal University of Science and TechnologySchool of Science, Technology and Engineering and Forest Research Institute, University of the Sunshine CoastDepartment of Forestry and Environmental Science, Shahjalal University of Science and TechnologyDepartment of Forestry and Environmental Science, Shahjalal University of Science and TechnologyRubenstein School of Environment and Natural Resources, The University of VermontSchool of Science, Technology and Engineering and Forest Research Institute, University of the Sunshine CoastNatural Resources Institute Finland (Luke)Abstract 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.https://doi.org/10.1007/s44274-025-00256-0Forest structureBasal areaTree densityVolumeRandom forest algorithm |
| spellingShingle | Ariful Khan Md. Shawkat Islam Sohel Md. Shamim Reza Saimun Mohammed Abu Sayed Arfin Khan M. Salim Uddin Melanie L. Harris Parvez Rana Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data Discover Environment Forest structure Basal area Tree density Volume Random forest algorithm |
| title | Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data |
| title_full | Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data |
| title_fullStr | Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data |
| title_full_unstemmed | Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data |
| title_short | Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data |
| title_sort | estimation of tree attributes in mixed tropical hill forests using landsat 8 and sentinel 1 data |
| topic | Forest structure Basal area Tree density Volume Random forest algorithm |
| url | https://doi.org/10.1007/s44274-025-00256-0 |
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