Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia
Mapping forest above-ground biomass (AGB) is crucial for monitoring forest ecosystems and assessing the success of conservation initiatives such as the REDD + carbon projects. Traditional field-based approaches to measuring AGB, however, face significant challenges, due to high financial costs and l...
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001414 |
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| author | Kennedy Kanja Ce Zhang Peter M. Atkinson |
| author_facet | Kennedy Kanja Ce Zhang Peter M. Atkinson |
| author_sort | Kennedy Kanja |
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| description | Mapping forest above-ground biomass (AGB) is crucial for monitoring forest ecosystems and assessing the success of conservation initiatives such as the REDD + carbon projects. Traditional field-based approaches to measuring AGB, however, face significant challenges, due to high financial costs and logistical constraints. Remote sensing, including both active and passive sensors, presents a promising and cost-effective alternative, yet its practical utility and accuracy for capturing forest AGB in diverse and complex ecosystems remains largely unexplored. This research used an extensive national forest inventory (NFI) dataset to evaluate the ability to map the AGB of the Miombo woodlands in Zambia across four agro-ecological zones using both multi-seasonal SAR (Sentinel-1A) and optical (Landsat-8 OLI) imagery. A multi-level experiment was designed to (i) compare the accuracy of AGB estimation using SAR and optical data when used independently, and in combination, using a Random Forest regression model, (ii) assess the effect of seasonality on the accuracy of AGB estimation when using SAR and optical datasets, and (iii) evaluate the effect of variation in climatic and environmental conditions on AGB estimation. Experimental results show that multi-seasonal images (across the rainy, hot and dry seasons) outperformed single-season and annual images. Combining SAR backscatter in the hot season, optical bands in the dry season, and vegetation indices in the hot season produced the most accurate AGB model (R = 0.69, MAE = 14.01 Mg ha−1 and RMSE = 18.23 Mg ha−1). The models performed distinctly across different agro-ecological zones (R = 0.44 – 0.79), suggesting that fitting local models could be beneficial. These results based on the extensive NFI of Zambia demonstrate that seasonal effects and fitting local models can lead to more accurate AGB estimation within the Miombo woodlands, which is of significance for ongoing REDD + carbon projects in Zambia and other African countries. |
| format | Article |
| id | doaj-art-e6a93b935898472ab7b409cc1f991a59 |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-e6a93b935898472ab7b409cc1f991a592025-08-20T02:58:25ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910449410.1016/j.jag.2025.104494Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of ZambiaKennedy Kanja0Ce Zhang1Peter M. Atkinson2Lancaster Environment Centre, Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YG, UK; School of Natural Resources, Copperbelt University, Kitwe, Zambia; School of Applied Sciences, Kapasa Makasa University, Chinsali, Zambia; Corresponding author.School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UKLancaster Environment Centre, Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YG, UK; Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; College of Surveying and Geo-Informatics, Tongji University, No. 1239, Siping Road, Shanghai 200092, PR ChinaMapping forest above-ground biomass (AGB) is crucial for monitoring forest ecosystems and assessing the success of conservation initiatives such as the REDD + carbon projects. Traditional field-based approaches to measuring AGB, however, face significant challenges, due to high financial costs and logistical constraints. Remote sensing, including both active and passive sensors, presents a promising and cost-effective alternative, yet its practical utility and accuracy for capturing forest AGB in diverse and complex ecosystems remains largely unexplored. This research used an extensive national forest inventory (NFI) dataset to evaluate the ability to map the AGB of the Miombo woodlands in Zambia across four agro-ecological zones using both multi-seasonal SAR (Sentinel-1A) and optical (Landsat-8 OLI) imagery. A multi-level experiment was designed to (i) compare the accuracy of AGB estimation using SAR and optical data when used independently, and in combination, using a Random Forest regression model, (ii) assess the effect of seasonality on the accuracy of AGB estimation when using SAR and optical datasets, and (iii) evaluate the effect of variation in climatic and environmental conditions on AGB estimation. Experimental results show that multi-seasonal images (across the rainy, hot and dry seasons) outperformed single-season and annual images. Combining SAR backscatter in the hot season, optical bands in the dry season, and vegetation indices in the hot season produced the most accurate AGB model (R = 0.69, MAE = 14.01 Mg ha−1 and RMSE = 18.23 Mg ha−1). The models performed distinctly across different agro-ecological zones (R = 0.44 – 0.79), suggesting that fitting local models could be beneficial. These results based on the extensive NFI of Zambia demonstrate that seasonal effects and fitting local models can lead to more accurate AGB estimation within the Miombo woodlands, which is of significance for ongoing REDD + carbon projects in Zambia and other African countries.http://www.sciencedirect.com/science/article/pii/S1569843225001414Miombo woodlandsAbove ground biomassNational Forest InventorySAR-opticalRemote SensingRandom Forest |
| spellingShingle | Kennedy Kanja Ce Zhang Peter M. Atkinson Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia International Journal of Applied Earth Observations and Geoinformation Miombo woodlands Above ground biomass National Forest Inventory SAR-optical Remote Sensing Random Forest |
| title | Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia |
| title_full | Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia |
| title_fullStr | Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia |
| title_full_unstemmed | Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia |
| title_short | Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia |
| title_sort | evaluating multi seasonal sar and optical imagery for above ground biomass estimation using the national forest inventory of zambia |
| topic | Miombo woodlands Above ground biomass National Forest Inventory SAR-optical Remote Sensing Random Forest |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001414 |
| work_keys_str_mv | AT kennedykanja evaluatingmultiseasonalsarandopticalimageryforabovegroundbiomassestimationusingthenationalforestinventoryofzambia AT cezhang evaluatingmultiseasonalsarandopticalimageryforabovegroundbiomassestimationusingthenationalforestinventoryofzambia AT petermatkinson evaluatingmultiseasonalsarandopticalimageryforabovegroundbiomassestimationusingthenationalforestinventoryofzambia |