An Above-Ground Biomass Map of African Savannahs and Woodlands at 25m Resolution Derived from ALOS PALSAR. Bouvet, A., Mermoz, S., Le Toan, T., Villard, L., Mathieu, R., Naidoo, L., & Asner, G. P. 206:156–173. ![An Above-Ground Biomass Map of African Savannahs and Woodlands at 25m Resolution Derived from ALOS PALSAR [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex [Highlights] [::] Estimation of above ground biomass over the whole Africa at a 25 m resolution. [::] New methodology using ALOS PALSAR mosaics and in situ data. [::] First map with a special focus on low biomass areas (savannahs and woodlands). [Abstract] Savannahs and woodlands are among the most important biomes in Africa: they cover half of sub-Saharan Africa, provide vital ecosystem services to the rural communities, and play a major part in the carbon budget. Despite their importance and their fragility, they are much less studied than other ecosystems like rainforests. In particular, the distribution and amount of the above-ground woody biomass (AGB) is largely unknown. In this paper, we produce the first continental map of the AGB of African savannahs and woodlands at a resolution of 25 m. The map is built from the 2010 L-band PALSAR mosaic produced by JAXA, along the following steps: a) stratification into wet/dry season areas in order to account for seasonal effects, b) development of a direct model relating the PALSAR backscatter to AGB, with the help of in situ and ancillary data, c) Bayesian inversion of the direct model. A value of AGB and its uncertainty has been assigned to each pixel. This approach allows estimating AGB until 85 Mg·ha− 1 approximately, while dense forests and non-vegetated areas are masked out using the ESA CCI Land Cover dataset. The resulting map is visually compared with existing AGB maps and is validated using a cross-validation approach and a comparison with AGB estimates obtained from LiDAR datasets, leading to an RMSD of 8 to 17 Mg·ha− 1. Finally, carbon stocks for savannahs in Africa and in 50 countries are estimated and compared with estimates by FAO and from AGB maps available over Africa.
@article{bouvetAbovegroundBiomassMap2018,
title = {An Above-Ground Biomass Map of {{African}} Savannahs and Woodlands at 25m Resolution Derived from {{ALOS PALSAR}}},
author = {Bouvet, Alexandre and Mermoz, Stéphane and Le Toan, Thuy and Villard, Ludovic and Mathieu, Renaud and Naidoo, Laven and Asner, Gregory P.},
date = {2018-03-01},
journaltitle = {Remote Sensing of Environment},
shortjournal = {Remote Sensing of Environment},
volume = {206},
pages = {156--173},
issn = {0034-4257},
doi = {10.1016/j.rse.2017.12.030},
url = {https://doi.org/10.1016/j.rse.2017.12.030},
urldate = {2019-05-30},
abstract = {[Highlights]
[::] Estimation of above ground biomass over the whole Africa at a 25 m resolution.
[::] New methodology using ALOS PALSAR mosaics and in situ data.
[::] First map with a special focus on low biomass areas (savannahs and woodlands).
[Abstract]
Savannahs and woodlands are among the most important biomes in Africa: they cover half of sub-Saharan Africa, provide vital ecosystem services to the rural communities, and play a major part in the carbon budget. Despite their importance and their fragility, they are much less studied than other ecosystems like rainforests. In particular, the distribution and amount of the above-ground woody biomass (AGB) is largely unknown. In this paper, we produce the first continental map of the AGB of African savannahs and woodlands at a resolution of 25 m. The map is built from the 2010 L-band PALSAR mosaic produced by JAXA, along the following steps: a) stratification into wet/dry season areas in order to account for seasonal effects, b) development of a direct model relating the PALSAR backscatter to AGB, with the help of in situ and ancillary data, c) Bayesian inversion of the direct model. A value of AGB and its uncertainty has been assigned to each pixel. This approach allows estimating AGB until 85 Mg·ha− 1 approximately, while dense forests and non-vegetated areas are masked out using the ESA CCI Land Cover dataset. The resulting map is visually compared with existing AGB maps and is validated using a cross-validation approach and a comparison with AGB estimates obtained from LiDAR datasets, leading to an RMSD of 8 to 17 Mg·ha− 1. Finally, carbon stocks for savannahs in Africa and in 50 countries are estimated and compared with estimates by FAO and from AGB maps available over Africa.},
keywords = {~INRMM-MiD:z-4RFIS4CA,above-ground-biomass,africa,biomass,comparison,mapping,model-comparison,remote-sensing,uncertainty}
}
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P."],"bibdata":{"bibtype":"article","type":"article","title":"An Above-Ground Biomass Map of African Savannahs and Woodlands at 25m Resolution Derived from ALOS PALSAR","author":[{"propositions":[],"lastnames":["Bouvet"],"firstnames":["Alexandre"],"suffixes":[]},{"propositions":[],"lastnames":["Mermoz"],"firstnames":["Stéphane"],"suffixes":[]},{"propositions":[],"lastnames":["Le","Toan"],"firstnames":["Thuy"],"suffixes":[]},{"propositions":[],"lastnames":["Villard"],"firstnames":["Ludovic"],"suffixes":[]},{"propositions":[],"lastnames":["Mathieu"],"firstnames":["Renaud"],"suffixes":[]},{"propositions":[],"lastnames":["Naidoo"],"firstnames":["Laven"],"suffixes":[]},{"propositions":[],"lastnames":["Asner"],"firstnames":["Gregory","P."],"suffixes":[]}],"date":"2018-03-01","journaltitle":"Remote Sensing of Environment","shortjournal":"Remote Sensing of Environment","volume":"206","pages":"156–173","issn":"0034-4257","doi":"10.1016/j.rse.2017.12.030","url":"https://doi.org/10.1016/j.rse.2017.12.030","urldate":"2019-05-30","abstract":"[Highlights] [::] Estimation of above ground biomass over the whole Africa at a 25 m resolution. [::] New methodology using ALOS PALSAR mosaics and in situ data. [::] First map with a special focus on low biomass areas (savannahs and woodlands). [Abstract] Savannahs and woodlands are among the most important biomes in Africa: they cover half of sub-Saharan Africa, provide vital ecosystem services to the rural communities, and play a major part in the carbon budget. Despite their importance and their fragility, they are much less studied than other ecosystems like rainforests. In particular, the distribution and amount of the above-ground woody biomass (AGB) is largely unknown. In this paper, we produce the first continental map of the AGB of African savannahs and woodlands at a resolution of 25 m. The map is built from the 2010 L-band PALSAR mosaic produced by JAXA, along the following steps: a) stratification into wet/dry season areas in order to account for seasonal effects, b) development of a direct model relating the PALSAR backscatter to AGB, with the help of in situ and ancillary data, c) Bayesian inversion of the direct model. A value of AGB and its uncertainty has been assigned to each pixel. This approach allows estimating AGB until 85 Mg·ha− 1 approximately, while dense forests and non-vegetated areas are masked out using the ESA CCI Land Cover dataset. The resulting map is visually compared with existing AGB maps and is validated using a cross-validation approach and a comparison with AGB estimates obtained from LiDAR datasets, leading to an RMSD of 8 to 17 Mg·ha− 1. 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Despite their importance and their fragility, they are much less studied than other ecosystems like rainforests. In particular, the distribution and amount of the above-ground woody biomass (AGB) is largely unknown. In this paper, we produce the first continental map of the AGB of African savannahs and woodlands at a resolution of 25 m. The map is built from the 2010 L-band PALSAR mosaic produced by JAXA, along the following steps: a) stratification into wet/dry season areas in order to account for seasonal effects, b) development of a direct model relating the PALSAR backscatter to AGB, with the help of in situ and ancillary data, c) Bayesian inversion of the direct model. A value of AGB and its uncertainty has been assigned to each pixel. This approach allows estimating AGB until 85 Mg·ha− 1 approximately, while dense forests and non-vegetated areas are masked out using the ESA CCI Land Cover dataset. 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