Modelling and mapping forest above-ground biomass using earth observation data

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dc.contributor.authorMadundo, Sami Dawood
dc.contributor.authorMauya, Ernest William
dc.contributor.authorLolila, Nandera Juma
dc.contributor.authorMchelu, Hadija Ahmed
dc.date.accessioned2023-08-25T09:10:59Z
dc.date.available2023-08-25T09:10:59Z
dc.date.issued2022
dc.descriptionJournal articleen_US
dc.description.abstractAccurate information on above-ground biomass (AGB) is important for sustainable forest management as well as for global initiatives aimed at combating climate change in the Tropics. In this study, AGB was estimated using a combination of field and Sentinel-2 earth observation data. The study was conducted at Magamba Nature Reserve in Lushoto district, Tanzania. Field plot-based AGB values were regressed against eighteen Sentinel-2 remote sensing variables (bands and vegetation indices) using Random Forest (RF) models based on centroid and weighted approaches. Results showed that the weighted model had the highest fit and precision (pseudo-R 2 = 0.21, rRMSE = 68.23%). A prediction map was produced with a mean AGB of 223.47 Mg ha -1 which was close to that of the field (225.19 Mg ha -1 ). Furthermore, the standard deviation of the AGB obtained from the map (i.e 174.04 Mg ha -1 ) was relatively lower as compared to the one obtained from the field-based measurements (i.e 97.42 Mg ha -1 ). This study demonstrated that Sentinel-2 imagery and RF-based regression techniques have potential to effectively support large scale estimation of forest AGB in the tropical rainforests.en_US
dc.identifier.citationSami Dawood Madundo, Ernest William Mauya, Nandera Juma Lolila, Hadija Ahmed Mchelu. Modelling and Mapping Forest Above- Ground Biomass Using Earth Observation Data. International Journal of Natural Resource Ecology and Management. Vol. 7, No. 1, 2022, pp. 15-21. doi: 10.11648/j.ijnrem.20220701.13en_US
dc.identifier.issn2575-3088
dc.identifier.urihttp://www.suaire.sua.ac.tz/handle/123456789/5681
dc.language.isoenen_US
dc.publisherScience Publishing Groupen_US
dc.subjectAbove-ground Biomassen_US
dc.subjectEarth observation dataen_US
dc.subjectModellingen_US
dc.subjectSentinel-2en_US
dc.subjectRandom forest,en_US
dc.titleModelling and mapping forest above-ground biomass using earth observation dataen_US
dc.typeArticleen_US
dc.urlhttps://www.researchgate.net/publication/358965501en_US

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