Historical trends of degradation, loss, and recovery in the tropical forest reserves of Ghana

dc.contributor.authorWimberly, M.C.
dc.contributor.authorDwomoh, F.K.
dc.contributor.authorNumata, I.
dc.contributor.authorMensah, F.
dc.contributor.authorAmoako, J.
dc.contributor.authorNekorchuk, D.M.
dc.contributor.authorMcMahon, A.
dc.date.accessioned2022-07-04T20:21:17Z
dc.date.available2022-07-04T20:21:17Z
dc.date.issued2022
dc.descriptionResearch Articleen_US
dc.description.abstractThe Upper Guinean Forest region of West Africa, a globally significant biodiversity hotspot, is among the driest and most human-impacted tropical ecosystems. We used Landsat to study forest degradation, loss, and recovery in the forest reserves of Ghana from 2003 to 2019. Annual canopy cover maps were generated using random forests and results were temporally segmented using the LandTrendr algorithm. Canopy cover was predicted with a predicted-observed r2 of 0.76, mean absolute error of 12.8%, and mean error of 1.3%. Forest degradation, loss, and recovery were identified as transitions between closed (>60% cover), open (15–60% cover) and low tree cover (< 15% cover) classes. Change was relatively slow from 2003 to 2015, but there was more disturbance than recovery resulting in a gradual decline in closed canopy forests. In 2016, widespread fires associated with El Niño drought caused forest loss and degradation across more than 12% of the moist semi-deciduous and upland evergreen forest types. The workflow was implemented in Google Earth Engine, allowing stakeholders to visualize the results and download summaries. Information about historical disturbances will help to prioritize locations for future studies and target forest protection and restoration activities aimed at increasing resilience.en_US
dc.identifier.otherhttps://doi.org/10.1080/17538947.2021.2012533
dc.identifier.urihttp://ugspace.ug.edu.gh/handle/123456789/38206
dc.language.isoenen_US
dc.publisherTaylor & Francis Groupen_US
dc.subjectLandsaten_US
dc.subjectrandom forestsen_US
dc.subjectmachine learningen_US
dc.subjecttime seriesen_US
dc.subjectLandTrendren_US
dc.subjectdroughten_US
dc.subjectwildfireen_US
dc.titleHistorical trends of degradation, loss, and recovery in the tropical forest reserves of Ghanaen_US
dc.typeArticleen_US

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