Characterization of Land-Cover Changes and Forest-Cover Dynamics in Togo between 1985 and 2020 from Landsat Images Using Google Earth Engine

Arifou Kombate (), Fousseni Folega, Wouyo Atakpama, Marra Dourma, Kperkouma Wala and Kalifa Goïta
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Arifou Kombate: Centre d’Applications et de Recherches en Télédétection (CARTEL), Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Fousseni Folega: Géomatique et Modélisation des Écosystèmes, Laboratoire de Botanique et Écologie Végétale (LBEV), Département de botanique, Faculté des sciences, Université de Lomé, 01 BP 1515 Lomé, Togo
Wouyo Atakpama: Unité de Recherche en Systématique et Conservation de la Biodiversité, Laboratoire de Botanique et Écologie Végétale (LBEV), Département de botanique, Faculté des sciences, Université de Lomé, 01 BP 1515 Lomé, Togo
Marra Dourma: Géomatique et Modélisation des Écosystèmes, Laboratoire de Botanique et Écologie Végétale (LBEV), Département de botanique, Faculté des sciences, Université de Lomé, 01 BP 1515 Lomé, Togo
Kperkouma Wala: Géomatique et Modélisation des Écosystèmes, Laboratoire de Botanique et Écologie Végétale (LBEV), Département de botanique, Faculté des sciences, Université de Lomé, 01 BP 1515 Lomé, Togo
Kalifa Goïta: Centre d’Applications et de Recherches en Télédétection (CARTEL), Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada

Land, 2022, vol. 11, issue 11, 1-31

Abstract: Carbon stocks in forest ecosystems, when released as a result of forest degradation, contribute to greenhouse gas (GHG) emissions. To quantify and assess the rates of these changes, the Intergovernmental Panel on Climate Change (IPCC) recommends that the REDD+ mechanism use a combination of Earth observational data and field inventories. To this end, our study characterized land-cover changes and forest-cover dynamics in Togo between 1985 and 2020, using the supervised classification of Landsat 5, 7, and 8 images on the Google Earth Engine platform with the Random Forest (RF) algorithm. Overall image classification accuracies for all target years ranged from 0.91 to 0.98, with Kappa coefficients ranging between 0.86 and 0.96. Analysis indicated that all land cover classes, which were identified at the beginning of the study period, have undergone changes at several levels, with a reduction in forest area from 49.9% of the national territory in 1985, to 23.8% in 2020. These losses of forest cover have mainly been to agriculture, savannahs, and urbanization. The annual change in forest cover was estimated at −2.11% per year, with annual deforestation at 422.15 km 2 per year, which corresponds to a contraction in forest cover of 0.74% per year over the 35-year period being considered. Ecological Zone IV (mountainous, with dense semi-deciduous forests) is the one region (of five) that has best conserved its forest area over this period. This study contributes to the mission of forestry and territorial administration in Togo by providing methods and historical data regarding land cover that would help to control the factors involved in forest area reductions, reinforcing the system of measurement, notification, and verification within the REDD+ framework, and ensuring better, long-lasting management of forest ecosystems.

Keywords: land-cover change; REDD+; Google Earth Engine; random forest; landsat; Togo (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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