Strengthening Forest Landscape Restoration through Understanding Land Use Dynamics: Case Study of Tchamba Prefecture (Togo)

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Abstract

Land use and land cover (LULC) change remains a major concern in many countries. To examine how land is used for increased actions on forest landscape restoration (FLR) issues, this study analyses LULC changes from 1991 to 2022 and predict future patterns for the years 2050 in Tchamba Prefecture, Togo, using remote sensing and the Cellular Automata-Markov modelling technique. Results show that dense forests and gallery forests (18.85%), open forest/woodland (20.17%), and tree and shrub savannah (50.04%) were the predominant classes of the landscape in 1991. However, by 2022, most tree and shrub savannah had been converted to mosaic crop/fallow land (52.27%), open forest/woodland (19.32%), and plantation (7.09%). Thus, between 1991 and 2022, forest area decreased from 89.87% to 48.96%; a 45.52% reduction in coverage. Predictions are that open forest/woodland, tree and shrub savannah will decline, while mosaic crop/fallow land will increase (51.66%). However, slight increases were recorded in plantation (9.43%) and a slight balance in dense forests/gallery forests (16.17%). Consequently, forest area will slightly change between 2022 (48.96%) and 2050 (47.41%). These changes in LULC are associated to socio-economic pressures and political instability in the 1990s. So, the return to stability, the promotion of community forests as a key restoration model, and the valorisation of agroforestry species allows for the stabilisation of dense forests/gallery forests areas and an increase in plantation. Thus, it is therefore for all stakeholders involved in restoration processes to consider this information when planning FLR activities.

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APA

Hounkpati, K., Adjonou, K., Moluh Njoya, H., Hlovor, A. K. D., Kipkulei, H. K., Sieber, S., … Kokou, K. (2024). Strengthening Forest Landscape Restoration through Understanding Land Use Dynamics: Case Study of Tchamba Prefecture (Togo). Journal of the Indian Society of Remote Sensing, 52(5), 1117–1134. https://doi.org/10.1007/s12524-024-01862-w

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