Spatio-Temporal Distribution of Southeastern Community Forests in Togo (West Africa)

The present study aimed to provide the basics needed to reconcile the fight against poverty with the need of a good management of community resources in the context of local and sustainable development. It focused on the identification of geographic location and spatio-temporal dynamics of these resources in the southeastern part of Togo. The methodological approach was based on the spatial analysis of the area for the years 1988, 2000 and 2018 by combining optical imagery from Landsat TM, ETM+ and Sentinel-2A MSI satellites with radar imagery from Sentinel-1A IW/GRDH. Spatial analysis showed a fragmented spatial structure undergoing major changes for the period 1988-2018. Plantations, riparian formations, agglomerations, water bodies and wetlands increased respectively by 4.61%, 2.09%, 1.07%, 0.43% and 0.35% annually, while forests, savannahs, crops and fallow lands decreased annually by 0.71%, 5.87% and 1.97%. For the “forests” class, seven community forests geographically organized in three sub-groups were identified and mapped. The analysis of their areas pointed to the fact that out of 667 ha of community forests in 1988, only 415 ha remain in 2018, which means a loss of 37.78% in forest areas over the 30 years, or an annual deforestation rate of 1.64%. The different spatial changes observed could be attributed to several unsustainable human activities. The land use maps for the years 1988, 2000 and 2018 will contribute to resource localization and protection in sensitive areas or, in other words, to the integrated and rational management of these resources. The different identified and mapped community forests could serve as management units for managers in developing their management plans.

Monitoring of Residual Forest Ecosystems Dynamics in the Mono Biosphere Reserve (Southeast Togo)

The forest ecosystems of the Lacs 2 commune (South-East, Togo) are undergoing severe degradation, which has not yet been documented. This study is carried out in order to assess and quantify the spatio-temporal dynamics of residual forests and to identify the determinants of deforestation in South East Togo. The methodological approach is based on the use of historical aerial photographs from 1976 and drone images from 2019 in addition to field investigations. Several spatial structure indices were also calculated in order to quantify the fragmentation of classes and of the forest landscape. The results show that the forest landscape is changing. The classes of forests, plantations and palm groves show an annual rate of decline of 7.5%, 0.8% and 9.4% respectively while the classes of savannahs, agglomerations, surface water and swamps increased by 16.4%, 0.4%, 0.7% and 0.1%. The results also reveal a high fragmentation within the forest, plantation, surface water and swamp class and moderate fragmentation for the savannah and palm trees classes. At the landscape level, the savannah class is dominant by more than 70%, thus making the landscape little diversified from an ecological point of view. The main driver of deforestation in the study area remains shifting slash-and-burn agriculture. It is accentuated by the establishment of perennial oil palm crops, which has influenced the annual deforestation rate by 0.72%.

Coastline Change Modelling Induced by Climate Change Using Geospatial Techniques in Togo (West Africa)

Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and human habitats. This research, basing on geospatial techniques, focuses on coastal erosion monitoring from 1988 to 2018 in Togo. It is interested in the extraction of shoreline and in the analysis of change. Various satellite images indexes have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline on NDWI index. The topographic morphology such as linear and non-linear coastal surfaces have been considered. The estimation of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population and disturbance of human habitats.