Modelling Land Use and Land Cover in the Transboundary Mono River Catchment of Togo and Benin Using Markov Chain and Stakeholder’s Perspectives

Abstract

Integrating both modeling approach and stakeholders’ perspectives to derive past and future trends of land use land cover (LULC) is a key to creating more realistic results on LULC change trajectories and can lead to the implementation of appropriate management measures. This article assessed the past changes of LULC in the Mono River catchment using Landsat images from the years 1986, 2000, 2010, and 2020 by performing Machine Learning Classification Method Random Forest (RF) technique, and using Markov Chain method and stakeholder’s perspective to simulate future LULC changes for the years 2030 and 2050. LULC was classified as savanna, cropland, forest, water bodies, and settlement. The results showed that croplands and forests areas declined from 2020 to 2050 with decreases of −7.8% and −1.9%, respectively, a modest increase in settlement (1.3%), and savanna was the dominant LULC in the study region with an increase of 8.5%. From stakeholders’ perspective, rapid population growth, deforestation, rainfall variability/flood, urbanization, and agricultural expansion were the most important drivers associated with the observed LULC changes in the area. Other factors, such as lack of political commitment, distance to river, and elevation were also mentioned. Additionally, most the land-use scenarios identified by stakeholders would intensify land degradation and reduce ecosystem services in the area. By considering all of these potential LULC changes, decision-makers need to develop and implement appropriate solutions (e.g., land use planning strategies, reforestation campaigns, forest protection measures) in order to limit the negative effects of future LULC changes.

A Multi Criteria Decision Analysis Approach for Regional Climate Model Selection and Future Climate Assessment in the Mono River Basin, Benin and Togo

“…The lower part of the basin is prone to recurrent flood events which trigger economic losses and deaths in both countries [15]. An average of 1000 mm precipitation per year is recorded in the south and 1200 mm in the northern part [30,31].…”

“…Historically, the Mono catchment experiences recurrent flood events [62]. Nonetheless, considering the potential decrease of rainfall (even statistically not significant), the increase of temperature discussed above, and the changes in future land use/cover mainly characterized by a savannahfication of forests and agricultural lands [31], drought-related studies should be undertaken alongside flood assessments in the Mono river catchment.…”

Recovering from Financial Implications of Flood Impacts—The Role of Risk Transfer in the West African Context

“…Moreover, in the light of climate change, the annual maxima of daily precipitation in the area are expected to increase further, leading to a more substantial impact of heavy rainfall events on discharge within the river basin and thus to flood events of higher severity [5]. Apart from climatic changes, there are also other anthropogenic factors contributing to the flooding problem in the area, such as deforestation as well as the expansion of settlements, farmland, and infrastructure into exposed areas [57,66,67]. The floods in the largely rural LMRB usually cause extensive damage, for example, to houses, infrastructure, public buildings, and human health, due to the flood water remaining in the living environment for some time [6,55,56].…”

The use of texture analysis and band transformation on multispectral imagery to map open-pit mines using machine learning

“…Each tree's decision is made by referring to the provided training samples. This algorithm offers reasonable accuracy for land cover classification [11,[22][23][24][25].…”