Development of a geospatial framework for monitoring and assessing desertification in Kebbi state, Nigeria

Terrestrial degradation nowadays presents one of the devastating environmental problems of the earth caused by natural as well as man-made factors. According to UNCCD, almost one-third of the world agricultural land is facing a degradation phenomenon. Desertification phenomenon is threatening Kebbi state with huge drawback relating to the diminishing of ecosystem services and negative socioeconomic impact on community livelihood. However, the extent, trends and severity of desertification in Kebbi state has neither fully recognised, nor the rate of its development is accurately documented. Subsequently, the primary aim of the study is to develop a geospatial framework to monitor and assess land degradation in the area with specific emphasis on quantifying land cover (LC) change and vegetation depletion from 1986-2016, modelling the LC change for the year 2026 and characterising desertification sensitivity in the area. Remotely sensed data were classified into five thematic LC classes namely dense vegetation (DV), shrubs land (SL), farmland (FL), bare/grassland (BGL) and water body (WB). The Driver-Pressure-State-Impact-Response (DPSIR) framework was used for better understanding of the drivers, the state of the environmental condition, the causes and the impact of the LC changes. Modelling the future (2016-2026) LC of the area was conducted using the CA-Markov model. Desertification sensitivity of the area was assessed using Mediterranean Desertification and Land Use-Environmental Sensitivity Area Index (MEDALUS-EASI) methodology. The analysis of LC change from 1986-2016, indicates a gradual decrease of DV by about 61%, the downturn in the SL by about 25.7%. BGL has increased by about 18.2% while FL increases by 35.9%. WB remains unchanged. The drivers of the change are mainly the demand for farming land as the population increases as well as socio-economic stresses.The pressures of the change include expansion of farming land and the use of wood as fuelwood, construction material and other domestic use. The state of the condition indicates a decline in vegetated and shrubland, however; farming land and bare grassland are increasing. The impacts include severe land degradation, soil erosion, the decline in the provision of ecosystem goods and services and biodiversity loss. The responses include afforestation programs by government and Non-Governmental Organisations (NGO’s) however, communities and individuals organise an annual tree planting campaign. The future LC prediction (2026) illustrates that FL and DV may probably increase while SL and BGL may probably decline. WB may decrease slightly. The spatial assessment of desertification sensitivity of the area indicates that 36% of the area is not affected. 17%, 30%, 15 % fall within a low, moderately and sensitive categories respectively. Only 1% of the area is highly sensitive to desertification. The impact of the different quality index to the desertification in the area show that climatic indices have a high impact on desertification in the area (r2=0.64) followed by soil indices (r2=0.47) and human indicators to desertification (r2=0.45). Vegetation indices have the least impact to the desertification in the area with (r2=0.38). It is recommended that Governmental and NGO’s should make an essential strategic plan for the continued in-depth assessment and monitoring of land degradation using advanced tools. Adaptation and mitigation measures such as agroforestry system of farming, robust afforestation and land restoration activities need to be strengthened. Environmental education needs to be integrated into both primary and post-primary teaching curricula to enhance environmental awareness. There is also the need to improve the livelihood of the rural people by providing alternative income and domestic energy sources since they depend heavily on natural resources for sustenance. The novelty of the study laid on the integration of remote sensing and GIS, the MEDALUS-EASI and the DPSIR frameworks for developing a unique and explanatory platform for modelling LC cover trend and magnitude of land degradation in the area.

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