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Monitoring Desertification and its Environmental Effects in Sokoto State using Geospatial Approach

Domain:

geospatialenvironment and energy

Record type:

paper
Creator:
Bab
Publisher:
Fac
Host:
Report by the International Food Policy Research Institute highlights that Nigeria loses approximately 0.35% of its total land area estimated at 923,768 km² to desertification annually. As of the year 2020, about 35% of the country's landmass is under threat, putting the livelihoods of over 40 million land dependent individuals at risk. This study employs Remote Sensing and Geographic Information System (GIS) techniques to monitor the rate of desertification in Sokoto State, Nigeria. Multi-temporal satellite imagery, including Landsat TM (2000), Landsat 4 (2005), Landsat 7 (2010), and Landsat 8 OLI (2015, 2018), was obtained from the United States Geological Survey (USGS). Geometric and atmospheric corrections were applied to enhance image accuracy. Supervised classification using the Maximum Likelihood algorithm was adopted, and desertification indicators precipitation, temperature, and population were integrated with land use/land cover (LULC) outputs for comprehensive analysis. Findings reveal a substantial decline in vegetated areas from 33.4% in the year 2000 to 12.3% in the year 2020, largely driven by urbanization, agricultural encroachment, and possible climate factors. Built-up areas significantly increased from 15.1% to 32.3% over the same period, indicating rapid urban development. Bare soil coverage exhibited fluctuations, with a notable rise from 41.6% in the year 2015 to 52.8% in 2020. Water bodies declined from 2.6% in 2000 to 1.6% in 2020, raising concerns over water availability. Projections using Markov cellular automata algorithm for the year 2025 and 2030 indicated continued degradation, with vegetated areas recording a decrease trend pattern by 8.2% to 10% and built-up areas to record an increasing trend of 40% to 53% by 2030. These trends underscore the urgent need for strategic interventions and policies aimed at mitigating desertification and restoring degraded lands to sustainable productivity.

Visit

doi.org

Tasks

image classificationcomputer vision

Languages

Fulfulde, NigerianHausa

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