A study of land degradation pattern in the Mahin mud-beach coast of southwest Nigeria with spatial-statistical modelling geostatistics.
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Canadian Center of Science and Education
This study utilized remote sensing and spatial-statistical geostatistics to model future land degradation in the mud-beach coast of southwest Nigeria. Current landcover was derived from Landsat ETM+data. Model input data c onsists of 12 predictor variables. Attribution of weights to variables was done through multi-criteria evaluation. These weights were used to develop logistic regression function for simulating probability surfacemaps. Degraded lands accounted for about 30.2% of the total landcover with permanently inundated lands and bare surfaces contributing 22.4%. The results suggest soil, geology, elevation, distance to ocean, and location of old bitumen wells as the most important predictor variables. Simulated composite probabilities for transiting into degraded lands range between 0.4184 and 0.4871 in the next 20 years (from 2001) to between 0.4284 and 0.4973 in the next 100 years. Mangrove,,scrub/coastal grassland, farmland/fallow and built-up areas appear to have higher probabilities, while the palm swamp ecosystems have the least.
Landcover , Land degradation , Mud-beach coast , Geostatistics , Spatial-statistics , Research Subject Categories::SOCIAL SCIENCES
Journal of Geography and Geology, 3(1), p141-159. ISSN 1916-9779. http://www.ccsenet.org/journal/index.php/jgg/article/view/7453. DOI:10.5539/jgg.v3n1p141