No abstract

Landfill has been recognized as the cheapest form for the final disposal of municipal solid waste and as such has been the most used method in the world. However, siting landfill is an extremely complex task mainly due to the fact that the identification and selection process involves many factors and strict regulations. For proper identification and selection of appropriate sites for landfills careful and systematic procedures need to be adopted and followed. Wrong siting of landfill many result in environmental degradation and often time public opposition. In this study, attempts have been made to determine sites that are appropriate for landfill siting in Damaturu town Nigeria, by combining geographic information system (GIS) and a multi-criteria decision making method (MCDM) known as the analytic network process (ANP) for the determination of the relative importance weights of factors (criteria). The land suitability output is presented from less suitable to the most suitable areas. The final map produced show areas that are suitable for landfill siting. Based on the analysis fourteen sites were identified to fulfill the required criteria, however, only seven met the land availability criteria of twenty hectares and above. The results showed the efficacy of GIS and multi-criteria decision making method in decision making

ABSTRACT

This paper presents a comprehensive assessment of the locations, extent and the impact of forest fire in University of Ilorin Teak Plantation using pre- and post-fire Sentinel-2 level 1C products. First, the pre-fire image was classified into three classes: vegetation area, bare soil and water body, using supervised classification (Maximum Likelihood method) to distinguish between vegetation and non-vegetation areas. Then, from the post-fire image, the burn areas were detected and extracted using Normalized Burnt Ratio. With the burn area polygon, impact of the fire on the planted forest was determined by isolating the vegetation class within the classified map so estimating the number of teak trees affected through extrapolation of the burn area and the tree spacing grid of 3m. The classification result shows that vegetation land cover type accounted for about 419.7 ha (66 %) of the total area while bare soil and water body take 204.3 ha (32 %) and 12.9 ha (2 %), respectively. Also, the resulting classified map produced overall classification accuracy of 95 %. Impact assessment result reveals that a total number of 49156 tree stands were affected by the fire within burnt area of 54.8 ha (8.6%). Analysis of the estimation success rate using one of the burn areas as validation site yielded approximation in excess of 3% with 17621 counted and 18222 estimated. Planted forest management and planning has many phases; so, it is necessary to understand the current and future condition of what is being manage. The fire burn map derived from this study will assist the University teak plantation management team update its current management strategy to protect it from continuous exposure to fire. From fire management perspective, the list of planning activities that require future assessments include pruning preferences, replanting, commercial thinning, spacing of planted trees, and perimeter buffering.

Generation of land use/land cover map at different spatial scales using satellite remote sensing data has been in practice as far back as early 1970s. Since then, research focus has been on the development of classification steps and improving the quality of the resulting maps. In recent times, the demand for detailed high accuracy land-use and land-cover (LULC) data has been on the increase due to the growing complexity of earth processes, while, at the same time, processing step is becoming more complex. This paper explores Landsat 8 derived normalized difference vegetation index (NDVI) threshold for the purpose of simplifying land cover classification process. NDVI images of January, May and December, 2018, representing dry, wet and harmattan seasons were generated. Thereafter, NDVI values corresponding to the location of a set of training data representing the target urban land covers (water, built-up area, soil, grassland and shrub) were extracted. Using the statistics of the extracted values, NDVI threshold for the respective land cover type were determined for the classification process. Finally, the classification accuracy was evaluated using the unbiased matrix coefficient technique which produced overall accuracy of 71.3%, 46.4% and 75.6% at 95% confidence limit for the months of January, May and December of the year review respectively. The result has shown that NDVI threshold is a simple and practical alternative to obtain LULC map at a reasonable time with a few data.