A simplified multi-criteria evaluation model for landfill site ranking and selection based on AHP and GIS

This study used GIS based Multi-criteria Decision Analysis (MCDA) approach for evaluating the most environmentally suitable landfill sites in the study area. The weights of relative importance of the factors guiding landfill siting were estimated using pair-wise comparisons in AHP. The maps showing suitable landfill sites were generated applying a weighted linear combination (WLC) in GIS using a comparison matrix to aggregate different significant scenarios associated with environmental and economic objectives. To determine the appropriate areas where landfill sites can be located, thematic maps for all the criteria were generated using GIS. A final map was produced showing suitability for the location of the landfill sites. The suitable sites having an area equal to or above 4 ha at one place and 90% of which is barren land were considered suitable for landfill. The selected candidate sites were ranked to get the most desirable sites for landfill

Dual-tree complex wavelet transform-based image enhancement for accurate long-term change assessment in coal mining areas

The main objective of this study was to improve the long-term land use change detection by improving classification accuracy of previous generation satellite image using a recent super-resolution technique. The study also analysed the change in land cover over a period of 41 years in a coal mining area. A dual-tree complex wavelet transform-based image super-resolution technique was used to enhance Landsat images of 1975 and 2016. Separating pixels with similar spectral response is an enigmatical task, especially when those pixel represent different ground features. Therefore, an advanced neural net supervised classifier was used to minimize classification errors. Accuracy of the classified images (both super-resolved and original) were measured using confusion matrices and kappa coefficients. A significant improvement of more than 10% was observed in the overall classification accuracy for the image of 1975, highlighting that the classification accuracy of earlier generation satellite data can be improved substantially