Studies on land use and cover changes (LULCC) have been a great concern due to their contribution the policies formulation strategic plans in different areas at scales. The LULCC when intense global scale can be catastrophic if not detected monitored affecting key aspects of ecosystem’s functions. For decades, technological developments tools geographic information systems (GIS), remote sensing...

Remote sensing in urban areas has been a challenger for quite some time due to their complexity and fragment with combination of man-made features and natural features. High-resolution satellite images and airborne laser altimetry data offered potential possibilities for feature extraction and spatial modelling in urban areas. Land use classification of urban areas may become possible by exploi...

This doctoral thesis investigates the potential of classification methods based on spatial context to infer specific forms of land use from remote sensing data. The problem is that some types of land use are characterized by a complex configuration of land covers that traditional per-pixel based methods have problems classifying due to spectral heterogeneity. The problem of spectral heterogenei...

Mapping land cover change is an important problem for the scientific community as well as policy makers. Traditionally, bi-temporal classification of satellite data is used to identify areas of land cover change. However, these classification products often have errors due to classifier inaccuracy or poor data, which poses significant issues when using them for land cover change detection. In t...

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classi...