Land cover and forest mapping supports decision makers in the course of making informed decisions for implementation of sustainable conservation and management plans of the forest resources and environmental monitoring. This research examines the value of integrating of ALOS PALSAR and Landsat data for improved forest and land cover mapping in Northern Tanzania. A separate and joint processing ...

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...

This study aims to develop a land cover texture-based classification scheme applicable for ALOS PALSAR imageries of the upper Marikina watershed acquired 2007 – 2010. From the raw dual polarization bands of HH+HV that has a ground resolution of 25m, additional bands HH/HV and NL was computed for surface texture normalization. The classification scheme was based on texture analysis using grey le...

This study investigates the possibility providing gully erosion map by the supervised classification of satellite images (ETM) in two mountainous and plain land types. These land types were the part of Varamin plain, Tehran province, and Roodbar subbasin, Guilan province, as plain and mountain land types, respectively. The position of 652 and 124 ground control points were recorded by GPS respe...

Urban land use classification from remotely sensed images has drawn great attention in the past decades. Most researchers derive land use data from remotely sensed images alone, but the results are not quite satisfying for detecting detailed land use classes in urban areas. Fuzzy urban land use classes proposed here consist of a number of fuzzy memberships that offer direct links to findings fr...

Study deals with the land cover dynamics analysis using remote sensing data over urban, suburban area in northern Belarus (Polotsk and Novopolotsk cities and surroundings) over the period 1994 – 2002. SPOT 3 and 5 images are used for the study. Land cover change detection is conducted using image differencing and post-classification comparison methods. Several classification methods are tested ...

A refined land cover classification for the arid Phoenix (Arizona, USA) metropolitan area and some simple modifications to the surface energetics were introduced in the fifth-generation PSU/NCAR mesoscale meteorological model MM5. The single urban category in the existing 24-category United States Geological Survey (USGS) land cover classification used in MM5 was divided into three classes to a...

Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classific...

Land cover classification remains an important and complex problem when land observational satellites are involved. Conventional methods of image classification have been shown to provide adequate results; however, when complex surface arrangements are encountered the reliability of conventional approaches can be called into question. This situation is particularly apparent when land cover clas...

This paper examines the application of object-orientated processing and artificial intelligence techniques to high spatial resolution satellite sensor images for urban land-use monitoring. Although these techniques have been applied to aerial photography for some time, their use in the analysis of digital images acquired by satellite sensors is much less well developed. Within this study, a two...