The use of post-classification change methods for the analysis of land cover change provides intuitive and potentially reliable results. A recurring problem is the difference in land cover nomenclature that can occur over time or across space when multiple data sources are required. Building on work that uses category semantics as a foundation for reasoning with land cover classes, this paper u...

land degradation is a major danger which restricting different areas of iran. systematic description of the environmentfor detection of environmental changes and the human-related causes and responses is essential in land cover changestudy. use of land cover data allow detection of where certain changes occur, what type of change, as well as how theland is changing. existing systems for classif...

Geographic information systems and satellite remote sensing information are emerging technologies in land-cover change assessment. They now provide an opportunity to gain insights into land-cover change properties through the spatio-temporal data capture over several decades. The time series of Landsat images covering the 1985–2009 period is used here to explore the impacts of surface mining an...

Environmental remote sensing is dominated by land use and land cover classifications. Different land cover features reflect electromagnetic radiation in different ways and remotely sensed images provide a basic and synoptic representation of land cover variation on the Earth’s surface. The pixel based analysis has been ‘the holy grail’ for many years. Land-based features may be categorized acco...

Per-pixel and sub-pixel are two common classification methods in land cover studies. The characteristics of a landscape, particularly the land cover itself, can affect the accuracies of both methods. The objectives of this study were to: (1) compare the performance of sub-pixel vs. per-pixel classification methods for a broad heterogeneous region; and (2) analyze the impact of land cover hetero...

Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, however, assumes that each land cover type has an equal probability of being included in the model...

Land-use and land-cover (LULC) change is a primary driver of terrestrial carbon release, often through the conversion forest into agriculture or expansion urban areas. Classification schemes are key component landscape analyses. This study creates novel LULC classification scheme by incorporating ecological data to redefine classes an existing based on variation in above-ground tree carbon. A i...

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...