This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies ( i.e. the context ) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into u...

A stochastic, spatially explicit method for assessing the impact of land cover classification error on distributed hydrologic modeling is presented. One-hundred land cover realizations were created by systematically altering the North American Landscape Characterization land cover data according to the dataset’s misclassification matrix. The matrix indicates the probability of errors of omissio...

the research used the satellite image (landsat 7 etm ) within the thermal infrared sixth band (tir6) and geographic information system (gis) to determine the air pollution and its relationship with the land cover (lc) and land use (lu) of baghdad city. concentration of total suspended particles (tsp), lead (pb), carbon oxides (co, co2), and sulphur dioxide (so2) were obtained from 22 ground mea...

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodi...

Land cover maps are widely used to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for LAI and FAPAR retrievals from MODIS and MISR. As part of this analysis, we examine the ...

Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based...

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 cover maps are used widely to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrie...

Multi-Resolution Land Characterization 2000 (MRLC 2000) is a second-generation federal consortium to create an updated pool of nation-wide Landsat 7 imagery, and derive a second-generation National Land Cover Database (NLCD 2000). This multi-layer, multisource database will include a suite of 30-meter resolution data that will serve as standardized ingredients for the production of land cover –...