The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sens...

After the Geometric correction and resampling the image should be classified. This paper introduced a new method for classifying the areas in a remotely sensed image under the category of supervised classification techniques. This classification technique describes how to classify the geographical areas in given image under supervised classification techniques conventions. So to tell the abstra...

Modification of the original bands and integration of ancillary data in digital image classification has been shown to improve land use land cover classification accuracy. There are not many studies demonstrating such techniques in the context of the mountains of Nepal. The objective of this study was to explore and evaluate the use of modified band and ancillary data in Landsat and IRS image c...

land cover information is one of the most important prerequisite in urban management system. in this way remote sensing, as the most economic technology, is mainly used to produce land cover maps. considering the complicated and dense urban areas in third world countries, object based approaches are suggested as an effective image processing technique. the purpose of this paper are the introduc...

this research was performed to evaluate the potentials of landsat mss data for map-ping land features in arid zones of southeastern esfahan, iran. databases of the area were formed using all available relevant maps and reports which were supported by fieldwork. a supervised image classification approach was used and thirty-two training areas were applied. separability of the spectral classes wa...

An ecological land classification was developed for a complex region in southern California using geographic information system techniques of map overlay and contingency table analysis. Land classes were identified by mutual information analysis of vegetation pattern in relation to other mapped environmental variables. The analysis was weakened by map errors, especially errors in the digital el...

Google Street View (GSV) was used for urban land use classification, together with airborne light detection and ranging (LiDAR) data and high resolution orthoimagery, by a parcel-based method. In this data article, we present the input raw GSV images, intermediate products of GSV images, and final urban land use classification data that are related to our research article "Parcel-based urban la...

Medium-scale land cover maps are traditionally created on the basis of a single cloud-free satellite scene, leaving information present in other scenes unused. Using 1309 field observations and 20 cloudand error-affected Landsat scenes covering Zanzibar Island, this study demonstrates that the use of multiple scenes can both allow complete coverage of the study area in the absence of cloud-free...

Information on a well-scale urban land cover is important for a number of urban planning practices involving tree shade mapping, green space analysis, urban hydrologic modeling and urban land use mapping. In this study, an urban land cover dataset received from the database of UCI (University of California at Irvine) machine learning was used as the urban land cover data. This dataset is the ur...