In recent years, large scale land cover maps constructed from remotely sensed data have become important information sources for resource management. For many applications, poor map accuracy limits their usability. This article investigates methods of combining classification rules for improving accuracy in general, and for exploiting spatial information in particular. We examine the performanc...

Changes in land use and land cover can lead to irreversible changes in forests that result in overall reductions in biodiversity and loss of elements of high ecological and cultural value. Land use and cover change models can be an important resource for scientists to develop a sustainable land management program. This paper presents a method to assess the accuracy of a forestation predictive m...

Due to the relatively low temporal resolutions of high spatial resolution (HR) remotely sensed images, land-cover change detection (LCCD) may have to use multi-temporal images with different resolutions. The low spatial resolution (LR) images often have high temporal repetition rates, but they contain a large number of mixed pixels, which may seriously limit their capability in change detection...

A set of land-cover pattern maps for the Gunma Prefecture was produced to help understand and assess the ecological implications of regional spatial patterns. Covering a total area of approximately 6,360 square kilometers, about 77 percent of this area can be regarded as “vegetation cover”. The land cover map was derived from the supervised classification process with the combined use of TM, SP...

Super-resolution mapping or sub-pixel mapping is a set of techniques to produce the hard land cover map at sub-pixel spatial resolution from the land cover proportion images obtained by soft-classification methods. In addition to the information from the land cover proportion images at the original spatial resolution, supplementary information at the higher spatial resolution can be used to pro...

Fuzzy c-means clustering (FCM) algorithm has been used to analyze the sub-pixel composition of medium spatialresolution satellite image (i.e., Landsat ETM). As urban landscape shows complex patterns of land cover composition and setting, it is difficult to have high accuracy in estimating urban land cover composition from Landsat image because of the mixed pixel problem. This study evaluates th...

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cov...

A ca. 2010, 30 m resolution map depicting annually tilled areas across the conterminous United States was developed. Input sources included four years, spanning 2008–2011, of annual national-level coverage Cropland Data Layer (CDL) land cover classifications as produced by the National Agricultural Statistics Service. Derived total land area under tillage from the aggregate CDL product equaled ...

Advances in remote sensing and GIS make possible construction of high resolution categorical raster-based maps depicting spatial distribution of natural and/or anthropogenic features. The best know example is a land cover/land use (LCLU) map. GIS tools for performing queries for spatial extent of single map category are readily available. However, often, an analyst would like to query a map for...

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust t...