Subpixel Estimates of Impervious Surface Cover Using Landsat TM Imagery
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منابع مشابه
Mapping Impervious Surface Area Using High Resolution Imagery: a Comparison of Object-based and per Pixel Classification
Impervious surface area is a key indicator of environmental quality. Satellite remote sensing of impervious surface has focused on subpixel analysis via various forms of statistical estimation, subpixel classification, and spectral mixture analysis, using medium resolution Landsat TM or ETM+ data. Maps of impervious surface area from these studies provide useful inputs to planning and managemen...
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Impervious surfaces (IS) such as asphalt, concrete and rooftops prevent percolation of water into the soil, creating water quantity and water quality impacts that have been extensively documented in the literature. Impervious surfaces can therefore be considered a direct indicator as to the quality of surrounding surface water including streams, lakes, and estuaries. Simply put, as the amount o...
متن کاملSubpixel Impervious Surface Mapping
Identified by the EPA as the leading threat to surface water quality in the United States, nonpoint source (NPS) pollution is channeled into rivers and streams via impervious surfaces. Impervious surfaces are anthropogenic features, such as roads, buildings, and parking lots, through which water cannot infiltrate into the soil. Research from the past 15 years shows a consistent, inverse relatio...
متن کاملLandsat Etm Sub-pixel Analysis of Urban Landscape Using Fuzzy C- Means Clustering and Differentiated Impervious Surface Classes
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...
متن کاملSubpixel Urban Land Cover Estimation: Comparing Cubist, Random Forests, and Support Vector Regression
Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (reflectance, tasseled cap, and bot...
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تاریخ انتشار 2000