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Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project

View Article: PubMed Central - PubMed

ABSTRACT

The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery has been widely used in classification process of land cover. However, atmospheric corrections have to be made by preprocessing satellite sensor imagery since the electromagnetic radiation signals received by the satellite sensors can be scattered and absorbed by the atmospheric gases and aerosols. In this study, an ASTER sensor imagery, which was converted into top-of-atmosphere reflectance (TOA), was used to classify the land use/cover types, according to COoRdination of INformation on the Environment (CORINE) land cover nomenclature, for an area representing the heterogonous characteristics of eastern Mediterranean regions in Kahramanmaras, Turkey. The results indicated that using the surface reflectance data of ASTER sensor imagery can provide accurate (i.e. overall accuracy and kappa values of 83.2% and 0.79, respectively) and low-cost cover mapping as a part of inventory for CORINE Land Cover Project.

No MeSH data available.


Mean pixel value of the ten classes generated by supervised classification.
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f4-sensors-08-01237: Mean pixel value of the ten classes generated by supervised classification.

Mentions: Supervised classification provided satisfactory results in terms of distinguishing irrigated crops, forest, stubble, water courses, and rangeland; however, accuracy for fallow, sparse forest, bare land, and residential were relatively low due to large variation of spectral signatures. The highest producers and users accuracy was reached in classification of irrigated crops (96.61%) and stubble (94.87%), respectively. The lowest producers and users accuracy was for fallow (57.69%) and bare land (35.29%), respectively. It was assumed that low accuracy of the follow is due to close reflection values received from follows, sparse forest, and wetland (Figure 4). The results also indicated that supervised classification overestimated rangeland and bare land, while underestimated irrigated, forest, residential, and water courses. The roads in the study area could not be distinguished during the classification process due to close reflectance values with adjacent raster cells.


Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project
Mean pixel value of the ten classes generated by supervised classification.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927522&req=5

f4-sensors-08-01237: Mean pixel value of the ten classes generated by supervised classification.
Mentions: Supervised classification provided satisfactory results in terms of distinguishing irrigated crops, forest, stubble, water courses, and rangeland; however, accuracy for fallow, sparse forest, bare land, and residential were relatively low due to large variation of spectral signatures. The highest producers and users accuracy was reached in classification of irrigated crops (96.61%) and stubble (94.87%), respectively. The lowest producers and users accuracy was for fallow (57.69%) and bare land (35.29%), respectively. It was assumed that low accuracy of the follow is due to close reflection values received from follows, sparse forest, and wetland (Figure 4). The results also indicated that supervised classification overestimated rangeland and bare land, while underestimated irrigated, forest, residential, and water courses. The roads in the study area could not be distinguished during the classification process due to close reflectance values with adjacent raster cells.

View Article: PubMed Central - PubMed

ABSTRACT

The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery has been widely used in classification process of land cover. However, atmospheric corrections have to be made by preprocessing satellite sensor imagery since the electromagnetic radiation signals received by the satellite sensors can be scattered and absorbed by the atmospheric gases and aerosols. In this study, an ASTER sensor imagery, which was converted into top-of-atmosphere reflectance (TOA), was used to classify the land use/cover types, according to COoRdination of INformation on the Environment (CORINE) land cover nomenclature, for an area representing the heterogonous characteristics of eastern Mediterranean regions in Kahramanmaras, Turkey. The results indicated that using the surface reflectance data of ASTER sensor imagery can provide accurate (i.e. overall accuracy and kappa values of 83.2% and 0.79, respectively) and low-cost cover mapping as a part of inventory for CORINE Land Cover Project.

No MeSH data available.