LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS
Autor: | Shoab Ahmad Khan, M. A. Maud, Ejaz Hussain, Wasim Pervez, Faisal Amir |
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Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
Hydrology
lcsh:Applied optics. Photonics geography geography.geographical_feature_category 010504 meteorology & atmospheric sciences lcsh:T 0211 other engineering and technologies lcsh:TA1501-1820 02 engineering and technology Vegetation 01 natural sciences lcsh:Technology Technical design Waves and shallow water Operational land imager lcsh:TA1-2040 Spring (hydrology) Satellite image processing lcsh:Engineering (General). Civil engineering (General) Channel (geography) Change detection 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-1-W1, Pp 607-612 (2017) |
ISSN: | 2194-9034 1682-1750 |
Popis: | This paper investigated the potential utility of Landsat-8 Operational Land Imager (OLI) for change detection analysis and mapping application because of its superior technical design to previous Landsat series. The OLI SVM classified data was successfully classified with regard to all six test classes (i.e., bare land, built-up land, mixed trees, bushes, dam water and channel water). OLI support vector machine (SVM) classified data for the four seasons (i.e., spring, autumn, winter, and summer) was used to change detection results of six cases: (1) winter to spring which resulted reduction in dam water mapping and increases of bushes; (2) winter to summer which resulted reduction in dam water mapping and increase of vegetation; (3) winter to autumn which resulted increase in dam water mapping; (4) spring to summer which resulted reduction of vegetation and shallow water; (5) spring to autumn which resulted decrease of vegetation; and (6) summer to autumn which resulted increase of bushes and vegetation . OLI SVM classified data resulted higher overall accuracy and kappa coefficient and thus found suitable for change detection analysis. |
Databáze: | OpenAIRE |
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