Evaluate the Accuracy of Supervisor Classification for Al-Shatrah Image, Using Random Points by Remote Sensing and GIS
DOI:
https://doi.org/10.61841/yre7t173Keywords:
Landuse Landcover, Remote Sensing, GIS, ClassificationAbstract
Remote sensing and GIS techniques are one of the very important tools for producing land-use maps and land cover through a process called image classification. This study examines the evaluation of the accuracy of the supervisor classification of the land cover for land use using Google Earth in the case of Shatrah city in Iraq for the year 2017, where a Landsat-8 OLI_TIRS image was used and analyzed using ArcGIS 10.6. After classifying the land cover/land use types, 100 random points were created in ArcGIS and converted to KML to open in Google Earth. The value of each random point in Google Earth has been validated to assess accuracy. This research includes two parts: (1) land use/land cover (LULC) classification and (2) accuracy evaluation. The supervised classification was performed. The major classified LULC were uncovered agricultural (66.6%), water (1.6%), urban areas (8.5%), dense agricultural (9.9%), and barren lands (13.4%). The results indicate that the overall accuracy of the rating was 78% and the Kappa Coefficient (K) was 0.73. The kappa coefficient is classified as acceptable, and therefore the categorized image was found to be suitable for further research. This study provides an essential source of information that planners and decision makers can use for sustainable environmental planning.
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