Abstract
This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.
Highlights
Land cover and land use mapping and analysis are vital for different environmental and mapping applications
The main purpose of this study is to evaluate and compare the classification accuracies of new generation Sentinel-2 and Landsat-8 optical satellite images in Istanbul metropolitan area
As a result of the study; for Landsat-8 image, 70.60% classification accuracy with MLC method and 81.67% classification accuracy with Support Vector Machine (SVM) method have been acquired
Summary
Land cover and land use mapping and analysis are vital for different environmental and mapping applications. Mapping and monitoring of land cover have been widely recognized as an important scientific goal since created information could be used to support environmental and atmospheric models, decision making procedures etc. Since it is possible to obtain rapid, periodic and accurate data from remote sensing system, satellite images are important source of creating land cover/use information. Land cover/ use maps are created using image classification approaches. Land cover/ use classification is an important and challenging research field in remote sensing and multi-temporal land cover/use maps emphasizing the land changes have become a basic information source to analyse several environmental issues. Image classification has a long history in the remote sensing and is the fundamental for many applications, such as carbon modeling, land cover/use change, forest monitoring and management, and crop yield estimation (Woodcock et al, 2001; Zhu et al, 2012). Many classifiers have been developed and tested for land cover classification, such as Maximum Likelihood, neural networks, decision trees, support vector machines, and random forest (Zhu et al, 2012)
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