# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
###### Abstract  
In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification. multi-index approach; land cover/use mapping; SVM classification; Sentinel-2A; bare land; built-up area 2019 11 345 10.3390/rs11030345 10.  
and formation of heat islands, which trigger other climatic phenomena [3; 4; 5; 6]. Thus, there is a need for accurate, up-to-date, and periodical LCU maps to develop efficient decision-making mechanisms to cope with climate change and effectively manage and plan cities [3; 4; 7]. With the advances in satellite technologies, traditional methods have been mostly replaced by remotely-sensed data analysis to monitor LCU change [8; 9]. The availability of free global and historical satellite imagery provides a valuable opportunity for mapping and monitoring LCU, constantly and effectively [10; 11]. The separate or combined use of optical and SAR data provide valuable information about the physical characteristics of land surface; different analysis methods have been developed to determine different object types. Among these methods, image classification has become the most popular method for mapping LCU and its changes [12; 13]. [PERSON] and [PERSON], 2007, reviewed the classification algorithms and accuracy of several image classification studies. They reported that the classification accuracy was affected by several factors and these factors can be grouped as: (i) Use of advanced classification algorithms, such as Support Vector Machine (SVM), Random Forest (RF), and regression tree (CART); (ii) selection of multiple remote sensing features, such as multi-spectral, multi-temporal, or multi-sensor data fusion; and (iii) integration of additional data, such as topographic maps or soil maps [14]. The use of non-parametric classification algorithms, such as machine learning algorithms, decision trees and knowledge-based classifiers have increased [14]. Among these, SVMs, which use a set of related learning algorithms for classification, showed a superior performance, compared to traditional classification techniques such asmaximum likelihood, minimum distance, or parallelepiped classifiers [15; 16; 17].