Keywords: Remote sensing, Soil salinity, Landsat data, Salinity index, Vegetation index Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM⁺ satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

The global population is experiencing exponential growth, resulting in a substantial increase in urbanization and subsequent urban expansion. This uncontrolled expansion, often termed urban sprawl, poses significant challenges to sustainable urban development. Understanding the importance of this concern, obtaining precise information about changes in Land Use/Land Cover (LULC) becomes crucial. This research examines the transformative processes of LULC and urban expansion over a 20-year period in two distinct study areas. For this purpose, built-up areas are first analysed using satellite-derived land surface temperature data, revealing temperature increase over time attributed to urban expansion. To project future trends using CA-Markov model, the suitability maps for each LULC class aggregated through the Multi Criteria Evaluation (MCE) method. Subsequently, the CA-Markov simulates the LULC maps for 2017 and 2018 for each study area, with a focus on the year 2050. Model calibrated by comparing the simulated maps with the actual maps in both study areas, and the reliability is affirmed by high Kappa coefficients (> 80%). Consequently, the study predicts LULC maps for 2050, revealing that both areas will experience a continued increase in built-up areas, a decrease in forested areas, and a relative stability in agricultural zones over the next 33 years.

In recent years, especially in metropolitan cities such as Istanbul, the emerging needs of the increasing population and demand for better air transportation capacity have led to big environmental changes. One of them is originated due to the construction of the new airport (Istanbul Grand Airport – IGA), located on the Black Sea coast on the European side of Turkey and expected as “The biggest hub in Europe” by the early 2020s. The construction has five phases and first construction phase is scheduled to finish up by the end of 2018. With an advanced space technologies including remote sensing, environmental consequences due to Land Use/Land Cover changes (LULC) can be monitored and determined efficiently. The aim of this paper is to analyse LULC changes especially in the forest areas and water bodies by using two different satellite image dataset. In this context, supervised classification method and different spectral indices are applied to both Landsat-8 (2013–2017) and Sentinel 2A (2015–2017) image datasets to demonstrate the total and annual changes during the construction of the first phase. The efficiency of two datasets is outlined by comparison of the output thematic map accuracies.

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performance.

Coastal management requires rapid, up-to-date, and correct information. Thus, coastal movements have primary importance for coastal managers. For monitoring the change of shorelines, remote sensing data are some of the most important information and are utilized for differentiating any detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline from satellite images. In the literature most of the algorithms developed for optical images have been discussed in detail. In this study, an algorithm which extracts coastlines efficiently and automatically by processing SAR (Synthetic Aperture Radar) satellite images has been developed. A data set of ALOS Palsar image of Fine Beam Double (FBD) HH-HV polarized data has been used. PALSAR image has L-band data, and has a 14 MHz bandwidth and 34.3 degrees look angle. Data were acquired in ascending geometry. Ground resolution of PALSAR image was resampled to 15 m to amplitude image. Zonguldak city, lies on the northwest costs of Turkey, has been selected as the test area. An algorithm was developed for automatic coastline extraction from SAR images. The algorithm is encoded in a C__ environment. To verify the results the algorithm was applied on two PALSAR images gathered in two different date as 2007 and 2010. The results of automatic coastline extraction obtained from SAR images were compared to the results derived from manual digitizing. Random control points which are seen on each image were used. The average differences of selected points were calculated.