This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.

With the latest development and increasing availability of high spatial resolution sensors, earth observation technology offers a viable solution for crop identification and management. There is a strong need to produce accurate, reliable and up to date crop type maps for sustainable agriculture monitoring and management. In this study, RapidEye, the first high-resolution multi-spectral satellite system that operationally provides a Red-edge channel, was used to test the potential of the data for crop type mapping. This study was investigated at a selected region mostly covered with agricultural fields locates in the low lands of Menemen (İzmir) Plain, TURKEY. The potential of the three classification algorithms such as Maximum Likelihood Classification, Support Vector Machine and Object Based Image Analysis is tested. Accuracy assessment of land cover maps has been performed through error matrix and kappa indexes. The results highlighted that all selected classifiers as highly useful (over 90%) in mapping of crop types in the study region however the object-based approach slightly outperforming the Support Vector Machine classification by both overall accuracy and Kappa statistics. The success of selected methods also underlines the potential of RapidEye data for mapping crop types in Aegean region.

In this study, using Landsat-TM Images (1984 and 1992), land-use/land-cover characteristics in Istanbul's Elmali Water Basin Area were analysed and evaluation of thematic and positional accuracy of images was performed. High accuracy raster data were converted to vectorial data and used in Geographic Information Systems. A data base to be used for water basin planning and managing, utilising statistical attribute data covering population, water potential, topographic position, industry and labour force was prepared and overlay analysis was carried out. Finally, comparison between water basin protected areas in 1984 and 1992 using GIS techniques was performed and results were illustrated on three dimensional surface models.

Human activities in many parts of the world have greatly affected natural areas. Therefore, monitoring and forecasting of land-cover changes are important components for sustainable utilization, conservation, and development of these areas. This research has been conducted on Igneada, a legally protected area on the northwest coast of Turkey, which is famous for its unique, mangrove forests. The main focus of this study was to apply a land use and cover model that could quantitatively and graphically present the changes and its impacts on Igneada landscapes in the future. In this study, a Markov chain-based, stochastic Markov model and cellular automata Markov model were used. These models were calibrated using a time series of developed areas derived from Landsat Thematic Mapper (TM) imagery between 1990 and 2010 that also projected future growth to 2030. The results showed that CA Markov yielded reliable information better than St. Markov model. The findings displayed constant but overall slight increase of settlement and forest cover, and slight decrease of agricultural lands. However, even the slightest unsustainable change can put a significant pressure on the sensitive ecosystems of Igneada. Therefore, the management of the protected area should not only focus on the landscape composition but also pay attention to landscape configuration.

The Riva River is located on the Anatolian (Asian) side of Istanbul by the Black Sea coast, with a wonderful sandy beach and delta formation which has been selected as the study area. The Riva is the largest river in this region, which flows into the Black Sea and also gives its' name to the area. The river carries some amount of sediments to the Black Sea. These particles cause considerable changes not only in the bed bathymetry and coastal boundaries of the river but also along the coastline of the Black Sea. The aim of this study is to determine these variations by means of satellite data and GIS. In the study, satellite images dated 1975, 1984, 1992 and 2001 have been used. At the end of the study, the possibility of observing bathymetric changes in front of the coastline by means of satellite data has been investigated and results were evaluated and visualised by means of GIS.