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.

Accurate and reliable greenhouse mapping using remotely sensed data and image classification methods have a significant role since it can comprehensively improve the urban and rural planning, natural resource management, sustainable development, agricultural yield estimation, and monitoring and planning of crop proportion. This research is mainly focused on the determination of greenhouses from SPOT-7 and Sentinel-2 MultiSpectral Instrument (MSI) images by using an object-based image classification method with three different classifiers which are k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) in the selected test region. First, the image objects were obtained by segmenting the images. Second, spectral features, textural features, and remote sensing indices were extracted for each image object. Third, different classifiers were employed to classify greenhouses. Then, accuracy assessment analysis was applied to test the agreement between the classified data and ground truth data using the error matrix. The results highlighted that the KNN and RF classifier have a slightly higher overall accuracy and Kappa statistics for SPOT-7 image with the 91.43% and 0.88. Furthermore, the KNN classifier for Sentinel-2 MSI image has the highest overall accuracy and Kappa statistics of 88.38% and 0.83. The achieved results underlined the potential of Sentinel-2 MSI and SPOT-7 data for object-based greenhouse mapping using different machine learning classifiers in Mediterranean Region.

Efficient methodologies to map greenhouses are very important for the implementation of sustainable agricultural practices, natural resource management, and sustainable urban and rural development. Remote sensing imagery provides a great potential with different spatial and spectral resolutions for greenhouse monitoring and mapping. The conventional techniques for greenhouse mapping are time consuming, and expensive. Because of this reason, many different image processing methods such as classification methods including pixel-based or object based classification and remote sensing indices have been applied for greenhouse mapping. In this study, greenhouses in Anamur, Mersin, Turkey were determined by using object based classification and selected remote sensing indices. Freely available new generation 2018 dated Sentinel-2 MSI data which has 10-meters spatial resolution was used to detect the greenhouse in the selected region. Multi-resolution segmentation (MRS) method was conducted to Sentinel-2 MSI data for object-based image analysis (OBIA). In the first stage, the image segmentation process was performed. Spectral features (mean values of the layers) and remote sensing indices such as Normalized difference vegetation index (NDVI), Normalized difference water index (NDWI) and Retrogressive plastic greenhouse index (RPGI) were extracted from the segmented image. Then, four different datasets were created and the OBIA classification process was performed by applying the nearest neighbor classifier to the created data sets. Reference dataset for training and validation has been created by field survey, apart from this some of the sample are taken with the help of high resolution Google earth images. On the final stage, the accuracy assessment analysis was performed to test the agreement between classification results and ground truth data using error matrix. Dataset-4 (mean values of the layers, NDVI, NDWI and RPGI) has the highest producer and overall accuracies with 82% and 74%, respectively.

Coastline extraction is a fundamental work for coastal resource management and coastal environmental protection. Today, by using digital image processing techniques, coastline extraction can be done with remote sensing imagery systems. In this study, Landsat 8 Operational Land Imagery (OLI) data have been the main data source due to free access and sufficient spatial resolution for coast line extraction. This research is focused on determining the coastline length and measuring land area by using Landsat 8 OLI satellite image for Bodrum Peninsula, Turkey. Three commonly used methods have been applied in order to determine sea-land boundary line and its length, and area of the study area. The Automatic Water Extraction Index (AWEI), Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification technique and on screen digitizing method was chosen for identification of coastal boundaries. Results of coastline length and land areas of Bodrum by using AWEI, ISODATA and on-screen digitizing are compared with each other. This study shows that with using optimal threshold value, AWEI can be used for coast line extraction method with coherently for Landsat 8 OLI satellite imagery. The overall results show that coastline extraction from satellite imagery can be done with sufficient accuracy using spectral water indices instead of time consuming on-screen digitizing.