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Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data limitations, it is advised to employ a data collection consisting of images with homogeneously distributed intensity values of land use/cover objects at various resolution values. Thus, two different datasets were created. In line with the proposed approach, images of bare land, farmland, residential areas, and forested regions were extracted from orthophotos of different years with different spatial resolutions. In addition, images with intensity values in a more limited range for the same categories were obtained from a single year's orthophoto to highlight the contribution of the suggested approach. Training of two different datasets was performed independently using a deep learning-based super-resolution model, and the same test images were enhanced individually with the weights of both models. The results were assessed using a variety of quality metrics in addition to visual interpretation. The findings indicate that the suggested dataset structure and content can enable the recovery of more details and effectively remove the smoothing effect. In addition, the trend of the metric values matches the visual perception results. [ABSTRACT FROM AUTHOR]

For more than 50 years, marine and remote sensing researchers have investigated the methods of bathymetry extraction by means of active (altimetry) and passive (optics) satellite sensors. These methods, in general, are referred to as satellite-derived bathymetry (SDB). With the advances in sensor capabilities and computational power and recognition by the International Hydrographic Organization (IHO), SDB has been more popular than ever in the last 10 years. Despite a significant increase in the number of studies on the topic, the performance of the method is still variable, mainly due to environmental factors, the quality of the deliverables by sensors, the use of different algorithms, and the changeability in parameterization. In this study, we investigated the capability of Gokturk-1 satellite in SDB for the very first time at Horseshoe Island, Antarctica, using the random forest- and extreme gradient boosting machine learning-based regressors. All the images are atmospherically corrected by ATCOR, and only the top-performing algorithms are utilized. The bathymetry predictions made by employing Gokturk-1 imagery showed admissible results in accordance with the IHO standards. Furthermore, pixel brightness values calculated from Sentinel-2 MSI and tasseled cap transformation are introduced to the algorithms while being applied to Sentinel-2, Landsat-8, and Gokturk-1 multispectral images at the second stage. The results indicated that the bathymetric inversion performance of the Gokturk-1 satellite is in line with the Landsat-8 and Sentienl-2 satellites with a better spatial resolution. More importantly, the addition of a brightness value parameter significantly improves root mean square error, mean average error, coefficient of determination metrics, and, consequently, the performance of the bathymetry extraction. [ABSTRACT FROM AUTHOR]

Sa tellite images have been widely used in the production of geospatial information such as land use and land cover mapping and the generation of several thematic layers via image processing techniques. The systematic sensor and platform-induced geometry errors influence images acquired by sensors onboard various satellite platforms. Thus, geometric correction of satellite images is essential for image pre-processing to extract accurate and reliable locational information. Geometric correction of satellite images obtained from two different satellites, Pleiades 1A (PHR) and SPOT-6, was performed within the scope of this study using empirical models and a physical model. The 2D polynomial model, 3D rational function model with calculated RPCs from GCPs, 3D rational function model with RPCs from satellite, RPC refinement model using GCPs, and Toutin's physical model were used. Several experiments were carried out to investigate the effects of various parameters on the performance of the geometric correction procedure, such as GCP reference data source, GCP number and distribution, DEM source, spatial resolution, and model. Our results showed that lower RMSE values could be achieved with the model that uses RPC from data providers for PHR and SPOT, followed by the RPC refinement method for PHR and Toutin method for SPOT. In general, GCPs from the HGM data source and ALOS DEM combination provided better results. Lastly, lower RMSE values, thus better locational accuracy values, were observed with the PHR image except for a single test. [ABSTRACT FROM AUTHOR]

Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation. [ABSTRACT FROM AUTHOR]

In this research, multicriteria decision analysis with pairwise comparison weighting method was utilized to determine the suitable locations for vineyard plantation in Sarkoy region of Turkey. Soil maps, meteorological measurements, slope, aspect and elevation maps were used as input to conduct spatial analysis. Different methods were compared and pairwise comparison method was identified as the most appropriate method of weighting for this spatial analysis. Current vineyard areas were determined using Worldview-2 imagery and their spatial distribution compared with the resulting suitability map to determine the current suitability. Comparisons showed current vineyards were mostly established in locations where suitability map expresses low capability. Further inspection unveiled that, these low capability lands are closer to the transportation networks and city/county centres that tend to be in sea level elevations as opposed to vine grapes thriving in higher altitudes. Results also enabled providing suggestions on alternative sites for new vineyard plantation. [ABSTRACT FROM AUTHOR]

For spatial analyses, raster land cover/use maps are converted into points, where each point holds attribute of its corresponding land cover/use. However, these are not identical in terms of areas or shapes; thus assigning a point to each isolated shape is not an adequate solution and for that gridding is suggested. Square, hexagon and triangle are among the basic land use gridding systems where each of them has its own advantages in such process. This research aims to compare the systems in providing accurate representations of the original land cover/use maps, assess the data loss while increasing resolution and suggest suitable gridding system. The research finds the errors in area and feature numbers as criteria for selected classes. Modules that find out errors in each scale considering each criterion and class alone are proposed. The modules suggest both the best system for each criterion alone and for combined criteria. [ABSTRACT FROM AUTHOR]

The mam objective of this study is to compare the performance of two interpolation approaches, namely Inverse Distance Weighting (IDW) and Or­dinary Kriging (OK) methods, for mapping spatial distribution of soil characteristics including electri­cal conductivity (EC), organic matter, phosphorus, lime and boron in the Tuz Lake Basin of Turkey. A total number of 312 soil samples were used for anal­yses, in which 80% of the data was employed to gen­erate interpolation models; whereas, the rest 20% was engaged for validation of the estimated outputs. Additionally, linear regression analysis was per­formed for further comparative evaluation. The re­sults demonstrate that OK provided better results than IDW in estimating lime and EC parameters. On the other hand, IDW provided better results in esti­mating phosphorus, organic matter and boron. Both methods are useful to produce areal maps of the re­lated soil parameters by using discrete number of point observations. [ABSTRACT FROM AUTHOR]

Determination of cotton and maize cultivated areas with multi-temporal satellite images using veg­etation indices is the mam objective of this research. Study area was located on Sanlmrfa province, Tur­key/which hosts huge amount of agricultural pro­duction of cotton and maize in spring-summer sea­son with its suitable and effective irrigation system. The Landsat 8 OLI multi-temporal images acquired with 16-day interval were used to identify cultivated areas in the study area. Image acquisition dates from 30 April 2015 to 21 September 2015 completely co­vers the phenological development period of culti­vated crop types. Terrain corrected images were ra-diometncally calibrated to produce Top of Atmos­phere (ToA) reflectance images, in order to reduce {he atmospheric and illumination effects, thus providing efficient multi-temporal analysis. ToA re­flectance images were then used in vegetation index (VI) production. Normalized Difference Vegetation Index (NDVI), Transformed Difference Vegetation Index (TDVI), Enhanced Vegetation Index (EVI) and Green Normalized Difference Vegetation Index (GNDVI) were used in this research as vegetation suppression and data dimension reduction methods. Then VI image stacks were classified with pixel based Support Vector Machine (SVM) algorithm and results were compared with statistical production da­tabase to evaluate the effectiveness of broadband Vis in cultivated area and crop pattern detection. Results of the analysis provided that, GNDVI based dataset provided highest accuracies according to areal com­parison and point based accuracy assessment. NDVI and TDVI based datasets were ranked as the second with similar accuracy results, while EVI based da­taset was in the last place when compared to remain­ing VI datasets. Additionally, area determination ef­ficiency and classification accuracy for the cotton was higher than the maize nearly in all regions. [ABSTRACT FROM AUTHOR]

Performing point-based land cover/use analysis traditionally compels converting thematic clusters into representative points located at their corresponding centroids. Yet, various spatial analyses require to grid thematic maps into smaller regular geometric grids such as square and triangle, where the attribute of the dominant land cover/use class is assigned as the grid value. Hence, the class is represented geometrically via the selected gridding system and the accuracy of the later intended spatial analysis is altered. Moreover, the grid size that provide acceptable errors within determined thresholds for each grid is also utmost importance, since it is necessary to represent the original land cover/use thematic, to reduce the complexity and computing time of the analysis. Both of these problems remained unsolved especially for the green classes such as vegetation, forest etc. This study proposes a methodology to identify the best suitable gridding system for green areas, where the relationship of scale for each gridding system is analyzed. The study grids a reference land cover/use map, where both square and triangle systems at 23 different scales are tested. The relative errors of each gridding system at each scale are found via comparing the areal results of each grid with the reference map. According to the achieved results, the triangle gridding system is identified as more accurate in providing areal representation. Furthermore, the developed model demonstrates the associated errors of each scale for both gridding systems. Thus, the developed model could be utilized to choose appropriate grid cell size according to the application. [ABSTRACT FROM AUTHOR]

Climate change, rapid urbanization and indus­trialization have so far resulted in substantial loss of agricultural land, forestry and green areas that are environmentally of utmost importance. This is noted considerably in many coastal regions and highly populated cities such as metropolis Istanbul in Tur­key. In that sense, there is an urgent need for evalu­ating the magnitude, pattern, and type of land cover/use (LCLU) changes for projecting future land development in the most sustainable manner in this highly crowded city. Remote sensing (RS), in con­junction with Geographic Information Systems (GIS), has been widely applied and recognized as powerful and effective tools of modern technology in detecting LCLU. Five satellite imageries acquired in 1975, 1987, 1997, 2007 and 2014 were classified and corresponding LCLU thematic maps were achieved. The change detection results revealed great loss in the green areas such as forests, and in turn, huge increase in urbanized and industrialized areas. Sharp increases were detected for land cover classes of urban fabric, industrial and commercial units, and for open spaces covered with slight or no vegetation, in ratios of 227%, 410% and 402%, re­spectively over time. Hence, such quantitative LCLU results of this strategically important region of the world are sufficiently robust to alert urban and regional planners for efficient management practices intending to form a balance between utilization and protection of natural resources. [ABSTRACT FROM AUTHOR]

Rapid and uncontrolled urbanization is one of the most important land cover change phenomenon, which has important environmental impacts on ecosystem and climate. Due to the urbanization, green areas and natural permeable surfaces transform into non-permeable impervious surfaces, yielding an increase in urban land surface temperatures (LST). This causes an environmental phenomenon called urban heat islands (UHI), where urban temperatures are higher than the surrounding rural areas. Satellite remote sensing technology is an effective tool to monitor and quantify the effects of urbanization process with synoptic viewing capability and high temporal resolution. This study focuses on determining the land surface temperature and land use/land cover (LULC) changes in Istanbul for the last three decades and identifying the relation between these parameters using multi-temporal optical and thermal remote sensing data. The study is conducted using Landsat 5 TM and Landsat 8 OLI/TIR images, which were acquired on June 1984 and June 2017 respectively. In order to assess the land cover change between 1984 and 2017, a vegetation impervious surface- soil (V-I-S) model was applied to the fraction images obtained from linear spectral unmixing process. End-member spectra used in linear spectral umixing were extracted from ASTER spectral library and resampled to band passes of Landsat 5 TM and Landsat 8 OLI data. In addition to V-I-S model outputs, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) images were produced. A maximum-likelihood classification was also performed to multispectral bands in order to derive the LULC maps. Water bodies were extracted from imagery using automated water extraction index (AWEI). Thermal infrared bands of Landsat 5 TM and Landsat 8 are used for the derivation of land surface temperatures. The results of the study indicates that, the amount of impervious surfaces substantially increased along with land surface temperatures over three decades. [ABSTRACT FROM AUTHOR]

Determining the hydroelectric power potential of ungauged or poorly gauged basins gains importance parallel with the increasing electricity consumption. This study presents some simple methods to predict flow to determine the hydroelectric power potential of poorly gauged basins, such as the precipitation-elevation, average precipitation, and average basin elevation methods. Results of these methods are compared with the available flow measurements. The poorly gauged Solaklı Basin, which is located in Trabzon, in the Eastern Black Sea Region of Turkey, is selected as the pilot area. The hydroelectric power potential of the planned small hydroelectric power plants in this area is estimated using different flow prediction methods. [ABSTRACT FROM AUTHOR]

Turkey is divided into 25 hydrologic basins depending on the topographic structure. These basins have different productivities and potentials in relation with the annual rainfall they receive. Among them, Firat-Dicle Basin that constitutes approximately 47.7% of the water potential of the whole country, takes the first place in terms of water productivity with 52.94 billion m3. Fishery is the most effective activity within the different usage areas in these basins. Determination of the current situation of the fishery facilities in the region, the potential of aquaculture, point and spread pollution load, determined pressures-effects and hot spots have great importance in the management of the basin. Within the scope of this study, remote sensing being one of the current and quick resulting technologies, and GIS were used in order to provide information about the current usage of basin's water resources in terms of water products and sustainable management. Apart from the pressures and impacts on the ecosystem, the possible effects of total N/P load were also calculated in order to improve the existing potential, to control the feasibility of decisions to be made about the region and to enlighten decision makers are among the primary objectives. [ABSTRACT FROM AUTHOR]

On 30 May 2013, a forest fire occurred in Izmir, Turkey causing damage to both forest and fruit trees within the region. In this research, pre- and post-fire SPOT-6 images obtained on 30 April 2013 and 31 May 2013 were used to identify the extent of forest fire within the region. SPOT-6 images of the study region were orthorectified and classified using pixel and object-based classification (OBC) algorithms to accurately delineate the boundaries of burned areas. The present results show that for OBC using only normalized difference vegetation index (NDVI) thresholds is not sufficient enough to map the burn scars; however, creating a new and simple rule set that included mean brightness values of near infrared and red channels in addition to mean NDVI values of segments considerably improved the accuracy of classification. According to the accuracy assessment results, the burned area was mapped with a 0.9322 kappa value in OBC, while a 0.7433 kappa value was observed in pixel-based classification. Lastly, classification results were integrated with the forest management map to determine the effected forest types after the fire to be used by the National Forest Directorate for their operational activities to effectively manage the fire, response and recovery processes. [ABSTRACT FROM PUBLISHER]