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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]

Satellite-derived bathymetry (SDB) is the process of estimating water depth in shallow coastal and inland waters using satellite imagery. Recent advances in technology and data processing have led to improvements in the accuracy and availability of SDB. The increased availability of free optical satellite sensors, such as Landsat missions and Sentinel 2 satellites, has increased the quantity and frequency of SDB research and mapping efforts. In addition, machine learning (ML)- and deep learning (DL)-based algorithms, which can learn to identify features that are indicative of water depth, such as color or texture variations, have started to be used for extracting bathymetry information from satellite imagery. This study aims to produce an initial optical image-based SBD map of Horseshoe Island's shallow coasts and to perform a comprehensive and comparative evaluation with Landsat 8 and Sentinel 2 satellite images. Our research considers the performance of empirical SDB models (classical, ML-based, and DL-based) and the effects of the atmospheric correction methods ACOLITE, iCOR, and ATCOR. For all band combinations and depth intervals, the ML-based random forest and XGBoost models delivered the highest performance and best fitting ability by achieving the lowest error with MAEs smaller than 1 m up to 10 m depth and a maximum correlation of R2 around 0.80. These models are followed by the DL-based ANN and CNN models. Nonetheless, the non-linearity of the reflectance–depth connection was significantly reduced by the ML-based models. Furthermore, Landsat 8 showed better performance for 10–20 m depth intervals and in the entire range of (0–20 m), while Sentinel 2 was slightly better up to 10 m depth intervals. Lastly, ACOLITE, iCOR, and ATCOR provided reliable and consistent results for SDB, where ACOLITE provided the highest automation. [ABSTRACT FROM AUTHOR]

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The classification of maritime boats and ship targets using optical satellite imagery is a challenging subject. This research introduces a unique and rich ship dataset named Very High-Resolution Ships (VHRShips) from Google Earth images, which includes diverse ship types, different ship sizes, several inshore locations, and different data acquisition conditions to improve the scalability of ship detection and mapping applications. In addition, we proposed a deep learning-based multi-stage approach for ship type classification from very high resolution satellite images to evaluate the performance of the VHRShips dataset. Our "Hierarchical Design (HieD)" approach is an end-to-end structure that allows the optimization of the Detection, Localization, Recognition, and Identification (DLRI) stages, independently. We focused on sixteen parent ship classes for the DLR stages, and specifically considered eight child classes of the navy parent class at the identification stage. We used the Xception network in the DRI stages and implemented YOLOv4 for the localization stage. Individual optimization of each stage resulted in F1 scores of 99.17%, 94.20%, 84.08%, and 82.13% for detection, recognition, localization, and identification, respectively. The end-to-end implementation of our proposed approach resulted in F1 scores of 99.17%, 93.43%, 74.00%, and 57.05% for the same order. In comparison, end-to-end YOLOv4 yielded F1-scores of 99.17%, 86.59%, 68.87%, and 56.28% for DLRI, respectively. We achieved higher performance with HieD than YOLOv4 for localization, recognition, and identification stages, indicating the usability of the VHRShips dataset in different detection and classification models. In addition, the proposed method and dataset can be used as a benchmark for further studies to apply deep learning on large-scale geodata to boost GeoAI applications in the maritime domain. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based approaches have shown promising results in the pansharpening of the satellite images in recent years. However, they still exhibit limitations in producing high-quality pansharpening outputs. To that end, we propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem, which brings an entirely novel perspective and solution to the problem compared with the existing methods that base their solution solely on producing a super-resolution version of the multispectral image. Whereas the CNN-based methods provide a reduced-resolution panchromatic image as the input to their model along with the reduced-resolution multispectral images and, hence, learn to increase their resolution together, we instead provide the grayscale transformed multispectral image as the input and train our model to learn the colorization of the grayscale input. We further address the fixed downscale ratio assumption during training, which does not generalize well to the full-resolution scenario. We introduce a noise injection into the training by randomly varying the downsampling ratios. Those two critical changes, along with the addition of adversarial training in the proposed PanColorization generative adversarial network (PanColorGAN) framework, help overcome the spatial-detail loss and blur problems that are observed in CNN-based pansharpening. The proposed approach outperforms the previous CNN-based and traditional methods, as demonstrated in our experiments. [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]

Digital elevation models (DEMs), which can occur in the form of digital surface models (DSMs) or digital terrain models (DTMs), are widely used as important geospatial information sources for various remote sensing applications, including the precise orthorectification of high-resolution satellite images, 3D spatial analyses, multi-criteria decision support systems and deformation monitoring. The accuracy of DEMs has direct impacts on specific calculations and process chains; therefore, it is important to select the most appropriate DEM by considering the aim, accuracy requirement and scale of each study. In this research, DSMs obtained from a variety of satellite sensors were compared to analyze their accuracy and performance. For this purpose, freely available Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30 m and Advanced Land Observing Satellite (ALOS) 30 m resolution DSM data were obtained. Additionally, 3 m and 1 m resolution DSMs were produced from tri-stereo images from the SPOT 6 and Pleiades high-resolution (PHR) 1A satellites, respectively. Elevation reference data provided by the General Command of Mapping, the national mapping agency of Turkey--produced from 30 cm spatial resolution stereo aerial photos, with a 5 m grid spacing and ±3 m or better overall vertical accuracy at the 90% confidence interval (CI)--were used to perform accuracy assessments. Gross errors and water surfaces were removed from the reference DSM. The relative accuracies of the different DSMs were tested using a different number of checkpoints determined by different methods. In the first method, 25 checkpoints were selected from bare lands to evaluate the accuracies of the DSMs on terrain surfaces. In the second method, 1000 randomly selected checkpoints were used to evaluate the method's accuracies for the whole study area. In addition to the control point approach, vertical cross-sections were extracted from the DSMs to evaluate the accuracies related to land cover. The PHR and SPOT DSMs had the highest accuracies of all of the testing methods, followed by the ALOS DSM, which had very promising results. Comparatively, the SRTM and ASTER DSMs had the worst accuracies. Additionally, the PHR and SPOT DSMs captured man-made objects and above-terrain structures, which indicated the need for post-processing to attain better representations. [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]

Highlights: [•] We estimate crop cover fraction in cultivated areas with digital camera pictures. [•] We develop a light use efficiency based model for yield estimation. [•] We include meteorological yield limiting factors in model calculation. [•] We derive relational equations between crop cover and spectral vegetation indexes. [•] We reach 5% estimation error in test parcels and 10% in region based analysis. [Copyright &y& Elsevier]

This research investigates the accuracy of pixel- and object-based classification techniques across varying spatial resolutions to identify crop types at parcel level and estimate the area at six test sites to find the optimum data source for the identification of crop parcels. Multi-sensor data with spatial resolutions of 2.5 m, 5 m and 10 m from SPOT5 and 30 m from Landsat-5 TM were used. Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machines (SVM) were used as pixel-based methods in addition to object-based image classification (OBC). Post-classification methods were applied to the output of pixel-based classification to minimize the noise effects and heterogeneity within the agricultural parcels. In addition, processing-time performance of the algorithms was evaluated for the test sites and district scale classification. OBC results provided comparatively the best performance for both parcel identification and area estimation at 10 m and finer spatial resolution levels. SVM followed OBC at 2.5 m and 5 m resolutions but accuracies decreased dramatically with coarser resolutions. ML and SAM results were worse up to 30 m resolution for both crop type identification and area estimation. In general, parcel identification efficiency was strongly correlated with spatial resolution while the classification algorithm was a more effective factor than spatial resolution for area estimation accuracy. Results also provided an opportunity to discuss the effects of image resolution and the classification algorithm independent factors such as parcel size, spatial distribution of crop types and crop patterns. [ABSTRACT FROM AUTHOR]

Precipitation is an important component of the hydrological and energy cycles, as well as a key input parameter for many applications in the fields of hydrology, climatology, meteorology, and weather forecasting research. As a result, estimating precipitation accurately is critical. The purpose of this research is to conduct a comprehensive and comparative evaluation of grid-based precipitation products over Turkey's Mediterranean region from 2017 to 2021 at monthly and grid scales, using data from 193 ground-based meteorological stations as a reference. PERCIANN CCS, PDIR-Now, GSMaP MVK, PERSIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 are the eight grid-based precipitation products. Several prospective were used to evaluate the products, including magnitude agreement with gauge stations for the entire region and the six hydrological sub-basins included in the region, performance in capturing various intensity categories, and elevation dependency. According to the evaluation results, PERCIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 performed well in all evaluation aspects, whereas PERCIANN CCS, PDIR-Now, and GSMaP MVK performed poorly in all metrics. The majority of the products underestimated heavy rainfall events, while all products performed better at low and moderate precipitation events. As a result, the products performed better in the summer and spring months (March to October) than in the winter months (December to February). Furthermore, the results showed that the performance of the majority of the products degraded for elevations greater than 1000 m. The evaluation suggests that PERSIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 can be used as good precipitation data sources and as a complement to ground-based meteorological stations in Turkey's Mediterranean region. [ABSTRACT FROM AUTHOR]

Extensive use of land and water resources results in changes in land-use and properties; wetlands are drained, reservoirs are built, settlements extend, and forests are converted to farmlands. Such changes affect the hydrology and ecology of a watershed. Further effects are pollution of the environment and deterioration of habitats. To follow the temporal change of land-use and corresponding properties, extensive laborious studies include data gathering from various state offices and field surveys. Remote sensing (RS). supported analyses, on the other hand, reduce the necessary manpower. The aim of this study is to investigate the change of land-use and corresponding properties on an average sized watershed with an area of 1000 km2. Remote Sensing was used to classify the watershed into four classes: Agricultural areas, forests, bare soil, and river bed. Landsat 5 TM images belonging to 1984, 2000, 2003 and 2010 were used for classification analysis. GIS was used to generate the land use and land property maps, and to make interpolations for the years where remote sensing data is missing. Finally, a yearly set of land use and land property maps is obtained. These maps are useful for understanding the land use changes and for estimating the future probable trend of land use changes. [ABSTRACT FROM AUTHOR]

The article presents a study which develops a fuzzy logic model by using the Mamdani method to estimate the monthly mean flows of poorly gauged basin. It examines the effectiveness of the model by estimating the mean flow at Solakli Basin located in the Eastern Sea Region of Turkey. It compares the result of the model with the actual measured data based on seven statistical characteristics, four different error modes and the contour map method.

Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy. [ABSTRACT FROM AUTHOR]

Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification. [ABSTRACT FROM AUTHOR]

Darlik Dam supplies 15% of the water demand of Istanbul Metropolitan City of Turkey. Water quality (WQ) in the Darlik Dam was investigated from Landsat 5 TM satellite images of the years 2004, 2005, and 2006 in order to determine land use/land cover changes in the watershed of the dam that may deteriorate its WQ. The images were geometrically and atmospherically corrected for WQ analysis. Next, an investigation was made by multiple regression analysis between the unitless planetary reflectance values of the first four bands of the June 2005 Landsat TM image of the dam and WQ parameters, such as chlorophyll-a, total dissolved matter, turbidity, total phosphorous, and total nitrogen, measured at satellite image acquisition time at seven stations in the dam. Finally, WQ in the dam was studied from satellite images of the years 2004, 2005, and 2006 by pattern recognition techniques in order to determine possible water pollution in the dam. This study was compared to a previous study done by the authors in the Küçükçekmece water reservoir, also in Istanbul City.

This study focuses on the analysis of the Landsat-5 TM + SPOT-Pan (1992), IRS-1C/D LISS + Pan (2000), and Landsat-5 TM (2006) satellite images that reflect the drastic land use/land cover changes in the Küçükçekmece Lake region, Istanbul. Landsat-5 TM satellite data dated 2006 was used for mapping water quality. A multiple regression analysis was carried out between the unitless planetary reflectance values derived from the satellite image and in situ water quality parameters chlorophyll a, total phosphorus, total nitrogen, turbidity, and biological and chemical oxygen demand measured at a number of stations homogenously distributed over the lake surface. The results of this study provided valuable information to local administrators on the water quality of Küçükçekmece Lake, which is a large water resource of the Istanbul Metropolitan Area. Results also show that such a methodology structured by use of reflectance values provided from satellite imagery, in situ water quality measurements, and basin land use/land cover characteristics obtained from images can serve as a powerful and rapid monitoring tool for the drinking water basins that suffer from rapid urbanization and pollution, all around the world.

Accurate and timely information about land use and land cover (LULC) and its changes in urban areas are crucial for urban land management decision-making, ecosystem monitoring and urban planning. Also, monitoring and representation of urban sprawl and its effects on the LULC patterns and hydrological processes of an urbanized watershed is an essential part of water resource planning and management. This paper presents an image analysis study using multi temporal digital satellite imagery of LULC and changes in the Kucukcekmece Watershed (Metropolitan Istanbul, Turkey) from 1992 to 2006. The Kucukcekmece Basin includes portions of the Kucukcekmece District within the municipality of Istanbul so it faces a dramatic urbanization. An urban monitoring analysis approach was first used to implement a land cover classification. A change detection method controlled with ground truth information was then used to determine changes in land cover. During the study period, the variability and magnitude of hydrological components based on land-use patterns were cumulatively influenced by urban sprawl in the watershed. The proposed approach, which uses a combination of Remote Sensing (RS) and Geographical Information System (GIS) techniques, is an effective tool that enhances land-use monitoring, planning, and management of urbanized watersheds.