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After a disaster, ascertaining the operational state of extensive infrastructures and building clusters on a regional scale is critical for rapid decision-making and initial response. In this context, the use of remote sensing imagery has been acknowledged as a valuable adjunct to simulation model-based prediction methods. However, a key question arises: how to link these images to dependable assessment results, given their inherent limitations in incompleteness, suboptimal quality, and low resolution? This article comprehensively reviews the methods for post-disaster building damage recognition through remote sensing, with particular emphasis on a thorough discussion of the challenges encountered in building damage detection and the various approaches attempted based on the resultant findings. We delineate the process of the literature review, the research workflow, and the critical areas in the present study. The analysis result highlights the merits of image-based recognition methods, such as low cost, high efficiency, and extensive coverage. As a result, the evolution of building damage recognition methods using post-disaster remote sensing images is categorized into three critical stages: the visual inspection stage, the pure algorithm stage, and the data-driven algorithm stage. Crucial advances in algorithms pertinent to the present research topic are comprehensively reviewed, with details on their motivation, key innovation, and quantified effectiveness as assessed through test data. Finally, a case study is performed, involving seven state-of-the-art AI models, which are applied to sample sets of remote sensing images obtained from the 2024 Noto Peninsula earthquake in Japan and the 2023 Turkey earthquake. To facilitate a cohesive and thorough grasp of these algorithms in their implementation and practical application, we have deliberated on the analytical outcomes and accentuated the characteristics of each method through the practitioner's lens. Additionally, we propose recommendations for improvements to be considered in the advancement of advanced algorithms. [ABSTRACT FROM AUTHOR]

For rapid production of spatial data and topographic maps, map production systems should be transformed into a dynamic structure. Topographic map production system in Turkey, cartographic operations for each map are repeated in the same way, regardless of the amount of data updated in the geographic database. The implementation of similar actions on maps with very few changes is not time and cost effective. In this study, changes were determined by comparing the current and previous data from TOPODB and the resulting changes were integrated into the population class of 1:25.000 scale topographic maps in a third less time. [ABSTRACT FROM AUTHOR]

On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage. [ABSTRACT FROM AUTHOR]

Damage identification soon after a large-magnitude earthquake is a major problem for early disaster response activities. The faster the damaged areas are identified, the higher the survival chances of inhabitants. Current methods for damage identification are based on the application of artificial intelligence techniques using remote sensing data. Such methods require a large amount of high-quality labeled data for calibration and/or fine-tuning processes, which are expensive in the aftermath of large-scale disasters. In this paper, we propose a novel semi-supervised classification approach for identifying urban changes induced by an earthquake between images recorded at different times. We integrate information from a small set of labeled data with information from ground motion and fragility functions computed on large unlabeled data. A relevant consideration is that ground motion and fragility functions can be computed in real time. The urban changes induced by the 2023 Turkey earthquake sequence are reported as an evaluation of the proposed method. The method was applied to the interferometric coherence computed from C-band synthetic aperture radar images from Sentinel-1. We use only 39 samples labeled as changed and 9000 unlabeled samples. The results show that our method is able to identify changes between images associated with the effects of an earthquake with an accuracy of about 81%. We conclude that the proposed method can rapidly identify affected areas in the aftermath of a large-magnitude earthquake. [ABSTRACT FROM AUTHOR]

This study aims to describe, classify, and analyze the most common anthropogenic factors that have altered the landscape. For this reason, the city of Diyarbakır, one of Mesopotamia's largest and most urbanized cities, was chosen as an example of several anthropogenic factors. Several field surveys were conducted to determine the most common anthropogenic activities in the study area. They were then classified into four categories: marble quarries, aggregate quarries, water bodies, and built-up areas. A series of satellite images spanning 1975 to 2019 were analyzed by investigating time-based anthropogenic changes. The trends, distributions, and overall impacts of 177 locations were examined and monitored through remotely sensed images. Between 1975 and 2019, a total of 25224 ha of land were transformed, according to the findings. "Water bodies" were the most extended of the studied classes. The second most rapidly expanding class was "Built-up areas." The "Built-up areas" class was assumed to be the most effective agent that will continue to modify the land of the study area, given the need for more construction spaces. Surface mining activities can also be assumed to be an efficient agent that will continue to modify the lands of Diyarbakır in the future, considering the export potential of marble products and the demand for construction material. Water resources and building materials supply should be examined in depth throughout future development planning of such urbanized cities to minimize anthropogenic degradation. [ABSTRACT FROM AUTHOR]

The Present study focused on investigation of Land Use Land Cover (LULC) changes within three basins located in North-West part of Anatolia region, Turkey. The study area consisted of six provinces with 41 districts. Visible, near-infrared and shortwave-infrared bands of Landsat imageries, acquired in production seasons of 1984, 1999, and 2014, served as main data source. The area is covered by six Landsat scenes. Supervised classification maximum likelihood algorithm was adopted whereas LULC classes were considered as forest (F), grazing-other vegetation (G-O), agriculture (A), water surface (W), and residential area-bare soil (R-B). Prior to the classification process, scenes were subset according to district boundaries, and district-level images were classified to improve the classification precision, and then mosaicked to obtain LULC1984, LULC1999 and LULC2014 maps. The reliabilities of maps were evaluated by accuracy assessments using 900 stratified randomized control points with minimum of 90 points per class. LULC changes were evaluated in two terms; changes within study area (WSA) (ha, %) and changes within class area (WCA) (%). Moreover, distributions of class areas along three basins were presented. Finally, past statuses of each class were compared, particularly, impacts of urbanization on agriculture were highlighted, and population change patterns were evaluated as main underlying reason. [ABSTRACT FROM AUTHOR]

In this study, hydrologic regimes of the Palas basin have been investigated with land use/land cover (LULC) change. To investigate the relationship between precipitation and surface runoff, a SWAT (Soil and Water Assessment Tool) model was developed and runoff response was analysed under different LULC conditions. Firstly, Post classification change detection technique was used to prepare time series LULC maps of the years 1987, 2000 and 2011. The overall classification accuracy of 86% and Kappa Coefficient (K) of 0.82 were achieved. Then model was manually calibrated using monthly historical streamflow records. The calibration was successful with coefficient of determination (R 2) value of 0.61 and the Nash and Sutcliffe efficiency value of 0.55. Validation of the calibrated model using independent dataset shows even better model performance with Nash and Sutcliffe efficiency value of 0.62 and R 2 value of 0.85. The results of this research study are as follows. Agriculture area in Palas Basin considerably expanded (47%) in the last 24 years. Consequently, the area of bare soil declined (36%) markedly during the period 1987–2011. Simulations of runoff for the 2000–2011 period with the SWAT model showed that, under the LULC conditions of 1987, surface runoff would be 40% lower compared to runoff under the LULC conditions of 2011. For the development of sustainable water resource strategies, it is essential to establish interaction between land use changes and local hydrology. Although more observed data is needed for model accuracy, SWAT can provide useful data for water sources planners and policy makers. [ABSTRACT FROM AUTHOR]

Wetlands are among the most productive ecosystems that provide services ranging from flood control to climate change mitigation. Wetlands are also critical habitats for the survival of numerous plant and animal species. In this study, we used satellite remote sensing techniques for classification and change detection at an internationally important wetland (Ramsar Site) in Turkey. Sultan Marshes is located at the center of semi-arid Develi closed basin. The wetlands have undergone significant changes since the 1980s due to changes in water flow regimes, but changes in recent years have not been sufficiently explored yet. In this study, we focused on the changes from 2005 to 2012. Two multispectral ASTER images with spatial resolution of 15 m, acquired on June 11, 2005 and May 20, 2012, were used in the analyses. After geometric correction, the images were classified into four information classes, namely water, marsh, agriculture, and steppe. The applicability of three classification methods (i.e. maximum likelihood (MLH), multi-layer perceptron type artificial neural networks (ANN) and support vector machines (SVM)) was assessed. The differences in classification accuracies were evaluated by the McNemar's test. The changes in the Sultan Marshes were determined by the post classification comparison method using the most accurate classified images. The results showed that the highest overall accuracy in image classifications was achieved with the SVM method. It was observed that marshes and steppe areas decreased while water and agricultural areas expanded from 2005 to 2012. These changes could be the results of water transfers to the marshes from neighboring watershed. [ABSTRACT FROM AUTHOR]

One of the most important needs in contemporary landscape planning is quantitative land use/land cover (LU/LC) change information. The reason a strong emphasis is placed on landscape change information is that it serves as an ecological and geographical basis for preparing and implementing development plans in a more sustainable manner. Multi-temporal analysis of LU/LC changes on the Eastern Mediterranean coast of Turkey revealed that there is a marked preference for these areas primarily for building development. This paper demonstrates a methodology that relies on quantitative analysis techniques for assessing spatiotemporal changes in LU/LC in the case of the eastern Mediterranean coast of Turkey. In this respect, satellite image datasets (SPOT panchromatic, Landsat TM) acquired in 1989, 1995, 2001 and 2007 were enhanced. Resulting images were classified and compared to detect coastal urbanization and development trends. Post-classification change analyses were employed to quantify land cover conversions in three periods from 1985 to 1995, from 1995 to 2001 and from 2001 to 2007. This paper demonstrated that urban, agriculture and shrublands changed rapidly in this part of the Mediterranean coast. [ABSTRACT FROM PUBLISHER]

Earthquakes of 23 October 2011 and 9 November 2013 caused significant destruction on existing built-up areas of Van City, Turkey and had led to great loss of life and property. With the aim of identifying the effects of these two high-destructive earthquakes on spatial transformation process of the city, as well as to evaluate applied urban transformation strategies concerning to remove and mitigate the unfavourable effects of these earthquakes, maximum likelihood classification (MLC) is applied on related RapidEye satellite images to find out the urban macro-form changes, the change detection outputs by means of MLC results of the RapidEye satellite images dated before and after earthquakes which indicate that the overall accuracies are computed over 80%. The general results also reveal that the accuracies obtained are very promising to analyse spatial effect of the earthquakes on spatial transformation of the city. The identified findings of this analysis provide useful inputs to forthcoming researches aiming to evaluate the success of those kinds of urban transformation processes that may occur after similar natural disaster cases. [ABSTRACT FROM AUTHOR]

ABSTRACT This study classified land cover conversions by using quantitative change detection methods in the eastern Mediterranean coastal wetlands of Turkey. Three Landsat TM scenes acquired in 1984, 1998 and 2007 were analysed using image pairs of consecutive dates. The amounts of change were calculated for the periods from 1984 to 1998 and from 1998 to 2007, using binary change masks and supervised image classifications, in which eleven a priori defined land cover classes were used. Change areas accounted for 14.81 and 15.64 per cent of the total area, in the first and second periods, respectively. Conversions from/to sand dunes, mud flats and afforestation were by far the most extensive changes. Sand dunes and mud flats decreased, while afforestation and agriculture areas increased in the 23-year period between 1984 and 2007. Five types of qualitative change were identified on the basis of land cover transformations within and between natural and anthropogenic systems. Type I changes, which indicated conversions from (semi)natural to anthropogenic systems, took place as agricultural expansion over coastal sand dunes, while Type II changes mostly occurred within agricultural systems. Type III changes included changes in human uses, leading to conversions from one anthropogenic system to another. Conversions between semi(natural) systems such as salt marshes and mud-flats were typical Type IV changes. Type V changes were mainly due to conversions from agriculture to wetland categories or other semi(natural) cover types. Implications of these changes were discussed for management of coastal environmental resources. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The aim of this study is to compare various image algebra procedures for their efficiency in locating and identifying different types of landscape changes on the margin of a Mediterranean coastal plain, Cukurova, Turkey. Image differencing and ratioing were applied to the reflective bands of Landsat TM datasets acquired in 1984 and 2006. Normalized Difference Vegetation index (NDVI) and Principal Component Analysis (PCA) differencing were also applied. The resulting images were tested for their capacity to detect nine change phenomena, which were a priori defined in a three-level classification scheme. These change phenomena included agricultural encroachment, sand dune afforestation, coastline changes and removal/expansion of reed beds. The percentage overall accuracies of different algebra products for each phenomenon were calculated and compared. The results showed that some of the changes such as sand dune afforestation and reed bed expansion were detected with accuracies varying between 85 and 97% by the majority of the algebra operations, while some other changes such as logging could only be detected by mid-infrared (MIR) ratioing. For optimizing change detection in similar coastal landscapes, underlying causes of these changes were discussed and the guidelines for selecting band and algebra operations were provided. [Copyright &y& Elsevier]

Abstract: Rate and amount of carbon (C) storage in peatlands play a vital role in global biogeochemical cycles despite their globally small spatial extent. Carbon dioxide (CO2) emissions from drained peatlands is a function of drainage intensity and extent of peatlands, peat thickness, land use and land cover (LULC) type to which peatlands are converted, and management practices. Historical LULC change-detection of Yenicaga peatlands and wet flats yielded an estimated conservative decrease in C pool from 2.43 ± 0.15 million metric tons (Mt) in 1944 to 0.65 ± 0.04 Mt in 2009 due mostly to drainage by agricultural and afforestation activities. Drainage-induced C emission rate was estimated to range from 12.5 to 32.5 t C ha−1 yr−1. Our extrapolation of the C emission rate to the entire peatland area of 240 km2 in Turkey resulted in CO2 emissions of 0.30–0.78 Mt in 2009, equivalent to 0.01%–0.02% of the global LULC-related CO2 emissions of 3230 Mt in 2009. [Copyright &y& Elsevier]

Humans change the landscapes and especially the coastal zones for their requirements. The aim of the study was to analyse land use changes in the Çeşme coastal zone, Turkey, during a 48-year period (1957-2005) using remote sensing and GIS. In this study, land use changes in the Çeşme coastal zone were analysed using the four sets of land use maps derived from aerial photographs and classified satellite image taken in the years 1957, 1976, 1995 and 2005. Post-classification algorithm change detection was applied to observe land use changes during three time periods: Period 1 (1957-1976), Period 2 (1976-1995) and Period 3 (1995-2005). The results showed that natural land cover and agricultural lands decreased and built-up area increased throughout the study area during the 48-year period between 1957 and 2005. Furthermore, land use change was successively increased in Period 1, Period 3 and Period 2. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This research is focused on the coastline evolution monitoring and its potential change estimation by remote sensing techniques using multi-temporal Landsat images at the southeast coasts of the Mediterranean Sea in Turkey. The study area includes the coastal zone located in the Cukurova Delta coasts. The Cukurova Delta has accreted toward the Mediterranean Sea as a result of sediment discharge and transport from Seyhan and Ceyhan rivers. These processes have caused the morphological changes (accretion or erosion) of coastline along some parts of the southeast coasts of the Mediterranean Sea. In this study, coastline changes were researched by using radiometrically and geometrically corrected multi-temporal and multi-spectral data from Landsat Multispectral Scanner dated 1972, Thematic Mapper dated 1987, and Enhanced Thematic Mapper dated 2002. In the image processing steps, mosaicing, subset, Iterative Self-Organizing Data Analysis Technique classification, band ratioing (B5/B2), edge detection, and overlay techniques were used to carry out coastline extraction and the Digital Shoreline Analysis System was used to calculate rate of coastline changes. As a result of the analysis, in some parts of the research area, remarkable coastline changes (more than 2,900 m withdrawal and − 24.50 m/year erosion) were observed for a 30–year period. [ABSTRACT FROM AUTHOR]

Land cover of the Earth is changing dramatically because of human activities. Information about changes is useful for management of natural resources. Rapid land cover changes have taken place in many coastal areas of Turkey over the last two decades due to urbanization and land degradation. In this paper, land cover change dynamics were investigated by the combined use of satellite remote sensing and geographical information systems. The main objective of the study was to determine land-cover transition rates among land cover types in coastal areas of Turkey. A time series of Landsat TM and ASTER images were used to gather land cover change data of the coastal line of Candarli Bay, Izmir, Turkey. The images were classified using supervised classification and a post-classification comparison approach was used in change detection. The results show significant increase in urban areas but decrease in semi natural and agricultural areas. [ABSTRACT FROM AUTHOR]

This study assessed land cover (LC) changes in Kahramanmaraş (K.Maraş) and its environs by using multitemporal Landsat and ASTER imagery, respectively belong to 1989, 2000 and 2004. A priori defined nine land cover classes in the classification scheme were urban and built-up, forest, sparsely vegetated areas, grassland, vegetated stream beds, unvegetated stream beds, bare areas, crop fields, and water bodies. Individual classifications were employed using the combination of both unsupervised and supervised classification methods. Iterative Self Organizing Data Analysis (ISODATA) was used to reduce spectral variation in the scenes arising from complex pattern of crop fields. Maximum Likelihood classifier was used in the LC classification of the individual images. Image pairs of consecutive dates were compared by overlaying the thematic LC maps and cross-tabulating the LC statistics. Urbanization and expansion of agriculture were the major reasons for the dramatic LC conversions. The amount of conversion from crop fields to water occurred as large as 927.67 ha, accounting for 73% of the total land-to-water conversion. Conversions to agriculture have mainly been occurred from grasslands and sparsely vegetated areas as large as 1,314.95 and 1,325.84 ha, respectively. Urban coverage doubled in this period as a result of 1,443.45 ha of increase. Urban area increased in the second period from 2,920 to 3,526 ha. Conversions to agriculture occurred at high amounts. A total of 1,075.79 ha area changed from sparsely vegetated areas to crop fields. A landscape-level environmental monitoring scheme based on satellite remote sensing was proposed for effective environmental resource management. [ABSTRACT FROM AUTHOR]

Industrialization has been one of the major factors in the development of the countries, and has caused a population increase in cities, resulting in urban sprawl. Because industrialization and urbanization often advance in an uncontrolled or unorganized way in developing countries, they can have destructive effects on the environment, particularly on basic ecosystems, wildlife habitat, and global biodiversity. One of the places that has been subject to intense industrialization and resulting urbanization is the Gebze district of Kocaeli in Turkey. Land use and land cover changes that occurred in the region were investigated using satellite images acquired in 1987, 1997, and 2002. In the detection of changes postclassification comparison approach is employed using an artificial neural network classifier, specifically a multilayer perceptron with backpropagation learning algorithm. Results show some important findings regarding the size and nature of the change that occurred in the study area. In the despoiled areas, a large number of pixels of pasture and forest lands have been replaced by urban pixels; as a result, the total area of urban pixels doubled in the 15-year period with a higher urbanization rate between 1997 and 2002. A significant amount of forest land, about 38% for deciduous and 22% for coniferous forest, has been destroyed. In addition to the statistical estimates of the change, its spatial distribution was also investigated through a map of change that helps to determine the areas where considerable degradation and deforestation have taken place. [ABSTRACT FROM AUTHOR]

This paper focuses mainly on the coastline movements at the northeast coasts of the Aegean Sea in Turkey. The Aegean Sea is a semiclosed sea that has unique geographical features and covers an area of 191,000 km². The study area includes the coastal zone located between the southeastern part of the Meric River mouth and the Dalyan Lake coasts. The Meric Delta has accreted toward the Aegean Sea as a result of sediment discharge and transport. In contrast to this, the width of the natural land barrier between the Aegean Sea and the Dalyan Lake has decreased over the years because of coastal erosion. These processes have caused the morphological changes of coastline along some parts of the northeast coasts of the Aegean Sea. In this study, these changes were examined by using satellite data from Landsat MSS, TM, and ETM collected between 1975 and 2001. In the image processing step, registration, ISODATA classification, and temporal image ratioing techniques were used to carry out coastline change assessment. At the end of the study, significant coastline movements (in some parts more than 200 m) were detected for a 26- year period. [ABSTRACT FROM AUTHOR]

This paper investigates the determining land use changes in the Eastern Black Sea region of Turkey. Changes in land cover were analyzed using aerial photography from 1955–2002 within a Geographic Information System. As for method, digital photogrammetric techniques with high accuracy could be used to monitor land use/cover changes by comparing old and new aerial images. To implement the method successfully and display the results, three case study areas were selected and evaluated regarding the historical changes and land use capabilities of the land converted to forest areas: 1/35,000, 1/23,000 scale black and white (for the 1973 and 1982 periods) and 1/16,000 scale infrared aerial photographs (for the 2002 period), taken in 1955, 1973, 1982, and 2002, were assessed by scanning with 21-micron geometric resolution. These images were interpreted by using photogrammetric software and historical status of forested areas, and change detection were easily determined in digital form. It is found that while forest cover within the forest reserve has remained stable or increased, there has been a steady decline in forest cover outside the forest reserve's boundaries. It is also seen that human land use activities have been basic factors in shaping land cover change during the 50-year period. [ABSTRACT FROM AUTHOR]

• Coastal urbanisation patterns were analysed for four dates in the last thirty years. • Area/edge, shape, and aggregation characteristics of built-up patches were mapped. • Spatial and temporal patterns were analysed on a distance-to-coastline gradient. • Impacts of urbanisation on land capability were mapped. • Landscape planning and management proposals were made based on the results. Natural and anthropogenic forces on Earth create changes in biophysical characteristics of landscapes in the forms of modifications or transformations. Since landscapes are composed of spatially configured patches of different land cover types, changes in the pattern composition and configuration may be used as indicators for identifying environmental trends and their underlying causes. To this end, pattern indices hold a strong potential to conceptualize landscape structure in many different ways. This study aims to analyse landscape changes in an urbanized coastal landscape in the eastern Mediterranean region of Turkey using pattern indices as development indicators. For this purpose, the spatial distribution of built-up areas was extracted for the years 1989, 1995, 2001, 2007 and 2019 using geo-referenced SPOT and Sentinel images. Eight landscape structure indices were calculated to demonstrate spatial and temporal diversity of area/edge, shape and aggregation characteristics of built-up patches. These indices are (1) class area (CA), (2) percentage of landscape (PL), (3) largest patch index (LPI), (4) edge density, (5) perimeter-area ratio (PARA), (6) fractal dimension (FRAC), (7) related circumscribing circle (RCC) and (8) percentage of like-adjacencies (PLADJ). The study area was divided into 215 grid-units to represent spatial diversity of development. The aforementioned indices were calculated separately for each of the units. The spatial distribution of built-up areas was also associated with land capability to assess land degradation trends. Results showed that much of the development occurred on Class I and II lands that are in fact prime agricultural areas. Results also suggest that the structure of building patches and the way that it changes vary between the coast and inland. [ABSTRACT FROM AUTHOR]

Mining activities are important for the country's economy, but they cause serious threats to the environment. The geology of SW Turkey comprises Southern (Çine) Submassif of Menderes Metamorphic Massif and the Lycian Nappes. These geological units are unconformably overlain by lignite-bearing Miocene deposits, Upper Miocene–Pliocene conglomerates and Quaternary alluvial deposits. The aim of this study is to determine the geospatial change of mining activities in the Muğla-Aydın provinces, SW Turkey. For this purpose, Landsat-5 TM, Landsat-7 ETM, Landsat-8 OLI and Google Earth satellite image data for 1984, 1994, 2004, 2014 and 2018 have been used for change detection analysis. In 1984, only a Miocene lignite quarry was excavated. Then, in 1994, operations for the excavation of feldspar–quartz and marble quarries were started, and from 2004 to 2014, mining activities significantly accelerated. An aerial image of 2018 shows that an area of about 3800 ha has been exploited by mining at 149 quarries. Considering access roads to quarries, plants and dam reservoirs, the human impact extends over 3800 ha. The study area is home to several archaeological sites and endemic biota. Thus, there is an urgent need for the relocation and protection of archaeological heritages and endemic biota by creating special zones. Additionally, the rich geomorphologic features in the study area that require millions of years to form are at risk of totally disappearing. [ABSTRACT FROM AUTHOR]

The shoreline is constantly under the influence of physical elements, such as geomorphological events, earthquakes, and tectonic movements. In recent years, it has been changing due to the intensive use of coasts and the impact of human factors on coastal areas. This study's aim was to analyze the coastal change in the Gulf of Izmit, Turkey, using satellite images from different dates. In this context, coastal changes were analyzed in a 95% confidence interval by the end point rate (EPR), linear regression rate (LRR), and weighted linear regression (WLR) statistical methods, after the automatic extraction of shorelines from Landsat satellite images of 17 periods belonging to different dates between 1975 and 2017 in four different scenarios. Furthermore, the effects of the destructive earthquake (Mw, 7.4), which caused the loss of many lives and property in the Gulf of Izmit on August 17, 1999, were also examined in detail.

Abstract: The adoption of satellite imagery in archaeology is well recognized today, and is rapidly increasing with the greater availability of high resolution sensors, in the past limited solely to military applications. The metrical use of declassified satellite images from the intelligence CORONA project, the first US operational space photo reconnaissance project operating in the period 1960–1972, has been verified within the framework of a multidisciplinary research project carried out by the University of Bologna, in collaboration with Istanbul University and Gaziantep archaeological museum, at the archaeological site of Tilmen Höyük (south-eastern Turkey) and its surrounding landscape. These images, characterized by a panoramic geometry, indeed represent an important historical resource for photointerpretation in archaeology, but their metrical use is difficult, owing to the severe distortions affecting the acquisition together with the problems of finding technical data related to the missions. In order to produce metric products of sufficient quality for mapping purposes, a high resolution scanned copy of a CORONA image of the relevant area was processed using commercial digital photogrammetric workstations operating with different approaches, whose aim was to evaluate the potential and specific problems connected to these kinds of data. A full orientation and digital triangulation of these images is possible if all the technical data on the sensor and the acquisition are well-known, and in any case the existence of well-distributed and reliable ground control points in the study area is crucial. The results obtained are interesting as they allow for integration, on a common cartographic reference base, with current aerial and satellite imagery (medium to very high geometric resolution) to perform automatic or visual change analysis procedures. [Copyright &y& Elsevier]