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Coastal areas all over the world are usually exposed to intensive change and transformation processes resulting in a variety of natural, physical, and socio-economic problems. Körfez province, located along the İzmit Bay of Marmara Sea, Turkey, has been one of these coastal areas that has become a major point for industrial facilities and highly populated urbanized area since 1960s. Therefore, the analysis of the spatial changes in the land use patterns of the province has a critical role in the success of the physical planning processes and the protection of the coastal areas. In order to highlight this critical role, temporal change detection analysis for Körfez province covering a 6-year period and 5 land use classes was made using 2009 and 2015 SPOT imagery and thematic maps. Reclassified CORINE data, development plans, and land survey results were benefited for the supervised classification of the images. Four hundred eighty control points for each year were used to achieve a strong accuracy tested by Kappa coefficient. The spatio-temporal change detection results revealed a 22.5% and 2.3% decrease in agricultural lands and sea areas respectively, while there was an increase of 16.6% in forest-green areas, 6.4% in settlement areas, and 74.1% in lake areas. The results are believed to be significant input data to facilitate coastal and physical development planning over the area, and thus make sustainable land use decisions to protect the delicate landscape and coastal characteristics, while providing a sound risk management plan.

Forest fires globally cause severe losses in vegetation, soil and habitats and inevitably have direct and indirect negative environmental impacts such as deforestation, climate change and drought. According to the official records, there has been an increase of 58% in the number of the forest fires in Turkey in the last 30 years, between 1988 and 2018. Therefore, it is vital to determine the forest fire risks in the country and develop more effective methodologies to mitigate them. From this point, in the first phase, forest fire risk map of Kütahya-Ören region was prepared via the analyses of a variety of spatial data using geographical information system capabilities. The visibility analysis for the current fire towers was also performed. The results showed that very-high and high-risk, moderate-risk and low-risk zones respectively comprised 36.86%, 60.39% and 2.76% of the total study area, and 82.8% of the region was visible from the towers. In the second phase of the study, remote sensing methods were utilized for the detection of the areas burned in October 2001 in Ören-Çamdibi region, which was officially recorded as 4 hectares. The results revealed that the actual amount of the burned area was 5.6 hectares, and 83% of the burned surfaces was classified as moderate-risk areas in the fire risk map, while 17% of it was that of very-high and high-risk zones.

Urban Atlas (UA) data covering the large urban areas have been produced by the European Environment Agency for a variety of European countries including Turkey since 2006. The use of the UA data for the determination of spatiotemporal land use and density changes in urban areas. UA data of Eskisehir, Turkey, were used in order to detect the spatiotemporal changes between 2012 and 2018. CORINE data–based change detection and NDVI analysis were also made and compared with the results obtained from the UA data. The results based on the UA data revealed that the artificial surfaces in the study area increased by 17.65% and there was a 18.32% increase in the total amount of agricultural lands, natural lands, forests, and vegetation. Although CORINE data–based analyses showed a similar trend in land use/land cover changes, the amount of changes between 2012 and 2018 in CORINE and UA data–based analyses were found to be 4.99% and 17.55%, respectively. A 9.30% mismatch between the UA changes and NDVI difference data was also calculated. Research findings revealed that the utilization of the UA data in the urban territories would be advantageous especially in planning processes to detect and compare the changes in the artificial and non-artificial surfaces and NDVI analysis would be very supportive to control and compare the results. It is also concluded that this study may be a useful model to monitor the cities in accordance with the 2030 and 2050 policies of European Council on Land Use, Land Use Change and Forestry.

Wind energy is considered one of the cleanest and most sustainable resources among renewable energy sources. However, several negative environmental impacts can be observed, unless suitable sites are selected for the establishment of wind farms. The aim of this study is to determine the change in the soil organic carbon (SOC) stock resulting from land cover changes that were caused by wind farm establishments in the Karaburun peninsula. Within the scope of the study, remote sensing and geographic information system technologies were utilized. Maximum likelihood algorithm, one of the supervised classification techniques, was used to classify the land cover, and Normalized Difference Vegetation Index (NDVI) analyses were performed to determine land cover changes. The findings were correlated with the “Turkey Soil Organic Carbon Project” data. As a result, depending on the establishment of wind farms in the Karaburun Peninsula, a total decrease of 18,330.57 tons of SOC in the study area between 2000 and 2019 was determined. It should be taken into consideration that besides many other negative effects (effects on human health, effects on the ecosystem, effects on animals, etc.), land cover changes caused by wind farms may indirectly cause important problems such as climate change. Recently, this situation shows that there is an important dilemma in terms of current implementations. Wind farms are the most invested renewable energy sources and alternative energy supply to fossil fuels in terms of preventing climate change. However, the results of this study have reviewed that lack of proper approaches and methods to establish wind farms may result in various problems such as physical, chemical, and biological degradations and an increase in the amount of atmospheric carbon. Consequently, the investments in renewable energy sources should be comprehensively reevaluated in terms of current technologies, quality in the scope of environmental impact assessment and strategic environmental assessment processes, legal regulations and national policies, long-term environmental costs, etc.

The study investigates the impact of the land use changes on the urban heat island effect ratio (UHIER), focusing on the urban development fringe of Ankara, Türkiye. Initially characterized by rural land uses the areas has experienced significant transformations into residential estates, mostly including high-rise blocks and low-rise villas. Urban development patterns in 2013 and 2023 were compared with changes in UHIER and local climate zone classes (LCZCs) using RS and GIS techniques for UHIER calculation, and the World Urban Database and Access Portal Tools (WUDAPT) protocol for LCZ mapping. Overall, UHIER values have a tendency to rise, as areas with increaing UHIER are found to be twice as large as those with decreasing UHIER. Increasing UHIER is highly associated with increases in open high-rise and sparsely built areas, accompanied by decreases in low plants. UHIER, on the other hand, is mosly characterized by a reduction in large low-rise built-types. The parts where UHIER remains unchanged suggests that although compact high-rise, open high-rise, and sparsely built areas have increased, the reduction in other built types—particularly large low-rise areas—along with a rise in tree density, appears to balance these changes. Therefore, to prevent high UHI impact when the area is fully developed, more landscaping features, particularly trees, can be integrated and mid-rise and low-rise developments can be preferred over high-rises, ensuring the efficient land use.

Geographical information systems (GIS) facilitate both current landslide mapping processes and the prediction of potential landslides that may be experienced in the future. Within the scope of the study, landslide susceptibility maps were created to reduce the damage of possible landslides in the Karaburun Peninsula of İzmir province. A landslide inventory map was produced from related databases in the first place, followed by the creation of parameter maps (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers, and distance to fault lines). The frequency ratio (FR) method was utilized for producing the landslide susceptibility maps on a 5-level risk scale ranging from very low to very high-risk categories. Receiver operating characteristic (ROC) analysis was performed for accuracy testing. The resulting landslide susceptibility map revealed that 3% and 46% of the study area had high- and medium-risk categories, and the low landslide risk areas comprised 47% of the region. These results provide important inputs to guide sustainable strategic and physical planning processes in the region, which has been declared a special protection area and is a popular destination for tourism activities and energy facilities.

Demography researchers and scientists have been effectively utilizing advanced technologies and methods such as geographical information systems, spatial statistics, georeferenced data, and satellite images for the last 25 years. Areal interpolation methods have also been adopted for the development of population density maps which are essential for a variety of social and environmental studies. Still, a good number of social scientists are skeptical about such technologies due to the complexity of methods and analyses. In this regard, a practical intelligent dasymetric mapping (IDM) tool that facilitates the implementation of the statistical analyses was used in this study to develop the population distribution map for the Istanbul metropolitan area via night light data provided by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the census records of the study area. A population density map was also produced using the choropleth mapping method to enable to make a comparison of the traditional and intelligent population density mapping implementations. According to the dasymetric population density map, 38.5% of the study area fell into sparse density category while low, moderate, high, and very high population density class percentages were found to be 9.4%, 5.5%, 2.9%, and 0.1% respectively. On the other hand, the percentages of the same population density classes ranking from sparse to very high in the choropleth map were determined to be 90.7%, 7.3%, 1.7%, 0.3%, and 0%. In the change analysis made as a result of the classification, the changes between the city area and the population were revealed. During this period, the city area and population grew. Spatial change has also been interpreted by comparing it with population changes. There appears to be a remarkable increase in both surface area and population. It is observed that the increase is especially in the south and northwest of the city. With the population increase, the number of new residential areas has increased. It is thought that behind this growth, there are different reasons besides the effect of the increase in residential areas. When the environmental awareness of people has increased more than in the past centuries, new solutions should be produced in order to be more controlled, smart, and sustainable while planning the cities of the future. Considering that the development of technology and remote sensing techniques is progressing in parallel with this technology, this study in which GIS technologies integrated with satellite images are used, it is thought that it will contribute positively to the studies in this area in terms of regular development of urban areas, increasing the opportunity to make fast and correct decisions, and creating infrastructure for studies such as monitoring and prevention of illegal housing.

Kayseri, a densely urbanized province in Türkiye, grapples with pressing challenges of air pollution and limited green spaces, accentuating the need for strategic urban planning. This study, utilizing Landsat 8 and Landsat 9 satellite imagery, investigates the evolution of land surface temperatures (LST) and urban heat island (UHI) effects in key districts—Kocasinan, Melikgazi, Talas, and Hacılar—between 2013 and 2022. This research has been complemented with an analysis of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI), exploring correlations among the LST, UHI, NDVI, and NDBI changes. The findings indicate that a significant portion (65% and 88%) of the study area remained unchanged with respect to the NDVI and NDBI differences. This research’s findings reveal that a substantial portion (65% and 88%) of the study area exhibited consistency in the NDVI and NDBI. Noteworthy increases in the NDVI were observed in 20% of the region, while only 4% exhibited higher NDBI. Strikingly, the UHI displayed strong negative correlations with the NDVI and robust positive correlations with the NDBI. The LST changes demonstrated a reduced temperature range, from 21 to 51 °C in 2013, to 18 to 40 °C in 2022. Localized environmental factors, notably at the National Garden site, showcased the most significant temperature variations. Notably, the UHI exhibited strong negative correlations with the NDVI and strong positive correlations with the NDBI. The study’s results emphasize the interplay among the NDBI, LST, and UHI and an inverse relationship with the NDVI and NDBI, LST, and UHI. These findings hold implications for urban planning and policymaking, particularly in the context of resilient and sustainable land use planning and the UHI mitigation. This research underscores the intricate interplay among the NDBI, LST, and UHI, highlighting an inverse relationship with the NDVI. These findings hold crucial implications for resilient and sustainable urban planning, particularly in mitigating the UHI effects. Despite limited vacant spaces in Kayseri, geospatial techniques for identifying potential green spaces can facilitate swift UHI mitigation measures. Acknowledging Kayseri’s complex dynamics, future research should delve into the UHI responses to urban morphology and design, extending this methodology to analyze the UHI effects in other Turkish cities. This research contributes to a broader understanding of UHI dynamics and sustainable urban planning practices, offering valuable insights for policymakers, urban planners, and researchers alike.

Mining activities degrade the landscape and ecosystems by introducing new land uses that alter soil characteristics. Mapping of this degradation is critical, particularly in the context of environmental protection, including climate change research. Even though mining provides significant industrial and economic benefits to society, it also decreases soil organic carbon (SOC) stocks and increases atmospheric carbon levels. This study aims to develop a practical method for determining the changes in SOC in the Altintepe Gold Mine, Ordu, Turkey, due to land cover changes caused by the mining process. 2013 and 2021 Landsat 8 images and CORINE data were used to map the land cover of the study area. NDVI analyses were conducted to detect land cover and SOC stocks, while the NDWI method was used to identify the water surfaces. SOC stock changes on the changed lands were calculated compared to the Turkey Soil Organic Carbon Stock Project database. The results showed that 109.85 ha of forest and 5.30 ha of agricultural land, corresponding to 4450.82 tons of SOC loss, were destroyed in the research area. Since the alterations in SOC levels are commonly determined by verifying remote sensing based analysis results with the site surveys, it becomes quite challenging to conduct such research in areas like Altintepe due to site access restrictions or data unavailability. From this point, this study presents a practical and alternative approach that avoids the necessity of fieldwork and provides a quick SOC change estimation based on the comparison of available data. The results are expected to provide a comprehensive and holistic perspective for the future operations and management of the mine sites and the surrounding environments.

Forest fires are highly destructive phenomena in both ecological and economic terms. Therefore, it is significant to develop measures to detect and mitigate them. In this study, the forest fire risk map of the Milas district of Turkey was studied using geographical information systems and remote sensing methods. In the first part of the study, the forest fire risk map of the area was developed via a weighted overlay technique with analysis of stand characteristics, topographic features, distance from intermittent streams and built-up environment. According to the resulting forest fire risk map, extremely low-, low-, medium-, high- and extremely high-risk classes covered 0%, 0.5%, 65%, 30% and 0.5% of the forested areas in Milas district of Turkey, respectively. In the second part, the location of a major forest fire, which took place in 2007 in the study area, was determined using the normalized difference vegetation index, the normalized burn ratio, and the burn area index. When compared with the forest fire risk map, it was revealed that 45% of the burned areas in 2007 fell into the high-risk class, while 51% of it was from the extremely high-risk zones. Moreover, the forest risk map was compared with eleven forest fire cases between 2013 and 2019. The results show that eight of these fires took place in high-risk territories. According to these results, it was concluded that the created risk map coincides with the fire incidents.