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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.

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.

Within the scope of the study, the change occurring in the vegetation cover was examined in different temporal and spatial scales for in the example of Kırka boron mining site. Two different satellite images, namely Landsat and Sentinel, were utilized while the competence of these two satellite images were analyzed. Unsupervised classification and vegetation indexes have been used from remote sensing techniques. It was observed that the mining area boundary could not be determined using vegetation indexes, whereas unsupervised classification techniques showed that the miningral site occupies 1144.5 ha area. NDVI and NDRe analyses were conducted to determine the vegetation change outside the mining area. It has been determined that the analyses performed with the NDVI (normalized difference vegetation index) and NDRe (normalized difference red edge index) indexes show different results on water surfaces, while they show similar results in rest of the areas. In NDVI analysis, it has been determined that the two satellite images gave similar results in NDVI analyses, while having higher spatial resolution, sentinel satellite images were able to capture more details.

Agriculture is the largest consumer of freshwater and plays a critical role in addressing global water scarcity. While numerous studies have focused on the water footprint (WF) of various agricultural products, little attention has been paid to changing cropping patterns and their impact on WF. Here, we investigate the impact of conversion from hazelnut fields to kiwi orchards on green, blue, and gray WF between 2010 and 2021 in Ordu, Turkey. Our results show a total increase of 803,901 tons WF for all green, blue, and gray WF. Compared to the previous situation, changing the agricultural product and growing kiwifruit on previously established hazelnut fields increases green WF by 372,106 tons and blue WF by 334,167 tons. Thus, the change of cultivation pattern could significantly contribute to the water scarcity in the area, and at the same time, the increase in WF. Although kiwi cultivation might be advantageous economically, this economic benefit might be an ecological disadvantage as kiwi production is highly dependent on limited blue water resources. Therefore, it is suggested to further promote the rain-fed product, the hazelnut.

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.

The aim of this study is to monitor the change of the existing vegetation in the area after the construction of wind power plants (WPPs) by using remote sensing (RS) and geographic information systems (GIS) technologies. The effects of WPPs on green areas have not been fully explored. The aim of this study is to expand on current knowledge and create a diagnostic model that shows the relationship between the turbines and the surrounding vegetation. All inventory data obtained within the scope of this study were compiled in GIS, and their relations in the field, the establishment dates, and numbers of WPPs were revealed. In this study, NDVI method was preferred. As a result, some negative changes were found in terms of slope and aspect according to CORINE (broadleaf forest degradation (BLF) and agricultural lands with natural vegetation degradation (NVA)) classes. While this study is a pioneer for studies in which WPP-related deterioration is made in terms of slope and aspect, it reveals the importance of GIS and RS. At the same time, using the Python programming language in common in GIS studies shows that making calculations with bulk data makes it easier to work in relatively large areas, especially for landscape planning studies.

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.

This study employs advanced technologies, specifically remote sensing (RS) and geographic information systems (GIS), to investigate the impact of earthquakes on critical infrastructure in Kahramanmaraş. Critical infrastructure encompasses physical and digital systems crucial for national security, economic stability, and public well-being. Disruption or failure of these interdependent systems, including energy, transportation, communication, water supply, healthcare, and emergency services, can have profound impacts on regional security and societal necessities. Protecting and prioritizing critical infrastructure during disaster response is vital for minimizing damage and expediting recovery. The study employs an innovative approach by integrating building damage assessment results with Point of Interest (POI) data to swiftly assess earthquake effects on critical infrastructure in Kahramanmaraş. Real-time earthquake vulnerability of 57 critical infrastructure elements in 15 POI categories is analyzed. Results indicate financial institutions and commercial areas as the most damaged POIs, while muster points exhibit the least damage. Historical facilities, health facilities, governmental institutions, road facilities, and sports facilities also show varying degrees of damage. Overall, 34% of critical infrastructure structures experienced damage. The proposed method offers a pragmatic approach for rapidly identifying damaged critical infrastructure POIs during disaster-based assessments, addressing a research gap.

The study presents a GIS- and RS-based diagnostic model to determine the changes in the existing vegetation in the Urla, Çeşme, and Karaburun peninsulas, Turkey, between 2002 and 2017 after the installation of 239 wind power plants (WPP). The vegetation changes in 7 CORINE land cover classes within the 0–1 km (facility zone) and 1–2 km (control zone) buffer zones were detected in relation with the slope and aspect groups using NDVI analysis. The highest amount of negative change in broad-leaved forests, coniferous forests, and land principally occupied by agriculture, with significant areas of natural vegetation, was detected in the 3–5% slope group, while pasture lands, sclerophyllous vegetation and transitional woodland-shrubs showed the highest degradation in 1–2% slope areas. Negative changes in complex cultivation patterns were found to be on the flat surfaces. Except for the pasture lands and sclerophyllous vegetation classes, the highest degradations were observed on north-facing aspects. In all land cover classes, the most degraded areas were found to be within the facility zone. The results and the proposed model are expected to facilitate planning and decision-making processes for locations with similar landscape characteristics.