Natural heritage lakes in semi-arid regions are highly vulnerable to groundwater depletion and drought, yet their long-term volumetric dynamics are rarely quantified where in-situ hydrometric observations are limited. To move beyond conventional two-dimensional (2D) surface-area monitoring, this study integrates Landsat-derived annual water boundaries, a centimeter-level Unmanned Aerial Vehicle (UAV)-based Digital Elevation Model (DEM), and groundwater drought indicators to reconstruct historical stage-area-volume dynamics in a small, data-scarce volcanic lake. The framework was applied to Meke Lake, T & uuml;rkiye, a groundwater-dependent closedbasin maar lake. Landsat imagery from 1985 to 2025 was processed using the Modified Normalized Difference Water Index (MNDWI). Valid annual water boundaries from 1985 to 2020 were used for DEM-integrated area-volume reconstruction, whereas the 2021-2025 images were used only to verify the post-desiccation condition because no spatially continuous surface water boundary was detected after 2020. The results show that lake surface area declined from approximately 0.49 km2 in the mid-1980 s to less than 0.06 km2 by 2020. Water storage decreased more sharply, from peak values exceeding 600,000 m3 to 7,447 m3 by 2020, corresponding to a loss of more than 98% of peak storage capacity. The 2008-2009 and 2013-2014 periods mark critical decline phases driven by nonlinear basin morphology. This results indicate persistent groundwater drought after approximately 2007, while trend and change-point analyses confirm statistically significant longterm decline in lake area, volume, and groundwater drought indicators. The integrated satellite-UAV framework provides a transferable approach for assessing volumetric degradation in small, data-scarce natural heritage lakes.

University campuses located within cities function as small-scale urban living areas and are expected to be designed to ensure easy and equitable access for all segments of society. In this context, in the first stage of the present study, the criteria identified from the literature on campus-scale accessibility were presented to experts for weighting. The weighted criteria were scored using the AHP method by an expert group across four commonly used pedestrian axes within the campus. In the second stage, a Geographic Information System (GIS)-based composite Accessibility Suitability Index (ASI) was applied across the entire campus pedestrian network. Then, this index was combined with the renormalized AHP weights. The analyses revealed that at Osmaniye Korkut Ata University, Axis 1 fell into the “medium” category, while the other three axes were classified as “low.” Improvement proposals were developed to address the deficiencies identified in the study area. These recommendations are expected to positively influence environmental factors and boost pedestrian use on the Karacaoğlan Campus of Osmaniye Korkut Ata University.

The decision-making process for the adaptive reuse of cultural heritage requires the evaluation of multiple criteria because of its multifaceted structure. The criteria determined through a literature review were weighted by experts and ranked according to their degree of importance via the DEMATEL method, which is a multicriteria decision-making technique. This study, conducted by integrating the importance levels of the criteria determined by the DEMATEL method with Geographic Information Systems (GIS) techniques, was applied to Yediocak Primary School, one of the significant buildings in Osmaniye, affected by the 2023 Kahramanmara & scedil; Pazarc & imath;k Earthquake and heavily damaged during the event. The DEMATEL analysis demonstrated that economic value, regional potential, and compatibility with the new function are the primary cause-group criteria, whereas architectural, cultural, and social values are predominantly situated within the effect group. The spatial assessment yielded a low suitability score for the current primary school function (0.3954). The hybrid DEMATEL + GIS index (0.2598) confirmed that a building's reuse as a high-occupancy school is constrained by seismic risk, its position on a heavily trafficked corridor, and relatively limited access to healthcare and emergency assembly areas. This study aimed to establish a new framework for the adaptive reuse of historic buildings.

In this study, shoreline changes in Lake Egirdir, T & uuml;rkiye, were investigated over a 34-year period (1990-2024) through the application of remote sensing, GIS-based coastal analysis (DSAS- Digital Shoreline Analysis System) and statistical trend methodologies. Landsat images and meteorological data are analysed together to evaluate the relationship between climate variability and shoreline dynamics. The findings show that there is a significant shoreline erosion, especially in the northern and western regions, with the End Point Rate (EPR) values reaching -23.6 m/year and Linear Regression Rate (LRR) values reaching -11.23 m/year. According to the results of statistical trend analysis methods (Mann-Kendall-MK and Spearman Rho-SR) and Innovative Trend Analysis (ITA) methods (sorted and unsorted), while the change in precipitation remained relatively constant, it was determined that the dominant factors contributing to water loss are significantly increasing temperature and potential evaporation (PET) trends. In addition, the most remarkable correlation between lake parameters and PET values is found. This research uniquely combines climate-induced hydrological stressors with shoreline displacement patterns, providing new insights into lake evolution compared to other studies. The results highlight the need for sustainable water management strategies and predictive modeling to mitigate future degradation.

The present investigation emphasizes on the meteorological drought characteristics of the Marathwada region using the Standardized Precipitation Index (SPI) method. Rainfall data of eight districts in the region from 1901 to 2021 have been investigated. The analysis revealed that the region experienced a significant 49 per cent drought with some districts, such as Hingoli and Nanded, facing more frequent droughts than precipitation events. The longest drought, spanning from 1918 to 1927, and the most severe drought years, including 1918, 1920, 1929, 1972 and 2015, underscore the severity of the situation. Notably, extreme drought (ED) and severe drought (SD) events were experienced between 2 per cent and 5 per cent, with mild drought (MD) and mild wet (MW) classes being the dominant classes. Spatial analysis using the Kriging method revealed that the southern and eastern parts of the region are at higher risk of drought. Trend analysis using Mann-Kendall (MK) and Spearman Rho (SR) tests showed an increasing drought trend. In contrast, there is a significant increase in the extreme drought class using the Innovative Trend Analysis (ITA) method. The spatial occurrence rates of extreme and severe droughts have increased since 2010, highlighting the urgent need for improved water resource management and climate adaptation strategies to reduce future drought risks.

In this study, the meteorological drought of the Mediterranean region, which represents the most significant risk area in Turkey regarding drought and forms the southern border, was evaluated using the Standardized Precipitation Index (SPI) method with precipitation data measured at 28 stations between 1970 and 2022. The data are divided into different periods: the first period (FP) between 1970 and 1996, the second period (SP) between 1997 and 2022, and all period (AP) between 1970 and 2022 to assess the change in meteorological drought in the region from the past to the present. Applying the run theory to the SPI series obtained in these periods, duration, severity, intensity, and peak values are calculated for wet and dry conditions. The Mann-Kendall (MK), Spearman-Rho (SR), and Innovative Trend Analysis (ITA) methods are applied to ascertain the change in the spatial occurrence of drought classes. The study reveals that drought exhibits distinct characteristics and occurrence rates in different regions of the study area, with notable differences between the western and eastern regions. The study region exhibits considerable variability in average duration and severity. The results indicated that extreme and severe drought-wet conditions have increased again after a 30-year interval. Furthermore, it is determined that the region is experiencing significant shifts in extreme wet and dry conditions and extreme events related to flooding and drought.

Significant morphological transformations resulting from open-pit mining activities always present major problems with site safety and slope stability. This study investigates an active marble quarry in Dinar, Türkiye by combining geospatial analysis and photogrammetry based on unmanned aerial vehicles (UAV). Acquired in 2024 and 2025, high-resolution images were combined with dense point clouds produced by Structure from Motion (SfM) methods. Iterative Closest Point (ICP) registration (RMSE = 2.09 cm) and Multiscale Model-to-Model Cloud Comparison (M3C2) analysis was used to quantify the surface changes. The study found a volumetric increase of 7744.04 m3 in the dump zones accompanied by an excavation loss of 8359.72 m3, so producing a net difference of almost 615.68 m3. Surface risk factors were evaluated holistically using a variety of morphometric criteria. These measures covered surface variation in several respects: their degree of homogeneity, presence of any unevenness or texture, verticality, planarity, and linearity. Surface variation > 0.20, roughness > 0.15, and verticality > 0.25 help one to identify zones of increased instability. Point cloud modeling derived from UAVs and GIS-based spatial analysis were integrated to show that morphological anomalies are spatially correlated with possible failure zones.

This study examines the climatic impacts of industrial activities in the & Scedil;anliurfa Organized Industrial Zone, Turkey. It employs data from the Sentinel-5P satellite and Terra Climates dataset to analyse atmospheric methane concentrations and other climate parameters, including temperature, wind speed, and shortwave radiation, from 1958 to 2023. Statistical methods indicate significant changes in these parameters, suggesting a strong correlation between industrial activities and local climate variability. Results reveal a 1.8 degrees C increase in maximum temperature (p < 0.01), a 12% decline in wind speed (p < 0.05), and a continuous rise in atmospheric methane concentration, indicating a statistically significant industrial impact.

This study examined the historical shoreline evolution and predicted future changes for the Yeşilırmak Delta in Turkey, spanning from 1985 to 2024, with projections extending to 2044. The Yeşilırmak Delta has undergone substantial erosion due to a combination of factors, including sea-level rise and reduced sediment transport resulting from dam constructions along the river. The temporal shoreline changes were analyzed using Landsat satellite imagery and the Digital Shoreline Analysis System (DSAS), employing the Modified Normalized Difference Water Index (MNDWI) and object-based classification methods. In addition to the shoreline analysis, a comprehensive examination of streamflow data from 1985 to 2020 was conducted to assess the reduction in sediment transport. This analysis revealed a significant decline in flow rates at key stations, which exhibited a strong correlation with increased coastal erosion. The study forecasts future shoreline changes for 2034 and 2044, emphasizing the pressing need for effective coastal and water resource management strategies to mitigate further degradation and safeguard this vital ecosystem.

This study was conducted to determine the impact of the Covid-19 on road traffic accidents across Turkey's primary roads from 2015 to 2022. The accidents were analyzed within a space-time cube, characterized by spatial bins of 5 and 10 kilometers and temporal steps of 1 and 3 months using Emerging Hot Spot Analysis. The temporal behavior of the data was evaluated using the Mann-Kendall and Sen's slope methods, while stationarity using the Augmented Dickey-Fuller. On national scale, there was a significant decrease in accidents by 27.19% from 2019 to 2020, in injuries by 29.68%, and in deaths by 14.39%.

Urban forests are very important for the environment and for people, especially in semi-arid cities where there is not much greenery. This makes heat stress worse and makes the city less livable. This paper presents a comprehensive geospatial methodology for selecting afforestation sites in the expanding semi-arid urban area of Şanlıurfa, Turkey, characterized by minimal forest cover, rapid urbanization, and extreme weather conditions. We identified nine ecological and infrastructure criteria using high-resolution Sentinel-2 images and features from the terrain. These criteria include slope, aspect, topography, land surface temperature (LST), solar radiation, flow accumulation, land cover, and proximity to roads and homes. After being normalized to make sure they were ecologically relevant and consistent, all of the datasets were put together into a GIS-based Multi-Criteria Decision Analysis (MCDA) tool. The Analytic Hierarchy Process (AHP) was then used to weight the criteria. A deep learning-based semantic segmentation model was used to create a thorough classification of land cover, primarily to exclude unsuitable areas such as dense urban fabric and water bodies. The final afforestation suitability map showed that 151.33 km2 was very suitable and 192.06 km2 was suitable, mostly in the northeastern and southeastern urban fringes. This was because the terrain and subclimatic conditions were good. The proposed methodology illustrates that urban green infrastructure planning can be effectively directed within climate adaptation frameworks through the integration of remote sensing and spatial decision-support tools, especially in ecologically sensitive and rapidly urbanizing areas.

Within the contemporary urban development discourse, the paradigm of smart cities has gained prominence over the past two decades. Ensuring sustainability in smart cities requires coherent orchestration of processes that span design, construction, operations, and management. Central to this orchestration are technologies such as Building Information Modeling (BIM), which provides detailed architectural data, and Geographic Information Systems (GIS), which provide comprehensive geographic intelligence. However, a significant challenge remains: data degradation during BIM-GIS integration. This data inconsistency, exacerbated by the different data structures of BIM and GIS, is a barrier to true interoperability. One promising solution to this conundrum is the use of Semantic Web technologies. In this study, we leverage Semantic Linked Data and geometric conversion tools to develop an algorithm that mitigates the loss of semantic information during the BIM-to-GIS conversion process. The effectiveness of this approach is underscored by a 95% accuracy rate of the converted semantic information.

This paper proposes a multiscale dynamic correlation framework based on time-dependent intrinsic correlation (TDIC) to investigate the teleconnections between reconnaissance drought index (RDI) and standardized precipitation index (SPI). The SPI and RDI indices were calculated at 3-, 6-, and 12-month time scales using data from six stations in the & Ccedil;oruh and Aras (CA) basins in Turkey for the 1969-2020 period. The spatial variability of the evaluation of the drought class demonstrated that, with the exception of the northwestern region (Bayburt station), the correlation between RDI and SPI exceeded 97% and the difference in occurrence between drought classes is found to be marginal. In the multiscale analysis of the two indices, firstly, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN) was employed to decompose the series. The modes of RDI and SPI at different process scales exhibited a strong positive linear correlation, however, the association in their long-term trends may not be of the same nature. The TDIC analysis captured the long-range correlations between the modes of RDI and SPI at diverse process scales, and the newly proposed average significant correlation (ASC) measure could quantify their strength. Within the basin, there exists strong interconnections between SPI and RDI, with the exception of the observed alterations in their nature and strength at short time spells in some inter-annual scales. The proposed TDIC based framework is a generic and robust alternative for multiscale investigation studies while ASC can quantify the strength of non-linear associations at distinct process scales.

İklim değişikliğinin ani taşkınlar üzerindeki etkisi, yağış sıklığı ve yoğunluğunda, kar erimesinde ve atmosferik dolaşım düzenindeki değişikliklerde kayda değer bir artışla birlikte son yıllarda giderek daha belirgin hale gelmiştir. Sıcaklıklar yükseldikçe atmosferik nem tutma oranı artmakta ve bu da aşırı hava olaylarının yaşanma olasılığının artmasına neden olmaktadır. Bu süreçlerin tarım, altyapı ve insan yaşamı üzerinde önemli ekonomik ve sosyal etkileri vardır. Türkiye'de Karadeniz Bölgesi ve Akdeniz Havzası, coğrafi ve iklimsel özellikleri nedeniyle ani taşkınların olumsuz etkilerine karşı özellikle hassastır. Ani taşkınların yıkıcı potansiyeli, sağlam bir yerel altyapının yokluğunda meydana geldiklerinde daha da artmaktadır. Bu çalışma, iklim değişikliğinin tetiklediği ani taşkınların mekanizmalarını detaylı bir şekilde incelemektedir. Bölgesel yorumlamalar ve vaka çalışmalarıyla desteklenen çalışma, sürdürülebilir arazi kullanımı, yeşil altyapı uygulamaları ve erken uyarı sistemlerinin risk yönetimindeki önemini vurgulamaktadır. Hem çevresel hem de insani kayıpları en aza indirmek için doğal taşkın koruma stratejilerinin ve teknolojik yeniliklerin kullanılması zorunludur. Çalışma, iklim değişikliğiyle mücadelenin yalnızca teknik bir mesele değil, aynı zamanda sosyal bir sorumluluk olduğunu ortaya koymaktadır.

In this study, the coastal changes in Hersek Lagoon, have occurred in the 5 years (2015 to 2020) were examined. With the Modified Normalised Difference Water Index (MNDWI), the water areas in the lagoon were determined by object-based classification. When the changes are examined on a regional basis, it is seen that there are erosion and accretion areas were observed. Pearson's r value was examined between End Point Rate (EPR) and Linear Regression Rate (LRR) and a high correlation such as r = 0.921 was obtained. The maximum coastal change was observed as 18.74 m/year with EPR and 18.62 m/year with LRR.

Bu çalışma, Türkiye’nin Akdeniz Bölgesi’nde yer alan Mersin ili örneğinde, tarımsal sulama göletlerinin bölgesel iklim, bitki örtüsü ve su kaynakları üzerindeki uzun vadeli etkilerini uydu görüntüleri ve meteorolojik verilerle incelemektedir. Çalışmanın metodolojisi, 1985-2023 yılları arasındaki Landsat uydu verileri ile meteorolojik veri setlerinin entegrasyonuna dayanmaktadır. Arazi sınıflandırması için nesne tabanlı görüntü işleme teknikleri kullanılarak bitki örtüsünün ve su kütlelerinin değişimleri haritalanmış, sınıflandırma doğruluğu hata matrisi ve farklı doğruluk metrikleriyle değerlendirilmiştir. Ayrıca, kuraklık analizinde Standartlaştırılmış Yağış İndeksi kullanılarak Mann-Kendall, Spearman Rho ve Sen Slope gibi trend analiz yöntemleriyle kuraklık eğilimleri incelenmiştir. Sonuçlar, 1985 yılında 51 olan sulama göleti sayısının 2023'te 1935’e çıktığını ve bu artışın NDVI değerlerindeki yükselişle birlikte bitki örtüsünün korunmasına katkı sağladığını ortaya koymaktadır. Kuraklık analizleri, çalışma bölgesinde kurak dönemlerin bitki örtüsüne olan olumsuz etkilerinin sulama göletleri sayesinde azaldığını göstermektedir. Özellikle mikro iklim üzerinde düzenleyici bir etkisi olan bu göletler, yarı kurak bölgelerde tarımsal üretim sürdürülebilirliği için stratejik bir su yönetimi aracı olarak değerlendirilmektedir. Bu bulgular, tarımsal sulama göletlerinin su kaynaklarının sürdürülebilir yönetimi, iklim değişikliği ile mücadele ve çevresel direnci artırma gibi konularda önemli bir potansiyele sahip olduğunu göstermektedir. Bu kapsamda, yarı kurak ve kurak bölgelerde sulama göletlerinin sayısının artırılması ve bu yapıların planlamasında iklim dostu yaklaşımlar benimsenmesi önerilmektedir.

Eser, iklim değişikliğinin farklı boyutlarını ele alarak, küresel bir kriz olan iklim değişikliğine karşı hem teorik hem de pratik çözümler sunmayı amaçlamaktadır. Bu amaça yönelik olarak da toplumsal hareketlerden teknolojik yeniliklere, çevresel ve ekonomik etkilerden sanatsal yaklaşımlara kadar geniş bir perspektifle hazırlanmıştır. Eserde yer alan bölümler, iklim krizine dair çok yönlü bir analiz sunmaktadır. Toplumsal hareketler ve sosyal değişim bağlamında, yeni dalga iklim aktivizminin yükselişi ve feminist perspektiften iklim adaleti teorileri ele alınmaktadır. Teknolojik çözümler kapsamında, yapay zekâ destekli geri dönüşüm sistemleri ve karbonsuz yaşam için biyomimikri ve ahşap tasarım gibi yenilikçi yaklaşımlar tartışılmaktadır. Ekonomik ve sosyal etkiler, iklim değişikliğinin toplumsal yapılar üzerindeki etkileri ve krizle birlikte ortaya çıkan girişimcilik fırsatları üzerinden incelenmektedir. Ayrıca, eser, çevresel tarih ve estetik gibi konuları da kapsayarak, ekolojik sanat ve iklim estetiği gibi yaratıcı çözümleri gündeme getirmektedir. Doğal afetler ve çevresel etkiler bölümlerinde ise iklim değişikliğinin sel, taşkın ve bitki koruma yöntemleri üzerindeki etkileri detaylandırılmıştır. Yönetişim ve politika perspektifi, çok düzeyli kentsel yönetişim ağları ve çevresel performans ile kurumsal kalite ilişkileri gibi konular üzerinden değerlendirilmiştir. Disiplinler arası bir bakış açısıyla kaleme alınan bu kitap, farklı alanlardan akademisyenlerin katkılarıyla hazırlanmıştır ve bilimsel bir rehber niteliği taşımaktadır. İklim değişikliği ile mücadelede hem bireysel hem de toplumsal farkındalık yaratmayı hedefleyen bu eser, okuyucularına küresel sorunlara yönelik bütüncül çözümler sunmaktadır. Bu kitap, araştırmacılar, karar alıcılar, öğrenciler ve konuya ilgi duyan herkes için değerli bir kaynak olma özelliği taşımaktadır. İklim kriziyle mücadelede bilgi birikimini artırmayı ve daha sürdürülebilir bir geleceğe katkı sağlamayı amaçlayan bu çalışma, insanlık için ortak bir gelecek inşa etme bilincine hizmet etmektedir.

Bu çalışma, kentsel ortamlarda üç boyutlu (3B) taşkın simülasyonu için coğrafi bilgi sistemleri (CBS) ve LiDAR teknolojilerinin uygulanmasını ve buna ilişkin risklerin belirlenmesini incelemektedir. Bu çalışmada, akış yönlerini, havza bölgelerini ve olası taşkın bölgelerini belirlemek için LiDAR verilerinden ve bir sayısal yükseklik modeli (SYM) kullanılmıştır. Akış yönlerini belirlemek için D8 algoritması kullanılmış ve şiddetli yağış (200 mm) koşulları altında taşkın simülasyonları gerçekleştirilmiştir. Modeller, taşkınların bölgesel sonuçlarını ve kentsel altyapı üzerindeki zararlı etkilerini ortaya koymuştur. Çalışmanın bulguları, afet müdahale planlaması yapan karar vericilere stratejik rehberlik sağlamakta ve risk bölgelerinin hızlı ve kesin bir şekilde belirlenmesini kolaylaştırmaktadır. Bu çalışmanın bulguları, gelişmiş CBS ve simülasyon araçlarının etkin taşkın riski yönetimi ve önleme yöntemlerinin geliştirilmesi ve uygulanmasında etkili bir şekilde kullanılabileceğini göstermektedir.

It is important to determine car density in parking lots, especially in hospitals, large enterprises, and residential areas, which are used intensively, in terms of executing existing management systems and making precise plans for the future. In this study, cars in parking lots were detected using high-resolution unmanned aerial vehicle (UAV) images with deep learning methods. We tested the performance of the two approaches by determining the number of cars in a parking lot using the You Only Look Once (YOLOv3) and Mask Region–Based Convolutional Neural Networks (Mask R-CNN) approaches as deep learning methods and the deep learning tool of Esri ArcGIS Pro. High-resolution UAV images were processed by photogrammetry and used as input products for the R-CNN and YOLOv3 algorithm. Recall, F1 score, precision ratio/uncertainty accuracy, and average producer accuracy of products automatically extracted with the algorithm were determined as 0.862/0.941, 0.874/0.946, 0.885/0.951, and 0.776/0.897 for R-CNN and YOLOv3, respectively.

Deniz gözetiminde gemilerin tespiti, önemli pratik uygulamaları olan temel bir araştırmadır. Bu çalışmada Sentinel-1 verilerinin ve Faster R-CNN algoritmalarının gemi tespiti için kullanımını araştırdım ve %86.11 doğruluk elde ettim. Faster R-CNN algoritması, görüntülerdeki nesneleri algılamada olağanüstü performans sergileyen, derin öğrenmeye dayalı bir nesne algılama çerçevesidir. Sentinel-1, Avrupa Uzay Ajansı tarafından işletilen ve hassas mekansal çözünürlüğe sahip Sentetik Açıklıklı Radar (SAR) görüntüleri sağlayan ve onu gemi tespit uygulamaları için çok uygun hale getiren bir radar uydusudur. Önerilen metodoloji, doğru gemi tespiti için Sentinel-1 verilerini Faster R-CNN algoritması ile birleştirmenin etkinliğini göstererek, deniz gözetimi ve gemi trafiği yönetimindeki pratik uygulamalar için potansiyeli vurgulamaktadır. Çalışmanın sonuçları, deniz taşımacılığının emniyet ve güvenliğinin iyileştirilmesine katkıda bulunabilir ve denizcilik alanındaki çok çeşitli operasyonel ve araştırma faaliyetlerini desteklemeye yardımcı olabilir.