Nebiye Musaoğlu

While urban spatial structure and land-use distribution play an important role in shaping greenhouse gas (GHG) emissions, their sector-specific effects remain insufficiently understood in comparative urban studies. This study develops a scale-independent comparative framework to examine how functional land-use distribution, spatial configuration, alternative density representations, and broader economic and climatic context jointly shape per capita residential and transport emissions across 104 European cities. Using city-level GHG inventories reported to the Carbon Disclosure Project and harmonized CORINE land cover data, residential and transport emissions are analyzed separately through a grouped regression framework that systematically compares alternative variable combinations and identifies supported models using the Akaike information criterion. The results show that residential emissions are explained by multiple structural regimes, indicating that similar per capita outcomes can arise from different combinations of land-use distribution and urban spatial structure, whereas transport emissions converge on a single dominant regime reflecting a more stable configuration of land-use specialization, urban spatial structure, and population density. Across the supported models, 5 of the 21 landscapee metrics evaluated were significant for residential emissions and 3 for transport emissions. The findings demonstrate that urban emissions emerge from integrated structural configurations rather than isolated spatial indicators, underscoring the need for multivariate and sector-sensitive approaches in comparative urban climate research.

As lakes contribute significant amounts of methane (CH₄) to the atmosphere, they account for a significant share of the global greenhouse gases (GHGs) budget. Since lakes are ecosystems where physical and biological processes influencing CH₄ formation are concentrated, the study focuses on atmospheric CH₄ column concentrations over lake areas. This study aims to analyze the temporal variation in atmospheric CH₄ column concentrations (XCH₄) over Lake Eğirdir and Lake Burdur in Türkiye in 2023 and 2025 as well as the relationship between XCH₄ and environmental parameters such as Water Surface Temperature (WST), Normalized Difference Chlorophyll Index (NDCI), and Floating Algae Index (FAI). The temporal variability of XCH₄ observed over both lakes showed statistically significant positive correlations with lake-area-averaged WST, NDCI, and FAI (Pearson r = 0.49–0.65, p < 0.01). This outcome indicates consistent temporal patterns between XCH₄ and environmental conditions at the lake scale. Furthermore, time-series graphs show that monthly average XCH₄ values in both lakes reached their highest levels during the summer and autumn months. During these periods, XCH₄ concentrations exceeded 1860 ppb in Lake Eğirdir and 1900 ppb in Lake Burdur. The areas of land use/land cover (LULC) classes surrounding the lakes were evaluated together with XCH₄, and relatively higher XCH₄ values were observed over agricultural areas, which constitute the dominant class in the basins of both lakes. The distribution of XCH₄ throughout the lake depth showed higher values in the shallow and mid-depth zones and lower values in the deeper areas beyond 20 m, indicating that the distribution of XCH₄ varies throughout lake depth. The results obtained underline the importance of remote sensing data in monitoring XCH₄ in lake ecosystems.

Günümüzde veri hacminin ve çeşitliliğinin hızla artması, çok boyutlu veri yönetimi ve analizi alanında yenilikçi yöntemlerin geliştirilmesini gerekli kılmaktadır. Veri küpü teknolojisi bu konuda geliştirilmiş, çözüme yönelik kullanım hızı ve kolaylığı ön gören en güncel yöntemlerden biridir. Çok boyutlu mekânsal ve zamansal verilerin belirgin kullanım senaryoları dahilinde yapılandırılması, analiz edilmesi ve görselleştirilmesi süreçleri, bu teknolojinin “coğrafi veri küpü” tanımına evrilmesiyle anlamlı bir uygulama alanı kazanmıştır. Bu çalışmada, veri küpleri ve veri küpü tasarımının nasıl yapılacağı açıklanmıştır. Ayrıca Doğal Deneyler Kullanarak Kentsel Gelişim Keşifleri (Urban Development Explorations using Natural Experiments-UDENE) projesi kapsamında uydu görüntüleri ve coğrafi veri temelinde geliştirilen coğrafi veri küpü örnek olarak sunulmuştur. Geliştirilen platformda farklı uydu görüntülerinin tek bir veri küpü çatısı altında saklanması ve indekslenmesi sağlanmış; veri ön işleme, sorgu performansı ve sıkıştırma stratejileri açısından elde edilen sonuçlar değerlendirilmiştir. Bulgular, veri küpü yaklaşımının hem veri saklama verimliliği hem de çok boyutlu sorgulama performansı bakımından geleneksel yöntemlere kıyasla önemli ölçüde avantaj sağladığını göstermektedir. Bununla birlikte, gerçek zamanlı veri işleme ve donanım altyapısı gereksinimleri, teknolojinin yaygın olarak benimsenmesi önünde hâlâ çözülmesi gereken bazı sınırlılıklar olarak belirlenmiştir. Çalışmanın sonunda, gelecekteki araştırmalarda makine öğrenimi ve bulut bilişim mimarileriyle bütünleşik veri küpü sistemlerinin geliştirilmesine ve veri analitiği uygulamalarında çok boyutlu veri işleme yeteneklerinin genişletilmesine ilişkin öneriler sunulmuştur.

This study examines key features of natural lakes within a Lake Information Database (LID) and develops a lake assessment methodology (LAM) to identify the primary drivers of temporal water surface area (WSA) changes across Türkiye. The dominating driver for WSA changes is attributed either to natural factors or to human activities through harmonised analysis utilising LAM, which brings novelty to this study. Three lakes among the 27 with surface area over 2000 ha are displayed and discussed to show the applicability and performance of LAM that utilises remote sensing (RS) and geographical information system (GIS). The dominating factor on WSA decrease in the lakes was seen as climatic conditions in the higher altitude lakes, and human activities in the lower altitude lakes pointing out the key strength of the research. Thus, LAM offers a globally applicable and flexible methodology that produces fast results regarding the main drivers of WSA.

Road networks are crucial to urban infrastructure and significantly affect transportation, traffic management, and emergency response. Besides, accurate mapping is essential for detecting road networks effectively, but traditional methods like manual digitization and field surveys often struggle in fast-changing urban environments. Remote sensing and deep learning techniques have emerged as effective alternatives, although initial road segmentation faced challenges such as limited image resolution. Recent advances in satellite technology have alleviated these issues by providing ultra-high-resolution (sub-meter) imagery, which is vital for accurately representing road networks. Deep learning models like U-Net have enhanced road segmentation by accurately capturing complex features. This research examines the effectiveness of multi-resolution satellite imagery for road segmentation. This study aims to analyze the accuracy assessment of road segmentation using Sentinel-2 imagery (10 m resolution) and ultra-high-resolution Pléiades Neo imagery (sub-meter resolution). Ground truth data from the Google Maps API were used for validation. Among the tested resolutions, Pléiades Neo at 30 cm achieved the highest accuracy, with an F-score of 0.87. Pléiades Neo at 15 cm resolution followed closely with an F-score of about 0.85. Pléiades Neo at 1 m resolution (upscaled data) showed a moderate decline (F-score of 0.82), while Sentinel-2 had the lowest performance (F-score of 0.78). Overall, Pléiades Neo at 30 cm resolution offers the best balance of accuracy and data efficiency for road segmentation.

The fluxes of dissolved organic carbon (DOC) through tidal marsh‐influenced estuaries remain poorly quantified and have been identified as a missing component in carbon‐cycle models. The extreme variability inherent to these ecosystems of the land‐ocean interface challenge our ability to capture DOC‐concentration dynamics and to calculate accurate DOC fluxes. In situ discrete and continuous measurements provide high‐quality estimates of DOC concentration, but these strategies are constrained spatially and temporally and can be costly to operate. Here, field measurements and high‐spatial‐resolution remote sensing were used to train and validate a predictive model of DOC‐concentration distributions in the Plum Island Estuary (PIE), a mesotidal saltmarsh‐influenced estuary in Massachusetts. A large set of field measurements collected between 2017 and 2023 was used to develop and validate an empirical algorithm to retrieve DOC concentration with a ±15% uncertainty from Sentinel‐2 imagery. Implementation on 141 useable images produced a 6‐year time series (2017–2023) of DOC distributions along the thalweg. Analysis of the time series helped identify river discharge, tidal water level (WL), and a marsh enhanced vegetation index 2 as predictors of DOC distribution in the estuary, and facilitated the training and validation of a simple model estimating the distribution. This simple model was able to predict DOC along the PIE thalweg within ±16% of the in situ measurements. Implementation for three years (2020–2022) illustrated how this type of remote‐sensing‐informed models can be coupled with the outputs hydrodynamic models to calculate DOC fluxes in tidal marsh‐influenced estuaries and estimate DOC export to the coastal ocean.

Bina çıkarımı; arazi kullanımı, şehir planlaması, afet izleme, navigasyon, coğrafi veri tabanlarının güncellenmesi ve kentsel dinamik izleme gibi çeşitli mekânsal uygulamalarda önemli rol oynar. Farklı bölgelerdeki binalar farklı yapısal ve geometrik özelliklere sahip olduğundan görüntülerden otomatik bina çıkarımı zor bir iştir. Son yıllarda uygun veri setleriyle eğitildiklerinde klasik makine öğrenme yöntemlerine göre daha yüksek doğruluklu sonuçlar üreten derin öğrenme modelleri, otomatik bina çıkarımında sıkça kullanılmaktadır. Modellerin yüksek doğrulukta eğitilmesi için kaliteli etiketlerin olduğu bina veri setleri büyük önem taşımaktadır. Bu çalışmanın amacı, bina tespiti için farklı çözünürlükteki uzaktan algılama görüntülerinden oluşturulmuş ve literatürde sıkça kullanılan açık kaynaklı bina veri setlerini tanıtmaktır. Veri setleri, kaydedildiği platformlara göre havadan, uydudan ve her iki platformdan kaydedilmiş görüntülerden oluşan veriler olarak üç kategoride gruplandırılıp, detayları açıklanmıştır. Bunun yanı sıra veri setleri ile yapılmış karşılaştırmalı çalışmaları içeren güncel literatür özeti verilmiştir. Bina tespiti işlemini doğru şekilde gerçekleştirmek için araştırmacılara rehberlik edecek ve bina veri seti oluşturulmasında dikkat edilmesi gereken kritik hususları içeren değerlendirmeler sunulmuştur.