The green divide and heat exposure: urban transformation projects in istanbul

Extreme heat events are happening more frequently and with greater severity, causing significant negative consequences, especially for vulnerable urban populations around the globe. Heat stress is even more common in cities with dense and irregular planning and lacking urban blue-green infrastructures. This study investigates the greening and cooling effects of five selected urban transformation projects and their surrounding areas (within a 10-min walking distance) in Istanbul from 2013 to 2021, with a focus on environmental justice and climate adaptation planning perspectives. By employing temporal analysis of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) values derived from Landsat data sets to detect changes in these five selected urban transformation projects in the megacity of Türkiye, Istanbul, this study finds that the distribution of green infrastructures (e.g., tree canopy) is only limited to project sites of long-running and state-supported urban transformation projects in Istanbul. Consequently, the unequal distribution of green infrastructures creates cooling effects only for the locals residing in the new residential projects. However, the surrounding areas have less urban green infrastructure and are exposed more to the urban heat over time. Urban development policies and planning highly contribute to increasing the climate vulnerabilities among those who do not benefit from the recently developed residential units in Istanbul. Such a trend can affect adaptive capacity of vulnerable communities and redress environmental injustices in urban planning in the megacity of Istanbul

Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

This study examines the use of snow avalanche susceptibility maps (SASMs) to identify areas prone to avalanches and develop measures to mitigate the risk in the Province of Sondrio, Italy. Various machine learning classifiers such as Random Forest, Gradient Boosting Machines, and AdaBoost, as well as newer classifiers like XGBoost, LightGBM, and NGBoost, were used with 17 conditioning factors and 1880 snow avalanche samples. The XGBoost classifier was found to have the best performance and McNemar’s test results indicated that certain classifier pairs, such as RF-AdaBoost, RF-XGBoost, and XGBoost-LightGBM, produced significant predictions while others did not. The XGBoost classifier found that 19.31% of Sondrio was very susceptible to avalanches. Instead of providing a global explanation of the classifier models, the study employs a local eXplainable Artificial Intelligence (XAI) approach called SHapley Additive eXplanations (SHAP) to give insight into how each conditioning factor contributes to the likelihood of snow avalanches. According to the SHAP values, the three most important factors in the XGBoost classifier model for determining the likelihood of snow avalanches are elevation, maximum temperature, and slope. The model shows that as elevation increases, the likelihood of avalanches also increases. On the other hand, a higher maximum temperature is found to decrease the likelihood of an avalanche. Slope is found to have a positive effect on the likelihood of an avalanche, meaning that steeper slopes increase the likelihood of an avalanche. This study also analyzes the avalanche susceptibility of ski resorts in the province and found that the majority of them are located in low and moderately susceptible areas, but some are in highly susceptible areas. The study used SHAP force plots to examine the local factors that contribute to the likelihood of avalanches in these specific ski resorts. The results show that ski resorts with elevations greater than 2000 m and slopes greater than 30 degrees, such as Livigno, Santa Caterina-Valfurva and Passo dello Stelvio, have a higher susceptibility to avalanches due to higher positive SHAP values. Conversely, ski resorts with elevations less than 2000 m and slopes less than 30 degrees, such as Aprica and Bormio, have a lower susceptibility to avalanches because of negative SHAP values. This study provides a valuable tool for creating new strategies to reduce the harm and damage caused by slow avalanches in the region

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes

Machine learning-based snow depth retrieval using GNSS signal-to-noise ratio data

GNSS-IR enables the extraction of environmental parameters such as snow depth by analyzing signal-to-noise ratio, indicating the strength of the GNSS signal. We propose a machine learning (ML) classifcation approach for snow depth retrieval using the GNSS-IR technique. ML classifer algorithms were studied to classify the strong and weak ground refections using input parameters (azimuth angle, satellite elevation angle, day of year, amplitude of refected signal, epoch number, etc.) as independent variables. GPS data collected by UNAVCO AB39 and daily snow depth data from SNOTEL Fort Yukon for a 6-year period (2015–2020) were considered. The frst 4-year data were trained by some well-known ML classifers to weight the input data and then used to classify the strong and weak signals. Tree-based classifers, Random Forest, AdaBoost, and Gradient Boosting overperformed the other classifers since they have more than 70% accuracy, so we performed our analysis with these three methods. The last 2-year data were used to validate both trained models and snow depth retrievals. The results show that ML classifer algorithms perform better results than traditional GNSS-IR snow depth retrieval; they improve the correlations by up to 19%. Moreover, the root-mean-square errors decrease from 15.4 to 4.5 cm. This study has a novel approach to the use of ML techniques in GNSS-IR signal classifcation, and the proposed methods provide a critical improvement in accuracy compared to the traditional metho

PSI Spatially Constrained Clustering: The Sibari and Metaponto Coastal Plains

PSI data are extremely useful for monitoring on-ground displacements. In many cases, clustering algorithms are adopted to highlight the presence of homogeneous patterns; however, clustering algorithms can fail to consider spatial constraints and be poorly specific in revealing patterns at lower scales or possible anomalies. Hence, we proposed a novel framework which combines a spatially-constrained clustering algorithm (SKATER) with a hypothesis testing procedure which evaluates and establishes the presence of significant local spatial correlations, namely the LISA method. The designed workflow ensures the retrieval of homogeneous clusters and a reliable anomaly detection; to validate this workflow, we collected Sentinel-1 time series from the Sibari and Metaponto coastal plains in Italy, ranging from 2015 to 2021. This particular study area is interesting due to the presence of important industrial and agricultural settlements. The proposed workflow effectively outlines the presence of both subsidence and uplifting that deserve to be focused and continuous monitoring, both for environmental and infrastructural purposes