Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

Climate change is thought to be responsible for the spread of various vector-borne diseases. The current study was conducted to evaluate the impact of different temperature and relative humidity regimes on the developmental stages of the yellow fever mosquito, Aedes aegypti (Diptera: Culicidae). The study also evaluated the impact of larval density on the survival of Ae. aegypti. In addition, the association between vector larval abundance, dengue incidence, and climatic factors were elucidated during 2016–2019 in three populated districts of Punjab, Pakistan, i.e., Lahore, Rawalpindi, and Multan. The results of the study revealed that at 10 °C and 35 °C, egg hatching and adult emergence were significantly reduced, regardless of the relative humidity. In contrast, at 20 °C and 30 °C, the rates of egg and adult survival increased with higher relative humidity. In addition, a density-dependent response was observed regarding larval survival of Ae. aegypti. Moreover, larval incidence was positively correlated with the number of dengue patients, Tmax, RH, and precipitation at Lahore (0.55, 0.23, 0.29, and 0.13), Rawalpindi (0.90, 0.30, 0.21, and 0.14), and Multan (0.05, 0.27, and 0.13) respectively, except in Multan, where a negative correlation (−0.09) with precipitation was observed. The inflow of patients had a positive correlation with the occurrence of a larval population, relative humidity, and precipitation at Lahore, Rawalpindi, and Multan districts, with the scale values of 0.55, 0.25, and 0.16; 0.90, 0.22, and 0.03; and 0.05, 0.06, and 0.03, respectively. In addition, a forecast model, ARIMA, predicted that there was a higher rate of larval occurrence in Rawalpindi, followed by Lahore. This study concluded that the role of precipitation > 200 mm prior to a 1–2-month lag, a 20–30 °C temperature range, and an RH exceeding 60% lead to the occurrence of larvae and dengue case spikes. This study will help to reinforce dengue surveillance and control strategies in Pakistan and to establish early management strategies based on changing climatic factors.

Urban resilience is essential for cities to endure and adjust to environmental and socioeconomic upheavals. The static indicators and rule-based spatial frameworks that are the mainstays of traditional resilience assessment models frequently fall short of capturing the dynamic character of coastal and urban resilience. This paper suggests a deep learning-based categorization framework for identifying resilience levels in urban and coastal settings by combining Long Short-Term Memory (LSTM) networks with multi-sensor remote sensing data. The Copernicus Marine Data Service's spatiotemporal ocean physics data, namely the eastward (uo) and northward (vo) seawater velocity, are used in the model to increase the precision of resilience evaluations. The methodology includes a multi-step deep learning pipeline, incorporating data preprocessing, feature extraction, class balancing with SMOTE, and LSTM-based classification. The proposed LSTM model is optimized to enhance performance with dropout regularisation (0.3), an Adam optimizer (learning rate = 0.0003), and class weighting strategies. The model is evaluated using accuracy, F1-score, confusion matrices, and loss curves, ensuring reliable classification across different resilience categories. Results indicate that the framework achieves high classification accuracy (91.5%), demonstrating superior performance compared to traditional machine learning approaches. Regarding multi-sensor fusion and deep learning, this study provides a scalable, adaptive, and data-driven solution for resilience classification, supporting climate adaptation strategies, disaster risk management, and sustainable urban development. The proposed methodology offers a robust tool for policymakers and urban planners, enabling more effective resilience monitoring and decision-making in rapidly evolving urban and coastal environments.

Monitoring glacial lakes is critical for assessing climate change impacts and mitigating glacial lake outburst flood risks. This study evaluates three deep learning architectures, U-Net, Simple Convolutional Neural Network (CNN), and Atrous Spatial Pyramid Pooling SegNet (ASPP SegNet), for binary semantic segmentation of glacial lakes using multi-sensor optical satellite imagery (Sentinel-2). Incorporating data augmentation and custom evaluation metrics (IoU, F1-score, validation loss), the results show that Simple CNN achieves the highest IoU (0.9155) and F1-score (0.9557). At the same time, ASPP SegNet demonstrates superior generalization with the lowest validation loss (0.03337). U-Net also delivers a reliable performance, albeit slightly lower. Visual and quantitative assessments highlight the advantage of multi-scale, context-aware architectures in delineating fragmented lake boundaries. This comparative study provides practical guidance for deep learning model selection in remote sensing-based glacial and coastal hydrology monitoring. Future work will explore temporal modeling, multi-class segmentation, and the integration of optical, radar, and elevation data for improved resilience.

The worldwide rise in soil salinization is among the most critical consequences of climate change, posing a significant threat to food security. Wheat (Triticum aestivum L.), a staple crop of paramount importance worldwide, encounters significant production limitations due to abiotic stressors, particularly salinity. Consequently, the development and cultivation of salt-tolerant wheat genotypes have emerged as an essential strategy to sustain agricultural productivity and safeguard global food security. The aim of the present study was to investigate the effect of salinity (150 mM) on the performance and combining ability of 10 hybrid combinations (F2) and their parents that were obtained through a line × tester mating design at the seedling stage. Morphological, physiological, and biochemical traits were assessed under both control and salt-stress conditions. Among the assessed traits, SFW emerged as the strongest predictor of salt tolerance, demonstrating the highest correlation with MFVS and the greatest contribution in the regression model. The results highlighted distinct responses among the studied genotypes. Hybrid H5 demonstrated particular promise, surpassing the performance of the superior parent for Na⁺, K⁺, K⁺/Na⁺ and proline (Pro). Furthermore, tester T1 emerged as a good combiner for proline (Pro), total soluble sugars content (Sug), chlorophyll content (Chl) and root length (RL) under saline conditions. In contrast, under control conditions, line L1 and testers T2, T3, and T5 exhibited superior performance, demonstrating significant general combining ability (GCA) effects for four traits simultaneously. Hybrid H4 emerged as outstanding under salt stress, exhibiting favorable specific combining ability (SCA) effects for Na⁺, K⁺/Na⁺ ratio, root length (RL), relative water content (RWC), and total soluble sugars content (Sug). Under normal conditions, hybrids H7 and H10 exhibited significantly superior performance across three traits simultaneously. Non-additive genetic effects predominantly influenced the studied traits under both conditions. The parental and hybrid combinations show promise for incorporation into breeding programs designed to improve salt tolerance under the specific conditions studied.

Boreal coastal habitats, with their complex hydrological, ecological, and geological functions, play a pivotal role in regulating regional climate, supporting biodiversity, and sustaining local communities. This study presents a comprehensive, multi-decadal assessment of land cover dynamics, a large and environmentally sensitive coastal-fluvial watershed. Using a 30-year Landsat satellite data archive (1990–2020) from Landsat 4, 5, 7, 8, and 9 sensors, we analyzed long-term changes in land cover patterns, focusing on vegetation health, surface water extent, and urban expansion. A suite of spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), was calculated for each time slice to capture key environmental variables related to vegetation productivity, urbanization, and hydrological dynamics. To ensure methodological consistency and computational efficiency, a cloud-based processing workflow was implemented using the Google Earth Engine (GEE) platform. This approach enabled automated preprocessing, atmospheric correction, cloud masking, and multi-decadal time-series analysis across the study area. Landsat surface reflectance and spectral index data were fused and subsequently classified using a Random Forest (RF) machine learning algorithm, allowing for enhanced land cover classification accuracy across seven time intervals spanning three decades. Our findings reveal distinct spatiotemporal trends in the Moose River Basin's landscape. A marked decline in vegetation cover was observed during the early 2000s, likely driven by a combination of climate variability, hydrological fluctuations, and local disturbances. Urban and built-up areas, primarily concentrated near river corridors and accessible regions, exhibited gradual but steady expansion over the study period, signaling increasing human settlement pressures.