This paper introduces a novel two-step method for predicting urban expansion using time-series satellite imagery. The approach combines semantic image segmentation with a CNN-LSTM model to learn temporal features. Experiments on satellite images from Riyadh, Jeddah, and Dammam in Saudi Arabia demonstrate improved performance compared to existing methods based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.
The Global Water Monitor Consortium, including KAUST, released its 2023 report, finding that 77 of 249 countries experienced record-high temperatures. Saudi Arabia had its third-hottest year but highest precipitation in 20 years. Vegetation vigor in Saudi Arabia was also the highest since 2001, almost 8% higher than the long-term average. Why it matters: The report highlights climate change impacts in Saudi Arabia, emphasizing the need for accessible information on water resources for stakeholders and the potential for increased vegetation due to higher rainfall.
KAUST's Urban Lab is developing green infrastructure solutions to combat the urban heat island (UHI) effect in arid climates. Researchers are focusing on using vegetation for shading and evapotranspiration, as well as increasing albedo with reflective surfaces. The team is using machine learning to analyze heat patterns and urban expansion to inform sustainable architecture and landscape design. Why it matters: This research is crucial for creating more livable and sustainable urban environments in Saudi Arabia and other regions facing desertification and water scarcity.
KAUST's Atmospheric and Climate Modeling group, led by Georgiy Stenchikov, is using high-resolution global and regional climate models to predict climate change in the Middle East, focusing on local atmospheric and oceanic processes. The group developed coupled regional atmospheric and oceanic models for the Red Sea, accounting for the climate effect of aerosols, especially dust, which is significant in the region. They found that dust strongly affects the Red Sea, causing high optical depth and solar cooling effect, particularly in the southern part, impacting energy balance and circulation. Why it matters: Improving regional climate models with specific attention to dust and aerosols is crucial for predicting and mitigating the environmental impacts of climate change in arid regions like the Middle East.
A KAUST-led study analyzed the impact of natural forests on local temperatures across the globe, finding that forests provide the greatest climate benefits when conserved or restored in their native locations. The analysis showed that forests generally have net global cooling effects in areas where dense tree cover would naturally exist. The research highlights that forests buffer against extreme temperatures, providing warming effects in freezing areas and cooling effects in warm regions. Why it matters: The findings emphasize the importance of regional scientific evidence in guiding land-use and restoration decisions, ensuring effective and sustainable nature-based solutions in the face of climate change.
KAUST's HALO group launched a CubeSat in 2023 for high-precision Earth observation in the Gulf region, combining GNSS Reflectometry and hyperspectral sensing. The satellite monitors vegetation, soil, agriculture, and ecosystem health, providing detailed estimates of irrigation water use and vegetation health. The Extreme Statistics (XSTAT) research group at KAUST focuses on the mathematical modeling and prediction of extreme weather and climate events. Why it matters: These KAUST initiatives enhance climate resilience in the region through advanced monitoring, statistical modeling, and predictive capabilities.