This study investigates the influence of land use and land cover change (LULCC) on urban heat island (UHI) effects in Zhengzhou, Henan, China, amid rapid urbanization and significant landscape transformations between 1993 and 2023. Employing remote sensing and geospatial analytical methods, we analyze the spatiotemporal dynamics of UHI intensity and its relationship with LULCC variables, including built-up areas, bare land, farmlands, forests, water bodies, and grasslands/shrubs. Our findings reveal a dramatic increase in UHI intensity, rising from 1.02°C in 1993 to 2.49°C in 2023, closely associated with a 130.78% expansion of built-up areas (from 1,951.13 sqkm to 4,502.84 sqkm). Concurrently, vegetative cover has declined significantly, with forests and farmlands decreasing by 43.37% and 70.90%, respectively. Geodetector analysis stresses the role of built-up areas as the primary driver of UHI, with a strong positive correlation with UHI (R = 0.729). Conversely, farmlands (R = −0.891) and forests (R = −0.962) demonstrate notable negative correlations, highlighting their critical cooling functions. K-means clustering analysis identifies spatial heterogeneity in UHI effects, with densely built-up clusters exhibiting the highest UHI intensities, whereas vegetated and water-dominated clusters exhibit significantly lower values. These findings emphasize the detrimental impact of urbanization on local thermal dynamics while emphasizing the importance of preserving green spaces and water bodies in mitigating UHI effects. Our standpoints drive the urgent need for sustainable land-use policies that integrate green infrastructure into urban planning frameworks. These insights are crucial for fostering climate-resilient cities in the face of ongoing urbanization and climate change.

This study investigates drought patterns in Uganda's Eastern (E), Northeastern (NE), Northwestern (NW), and Southwestern (SW) regions, focusing on the frequency, intensity, and duration of drought events and their impacts on water resources, agriculture, and livelihoods. Using data from ERA5, GPCC, and CRU TS (1960–2020), the study applies the Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and Standardized Runoff Index (SRI) to analyze drought trends. Ordinary Least Squares (OLS) regression and random forest models identify key predictors of drought events. Results show that the E and NE regions experience more frequent but shorter droughts, while the NW and SW regions face prolonged and intense droughts. SPI and Potential Evapotranspiration (PET) are key predictors of SPEI, with PET consistently influencing drought severity. The random forest model showed moderate predictive accuracy (MSE = 0.29, R² = 0.35) but struggled to predict SPI in the NW and SW. The study highlights the need for region-specific interventions and improved drought prediction models, integrating remote sensing, land-use data, and machine learning. Policy recommendations include enhancing climate data integration for water resource management and strengthening regional drought response systems.

Aim of the study: In rural and agricultural development, land plays a crucial role in driving productivity. To understand the impact of specific causes or combinations of causes on outcomes, it is essential to identify and establish clear causal relationships. Our study investigates the causal effects of different land use systems against Land Surface Temperature (LST) in Henan Province. We further make land use predictions based on current trends. Understanding these dynamics is essential for enhancing agricultural informatization, environmental management, and climate-smart choices of local districts, counties and villages across China’s agriculturally important regions and beyond.Material and methods: The study utilized integrated remote sensing data, techniques and a causality approach to investigate land use systems (LUS) and LST in Henan Province. We further used Modules for Land Use Change Evaluation (MOLUSCE) and Cellular Automata-Artificial Neural Network (CA-ANN) to predict LUS for the near future (2023–2053).Results and conclusions: Results revealed that built-up areas (+500%), forests (+50.88%) and water bodies (+83.56%) have expanded massively during the past 40 years. In contrast, cultivated (–20.81%) and barren areas (–60.53%) declined steadily. The temporal causal inference analysis demonstrated a strong convergence between built-up areas and land surface temperature (LST), which substantiates built-up areas’ profound impact on LST intensity. The spatial causal inference analysis shows moderate to robust positive indirect cross-mapping relationships between built-up areas (ρ = 0.63) and bare land (ρ = 0.32) against LST. Land use predictions (2023–2053) show a reduction in areas covered by forests and water bodies, and a reversed trend in cultivated lands. These are particularly important when formulating targeted policy-directives needed to regulate unsustainable land-use processes and undesirable economic trade-offs.

This comprehensive study delves into the intricate interplay between temperature trends and land cover dynamics in the Kara region, offering robust insights into the evolving climate and environmental patterns. Employing the Mann-Kendall trend test, our analysis reveals a statistically significant and consistent warming trend over the study period. The observed low p-values, positive Tau values, and upward-sloping regression lines emphasize the urgent need for proactive measures to address the climate-related challenges faced by the region. Simultaneously, a meticulous correlation analysis explores the relationships between land surface temperature (LST) and key land metrics- farms, barelands, built-ups, forests, and water bodies. Farmlands exhibit a noteworthy and statistically significant negative correlation of −0.74 with LST (p < 0.05), indicating a cooling influence and supported by a substantial 43.8% predictive power. Conversely, barelands and built-ups demonstrate strong positive correlations of 0.89 and 0.78, respectively, with LST both statistically significant at the 95% confidence level (p < 0.05), emphasizing their considerable warming impact. Forests and water bodies, with moderate negative correlations of −0.65 and −0.54, maintain statistical significance (p < 0.05), indicating their role in temperature moderation, supported by 8.5% and 13% significance. The inclusion of Sen’s slope values further enriches the analysis, providing quantitative insights into the rate of temperature change. The positive slope values underscore the increasing trend in temperatures over each respective decade. The observed statistical significances, Tau values, and Sen’s slope values accentuate the importance of these relationships for effective land management and environmental planning. Recognizing the cooling influence of farmlands suggests their potential use in strategic urban planning to mitigate temperature increases. Conversely, the warming impact of barelands and built-ups emphasizes the need for sustainable urban development practices to counteract rising temperatures in urbanized regions. Additionally, the cooling influence of forests and water bodies underscores their crucial role in temperature moderation.