Although the way in which vegetation phenology mediates the feedback of vegetation to climate systems is now well understood, the magnitude of these changes is still unknown. A thorough understanding of how the recent shift in phenology may impact on, for example, land surface temperature (LST) is important. To address this knowledge gap, it is important to quantify these impacts and identify patterns from the global to the regional scale. This study examines the trend and linear regression modeling of the leaf area index (LAI) and LST derived from the moderate resolution imaging spectroradiometer (MODIS) data, specifically to assess their spatial distribution and changing trends at the continental and regional scales. The change detection analysis of interannual variability in the global LAI and LST between two periods (2003–2010 and 2011–2018) demonstrates more positive LAI trends than negative, while for LST most changes were not significant. The relationships between LAI and LST were assessed across the continents to ascertain the response of vegetation to changes in LST. The regression between LAI and LST was negative in Australia (R² = 0.487 ***), positive but minimal in Africa (R² = 0.001), positive in North America (R² = 0.641 ***), negative in Central America (R² = 0.119), positive in South America (R² = 0.253 *) and positive in Europe (R² = 0.740 ***). Medium temperatures enhance photosynthesis and lengthen the growing season in Europe. We also found a significant greening trend in China (trendp = 0.16 ***) and India (trendp = 0.13 ***). The relationships between LAI and LST in these most prominent greening countries of the world are R² = 0.06 and R² = 0.25 for China and India, respectively. Our deductions here are twofold—(1) In China, an insignificant association appeared between greening trend and temperature. (2) In India, the significant greening trend may be a factor in lowering temperatures. Therefore, temperature may stabilize if the greening trend continues. We attribute the trends in both countries to the different land use management and climate mitigation policies adopted by these countries.

Background: GIS (Geographic Information Systems) based behavior maps are useful for visualizing and analyzing how children utilize their play spaces. However, a GIS needs accurate locational information to ensure that observations are correctly represented on the layout maps of play spaces. The most commonly used tools for observing and coding free play among children in indoor play spaces require that locational data be collected alongside other play variables. There is a need for a practical, cost-effective approach for extending most tools for analyzing free play by adding geospatial locational information to children’s behavior data collected in indoor play environments. Results: We provide a non-intrusive approach to adding locational information to behavior data acquired from video recordings of preschool children in their indoor play spaces. The gridding technique showed to be a cost-effective method of gathering locational information about children from video recordings of their indoor physical activities and social behaviors. Visualizing the proportions of categories and observed intervals was done using bubble pie charts which allowed for the merging of multiple categorical information on one map. The addition of locational information to other play activity and social behavior data presented the opportunity to assess what types of equipment or play areas may encourage different physical activities and social behaviors among preschool children. Conclusions: Gridding is an effective method for providing locational data when analyzing physical activities and social behaviors of preschool children in indoor spaces. It is also reproducible for most GIS behavior mapping focusing on indoor environments. This bypasses the need to have positioning devices attached to children during observations, which can raise ethical considerations regarding children’s privacy and methodological implications with children playing less naturally. It also supports visualizations on behavior maps making them easier to interpret.

The annual cycles of soil moisture and NO₂ have been analysed across the climate zones of West Africa using two satellite data sets (OMI on AURA and ASCAT on MetOp-A). Exploring the sources and sinks for NO₂ it is clear that the densely populated urban cities including Lagos and Abuja had the highest mean NO₂ concentrations (> 1.8 × 10¹⁵ molecules cm⁻²) indicative of the anthropogenic urban emissions. The data analysis shows that rising soil moisture levels may influence the sink of NO₂ concentrations after the biomass burning. The results also show significant soil moisture changes in areas of high humidity especially in the east equatorial monsoon climate zone where most of the Niger delta is located (4 %/yr.). A decline in NO₂ (0.9 %/yr.) was also observed in this climate zone. Beyond seasonal linear regression models, climate based Granger’s causality tests show that tropospheric NO₂ concentrations from soil emissions in the arid steppe (Sahel) and arid desert climate zones of West Africa are significantly affected by soil moisture variability (F > 10, p < 0.01). The arid steppe and arid deserts regions showed no significant changes in soil moisture levels but significant increase in tropospheric NO₂ concentrations (> 0.8 %/yr). The results demonstrate the critical sensitivity of the West African emissions of NO₂ on soil moisture and climate zone.

Wetlands play a crucial role in the global hydrological cycle and act as natural buffers that enhance resilience to extreme climatic events. However, they are increasingly being threatened due to human pressures. In this study, we assembled a deep learning (DL) framework to document recent changes in wetland dynamics in the Niger Delta using multi-temporal, multi-sensor satellite data. To achieve this, specific objectives were: (i) identifying optimal sensor combinations for wetland classification, (ii) evaluating DL model performance, (iii) performing land use/land cover (LULC) classification across two timeframes (2019/2020 and 2021/2022), and (iv) analyzing hydrological variability through satellite indicators and comparing with wetland changes. Several tests were undertaken to determine the best model for wetland mapping using the optimal multi-sensor image dataset as input in the DL models (Res-UNet and DeepLabV3). The results showed that the Res-UNet model, combined with the ResNet 152 backbone, outperformed the DeepLabV3 model (with identical backbones) in segmenting wetlands, achieving high F1 scores and overall accuracy (the F1 scores ranged from 0.83 to 0.91). Estimates of wetland transition across the landscape were obtained using the optimal DL model outputs as inputs for change detection analysis. The change detection analysis revealed that converting dense vegetation to wetlands was the main driver of wetland gain, while wetland loss primarily emanated from transitions to dense vegetation. The spatial extent of wetland loss was estimated to be 27,346 hectares, whereas gains totalled 98,453 hectares. Furthermore, the study established links between hydroclimatic indicators and changes in wetland, emphasizing the influence of climatic conditions and human activities on wetland expansion. Ultimately, our findings illustrate the potential of advanced remote sensing and deep learning for wetland monitoring and highlight the necessity of informed conservation strategies for fragile ecosystems.