Caleb Mensah

Gross Primary Productivity (GPP) is a fundamental component of the terrestrial carbon cycle, yet its long‐term dynamics and controlling mechanisms remain uncertain at continental scales, particularly where ecosystems respond differently to climate change. We investigated spatiotemporal trends and process‐level controls on annual GPP (AGPP) across China's major terrestrial ecosystems from 2002 to 2020 using an integrated Earth‐system diagnostic framework. Spatial trend analysis, machine learning prediction, functional response analysis, and spatial causal inference ‐ Geographical Pattern Causality (GPC) are combined to distinguish predictive associations from dominant mechanistic constraints. Nationally, AGPP increased marginally (+11.3 gC m ⁻² yr ⁻¹ ), masking strong ecosystem divergence: wetlands exhibited rapid gains (+72.6 gC m ⁻² yr ⁻¹ ), while forest AGPP declined significantly (−12.9 gC m ⁻² yr ⁻¹ ). Spatial causal analysis identifies a consistent national control hierarchy, with leaf area index (LAI), photosynthetically active radiation (PAR), and atmospheric CO ₂ (ρc,yr) exerting the strongest influence, while evaporative demand, precipitation, and temperature play secondary roles. Causal mechanisms are strongly ecosystem‐specific. Cropland productivity is dominated by PAR, LAI, and CO ₂ , with no detectable causal role of temperature. Grasslands are co‐limited by radiation, canopy structure, CO ₂ , and soil nitrogen. Forest productivity is thermally constrained but shows strong negative responses to atmospheric drying, indicating increasing hydraulic stress. Wetland productivity depends primarily on radiation and CO ₂ , with negligible influence from moisture or canopy structure. By separating prediction from spatially inferred causation, this study clarifies ecosystem‐specific controls and vulnerabilities within China's terrestrial carbon uptake.

This study examined the impacts of climate variability and cultivated land area on the yields of cassava, yam, and maize in Wenchi Municipality, Ghana, from 2000 to 2021. Using a quantitative approach, the study employed time-series data on rainfall, minimum and maximum temperatures, crop yields, and cultivated area. Multiple linear regression models with logarithmic transformations were used to assess the influence of climate variables and cultivated area on crop yields. Diagnostic tests confirmed the validity of model assumptions. The regression results revealed that temperature variables, especially minimum temperature, had a significant positive effect on all three crop yields. Maximum temperature also showed positive effects, although with varying levels of significance. Rainfall and cultivated area had no statistically significant impact on yields. The models explained 46.87%, 51.28%, and 61.57% of the variations in cassava, yam, and maize yields, respectively. Temperature played a more critical role than rainfall or cultivated land in influencing crop yields in Wenchi over the study period. These findings underscore the need for temperature-focused adaptation strategies and climate-smart agriculture to enhance food security and resilience in the transitional zones of Ghana.

In the field of environmental health, assessing air pollution exposure has historically posed challenges, primarily due to sparse ground observation networks. To overcome this limitation, satellite remote sensing of aerosols provides a valuable tool for monitoring air quality and estimating particulate matter concentration (PM) at the surface. In this study, we employ two predictive models to estimate Aerosol Optical Depth (AOD) levels over Ghana and selected localities from January 2003 to December 2019. Our investigation focuses on evaluating the capabilities of multiple linear regression (MLR) and artificial neural network (ANN) models in predicting AOD levels. Additionally, we introduce a novel approach to constructing the MLR model by leveraging the ANN architecture. These models utilize meteorological variables as input, to facilitate accurate predictions. Despite Ghana’s alarming air pollution health ranking and its substantial role in mortality, routine monitoring remains sparse. This research contributes a comprehensive sixteen-year assessment (2003-2019) of AOD at a 3 km resolution, obtained from MODIS Aqua and Terra satellites. The findings indicate that the southwestern part of the country displays elevated aerosol levels compared to other major cities. Given the region’s dense vegetation, this phenomenon can be attributed to biogenic emissions. Additionally, many small cities within this area are recognized as hotspots for surface mining operations, potentially contributing to increased local dust loadings in the atmosphere. Notably, the MLR model, implemented using the ANN model structure, outperformed the other utilized models. This endeavor aims to unravel the spatiotemporal distribution patterns of aerosols across Ghana, and its major urban hubs.

East Africa (EA) faces significant challenges related to greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and methane (CH₄), largely driven by biomass burning (BB) from agricultural fires and wildfires. Despite their importance, the spatiotemporal variability of these emissions remains poorly understood. This study quantifies CO₂ and CH₄ emissions across EA (2001-2022), revealing significant CO₂ emission peaks in 2005 (32.5 million tonnes), 2016 (29.8 million tonnes), and 2020 (31.2 million tonnes), alongside an 18% increase in CH₄ emissions between 2015 and 2020. BB accounted for approximately 54% of total CO₂ emissions (~17 million tonnes) and 74% of CH₄ emissions (~16 million tonnes), with regional hotspots including Northern Uganda (NUG), Tanzania (TZ), and South Sudan (SS) exhibiting the highest intensities, especially during dry seasons where emissions surged by up to 40%. Analysis of regional trends reveals a significant decline in CO₂ emissions in the Western Transition (WTZ) (slope = -13.02, p = 0.0004), likely reflecting effective mitigation such as forest restoration and REDD+ programs, supported by satellite-observed greening. In contrast, SS, NUG, and the Southeastern Tanzania (SETZ) showed negative but statistically insignificant CO₂ trends, indicative of fluctuating land-use pressures rather than sustained mitigation. CH₄ emissions rose significantly in the WTZ (slope = 15.67, p = 0.0001), driven by agricultural intensification, with a marginal increase in NUG, while remaining stable in SS and the SETZ. Non-stationarity in emissions (ADF p > 0.05) across regions highlights the influence of dynamic socio-environmental factors such as land-use changes, policy shifts, and climate variability. These findings emphasize the urgent need for improved fire management, sustainable land practices, and integrated mitigation strategies to address EA's growing environmental threats and contribute to global climate goals under the Paris Agreement.

Aim of the study: This study analyses LULC changes in Wakiso and Kampala districts from 1995 to 2024. Objectives include mapping LULC types, assessing changes, evaluating transitions among categories (agriculture, water, built-up areas, forests), and examining trends and drivers. It provides insights into the impacts of urbanization and agricultural expansion, offering practical implications for sustainable land management.Material and methods: Landsat images from 1995, 2010, and 2024 were used, sourced from NASA's Earth Explorer. January images minimized seasonal variation, and data were reprojected to UTM Zone 36N with WGS84. Pre-processing included corrections and haze reduction in ArcMap 10.4. Supervised classification used a False Colour Composite of blue, green, and red bands, with 2,500–3,000 pixels per LULC category sampled. Accuracy was assessed with Kappa statistics and overall accuracy, and NDVI and NDBI were calculated for vegetation and urban growth analysis.Results and conclusions: The analysis showed significant land use changes: built-up areas increased by 163%, reducing vegetation cover by 30.7%. Agricultural land grew by 112.4%, while forest cover rose by 29.2%. NDVI trends revealed a vegetation density increase until 2010, then a slight decline by 2024 due to urban sprawl. NDBI showed substantial urban growth, stabilizing between 2010 and 2024. The study highlights the impact of urbanization and agricultural expansion in Wakiso and Kampala, emphasizing the need for sustainable land management to balance development and conservation.

The strong effects of climate change are expected to negatively impact the long-term resilience and function of forest ecosystems, which could lead to changes in forest carbon balance and productivity. However, these forest responses may vary with local conditions and forest types. Accordingly, this study was carried out to determine gross primary productivity (GPP) sensitivity to changes in environmental parameters. Central European beech (at Štítná) and spruce species (at Bílý Kr̆íz̆ and Rájec), growing under contrasting climatic conditions, were studied. The comparative analyses of GPP were based on a five-year-long dataset of eddy covariance fluxes during the main growing season (2012–2016). Results of forest GPP responses with changes in environmental factors from a traditional Stepwise multiple linear regression model (SMLR) were used and compared with Random forest (RF) analyses. To demonstrate how actual GPP trends compare to potential GPP (GPP_pot) courses expected under near-optimal environmental conditions, we computed normalized GPP (GPP_norm) with values between 0 and 1 as the ratio of the estimated daily sum of GPP to GPP_pot. The study confirmed the well-known effect of total intensity of the photosynthetically active radiation and its diffuse fraction on GPP_norm across all the forest types. However, the study also showed the secondary effects of other environmental variables on forest productivity depending on the species and local climatic conditions. The reduction in forest productivity at the beech forest in Štítná was presumed to be mainly induced by edaphic drought (anisohydric behaviour). In contrast, reduced forest productivity at the spruce forest sites was presumably induced by both meteorological and hydrological drought events, especially at the moderately dry climate in Rájec. Overall, our analyses call for more studies on forest productivity across different forest types and contrasting climatic conditions, as this productivity is strongly dependent on species type and site-specific environmental conditions.

The occurrence of extreme drought poses a severe threat to forest ecosystems and reduces their capability to sequester carbon dioxide. This study analysed the impacts of a central European summer drought in 2015 on gross primary productivity (GPP) at two Norway spruce forest sites representing two contrasting climatic conditions—cold and humid climate at Bílý Kříž (CZ-BK1) vs. moderately warm and dry climate at Rájec (CZ-RAJ). The comparative analyses of GPP was based on a three-year eddy covariance dataset, where 2014 and 2016 represented years with normal conditions, while 2015 was characterized by dry conditions. A significant decline in the forest GPP was found during the dry year of 2015, reaching 14% and 6% at CZ-BK1 and CZ-RAJ, respectively. The reduction in GPP coincided with high ecosystem respiration (R_eco) during the dry year period, especially during July and August, when several heat waves hit the region. Additional analyses of GPP decline during the dry year period suggested that a vapour pressure deficit played a more important role than the soil volumetric water content at both investigated sites, highlighting the often neglected importance of considering the species hydraulic strategy (isohydric vs. anisohydric) in drought impact assessments. The study indicates the high vulnerability of the Norway spruce forest to drought stress, especially at sites with precipitation equal or smaller than the atmospheric evaporative demand. Since central Europe is currently experiencing large-scale dieback of Norway spruce forests in lowlands and uplands (such as for CZ-RAJ conditions), the findings of this study may help to quantitatively assess the fate of these widespread cultures under future climate projections, and may help to delimitate the areas of their sustainable production.

Rapid urban expansion and development have resulted in the conversion of many natural green surfaces within cities to non-transpiring built-up surfaces, such as concrete and asphalt. These artificial urban surfaces cause substantial variation in land surface temperatures that affect the urban microclimate. Thus, there is the need to substantially quantify the extent of green cover loss within growing cities and its impact on surface temperatures. This study used LANDSAT data to spatially assess the extent of urban expansion and its effect on land surface temperature within Kumasi, Ghana. Subsequently, the results showed significant changes in the land cover, which had an effect on the observed land surface temperatures from 1986 to 2015. Generally, there was an overall increase in the built-up areas by 24.13% (55.81 km²) from 1986 to 2015, with a corresponding increase in the mean land surface temperature by 4.16°C. As such, there is the need for the adoption of sustainable urban planning strategies with green vegetation conservation initiatives for modern city planners. This would help reduce urban land surface temperatures while promoting clean air circulation within the city.