Data processing and climate characterisation to study its impact is becoming difficult due to insufficient and unavailable data, especially in developing countries. Understanding climate's impact on burnt areas in Ghana (Guinea-savannah (GSZ) and Forest-savannah Mosaic zones (FSZ)) leads us to opt for machine learning. Through Google Earth Engine (GEE), rainfall (PR), maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tmean), Palmer Drought Severity Index (PDSI), relative humidity (RH), wind speed (WS), soil moisture (SM), actual evapotranspiration (ETA) and reference evapotranspiration (ETR) have been acquired through CHIRPS (Climate Hazards group Infrared Precipitation with Stations), FLDAS dataset (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System) and TerraClimate platform from 1991 to 2021. The objective is to analyse the link and the contribution of climatic and environmental parameters on wildfire spread in GSZ and FSZ in Ghana. Variables were analysed (area burnt and the number of active fires) through Spearman correlation and the cross-correlation function (CCF) (2001 to 2021). The tests (Mann-Kendall and Sens's slope trend test, Pettitt test and the Lee and Heghinian test) showed the overall decrease in rainfall and increase in temperature respectively (−0.1 mm; + 0.8°C) in GSZ and (−0.9 mm; + 0.3°C) in FSZ. In terms of impact, PR, ETR, FDI, Tmean, Tmax, Tmin, RH, ETA and SM contribute to fire spread. Through the codes developed, researchers and decision-makers could update them at different times easily to monitor climate variability and its impact on fires.

Background: Two of Ghana’s ecological zones—Guinea-savanna zone (GSZ) and Forest-savanna mosaic zone (FSZ)—are practically homologous in terms of structure and floristic composition, with some differences. The various sub-ecosystems that make up these areas are being depleted and losing their natural values due to various threats. There is little understanding about the fire trends in these areas due to a lack of data and poor accessibility to existing fire statistics. This study aimed to contribute to the understanding of the trends of area burned and active fire in the Guinea-savanna and Forest-savanna mosaic zones in order to inform policy-makers about sustainable management options. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) daily active fire (MDC14ML) and burned-area (MCD64A1) products to characterize the fire regime in terms of seasonality, intensity, density, burned area, frequency, and trends during the study period of 2001 to 2021.Results: This study indicated that fire activity started in October and peaked in December (GSZ) and January (FSZ). The mean proportion burned was approximately 39.95% (burned area of 2659.31 km²; FSZ) and 60.05% (burned area of 3996.63 km²: GSZ), while the frequency was approximately 42.87% (1759.95 of active fires; FSZ) and 57.13% (2345.26 of active fires: GSZ). In 2018, GSZ recorded the largest burned area (19 811.2 km², which represents an average of 825.5 km²of the total area burned from 2001 to 2021) with 4719 active points detected. FSZ recorded its greatest burned area in 2015 (8727.4 km²; which represents an average of 363.6 km²of the total area burned from 2001 to 2021) with 5587 active points recorded. In addition, it was found that specific times of the day (1000 h to 1420 h) recorded the majority of burned areas. In overview, between 2001 and 2021, burned areas increased by an average of 1.4 km²(FSZ) and 4.6 km²(GSZ), and the number of active fires increased by an average of 4.7 (FSZ) and 4.4 (GSZ) active fires per km².Conclusions: In conclusion, burned areas and active fires are increasing in both ecological zones. This study demonstrated the relevance of remote sensing to describe spatial and temporal patterns of fire occurrence in Ghana and highlighted the need for fire control and fuel management by the policies and institutions (e.g., Ghana National Fire and Rescue Service) in these important and vulnerable zones (GSZ and FSZ). This is especially true in the Forest-savanna mosaic zone, which is increasingly affected by the disasters of wildfires and records more active fires than GSZ, indicating that this zone is becoming more and more vulnerable. Therefore, rigorous continuous monitoring is essential, and collaboration between organizations fighting for the conservation of natural resources in the field is strongly recommended.

Accurate estimation of above-ground biomass (AGB) is vital for carbon accounting, biodiversity conservation, and sustainable forest management, especially in tropical regions under strong anthropogenic pressure. This study estimated and mapped AGB in the Atacora Mountain Chain, Togo, using a multi-source remote sensing approach within Google Earth Engine (GEE). Field data from 421 plots of the 2021 National Forest Inventory were combined with Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, bioclimatic variables from WorldClim, and topographic data. A Random Forest regression model evaluated the predictive capacity of different variable combinations. The best model, integrating SAR, optical, and climatic variables (S1S2allBio), achieved R² = 0.90, MAE = 13.42 Mg/ha, and RMSE = 22.54 Mg/ha, outperforming models without climate data. Dense forests stored the highest biomass (124.2 Mg/ha), while tree/shrub savannas had the lowest (25.38 Mg/ha). Spatially, ~60% of the area had biomass ≤ 50 Mg/ha. Precipitation correlated positively with AGB (r = 0.55), whereas temperature showed negative correlations. This work demonstrates the effectiveness of integrating multi-sensor satellite data with climatic predictors for accurate biomass mapping in complex tropical landscapes. The approach supports national forest monitoring, REDD+ programs, and ecosystem restoration, contributing to SDGs 13, 15, and 12 and offering a scalable method for other tropical regions.

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