Towards understanding the environmental and climatic changes and its contribution to the spread of wildfires in Ghana using remote sensing tools and machine learning (Google Earth Engine)

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 degrees C) in GSZ and (-0.9 mm; + 0.3 degrees 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.

Automated summary

Data processing and climate characterisation for studying the impact of climate on wildfire spread in Ghana are hindered by insufficient and unavailable data, particularly in developing countries. Machine learning, combined with data obtained from various sources, such as CHIRPS, FLDAS, and TerraClimate, is used to analyze the link and contribution of climatic and environmental parameters on wildfire spread in Guinea-savannah (GSZ) and Forest-savannah Mosaic zones (FSZ) in Ghana. The analysis reveals a decrease in rainfall and an increase in temperature in both GSZ and FSZ, with precipitation (PR), reference evapotranspiration (ETR), fire danger index (FDI), and various temperature-related variables playing a significant role in fire spread. The developed codes allow for easy updating and monitoring of climate variability and its impact on fires by researchers and decision-makers.