Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


  • Tichaona Mukunga - , Technische Universitat Wien (Autor:in)
  • Matthias Forkel - , Juniorprofessur für Umweltfernerkundung (Autor:in)
  • Matthew Forrest - , Senckenberg Biodiversität und Klima Forschungszentrum (Autor:in)
  • Ruxandra Maria Zotta - , Technische Universitat Wien (Autor:in)
  • Nirlipta Pande - , Technische Universitat Wien (Autor:in)
  • Stefan Schlaffer - , Technische Universitat Wien (Autor:in)
  • Wouter Arnoud Dorigo - , Technische Universitat Wien (Autor:in)


Fires are a pervasive feature of the terrestrial biosphere and contribute large carbon emissions within the earth system. Humans are responsible for the majority of fire ignitions. Physical and empirical models are used to estimate the future effects of fires on vegetation dynamics and the Earth’s system. However, there is no consensus on how human-caused fire ignitions should be represented in such models. This study aimed to identify which globally available predictors of human activity explain global fire ignitions as observed by satellites. We applied a random forest machine learning framework to state-of-the-art global climate, vegetation, and land cover datasets to establish a baseline against which influences of socioeconomic data (cropland fraction, gross domestic product (GDP), road density, livestock density, grazed lands) on fire ignition occurrence were evaluated. Our results showed that a baseline random forest without human predictors captured the spatial patterns of fire ignitions globally, with hotspots over Sub-Saharan Africa and South East Asia. Adding single human predictors to the baseline model revealed that human variables vary in their effects on fire ignitions and that of the variables considered GDP is the most vital driver of fire ignitions. A combined model with all human predictors showed that the human variables improve the ignition predictions in most regions of the world, with some regions exhibiting worse predictions than the baseline model. We concluded that an ensemble of human predictors can add value to physical and empirical models. There are complex relationships between the variables, as evidenced by the improvement in bias in the combined model compared to the individual models. Furthermore, the variables tested have complex relationships that random forests may struggle to disentangle. Further work is required to detangle the complex regional relationships between these variables. These variables, e.g., population density, are well documented to have substantial effects on fire at local and regional scales; we determined that these variables may provide more insight at more continental scales.


PublikationsstatusVeröffentlicht - 10 Mai 2023

Externe IDs

WOS 001007355400001
ORCID /0000-0003-0363-9697/work/142252107