Assessing GHG Emissions from Forest Fire and Stubble Burning using Satellite Remote Sensing

Stubble burning has become a recurrent event in many parts of India, including the central Indian state of Madhya Pradesh. The farmers burn the crop residue in the post-harvest period of the Kharif and Rabi seasons, to quickly prepare fields for the next cropping cycle. This study used MODIS satellite data-derived burned area product and Sentinel-5P derived NO2 column number density to identify fire burned areas and assess the corresponding GHG emission, respectively. The uncertainty layer was utilized in the MODIS data pre-processing, followed by monthly data aggregation. The ESRI land use land cover (LULC) layer was used to differentiate the burned areas in cropland and forest. The study observed a total of 5787 sq km fire-affected area in 2024, wherein crop residue burned area was 5346 sq km that was more than 12 times the forest fire area (441 sq km). More than 75% of the forest fire burning occurred in March, April, and May; with around 15% in post-monsoon November and December. Similarly, more than 74% of the crop residue burning occurred in March, April, and May; and around 20% in post-monsoon November. Overall, the maximum total fire events occurred in April (42%), followed by March (18%) and May (15%). On the contrary, the maximum NO₂ concentration was recorded in May and June, followed by November and December. The study hypothesizes that the temporal lag in NO₂ concentration relative to the total burned area may indicate the accumulation of NO₂ along with the contributions from neighboring states with higher anthropogenic GHG emissions. Moreover, the forest dwellers in central India often apply surface fire for forest floor cleaning during the peak summer months (May and June) before minor forest product collection. These under canopy surface fire events often remain undetected due to dense forest canopy, although they emit a significant amount of GHG. Thus, the GHG emissions from surface fire events remain unaccounted for. The study proposes developing a citizen science-based surface forest fire monitoring module. The accurate canopy and surface fire events, and the crop residue burned area would help assess GHG emission. Such as robust data can be used to develop predictive models for future surface fire risk estimation and quantification of the associated GHG emissions.