Uncertainty assessment of remote sensing- and ground-based methods to estimate wildfire emissions: a case study in Calabria region (Italy)

Abstract

In a general framework characterized by ever-increasing evidence of impacts attributable to climate change, the quantitative estimation of wildfire emissions (e.g., black carbon, carbon monoxide, particulate matter) and the evaluation of its uncertainty are crucial for mitigation and adaptation purposes. Global atmospheric emission models use mainly remote sensing fire datasets, which are affected by significant uncertainties. To assess the errors of remote sensing-based inventories, we compared the temporal and spatial behavior of the last version of the satellite-based Global Fire Emissions Database (GFED4s) with a more accurate ground-based wildfire emissions inventory, for the 2008–2016 period. The study area was Calabria (southern Italy), among the Italian regions with the highest contribution to national wildfire emissions. This study highlights a reliable agreement of time evolution of Burned Areas (R2 = 0.87), but an overestimation of their extent by satellite compared to ground observations (approximately + 18%). Nevertheless, satellite data systematically underestimated Dry Matter and emissions by forest and grassland wildfires (ranging between -66% and -97%). Furthermore, detailed information on land cover allowed assessing the vegetation parameters uncertainties on ground-based emission inventory. The Mass Available Fuel values, which are constantly modified by wildfires, and land use changes, and not frequently updated, showed not to affect the emission estimations. Finally, the relationship between ground-based and remote sensing-based inventories for the analyzed period highlighted that the preliminary satellite emissions related to 2017–2019 require careful validation before any applications.

FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and Interpretability

“…Traditional wildfire detection methods, such as satellite imagery [10,11] and groundbased sensor systems [12,13], are increasingly inadequate in the face of these increasing threats. While these traditional methods provide tangible data, they often face limitations due to the complex and unpredictable nature of wildfires.…”