Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems

Abstract

The catastrophic impact of wildfires on the economy and ecosystems of Mediterranean countries in recent years, along with insufficient policies that favor disproportionally high funding for fire suppression, demand a more comprehensive understanding of fire regimes. Satellite remote sensing products support the generation of relevant burned-area (BA) information, since they provide the means for the systematic monitoring of large areas worldwide at low cost. This research study assesses the accuracy of the two publicly available MODIS BA products, MCD64A1 C6 and FireCCI51, at a national scale in a Mediterranean country. The research period covered four fire seasons, and a comparison was conducted against a higher-resolution Sentinel-2 dataset. The specific objectives were to assess their performance in detecting fire events occurring primarily in forest and semi-natural lands and to investigate their spatial and temporal uncertainties. Monthly fire observations were processed and analyzed to derive a comprehensive set of accuracy metrics. We found that fire size has an impact on their detection accuracy, with higher detection occurring in fires larger than 100 ha. Detection of smaller (<100 ha) fires was favored by the 250 m FireCCI51 product, but not from MCD64A1 C6, which exhibited less than 50% detection probability in the same range. Their spatial estimates of burned area exhibited a fairly satisfactory agreement with the reference data, reaching an average of 78% in detection rate. MCD64A1 C6 exhibited a more consistent spatial performance overall and better temporal accuracy, whereas FireCCI51 did not substantially outperform the former despite its finer resolution. Additional research is required for a more rigorous assessment of the variability of these burned area products, yet this research provides further insight and has implications for their use in fire-related applications at the local to the national scale.

Comparative Analysis of Multisensor Burned Area Products for the Brazilian Amazon – Region of the APA Triunfo do Xingu

“…Among optical sensor data, the BA-S2L8 datasets for the years 2018 and 2020, and BA-MAPBIOMAS, for the year 2019, presented the highest rates, being, respectively, 13% and 56% for BA -S2L8 and 50% for BA-MAPBIOMAS. We emphasize that the overlaps focused on polygons of burned areas smaller than 6.5 hectares, demonstrating the high spatial similarity between the datasets for small fire areas, despite the BA-MCD64A1 not being the best sensor to detect burns more minor than 100 hectares (Katagis and Gitas, 2022).…”

Mathematical Simulation of Forest Fuel Pyrolysis and Crown Forest Fire Impact for Forest Fire Danger and Risk Assessment

“…The developed mathematical model enables modernization or replacement of systems for predicting forest fire danger [77][78][79][80]. In addition, the technologies of mathematical modeling, geoinformation technologies, and remote sensing data can be integrated into a big multifunctional system [81][82][83][84].…”

Fire regime of peatlands in the Angolan Highlands

“…This study makes use of the MODIS FireCCI51 fire data (Chuvieco et al, 2018a ). This product has been extensively validated for wildfires and larger burns (Hall et al, 2021 ), is shown to have a higher detection of smaller fires (< 1 km 2 ) compared to other products (Katagis & Gitas, 2022 ), and has comparable accuracy in detecting tropical peatland fires (Vetrita et al, 2021 ). Despite this, limitations of the MODIS burned area data have been documented in the literature (Alves et al, 2018 ; Chuvieco et al, 2018b ; Ying et al, 2019 ), with omission errors up to 72% (Boschetti et al, 2019 ).…”