Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

Domaine:

environment and energygeospatial

Type de record:

paper
Créateur:
PatMarGareth RobertsWei
Éditeur:
MDP
Hôte:
Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI’s omission rate can be attributed to a coupling between SEVIRI’s low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI’s commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/3.0/

Similaires

Characterizing the spatio-temporal fire regime in Ethiopia using the MODIS-active fire product: a replicable methodology for country-level fire reportingAnalysing Threshold Value in Fire Detection Algorithm Using MODIS DataBiomass Burning in Africa: An Investigation of Fire Radiative Power Missed by MODIS Using the 375 m VIIRS Active Fire ProductEstimating biomass consumed from fire using MODIS FREIDENTIFYING ACTIVE FIRE IN SOUTHWESTERN NIGERIA WITH MODIS DATA AND GEOGRAPHICAL INFORMATION SYSTEMSForest Fire Detection using UAV Imaginary Data

Characterizing the spatio-temporal fire regime in Ethiopia using the MODIS-active fire product: a replicable methodology for country-level fire reporting

Analysing Threshold Value in Fire Detection Algorithm Using MODIS Data

MODIS instruments have been designed to include special channels for fire monitoring by adding more

Biomass Burning in Africa: An Investigation of Fire Radiative Power Missed by MODIS Using the 375 m VIIRS Active Fire Product

Biomass burning plays a key role in the interaction between the atmosphere and the biosphere. The ne

Estimating biomass consumed from fire using MODIS FRE

Biomass burning is an important global phenomenon impacting atmospheric composition. Application of

IDENTIFYING ACTIVE FIRE IN SOUTHWESTERN NIGERIA WITH MODIS DATA AND GEOGRAPHICAL INFORMATION SYSTEMS

The potentials of active fire product derived from Moderate Resolution Imaging Spectroradiometer (MO

Forest Fire Detection using UAV Imaginary Data

Forest fires are a major threat to ecosystems and human lives. Early detection of forest fires can g