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arxiv: 2606.06016 · v1 · pith:JGRSFRCInew · submitted 2026-06-04 · ⚛️ physics.ao-ph

Leveraging MTG-FCI fire observations for event-based fire behavior monitoring from near-real-time operation to seasonal analysis

Ronan Paugam , Jean-Baptiste Filippi , Akli Benali , Jorge Gomes , Weidong Xu , Emanuel Dutra , Francois Andre , Damien Boulanger
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Vianney Retornard Andrea Meraner Julia Harvie Victor Penot Cyrielle Denjean
This is my paper

Pith reviewed 2026-06-27 22:52 UTC · model grok-4.3

classification ⚛️ physics.ao-ph
keywords active fire detectionMTG-FCIspatio-temporal clusteringfire event trackingwildfire monitoringfire radiative powerrate of spread
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The pith

The Fire Event Tracker clusters MTG-FCI fire detections into persistent events using fixed spatio-temporal rules, delivering consistent updates on fire geometry, power, and spread every 10 minutes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a Fire Event Tracker algorithm that groups individual active fire detections from the MTG-FCI sensor into coherent events by applying a single set of spatio-temporal clustering rules. These rules assign unique identifiers to each fire and recalculate its geometry, radiative power, and rate of spread at every 10-minute observation interval. The same fixed parameterization operates for both immediate wildfire response and later seasonal analysis of fire archives. A reader would care because raw satellite hotspots become usable records of how specific fires evolve, supporting resource decisions during events and pattern studies afterward.

Core claim

Event-based clustering of FCI active fire detections provides a consistent description of fire evolution. The FET algorithm performs spatio-temporal clustering on the LSA-SAF FCI active fire product, assigns persistent identifiers to fire events, and updates their geometry, fire radiative power, and rate of spread at each 10-min interval. The same parameterization supports both near-real-time operation and retrospective processing of the Mediterranean 2025 archive, as demonstrated in Portugal wildfire monitoring and the SILEX airborne campaign.

What carries the argument

The Fire Event Tracker (FET) algorithm, which performs spatio-temporal clustering of hotspot detections to assign persistent identifiers and update fire attributes at 10-minute intervals with one fixed parameterization.

Load-bearing premise

A single fixed set of spatio-temporal clustering rules can reliably link individual hotspot detections into coherent fire events across different fire sizes, observation conditions, and regions without frequent splitting or merging mistakes.

What would settle it

Systematic observation that the same fires fragment into multiple identifiers or that separate fires merge under the fixed rules when applied to fires of varying sizes or under different viewing angles and atmospheric conditions.

Figures

Figures reproduced from arXiv: 2606.06016 by Akli Benali, Andrea Meraner, Cyrielle Denjean, Damien Boulanger, Emanuel Dutra, Francois Andre, Jean-Baptiste Filippi, Jorge Gomes, Julia Harvie, Ronan Paugam, Vianney Retornard, Victor Penot, Weidong Xu.

Figure 1
Figure 1. Figure 1: Flow chart of the Fire Event Tracker (FET) algorithm [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Domain extension of the three configuration set-up of the Fire Event Tracker Algorithm (MED,PORTUGAL and SILEX) [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Panel (a) shows the yearly sum of the Fire Radiative Energy (FRE) for all fire event of 2025 within the MED domain. The sum [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Map of the 95th percentile of the fire event duration of all fire events from 2025 within the MED domain. The 95th percentile [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Panel (a) shows Map of the 95th percentile of the event FROS for all fire events from 2025 within the MED domain. The event [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Forward Rate Of Spread (FROS) classes (no value, null, and positive) distribution across fire event categories defined by their [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: RGB (top) and Middle-Wave Infrared (MWIR, 3.8 [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Time series of FRP for two fire events shown in Figure [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Spatial distribution of the 465 FCI triggered automated forecast launched for the SILEX Campaign, in blue more than 1 runs, [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison between FOREFIRE-MESONH plume simulation run within 1h after detection and FCI plume observation [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison between the final Area of Interest (AOI) delineated by FET and the EFFIS Burned Area (BA) for the same 2025 [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: View from the web interface develop to support the Fire Event Tracker algorithm. [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Update of the Figure [PITH_FULL_IMAGE:figures/full_fig_p020_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Distribution of the Area Of Interest (AOI) for fire events in which the FROS computation is successful (blue) and in which [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
read the original abstract

Wildfire monitoring and suppression require timely information on fire behavior, including fire energy release and rate of spread, to support operational decision-making and resource allocation. Active fire products from the Flexible Combined Imager (FCI) aboard the geostationary Meteosat Third Generation (MTG) satellites provide 10-min observations over Europe and Africa. Deriving fire behavior information from these observations requires associating individual hotspot detections into coherent fire events. We present a Fire Event Tracker (FET) algorithm that performs spatio-temporal clustering of hotspot detections from the LSA-SAF FCI active fire product. The algorithm assigns persistent identifiers to fire events and updates their geometry, fire radiative power, and rate of spread at each 10-min interval. The same parameterization is used for both near-real-time and retrospective processing. FET was applied retrospectively to the Mediterranean FCI hotspot archive of 2025 and operationally in two near-real-time contexts: wildfire monitoring in Portugal and support of the 2025 SILEX airborne campaign within the EUBURN project, where besides fire monitoring, FET products were also used to initialize coupled FOREFIRE-MesoNH simulations for plume forecasting. Results show that event-based clustering of FCI active fire detections provides a consistent description of fire evolution, enabling both tactical wildfire management and high-frequency seasonal fire analyses.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents the Fire Event Tracker (FET) algorithm that performs spatio-temporal clustering of hotspot detections from the LSA-SAF FCI active fire product to associate individual detections into coherent fire events with persistent identifiers. The algorithm updates fire geometry, radiative power, and rate of spread at 10-min intervals using the same parameterization for near-real-time operations in Portugal, support of the SILEX campaign, and retrospective analysis of the 2025 Mediterranean archive. The central claim is that this approach provides a consistent description of fire evolution suitable for both tactical management and high-frequency seasonal analyses.

Significance. Should the algorithm's consistency be demonstrated through quantitative validation, this contribution would enable timely fire behavior monitoring from geostationary satellite data at 10-minute resolution, which has potential value for operational wildfire suppression and detailed studies of fire regimes over Europe and Africa.

major comments (2)
  1. [Abstract] The statement 'Results show that event-based clustering of FCI active fire detections provides a consistent description of fire evolution' is presented without any quantitative performance metrics, validation against independent data, error analysis, or comparison to baselines, which is load-bearing for the central claim of providing a consistent description across NRT and seasonal contexts.
  2. [Results] No diagnostics are reported to address the assumption that a single fixed parameterization of spatio-temporal clustering reliably groups hotspots without substantial fragmentation or erroneous merging across varying fire sizes, cloud cover, or regional conditions, such as event lifetime distributions or overlap with independent perimeters.
minor comments (1)
  1. The abstract is quite long and could be condensed for clarity while retaining key claims.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive review. The comments highlight the need for stronger quantitative support for the consistency claim, which we address below by proposing targeted revisions while noting limitations in available validation data.

read point-by-point responses

  1. Referee: [Abstract] The statement 'Results show that event-based clustering of FCI active fire detections provides a consistent description of fire evolution' is presented without any quantitative performance metrics, validation against independent data, error analysis, or comparison to baselines, which is load-bearing for the central claim of providing a consistent description across NRT and seasonal contexts.

    Authors: We agree the abstract phrasing overstates the evidential basis. The manuscript shows consistency via identical parameterization applied to NRT operations in Portugal, SILEX campaign support, and the full 2025 Mediterranean archive, with practical utility illustrated through those deployments. We will revise the abstract to remove the load-bearing claim and instead state that the approach enables consistent monitoring across contexts, supported by the operational examples. No formal error analysis or baseline comparisons exist in the current work. revision: yes

  2. Referee: [Results] No diagnostics are reported to address the assumption that a single fixed parameterization of spatio-temporal clustering reliably groups hotspots without substantial fragmentation or erroneous merging across varying fire sizes, cloud cover, or regional conditions, such as event lifetime distributions or overlap with independent perimeters.

    Authors: The fixed parameterization is used uniformly across the reported applications, providing indirect support through successful event tracking in varied conditions. We will add event lifetime distributions and basic fragmentation/merging discussion drawn from the 2025 archive in a revised Results section. Overlap with independent perimeters is not feasible with data available to the authors. revision: partial

standing simulated objections not resolved
  • Quantitative overlap analysis with independent fire perimeters to validate clustering accuracy

Circularity Check

0 steps flagged

No circularity: algorithmic pipeline with fixed parameters

full rationale

The paper presents the FET algorithm as a fixed-parameter spatio-temporal clustering method applied to FCI hotspot data. No equations derive a target quantity from fitted inputs, no predictions are made by construction from the same data used to set parameters, and no self-citation chain or uniqueness theorem is invoked to justify the central claim. The results are outcomes of running the described pipeline on independent archives and operational streams; the consistency claim is an empirical observation of the output rather than a tautological reduction to the inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that spatio-temporal proximity clustering produces coherent fire events and on unspecified free parameters controlling the clustering thresholds and update logic.

free parameters (1)
  • spatio-temporal clustering thresholds
    The algorithm requires parameterization for distance and time windows to group hotspots; these values are stated to be fixed but not quantified in the abstract.
axioms (1)
  • domain assumption Individual hotspot detections from the LSA-SAF FCI product can be associated into coherent fire events via spatio-temporal clustering
    This is the core premise invoked when describing the FET algorithm in the abstract.

pith-pipeline@v0.9.1-grok · 5825 in / 1234 out tokens · 26622 ms · 2026-06-27T22:52:07.404892+00:00 · methodology

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Reference graph

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