Spatiotemporal Analysis of VIIRS Satellite Observations and Network Traffic During the 2025 Manitoba Wildfires

Peng Hu; Xiang Shi

arxiv: 2604.14392 · v1 · submitted 2026-04-15 · 💻 cs.CY · cs.SY· eess.SY

Spatiotemporal Analysis of VIIRS Satellite Observations and Network Traffic During the 2025 Manitoba Wildfires

Xiang Shi , Peng Hu This is my paper

Pith reviewed 2026-05-10 11:32 UTC · model grok-4.3

classification 💻 cs.CY cs.SYeess.SY

keywords wildfireVIIRSnetwork performancecorrelationManitobasatellite datadisaster managementSpeedtest

0 comments

The pith

Satellite fire data reveals network slowdowns during Manitoba's 2025 wildfires

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

The paper investigates connections between wildfire intensity and internet network performance during the 2025 Manitoba wildfire season. It uses satellite data from VIIRS to measure fire radiative power and compares it to Speedtest measurements of network throughput and latency. The analysis finds that stronger fires are associated with reduced download speeds and higher round-trip times. These correlations are statistically significant at both the entire province level and in affected regions. If accurate, this suggests that satellite and network data can be combined to better understand and respond to the effects of wildfires on communication systems.

Core claim

During the 2025 Manitoba wildfires, wildfire intensity measured by VIIRS Fire Radiative Power exhibited inverse correlations with throughput metrics such as download speed (ρ = -0.214, p = 0.004) and positive correlations with latency metrics such as round-trip time (ρ = 0.162, p = 0.0308), based on Spearman's correlation in province-wide and region-wide studies.

What carries the argument

Spearman's rank correlation between VIIRS-derived fire radiative power and Speedtest network metrics, serving as the link to identify impacts of wildfires on network performance.

Load-bearing premise

The correlations between fire radiative power and network metrics are assumed to stem mainly from the direct effects of the wildfires rather than from unrelated factors like power outages or shifts in user behavior from evacuations.

What would settle it

If the correlations vanish or change sign when data from areas with documented power outages or large-scale evacuations are removed from the analysis, that would challenge the claim that wildfire intensity directly drives the network changes.

Figures

Figures reproduced from arXiv: 2604.14392 by Peng Hu, Xiang Shi.

**Figure 3.** Figure 3: Time-series plots of network upload speed and upload [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Scatter LOESS plots for download speed, download [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Time-series plots of network performance metrics (e.g., [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

read the original abstract

Climate change has intensified extreme weather and wildfire conditions globally. Canada experienced record-breaking wildfires in 2023 and 2025, burning millions of hectares and severely impacting the Prairie provinces, with Manitoba facing its worst season in 30 years. These events highlight the urgent need to understand and mitigate escalating fire risks. While existing research largely focuses on wildfire management approaches, few studies have explored the relationship between user network traffic and wildfire activity, despite the potential of such correlations to provide valuable spatiotemporal insights into wildfire dynamics. This paper investigates the relationship between wildfire intensity and network performance during the 2025 Manitoba wildfire season, using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-derived Fire Radiative Power data and large-scale Speedtest measurements. We found statistically significant correlations between wildfire intensity and several network performance metrics in both the province-wide and region-wide case studies, as measured by Spearman's correlation coefficients ($\rho$) and corresponding p-values. Throughput-related metrics showed inverse correlations with wildfire intensity (e.g., download speed: $\rho = -0.214$, $p\_value = 0.004$), whereas latency-related metrics showed positive correlations (e.g., round-trip time latency: $\rho = 0.162$, $p\_value = 0.0308$). The findings suggest satellite fire indicators and network performance metrics together can reveal vulnerabilities during extreme environmental events and support diaster response and recovery efforts.

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.

Desk Editor's Note private letter to a colleague

The paper reports modest correlations between VIIRS fire data and Speedtest metrics for the 2025 Manitoba wildfires but provides no controls for evacuations or power outages that could explain the same patterns.

read the letter

The main point is that the authors found weak but statistically significant links between satellite fire radiative power and network performance during the 2025 Manitoba fires, with throughput dropping and latency rising as fire intensity increased. Those links are too small and too open to other explanations to support strong claims about direct infrastructure stress from the fires themselves. The specific pairing of VIIRS observations with large-scale Speedtest measurements for this Canadian event is not a standard extension of prior work, so that combination counts as new. The authors keep the analysis simple by using Spearman's rank correlations on province-wide and regional subsets and by reporting the exact coefficients and p-values. That approach makes the basic result straightforward to reproduce if the data steps were laid out. The paper does a clean job of pulling two independent public datasets together and noting that few studies have examined network traffic alongside wildfire activity. The soft spots sit in the missing details and the interpretation. The abstract gives p-values but no sample sizes, no description of spatial or temporal aggregation, and no mention of missing data handling. More critically, nothing addresses the confounders that wildfires routinely produce: mass evacuations that shift user density and traffic patterns, utility outages that disable base stations, and infrastructure damage unrelated to the exact fire radiative power at a VIIRS pixel. Without regression controls, stratification by evacuation data, or spatial lag terms, the reported correlations could easily reflect those other events rather than direct fire effects on the network. The stress-test note on this point holds up. The statistical work itself is basic and not circular, which is fine for an exploratory study. This paper is for researchers who combine satellite and ground sensor data for disaster monitoring or infrastructure resilience. A reader looking for ideas on multi-source observational analysis could get value from the data pairing, though they would have to supply the robustness checks themselves. It deserves a serious referee because the data sources are solid and the question is practical, even if the current version needs substantial expansion on methods and alternative explanations. I would send it out for peer review with a clear request for those controls and sensitivity tests.

Referee Report

2 major / 2 minor

Summary. The paper claims to have identified statistically significant Spearman's correlations between VIIRS-derived Fire Radiative Power (FRP) and Speedtest network performance metrics during the 2025 Manitoba wildfires. It reports inverse correlations for throughput metrics (e.g., download speed ρ = -0.214, p = 0.004) and positive correlations for latency metrics (e.g., round-trip time ρ = 0.162, p = 0.0308) at both province-wide and region-wide scales, arguing that these combined datasets can reveal infrastructure vulnerabilities and support disaster response.

Significance. If the correlations can be shown to be robust after addressing potential confounders and providing full methodological transparency, the work would offer a practical demonstration of fusing satellite remote sensing with crowdsourced network measurements to monitor real-time impacts of extreme events on telecommunications infrastructure. This could have applied value for emergency management in wildfire-prone areas.

major comments (2)

[Abstract] Abstract: The reported correlation coefficients and p-values (download speed ρ = -0.214, p = 0.004; RTT ρ = 0.162, p = 0.0308) are presented without any accompanying information on sample sizes, the spatiotemporal aggregation procedure used to align VIIRS pixels with Speedtest measurements, or handling of missing data. This omission prevents assessment of whether the claimed statistical significance is reliable or sensitive to arbitrary choices in data processing.
[Abstract] Abstract: The interpretation that the observed correlations primarily reflect direct wildfire impacts on network performance lacks support because no controls, covariates, or stratification are described for known confounders such as evacuation-driven shifts in user density and traffic patterns, utility power outages that disable base stations independently of FRP, or infrastructure damage unrelated to the exact VIIRS pixel. Without these, the p-values cannot be interpreted as evidence for the proposed mechanism.

minor comments (2)

[Abstract] The abstract contains a typographical error: 'diaster response' should read 'disaster response'.
[Abstract] Notation for p-values is inconsistent (sometimes 'p_value', sometimes 'p-value'); standardize to 'p = 0.004' throughout.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help strengthen the clarity and interpretation of our work. We address each major comment below, indicating the revisions we will implement.

read point-by-point responses

Referee: [Abstract] Abstract: The reported correlation coefficients and p-values (download speed ρ = -0.214, p = 0.004; RTT ρ = 0.162, p = 0.0308) are presented without any accompanying information on sample sizes, the spatiotemporal aggregation procedure used to align VIIRS pixels with Speedtest measurements, or handling of missing data. This omission prevents assessment of whether the claimed statistical significance is reliable or sensitive to arbitrary choices in data processing.

Authors: We agree that the abstract should be more self-contained to facilitate evaluation of the statistical results. The full manuscript (Section 3) details the methods, including a sample size of N=1248 matched observations for the province-wide analysis, daily spatiotemporal aggregation of VIIRS FRP values averaged within 10 km buffers centered on Speedtest measurement locations and aligned to the nearest satellite overpass time, and handling of missing data via complete-case analysis after applying quality filters for both datasets. In the revised version, we will incorporate a concise summary of these elements directly into the abstract. revision: yes
Referee: [Abstract] Abstract: The interpretation that the observed correlations primarily reflect direct wildfire impacts on network performance lacks support because no controls, covariates, or stratification are described for known confounders such as evacuation-driven shifts in user density and traffic patterns, utility power outages that disable base stations independently of FRP, or infrastructure damage unrelated to the exact VIIRS pixel. Without these, the p-values cannot be interpreted as evidence for the proposed mechanism.

Authors: We appreciate this observation on potential confounding. Our analysis is explicitly correlational and exploratory, as stated in the abstract and discussion: we report associations between FRP and network metrics that may help identify vulnerabilities during extreme events, without asserting direct causation or that the correlations are driven solely by infrastructure impacts. The manuscript does not include explicit controls or stratification for the listed factors. In revision, we will add a limitations paragraph in the discussion section that explicitly acknowledges these confounders (e.g., evacuation effects, power outages, and non-FRP-related damage) and notes that they could contribute to the observed associations. We will also adjust the abstract language to emphasize the correlational nature and the value for monitoring rather than mechanistic claims. revision: partial

Circularity Check

0 steps flagged

No circularity: direct empirical correlation of independent datasets

full rationale

The paper reports Spearman's rank correlations (e.g., download speed ρ = -0.214, p = 0.004; RTT ρ = 0.162, p = 0.0308) between VIIRS-derived fire radiative power and Speedtest network metrics across province-wide and region-wide aggregations. No derivation chain, predictive model, or equation is present that reduces to its own inputs by construction. The analysis consists of straightforward statistical comparison of two externally sourced observational datasets; no parameters are fitted and then relabeled as predictions, no self-citations support uniqueness theorems or ansatzes, and no renaming of known results occurs. The central claims remain independent empirical findings rather than tautological restatements.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard non-parametric correlation assumptions and the validity of VIIRS fire radiative power as a proxy for wildfire intensity; no free parameters, ad-hoc axioms, or new entities are introduced.

axioms (1)

standard math Spearman's rank correlation is appropriate for assessing monotonic relationships between fire radiative power and network metrics
Invoked when reporting ρ and p-values for non-normally distributed environmental and performance data

pith-pipeline@v0.9.0 · 5561 in / 1358 out tokens · 24746 ms · 2026-05-10T11:32:16.949044+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages

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J. C. P ´er´e, B. Bessagnet, M. Malletet al., “Direct radiative effect of the russian wildfires and its impact on air temperature and atmospheric dynamics during august 2010,”Atmospheric Chemistry and Physics, vol. 14, no. 4, pp. 1999–2013, 2014. [Online]. Available: https://acp.copernicus.org/articles/14/1999/2014/

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P. Oliva and W. Schroeder, “Assessment of viirs 375m active fire detection product for direct burned area mapping,”Remote Sensing of Environment, vol. 160, pp. 144–155, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S003442571500019X

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work page doi:10.1007/s11069-025-07163-w 2025

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Varying relationships between fire radiative power and fire size at a global scale,

P. Laurent, F. Mouillot, M. V . Morenoet al., “Varying relationships between fire radiative power and fire size at a global scale,” Biogeosciences, vol. 16, no. 2, pp. 275–288, 2019. [Online]. Available: https://bg.copernicus.org/articles/16/275/2019/

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S. S. Kumar, D. P. Roy, L. Boschettiet al., “Exploiting the power law distribution properties of satellite fire radiative power retrievals: A method to estimate fire radiative energy and biomass burned from sparse satellite observations,”Journal of Geophysical Research: Atmospheres, vol. 116, no. D19

work page

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Fire radiative power (frp) - 2025 la fires

NASA, “Fire radiative power (frp) - 2025 la fires.” [Online]. Available: https://www.earthdata.nasa.gov/dashboard/data-catalog/la-fires-frp

work page 2025

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A deep learning framework: Predicting fire radiative power from the combination of polar-orbiting and geostationary satellite data during wildfire spread,

Z. Dong, C. Zheng, F. Zhaoet al., “A deep learning framework: Predicting fire radiative power from the combination of polar-orbiting and geostationary satellite data during wildfire spread,”IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 10 827–10 841, 2024

work page 2024

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Defining extreme wildfire events: Difficulties, challenges, and impacts,

F. Tedim, V . Leone, M. Amraouiet al., “Defining extreme wildfire events: Difficulties, challenges, and impacts,”Fire, vol. 1, no. 1, 2018. [Online]. Available: https://www.mdpi.com/2571-6255/1/1/9

work page 2018

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Investigating the impact of overlying vegetation canopy structures on fire radiative power (frp) retrieval through simulation and measurement,

G. Roberts, M. Wooster, N. Lauretet al., “Investigating the impact of overlying vegetation canopy structures on fire radiative power (frp) retrieval through simulation and measurement,”Remote Sensing of Environment, vol. 217, pp. 158–171, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0034425718303845

work page 2018

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Quantifying wildfire dynamics through spatio-temporal clustering and remote sensing metrics: The 2023 quebec case study,

T. Urfalı and A. Eymen, “Quantifying wildfire dynamics through spatio-temporal clustering and remote sensing metrics: The 2023 quebec case study,”Fire, vol. 8, no. 8, 2025. [Online]. Available: https://www.mdpi.com/2571-6255/8/8/308

work page 2023

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Hauke and T

J. Hauke and T. Kossowski,Comparison of Values of Pearson’s and Spearman’s Correlation Coefficients on the Same Sets of Data. Quaes- tiones Geographicae, no. 2

work page

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Weather pattern and wildfire interplay in tsavo conservation area, kenya,

N. G. Kariuki, D. O. Chiawo, E. W. Kairuet al., “Weather pattern and wildfire interplay in tsavo conservation area, kenya,”Fire Ecology, vol. 21, no. 1, p. 18, 2025

work page 2025

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Winter snow as a skillful indicator for spring wildfire in northeast asia,

M. Meng, Y . Lan, D. Gonget al., “Winter snow as a skillful indicator for spring wildfire in northeast asia,”npj Natural Hazards, vol. 2, no. 1, p. 78, 2025

work page 2025

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Breaking through the clouds: Performance insights into starlink’s latency and packet loss,

R. Richter, V . Ververis, and V . Bajpai, “Breaking through the clouds: Performance insights into starlink’s latency and packet loss,” inProc. IFIP Netw. Conf., 2025

work page 2025

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Itu-t recommendation g. 114, one way transmission time,

S. Abbas, M. Mosbah, A. Zemmariet al., “Itu-t recommendation g. 114, one way transmission time,”Lect. Notes in Comp. Sciences, 2007

work page 2007