Russo-Ukrainian War monitoring with NASA's FIRMS

by Leonardo Möllmann Schöller

Why use FIRMS to detect war activity?

Distribution of data points by date, direct correlation

Motivation

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There are several motives to this topic, the biggest one is if this model can detect the intent score of the fire it can help start independent war crimes investigations and help identify climate damage concerning the war.

Sources

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• Center for information resilience (twitter data on confirmed attacks) - https://github.com/mauforonda/ukraine
• FIRMS archive data - https://firms.modaps.eosdis.nasa.gov/download/
• What is FIRMS - https://www.earthdata.nasa.gov/faq/firms-faq
• How to use FIRMS data - https://www.arcgis.com/home/item.html?id=b8f4033069f141729ffb298b7418b653
• FIRMS manual - https://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_C.pdf
• Ukraine regions - https://geodata.lib.utexas.edu/catalog/stanford-gg870xt4706
• Ukraine cities, Nature reserves, Forests, Rails, Roads - http://download.geofabrik.de/
• Ukraine forest fires - https://ceobs.org/countries/ukraine/

Model

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FIRMS' detected heat signatures are relatively big events of some tons of materials burning in a period, it is mainly used to track fire paths in wildfires for later be used in prevention and data collection about how our climate is changing through the years. These fires could also be triggered by artillery or sabotage, in other words, intentional fire.

The goal is to differentiate what class the FIRMS detected fire point is, is it a direct attack or a normal accidental fire? If we look just where the fire is happening we can get some clues on what is happening but there is no definitive answer, we have some different classifications that are hard to distinguish like:

	Forest	Urban
Accidental	Wildfire	Urban Fire
Attack	Attack on Defensible Positions	Attack on Civilian Infrastructure

With this model, we can mass classification data to track independently and in real-time where the fighting is occurring and the intensity of it.

Annotation

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The annotation is done by cross-relating temporal and geographical data from Twitter and NASA's FIRMS, this is done both manually and by bulk relation with data sources like the Center for Information Resilience using PostGIS queries.

Another event to consider is that with war, most services are overloaded or stopped working completely like wildfire fighting in occupied regions, one example is the Biloberezhia Sviatoslava national park which was on fire in the dry months of Russian occupation. We can hand annotate that data as an accidental fire in the period of war but this is when the lines begin to blur, we can argue a causal relation of because the war happened, this fire happened, but this will confuse our model a lot and overfit our data, so for these cases, we annotate as accidental.

Twitter report of wildfire

FIRMS detected fires around 2022/05/10

Data

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FIRMS attributes

Acquired Data:

• point or (lon, lat), coordinate of detection;
• date, datetime of detection;
• brightness, temperature (in Kelvin) using the MODIS channels 21/22;
• brightness_t31, temperature (in Kelvins) of the hotspot/active fire pixel;
• radiative_power, pixel-integrated fire radiative power in MW (MegaWatts), related to the rate at which fuel is being consumed;

Computed Geospatial Data:

• dist_road, distance to the nearest road, None if it's above 2km
• dist_rail, distance to the nearest railway, None if it's above 2km
• pop_density, average of nearest population centers weighted by distance using the following equation: $\frac{1}{\sqrt{x}+1}$

Low Density Area, 3.3 avg units	High Density Area, 1524 avg units

Model Data

• intent, category of the fire, 1 for accidental, 2 for attack
• source, where the classification comes from with 0 being predicted
• event, event associated with the annotation, used to intersect data with Twitter
• accident, model output of the accident classification
• attack, model output of the attack classification

FIRMS data categories

Type	Count	Class	Source	Event
2021-01-01 to 2021-12-31	10115	-
2022-02-24 to 2022-11-07	126027	-
1Km from a forest, before the war	6587	Accident	1
Biloberezhia wildfire	431	Accident	2	1
Center for information resilience	7269	Attack	3
Siege of Matiupol	527	Attack	4	2
Siege of Sievierodonetsk	724	Attack	5	3
Predicted Data	121555	?	6

• 1Km from a forest, before the war

Technically, every fire before the war began, outside the separatist Donbass region, was an accident, but to encourage the modal to learn more about natural forest fires, this bias was provided.

Geographic distribution

Attribute	Mean
brightness	316.512
brightness_t31	294.206
radiative_power	24.506
dist_road	333.939
dist_rail	453.745
avg_population	49.794

• Biloberezhia nature reserve wildfires (Russian occupation)

In the dry season during the Russian occupation, this nature reserve burnt down as a result of negligence, no firefighting was provided

Geographic distribution

Attribute	Mean
brightness	332.971
brightness_t31	294.260
radiative_power	16.170
dist_road	199.367
dist_rail	-
avg_population	6.340

• Center for information resilience, Twitter correlation with FIRMS

Geographic distribution

Current front lines

Attribute	Mean
brightness	325.193
brightness_t31	287.931
radiative_power	7.597
dist_road	138.415
dist_rail	419.653
avg_population	226.314

• Siege of Mariupol

Geographic distribution

Attribute	Mean
brightness	321.520
brightness_t31	279.804
radiative_power	4.242
dist_road	27.391
dist_rail	323.292
avg_population	839.082

• Siege of Sievierodonetsk-Lysychansk

Geographic distribution

Attribute	Mean
brightness	321.302
brightness_t31	292.513
radiative_power	5.396
dist_road	78.724
dist_rail	443.373
avg_population	271.664