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Russo-Ukrainian War monitoring with NASA's FIRMS

by Leonardo Möllmann Schöller

Why use FIRMS to detect war activity?

national-park-on-fire-twitter
Distribution of data points by date, direct correlation

Motivation


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


Model


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


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.

national-park-on-fire-twitter
Twitter report of wildfire
national-park-on-fire-firms
FIRMS detected fires around 2022/05/10

Data


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: $\frac{1}{\sqrt{x}+1}$
LowDensity HighDensity
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.

AccidentalFiresBeforeWar
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

Biloberezhia
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

Twitter
Geographic distribution
ControllMap
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

Twitter
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

Twitter
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