O lande al. Fi e Ecology (2025) 21:55
h ps://doi.o g/10.1186/s42408-025-00407-x
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Fi e Ecology
Nea eal- ime indica o s o bu n se e i y
in hewes e n U.S. omac i e i e acking
Elijah O land1,2* , Tempes D. McCabe1,3, Yang Chen4, Rebecca C. Schol en4, Zeb Becke 1,3,
Rachel A. Loehman5, James T. Rande son4, Shane R. Co ield1,3, Tianjia Liu6, Alexey N. Shiklomano 1,
Ku is Nelson7, Bi gi Pe e son7, Melanie B. Folle e‑Cook1 and Douglas C. Mo on1
Abs ac
Backg ound Timely in o ma ion on wild i e bu n se e i y is c i ical o assess and mi iga e po en ial pos ‑ i e impac s
on soils, ege a ion, and hillslope s abili y. T acking indi idual i e sp ead and in ensi y using sa elli e ac i e i e da a
p o ides a pa hway o nea eal‑ ime (NRT) in o ma ion. He e, we gene a ed a la ge da abase (n = 2177) o wild i e
e en s in he wes e n Uni ed S a es (U.S.) be ween 2012 and 2021 using ac i e i e de ec ions om he Visible In a ed
Imaging Radiome e Sui e (VIIRS) senso on he Suomi Na ional Pola ‑o bi ing Pa ne ship (SNPP) sa elli e and he Fi e
E en s Da a Sui e (FEDS) algo i hm o ack la ge i e g ow h e e y 12 h. We in eg a ed i e acking da a wi h inal i e
pe ime e s and bu n se e i y da a om he Moni o ing T ends in Bu n Se e i y (MTBS) p og am o e alua e he ela‑
ionship be ween bu n se e i y and i e beha io me ics de i ed om he i e acking app oach, including he a e
o i e sp ead and a e age i e adia i e powe (FRP) o i e de ec ions o each 12‑h g ow h inc emen .
Resul s When s a i ied by ege a ion ype, FRP and a e o sp ead me ics we e posi i ely co ela ed wi h classi ied
bu n se e i y o each 12‑h g ow h inc emen , highligh ing he po en ial o apidly iden i y a eas o high and low
se e i y bu ning. In o es s, in eg a ed measu es o FRP o e he i e li e ime cap u ed pe sis en laming and smolde ‑
ing ha compensa ed o ini ial di e ences be ween AM (01:30) and PM (13:30) i e de ec ions. P edic i e modeling
o hese ela ionships based on mul iple i e beha io indica o s and ege a ion ype om he LANDFIRE p og am
yielded an accu acy o 78% o he sepa a ion o unbu ned/low and mode a e/high bu n se e i y classes.
Conclusions These esul s demons a e he abili y o cap u e wi hin‑ i e di e ences in bu n se e i y using NRT
indica o s om i e acking o assis wi h eme gency managemen and disas e p epa edness o pos ‑ i e haza ds,
such as landslides, deb is lows, o changes in s eam low and wa e quali y. As VIIRS da a a e a ailable wi hin min‑
u es o each sa elli e o e pass in he U.S., apid es ima es o bu n se e i y based on i e acking can be made days
o weeks be o e a la ge wild i e is ully con ained.
Keywo ds Remo e sensing, Pos i e, Machine lea ning, Bu n se e i y, Fi e acking, Fi e in ensi y
Resumen
An eceden es La in o mación opo una sob e la se e idad de los incendios es c í ica pa a de e mina y mi iga
los po enciales impac os pos ‑ uego sob e los suelos, la ege ación, y la es abilidad de las lade as de mon aña. El
seguimien o de la elocidad de p opagación de cada incendio ac i o usando da os de sa éli es, p o ee de una
ía ápida pa a ob ene in o mación en iempo eal (NRT). En es e abajo, gene amos una g an base de da os de
*Co espondence:
Elijah O land
[email p o ec ed]; [email p o ec ed]
Full lis o au ho in o ma ion is a ailable a he end o he a icle
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O lande al. Fi e Ecology (2025) 21:55
e en os de incendios (n = 2.177) en el oes e de los EEUU en e 2012 y 2021, usando de ecciones ac i as del senso de
imágenes adiomé icas in a ojas (VIIRS) del sa éli e Suomi Na ional Pola -O bi ing Pa ne ship (SNPP), y de un algo‑
i mo de un conjun o de da os de e en os de incendios (FEDS). Es o pe mi e, cada 12 ho as, as ea el c ecimien o
de g andes incendios. In eg amos los da os de as eo de es os incendios con los pe íme os inales del uego y con
las de ecciones cada 12 ho as sob e la se e idad de es os incendios basados en el p og ama de moni o eo de las en‑
dencias en se e idad de las quemas (MTBS), pa a e alua la elación en e la se e idad de los incendios y las mé icas
de compo amien o del uego de i adas de la ap oximación del as eo, incluyendo la asa de p opagación del uego,
y el p omedio del pode adian e del uego (FRP) de las de ecciones pa a cada 12 h de inc emen o en el c ecimien o
del uego.
Resul ados Cuando ue on es a i icados po ipo de ege ación, el FRP y las mé icas de p opagación ue on posi i‑
amen e co elacionadas con la clasi icación de la se e idad del uego cada 12 h de inc emen o en el c ecimien o del
incendio, sub ayando el po encial pa a iden i ica ápidamen e á eas de al a y baja se e idad del uego. En bosques,
las medidas in eg adas de FRP sob e la du ación del incendio cap u a on llamas y ma e ial en combus ión de mane a
pe manen e, que compensa on las di e encias iniciales de de ección en e la 01:30 AM y las 13:30 PM. El modelo p e‑
dic i o de esas elaciones basadas en indicado es múl iples de compo amien o del uego y ipos de ege ación del
p og ama LANDFIRE, u ie on una exac i ud del 78% pa a la sepa ación de clases de se e idad de “no quemado/baja”,
y “mode ado/al a in ensidad”.
Conclusiones Es os esul ados mues an la habilidad pa a cap u a las di e encias de se e idad en e uegos usando
indicado es del as eo de NRT y pode asis i con el manejo de la eme gencia y la p epa ación del desas e y los
pelig os del pos uego como los deslizamien os de lade as, el lujo de esiduos, o cambios en el lujo de las co ien es
de los a oyos y la calidad del agua. Dado que los da os de VIIRS es án disponibles den o de pocos minu os luego
de que el sa éli e haya sob e olado los EEUU, las es imaciones ápidas de la se e idad basadas en el as eo del uego
puede hace se muchos días o semanas an es de que un g an incendio sea o almen e con enido.
Backg ound
Wild i es ha e subs an ial and in e connec ed impac s
on ege a ion, soils, and hyd ology (Bowman e al. 2009)
ha a y as a unc ion o bu n ex en and in ensi y (e.g.,
Adams 2013; Coop e al. 2019; Schwilk & Acke ly 2001).
Fuel consump ion and i e-induced ege a ion mo al-
i y al e biogeochemical cycles and con ibu e o g een-
house gas emissions (e.g., C u zen & And eae 1990;
Hao & Liu 1994; Kasischke e al. 1995; Seile & C u zen
1980; Van De We e al. 2003, 2017). Pos - i e changes
in ege a ion s uc u e and composi ion also change su -
ace albedo (Rande son e al. 2006), educe he in il a-
ion capaci y o bu ned soils (Debano 2000; Le ey 2001),
igge soil nu ien and chemical losses (Alexakis e al.
2021; Chen e al. 2010; Ne e al. 2005; Ro i a e al.
2012), and inc ease he a ailable sedimen o mobiliza-
ion downslope (Flo sheim e al. 1991, 2016; Gabe 2003;
Lamb e al. 2011, 2013). Combined, hese impac s lead
o longi udinal changes o he hyd ologic cycle, includ-
ing educ ions in e apo anspi a ion (Ahmad e al. 2024;
Bond-Lambe y e al. 2009; Kang e al. 2006; Roche e al.
2018) an inc ease in o e land low (Sco e al. 1998; Vega
& Díaz-Fie os Viquei a 1987; Wells 1981) and g ea e
isk o ca as ophic deb is lows (Cannon 2001; Can-
non & DeG a 2009; Kean e al. 2011; Lancas e e al.
2021). As wild i es in he wes e n Uni ed S a es and o he
i e-p one egions become mo e equen and in ense
(Aba zoglou & Williams 2016; Cunningham e al. 2024;
Muelle e al. 2020; Wes e ling e al. 2006), he expedi ed
deli e y o bu n se e i y da a is c ucial o assessing i e
e ec s and o alloca ing esou ces o manage pos - i e
haza ds.
One common app oach o assess bu n se e i y elies
on p e- i e and pos - i e sa elli e image y o es ima e he
di e enced o "del a" no malized bu n a io (dNBR)–a
me ic sensi i e o he loss o li e ege a ion co e and
soil exposu e ollowing bu ning (Eidenshink e al. 2007;
Key & Benson 2006). As such, mapped dNBR wi hin a
bu n sca based on Landsa o Sen inel-2 image y is a
common inpu o classi ying bu n se e i y, as used in
s anda d p oduc s om he Moni o ing T ends in Bu n
Se e i y (MTBS) o Bu ned A ea Eme gency Response
(BAER) p og ams. These same image sou ces can also be
used o de i e al e na i e indices such as he Rela i ized
dNBR (RdNBR) (Mille & Thode 2007) o he Rela i -
ized Bu n Ra io (RBR) (Pa ks e al. 2014). Remo e sens-
ing-based me ics o bu n se e i y a y in hei abili y o
accu a ely ep esen ield condi ions, as index sui abili y
changes based on uel ype and in ended use case (Ep -
ing e al. 2005; Mille & Thode 2007; Mo gan e al. 2014;
Pa ks e al. 2014; Pico e & Robe son 2011; Whi man
e al. 2018).
One o he key limi a ions o using emo e sensing-
based indices o assessing bu n se e i y is he need
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O lande al. Fi e Ecology (2025) 21:55
o pos - i e image y, gi en ha image acquisi ion du -
ing o immedia ely ollowing a wild i e may be delayed
by clouds, smoke, o sa elli e e isi ime. This ime
gap be ween he bu n da e and assessmen da e in o-
duces unce ain y in he es ima e o bu n se e i y and
delays he use o hese da a o si ua ional awa eness in
esponse o a i e e en . Fo g oups asked wi h eme -
gency esponse—such as BAER eams in he Uni ed
S a es—unce ain ies ied o he a ailabili y o cloud- ee
image y ep esen a ba ie o esponsi e planning and
managemen based on delays in mapping e o s o iden-
i y a eas o ele a ed isk equi ing immedia e assess-
men and/o ea men . Addi ionally, longi udinal i e
impac s such as delayed ee mo ali y may only become
isible du ing he ollowing g owing season; o cap u ing
hese e ec s, “ex ended” MTBS assessmen s adi ionally
ely on image y acqui ed 1 yea a e he i e o p o ide
a mo e ho ough pic u e o ege a ion esponse (Key &
Benson 2006; Eidenshink e al. 2007). Gi en hese limi a-
ions, he e is an oppo uni y o de elop nea eal- ime
(NRT) app oaches ha d aw on complemen a y sa elli e
in o ma ion o assis wi h bu n se e i y assessmen s du -
ing and immedia ely ollowing a la ge i e e en p io o
he a ailabili y o s anda d MTBS and BAER p oduc s.
One pa hway o an icipa ing es ima es o bu n se e -
i y is o le e age p e- i e in o ma ion. Fo example, using
a combina ion o ai bo ne ligh de ec ion and anging
(lida ) and sa elli e-based land su ace albedo measu e-
men s, Fe nández-Guisu aga e al. (2021) examined
he link be ween p e- i e ege a ion s uc u e and bu n
se e i y, highligh ing he co ela ions be ween canopy
heigh and olume wi h Composi e Bu n Index (CBI) and
dNBR alues. Simila ly, S aley e al. (2018) linked his o i-
cal dis ibu ions o dNBR alues wi h exis ing ege a ion
ype (EVT) classi ica ions de i ed om LANDFIRE p od-
uc s (Rollins 2009). Using machine lea ning o ela ed
me hods, many da a-d i en s udies also demons a e he
impo an con ol o ele a ion on bu n se e i y (Dillon
e al. 2011; Es es e al. 2017; Holden e al. 2009; Wu e al.
2013). Finally, i e sp ead simula ions (e.g., Finney 2006;
Finney e al. 2011; Linn e al. 2002, 2020; Mell e al. 2007)
ha e also been used o model i e beha io and se e as
“scena io-based” assessmen s p io o bu ning. To da e,
hese app oaches ha e no inco po a ed ac i e i e in o -
ma ion made a ailable in NRT o accoun o diu nal o
day- o-day a iabili y in i e beha io o in ensi y.
Sa elli e ac i e i e de ec ions p o ide in o ma ion on
he loca ion and in ensi y o i e ac i i y, and da a a e yp-
ically a ailable wi hin minu es o hou s a e each sa elli e
o e pass. Fo example, he Mode a e Resolu ion Imaging
Spec o adiome e (MODIS) senso s on NASA’s Te a
and Aqua sa elli es ha e al eady p o ided o e 20yea s
o ac i e i e de ec ions a 1-km esolu ion (Giglio e al.
2016) and daily bu ned a ea es ima es a 500-m esolu-
ion (Giglio 2018). The weal h o MODIS ac i e i e and
bu ned a ea da a has spu ed a ange o app oaches o
delinea e indi idual i e e en s, bo h on egional and
global scales (Andela e al. 2019; A chibald & Roy 2009;
Balch e al. 2013, 2020; Han son e al. 2015; Lizundia-
Loiola e al. 2020; Loboda & Csisza 2007; Scadu o e al.
2020; Ve a e beke e al. 2014). Howe e , many o hese
p oduc s ely on da ase s no a ailable in NRT, and hus
a e mos app op ia ely used o e ospec i e analy-
sis. Imp o emen s in spa ial esolu ion, sensi i i y, and
geoloca ion accu acy o ac i e i e de ec ions om he
Visible In a ed Imaging Radiome e Sui e (VIIRS) sen-
so s (Sch oede e al. 2014) suppo new app oaches
o ack indi idual i e e en s e e y 12h (Andela e al.
2022; Chen e al. 2022). Recen wo k by Chen e al.
(2022) in oduced he Fi e E en s Da a Sui e (FEDS), an
app oach o use NRT ac i e i e obse a ions om he
Suomi-NPP VIIRS senso o i e a i ely ack and econ-
s uc i e p og ession in 12-h in e als o he s a e o
Cali o nia om 2012 o 2020. The esul ing FEDS da a
p o ide unp eceden ed insigh in o he a iabili y in i e
sp ead a e and in ensi y o la ge wild i es, hus p omo -
ing a amewo k o explo e he ela ionships be ween i e
beha io and bu n se e i y.
In his s udy, we applied he FEDS algo i hm o c ea e
a da ase o indi idual i e e en s o he Wes e n U.S.
om 2012 o 2021, aiming o sys ema ically in es iga e
he ela ionship be ween ac i e i e cha ac e is ics and
bu n se e i y. We e alua ed he po en ial o using mul-
iple me ics o i e beha io de i ed in NRT om FEDS,
explo ing he adeo s be ween accu acy and la ency o
apid assessmen s o bu n se e i y. As wild i es in he
U.S. can bu n o weeks o mon hs, hese NRT indica o s
may ill an unme need by p o iding imely upda es on
bu n se e i y. Such in o ma ion is c ucial o si ua ional
awa eness and esponsi e ac ion bo h du ing and imme-
dia ely a e wild i e e en s.
Me hods
Fi e acking
We used he FEDS algo i hm (Chen e al. 2022) o
gene a e 12-hou ly i e p og ession da a o he wes -
e n U.S. om 2012 o 2021. The FEDS algo i hm uses
VIIRS 375-m ac i e i e de ec ions (Sch oede e al.
2014) o ack indi idual i e p og ession a 12-h in e -
als ha co espond o he cadence o VIIRS o e -
passes o a gi en a ea, wi h daily o e passes occu ing
a app oxima ely 01:30 and 13:30 local ime. Ac i e i es
a e de ec ed as he mal anomalies by he VIIRS senso ,
whe e each 375-m ac i e i e pixel indica es likely laming
o smolde ing i e ac i i y. Theo e ical de ec ion limi s
o sub-pixel bu ning in he VIIRS 375-m da a p oduc
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O lande al. Fi e Ecology (2025) 21:55
a e epo ed o be as ine as 5-m2, wi h his h esh-
old a ying as a unc ion o day/nigh he mal con as
be ween i es and backg ound condi ions a he ime o
o e pass, as well as he le el o smoke o cloud obscu a-
ion (Sch oede e al. 2014). VIIRS ac i e i e de ec ion
da a con ain supplemen a y in o ma ion such as con i-
dence lags, in a ed b igh ness empe a u es, and es i-
ma ed i e adia i e powe (FRP) in megawa s (MW),
ep esen ing he a e o ene gy ou pu o ha pixel a he
ime o obse a ion. FRP can be di ec ly linked o he a e
o biomass combus ion (Woos e e al. 2005) and he e-
o e is used as a snapsho indica o o i e in ensi y and
emissions a he ime o sa elli e o e pass.
To econs uc he p og ession o his o ical i es, we
used a chi ed 375-m VNP14IMGML ac i e i e loca ion
da a o ack he p og ession o all i es in he wes e n
U.S. om 2012 o 2021. The esul ing da ase p o ides
empo ally consis en obse a ions o i e sp ead in dis-
c e e 12-h pe iods ac oss he s udy domain. Addi ion-
ally, FEDS da a cap u e mul iple p ope ies ele an
o acking ac i e i e beha io , such as FRP, i e sp ead
a e, and i e line leng h. As he FEDS algo i hm was
de eloped wi h a ocus on acking wild i es in Cali o -
nia (Chen e al. 2022), he algo i hm’s applica ion o he
la ge domain o he wes e n U.S. in his s udy included
mino imp o emen s in he e iciency o he unde lying
clus e ing and me ging componen s o he wo k low o
mee he inc eased compu a ional demand. Addi ionally,
i e acking o he wes e n U.S. egion used p ojec ed
coo dina e sys ems (e.g., he US Na ional A las Equal
A ea sys em, EPSG:9311) in con as o he Wo ld Geo-
de ic Sys em (WGS) 84 geog aphic coo dina e sys em
(EPSG: 4326) used in Chen e al. (2022). See Da a A ail-
abili y o mo e in o ma ion on da a and code access.
Because he FEDS algo i hm elies on he p ep ocessed
VIIRS-based da a p oduc s ou lined in Sch oede e al.
(2014) and Sch oede & Giglio (2016), he same limi a-
ions discussed he ein apply. This includes he possibili y
o alse posi i e de ec ions om s a ic sou ce ho spo s
and alse nega i es due o cloud o smoke co e .
In his analysis, we included all FEDS i e objec s ha
in e sec ed MTBS pe ime e s designa ed as wild i es
wi h ma ching igni ion da es wi hin 10 days. Because
MTBS includes all i es > 1000 ac es in he wes e n U.S.,
smalle i es in he FEDS da abase we e excluded om
his s udy. We compu ed he in e sec ion-o e -union
(IOU) o all ma ches o allow addi ional il e ing based
on a quan i a i e ep esen a ion o hei spa ial ag ee-
men . In o al, he inal da ase con ains 2177 ma ched
wild i es in he wes e n U.S. be ween 2012 and 2021, ep-
esen ing a o al i e-a ec ed a ea o o e 166,722 km2 as
mapped by he FEDS algo i hm (Fig.1).
Calcula ion o i e sp ead a e, in ensi y, andpe sis ence
We analyzed i e sp ead based on he indi idual “inc e-
men s” o i e g ow h du ing each 12-h in e al (Fig.2).
Indi idual inc emen s o i e g ow h we e delinea ed by
aking he geome ic di e ence be ween i e pe ime e s
de i ed a ime and hose de i ed 12h la e . Each indi-
idual a ea o i e g ow h was assigned a unique index
such ha mul iple segmen s o i e sp ead (each wi h
di e en di ec ions and loca ions on he i e pe ime e )
du ing he same 12-h pe iod we e acked sepa a ely. We
e e o hese a eas as “sp ead inc emen s” o “g ow h
inc emen s,” whose a e o g ow h can be exp essed in
uni s o km2/12-h. Each inc emen is ca ego ized based
on he iming o ini ial de ec ion: inc emen s ma ked as
“PM” we e cons uc ed using ac i e i e de ec ions i s
obse ed a 13:30 local ime. These inc emen s include
ins an aneous measu es o PM i e beha io (e.g., FRP)
bu none heless ep esen mo ning i e g ow h be ween
01:30 ( he p eceding o e pass) and 13:30 ( he cu en
o e pass) (Fig.2). Simila ly, g ow h inc emen s linked o
he AM o e pass (01:30) ma k a e noon i e g ow h.
To eco d in o ma ion on i e in ensi y, we pe o med a
spa ial join be ween all sp ead inc emen s and all VIIRS
de ec ions eco ded o ha i e. No ably, we eco ded all
pixels de ec ed wi hin each sp ead inc emen , including
i e de ec ions om he ini ial pe iod o i e sp ead and
any pe sis en bu ning de ec ed wi hin each inc emen
o e he li e ime o he i e. This app oach p o ided mul-
iple me ics o i e in ensi y and i e pe sis ence (du a-
ion). Fo each inc emen o g ow h, we calcula ed he
mean, maximum, and a ea no malized o al FRP. Fi e
pe sis ence was es ima ed using wo me ics: (1) he
numbe o unique 12-h pe iods wi h one o mo e VIIRS
ac i e i e de ec ions wi hin a gi en sp ead inc emen ;
and (2) he ime di e ence be ween he i s and las
ac i e i e de ec ions, measu ed in hou s.
Fo compa ison wi h MTBS bu n se e i y da a, we
pe o med zonal s a is ics be ween indi idual sp ead
inc emen s and classi ied MTBS pixels, eco ding he
median MTBS pixel class wi hin each polygon. MTBS
se e i y classes in his analysis ange om low/unbu ned
(1), low (2), mode a e (3), and high (4) se e i y; classes
no pe aining o hese g oups, such as hose ep esen -
ing enhanced eg ow h (5) o no da a (0), we e excluded.
Compa isons wi h MTBS da a included bo h ini ial
assessmen s ocused on immedia e i e impac s in low
biomass sys ems, such as g asslands o small sh ublands,
as well as ex ended assessmen s using emo e sensing
image y 1 yea a e he i e o cap u e delayed ecosys-
em e ec s in high biomass en i onmen s like dense
sh ublands o o es s (Key & Benson 2006; Eidenshink
e al. 2007). We analyzed he combined da ase using
bo h assessmen ypes and sepa a ely e alua ed he
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O lande al. Fi e Ecology (2025) 21:55
Fig. 1 Wes e n U.S. wild i es om 2012 o 2021 included in he analysis (n = 2177), colo ed by he numbe o 12 h sp ead inc emen s in each i e
( o al n = 56,700)
Fig. 2 Example o he di e encing me hod used o isola e indi idual a eas o i e sp ead based on he 2021 Suga Fi e in Cali o nia. a FEDS
pe ime e on he a e noon o July 9 h, 2021 (13:30). b The FEDS pe ime e 12 h la e a 01:30. c The geome ic di e ence be ween hese wo
pe ime e s highligh ing a eas o i e sp ead in ha 12 h pe iod, e e ed o in his wo k as i e g ow h o sp ead inc emen s. Red shading in panel b
deno es ac i e i e de ec ions du ing he 01:30 o e pass, and a eas o indi idual i e sp ead ep esen all g ow h ha occu ed be ween 0 and 1
Page 6 o 18
O lande al. Fi e Ecology (2025) 21:55
in luence o ini ial e sus ex ended assessmen da a on
he ela ionships be ween i e beha io and bu n se e -
i y. We used he LANDFIRE da a p oduc s (Rollins 2009)
ma ched o he app op ia e i e yea o es ima e he mos
common exis ing ege a ion ype (EVT) p io o each i e
o s a i y he analysis o i e beha io and bu n se e i y
by ege a ion ype.
Finally, o ensu e p ope ag eemen be ween FEDS and
MTBS p oduc s, we compu ed he o e lap be ween each
12-h g ow h inc emen and he co esponding MTBS
i e pe ime e . Only inc emen s wi h a leas 50% o e lap
wi h MTBS we e included in he analysis. This h esh-
old e ained 84% o he o al sp ead inc emen da ase
(n = 47,098), demons a ing b oad ag eemen be ween
FEDS and MTBS despi e mo e han an o de o magni-
ude di e ence in he spa ial esolu ion o hei sou ce
da a (375-m s 30-m, espec i ely).
P edic i e modeling o ege a ion bu n se e i y
We de eloped wo models o explo e he po en ial o
p edic inal MTBS bu n se e i y class using FEDS
da a, whe e we es ed di e en e sions o he decision
ee-based ensemble model, XGBoos (Chen & Gues-
in 2016)—a model wi h ecen applica ions in s ud-
ies ela ed o bu n se e i y and emo e sensing (e.g., He
e al. 2024; Seydi e al. 2024). The i s (mul iclass) model
p edic ed he median MTBS class wi hin each inc e-
men o g ow h, and he second (bina y) model p o ided
he p obabili y ha he median MTBS class wi hin each
inc emen was mode a e/high se e i y (1) o no (0).
Each model was ained ia a g id sea ch, a ying
he decision ee coun om 25 o 1000 and ee dep h
om 1 o 5. This s a egy was chosen o achie e eason-
able pe o mance while being sensi i e o o e i ing and
diminishing e u ns o con inued model aining. Inpu
ea u es included g ow h inc emen sp ead a e, he
numbe o unique de ec ion pe iods, he mos commonly
occu ing LANDFIRE EVT alue, designa ion o ini ial
AM o PM obse a ion, and FRP cha ac e is ics sum-
ma ized as he mean, maximum, and a ea-no malized
o al (sum) on bo h he day o ini ial sp ead and o e he
li e ime o he i e. LANDFIRE EVT da a included he
ou -digi EVT code, in addi ion o he simpli ied “EVT_
LF” and “EVT_PHYS” a iables. Las ly, he eco egion
in which he i e occu ed—as de ined by Olson e al.
(2001)—was included o p o ide a seconda y, egional
ep esen a ion o ecological con ex . Fo each classi ica-
ion scheme (mul iclass o bina y), we compa ed models
wi h h ee di e en se s o inpu a iables: (1) a model
wi h EVT and eco egion cha ac e is ics only; (2) a model
wi h ac i e i e cha ac e is ics only; and (3) a model wi h
he combina ion o all cha ac e is ics.
Simila o he a ea-based il e ing h eshold used o
he da a analysis, model aining da a included sp ead
inc emen s wi h a leas 50% o e lap wi h MTBS pe im-
e e s om i es occu ing be ween 2012 and 2020
(n = 39,460, ac oss 1949 wild i es) wi h es ing occu ing
on i es in 2021 (n = 9000, ac oss 221 wild i es). E alua-
ion me ics included accu acy, p ecision, ecall, 1-sco e,
and he a ea unde he cu e (AUC, bina y only). No
o e lap c i e ia we e applied o he es ing da a o simu-
la e NRT applica ion, consis en wi h highe expec ed
unce ain y ega ding up- o-da e e e ence pe ime e
da a a ailabili y a he ime o sa elli e acquisi ion.
Resul s
Ac i e i e p ope ies andbu n se e i y
A he e en le el, il e ed and ma ched FEDS i es
be ween 2012 and 2021 (n = 2177, Fig.1) in he wes e n
U.S. p ima ily bu ned coni e o es s (76%), wi h smalle
con ibu ions om a eas domina ed by sh ublands (14%)
and g asslands (3.3%). Wi hin each dominan ege a-
ion ype, median MTBS class alues and FRP o each
12-h g ow h inc emen we e posi i ely co ela ed (Fig.3,
Table 1). In sp ead inc emen s domina ed by coni e
o es s, median o al FRP pe uni a ea (MW/km2) was
28% highe in a eas designa ed as high bu n se e i y (4)
as compa ed o mode a e se e i y (3) (Fig.3a). This di -
e ence was e en g ea e when compa ing he dis ibu-
ion o mean FRP alues o each inc emen , whe e FRP
in high se e i y sp ead inc emen s was 41% highe han
in mode a e se e i y inc emen s (Fig.3b). O e all, dis i-
bu ions o bo h he mean and a ea-no malized o al FRP
alues o coni e sp ead inc emen s we e s a is ically
di e en when compa ing neighbo ing se e i y classes
(Mann–Whi ney U es , wo-sided, p < 0.01).
Sh ubland and g assland domina ed i e g ow h inc e-
men s exhibi ed simila ela ionships be ween FRP and
MTBS bu n se e i y (Fig.3a–b, Table1). Fo bo h eg-
e a ion ypes, he s epwise posi i e ela ionship be ween
mean FRP and bu n se e i y was mo e consis en han
o a eano malized o al FRP. Sh ublands had he highes
mean FRP pe bu n se e i y class o he h ee ege a ion
ypes, consis en wi h e idence o ho e i es in sh ub
ecosys ems based on uel cha ac e is ics (e.g., Bu ge &
Bond 2015; De Luis e al. 2004; Keeley e al. 1999). Mean
FRP was no s a is ically di e en be ween high and mod-
e a e MTBS classes in sh ublands o g asslands. Small
sample sizes o high bu n se e i y inc emen s in g ass-
lands and sh ublands may pa ially con ibu e o his
inding (see Table1); he e ogenei y in ege a ion co e
may also lead o less consis en ela ionships be ween
FRP and bu n se e i y in g asslands. No ably, me ics o
o al FRP exhibi ed g ea e di e ences ac oss class ca -
ego ies when g ouped in o unbu ned/low (1 and 2) and
Page 7 o 18
O lande al. Fi e Ecology (2025) 21:55
mode a e/high (3 and 4) se e i y classes, illus a ing he
po en ial o ailo NRT me ics o suppo speci ic in o -
ma ion needs o eme gency esponse.
Highe MTBS se e i y consis en ly co esponded o
as e sp ead a es o coni e and sh ubland ege a ion
ypes om unbu ned/low (0) o mode a e (3) se e -
i y (Fig.3c). Fo sh ubland-dominan inc emen s, hose
classi ied a high (4) se e i y (n = 39) we e no conside ed
s a is ically di e en om mode a e se e i y (n = 1206).
Di e ences be ween sample sizes likely a ec hese
esul s. Fo coni e s, median sp ead a es we e ma gin-
ally slowe a high se e i y (0.74 km2/12 h) s mode a e
se e i y (0.88 km2/12 h), and hese di e ences we e s a-
is ically di e en . Fo g assland en i onmen s, a es o
sp ead inc eased be ween he unbu ned/low (1) and low
(2) se e i y classes, and di e ences be ween he mode -
a e (3) and low (2) ca ego ies we e no s a is ically di e -
en . Median sp ead a es declined by 71%— he highes
among all classes—be ween mode a e and high se e i y
ca ego ies. In addi ion o he limi ed sample size in he
highes se e i y class (n = 12), he 12-h e isi ime o he
VIIRS senso may no be su icien o cap u e he as -
mo ing na u e o g assland i es.
Di e ences in i e beha io by ege a ion ype unde -
sco e he alue o i e acking o assessing ecological
impac s o i e ac i i y (Fig.4). Fo example, sh ubland
and g assland i es sp ead as e han i es in coni e -
domina ed landscapes (Fig. 4a). The median alues o
VIIRS-based sp ead a es o g assland and sh ubland
classes we e 53% and 106% highe , espec i ely, han o
i e sp ead inc emen s in coni e s. Coni e -domina ed
g ow h inc emen s bu ned longe han hose domina ed
by o he ege a ion ypes, wi h median i e pe sis ence
o i e 12-h pe iods (Fig.4b). Fi e pe sis ence was also
mo e a iable in coni e o es s, measu ed as he hou s
be ween he i s and las ac i e i e pixel wi hin each
inc emen (Fig.4c), whe e he median du a ion was 96
h (in e qua ile ange: 168 h) compa ed o 12 h in bo h
sh ubland and g assland sp ead inc emen s (in e qua -
ile ange: 60 h). As such, mo e pe sis en i e ac i i y in
coni e en i onmen s boos ed o al FRP pe uni a ea,
consis en wi h he expec ed in luence o ele a ed uel
loading, uelbed dep h, and uel pa icle hea con en in
o es ed ecosys ems on i e beha io (Ro he mel 1972).
The in luence o ini ial e sus ex ended MTBS assess-
men ype on he ela ionship be ween bu n se e i y
and me ics o i e beha io a ied by ege a ion ype.
Es ima ed bu n se e i y o coni e inc emen s was
la gely sou ced om ex ended assessmen s (n = 30,345
o 35,740, o 85%). As a esul , he ela ionships be ween
bu n se e i y and i e beha io me ics we e compa-
able be ween ex ended assessmen s (Fig. S1) and he
combined da a shown inFig.3. Fo coni e inc emen s
wi h ini ial assessmen da a, he o e all pa e ns emain
unchanged, bu he dis ibu ions o mean FRP and i e
sp ead a e we e highe ac oss all se e i y classes han in
he combined da ase (Fig. S2). By con as , mos MTBS
da a o sh ubland and g assland g ow h inc emen s we e
d awn om ini ial assessmen s (58% and 60%, espec-
i ely). Fo sh ublands, he o e all ela ionships we e
Fig. 3 FEDS p ope ies delinea ed by dominan ege a ion ype and MTBS bu n se e i y class. “ns” designa ion indica es non‑signi ican a iable
di e ence be ween he assigned bu n se e i y class and he measu emen s in he class di ec ly below i . a A ea no malized cumula i e FRP
measu emen s, pe inc emen , o e he li e ime o he i e. Mean inc emen FRP alues o e he li e ime o he i e. c 12‑h sp ead a e as measu ed
by he inc emen ’s a ea. Whiske s ep esen he 5 h and 95 h pe cen iles, and ou lie s a e no shown
Page 8 o 18
O lande al. Fi e Ecology (2025) 21:55
Table 1 Di e ences in sp ead a e, a ea no malized o al FRP, and mean FRP ac oss coni e , sh ubland, and g assland en i onmen s o each MTBS class
Sp ead a e (km2/12h) To al FRP/sp ead a ea (MW/km2) Mean FRP (MW)
5 h pe cen ile 50 h
pe cen ile 95 h
pe cen ile 5 h pe cen ile 50 h
pe cen ile 95 h
pe cen ile 5 h pe cen ile 50 h
pe cen ile 95 h
pe cen ile Sample size
Dominan
ege a ion
ype
Median MTBS
class
Coni e Low/
unbu ned (1) 0.12 0.54 4.84 3.96 55.48 362.41 0.86 4.87 22.97 2112
Low (2) 0.13 0.71 7.49 10.39 115.01 639.6 1.47 6.85 34.17 21,205
Mode a e (3) 0.13 0.88 14.19 28.72 243.06 1101.3 2.66 12.17 65.98 10,672
High (4) 0.12 0.74 16.01 22.05 310.62 1478.78 2.73 17.14 104.36 1751
Sh ubland Low/
unbu ned (1) 0.11 0.68 10.82 3.64 67.19 557.51 0.96 7.27 36.21 513
Low (2) 0.11 1.57 21.13 4.48 69.07 599.68 1.25 10.32 59.77 4697
Mode a e (3) 0.18 2.08 30.78 7.97 173.39 905.88 2.71 20.54 112.93 1206
High (4) 0.11 1.11 8.43 8.17 182.41 849.17 1.44 27.11 126.31 39
G assland Low/
unbu ned (1) 0.11 0.5 2.55 3.16 56.75 384.46 0.96 6.44 26.97 185
Low (2) 0.11 1.31 16.57 3.36 55.51 482.89 1.09 8.01 47.04 1151
Mode a e (3) 0.11 1.27 17.53 6.97 121.49 1041.39 2.18 13.24 74.35 204
High (4) 0.1 0.37 11.76 20.7 403.34 1688.52 1.94 15.35 83.4 12
Page 9 o 18
O lande al. Fi e Ecology (2025) 21:55
consis en be ween ini ial assessmen da a and he com-
bined da ase , bu wi h a clea e sepa a ion o median
i e in ensi y and highe sp ead a es by ini ial assessmen
class. Ini ial assessmen da a o g asslands also p o-
ided g ea e sepa abili y by bu n se e i y class o mean
FRP and cumula i e FRP me ics. Remaining sh ubland
and g assland da a sou ced om ex ended assessmen s
exhibi ed highe in ensi y measu es and lowe sp ead
a es han hose shown in (Fig. S1).
Diu nal beha io
The 12-h cadence o he VIIRS obse a ions u he
allows o he compa ison o di e ences in i e beha -
io me ics be ween nigh ime and day ime o e passes,
including di e ences in sp ead a e and in ensi y ac oss
se e i y classes. MTBS bu n se e i y class dis ibu ions
sepa a ed by AM/PM o e pass designa ion we e s a is i-
cally di e en om one ano he (p < 0.01), whe e sp ead
inc emen s ied o PM VIIRS ac i e i e de ec ions exhib-
i ed highe se e i y classes o e all. Indeed, when limi ing
no malized o al FRP alues o only he ime o he ini ial
VIIRS o e pass, in ensi y alues we e consis en ly highe
o ini ial PM obse a ions han ini ial AM obse a-
ions ac oss all ege a ion ypes and bu n se e i y classes
(Fig.5a–c). Obse ed di e ences be ween AM and PM
VIIRS o e passes a e consis en wi h he expec ed diu -
nal cycle o i e in ensi y, wi h mo e in ense bu ning du -
ing a e noon hou s due o highe empe a u es, lowe
ela i e humidi y, and o en highe wind speeds (Andela
e al. 2015; Giglio 2007).
In eg a ing o e he li e ime o each i e e en esul ed
in mo e e en es ima es o FRP (Fig. 5d– ). Conside -
ing he ull li e ime o he i e, he a io o PM/AM
cumula i e FRP agg ega ed ac oss all inc emen s a ied
by a ac o o app oxima ely wo o less in coni e sys-
ems, wi h highe obse ed a ios in sh ubland (app oxi-
ma ely 2–4×) and g assland (app oxima ely 3×, excluding
ou lie s) ecosys ems (Table2). Fo coni e s, high se e i y
inc emen s we e ela i ely e enly dis ibu ed ac oss bo h
pe iods o mo ning and a e noon g ow h, highligh ing
he in luence o i e pe sis ence on bu n se e i y, whe e
longe du a ion bu ning leads o mo e comple e uel
consump ion in highe uel load sys ems. Con e sely,
he ime o ini ial i e sp ead may ha e a s onge in lu-
ence on bu n se e i y in sh ubland and g assland ecosys-
ems—especially hose conside ed o ini ial assessmen s
only.
We also obse ed diu nal a ia ion in sp ead a es, wi h
la ge a e noon sp ead ac oss nea ly all bu n se e i y
classes (Fig.6a–c). In coni e o es s, ele a ed a e noon
i e sp ead a es a e consis en wi h expec ed beha io
and suppo ed by he s ong di e ences in he ini ial AM
and PM FRP measu emen s. Fo example, highe PM
(13:30) FRP alues ack daily me eo ological condi ions
(e.g., highe empe a u es and lowe humidi y) amena-
ble o g ea e a e noon (13:30 o 01:30) i e sp ead. Fu -
he mo e, he inc easing a io be ween a e noon and
mo ning sp ead a es ac oss MTBS classes in coni e
en i onmen s poin s o he con ibu ions o diu nal a i-
abili y in beha io on bu n se e i y, whe e highe bu n
se e i y classes a e obse ed o coincide wi h pe iods o
inc easingly as e a e noon sp ead (Fig.6d). Fo sh ub-
lands bu ned a mode a e se e i y, a e noon sp ead a es
we e abou one and a hal imes as as as mo ning sp ead
a es (Fig.6e), be o e d opping o below 1 × a he high-
es se e i y class. This subsequen dec ease may be he
Fig. 4 a Dis ibu ions o inc emen sp ead a e o all i es s a i ied by dominan ege a ion ype (km2/12 h). b Dis ibu ions o he numbe
o unique pe iods in which one o mo e VIIRS pixel(s) we e de ec ed wi hin an indi idual sp ead inc emen , s a i ied by dominan ege a ion
ype. c Dis ibu ion o he hou s be ween he i s and las ac i e i e de ec ions wi hin a sp ead inc emen , s a i ied by dominan ege a ion ype.
Whiske s ep esen he 5 h and 95 h pe cen iles, and ou lie s a e no shown
Page 16 o 18
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Publishe ’s No e
Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in pub‑
lished maps and ins i u ional a ilia ions.
