Forest Fire Severity and Koala Habitat Recovery Assessment Using Pre- and Post-Burn Multitemporal Sentinel-2 Msi Data

Abstract

Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in Australia in 2019/2020, mostly areas dominated by eucalypts, which provide koala habitats. We studied the impact of fire and three subsequent years of recovery on a property in South-East Queensland, Australia. A classified Differenced Normalised Burn Ratio (dNBR) calculated from pre- and post-burn Sentinel-2 scenes encompassing the local study area was used to assess regional impact of fire on koala-habitat forest types. The geometrically structured composite burn index (GeoCBI), a field-based assessment, was used to classify fire severity impact. To detect lower levels of forest recovery, a manual classification of the multitemporal dNBR was used, enabling the direct comparison of images between recovery years. In our regional study area, the most suitable koala habitat occupied only about 2%, and about 10% of that was burnt by wildfire. From the five koala habitat forest types studied, one upland type was burnt more severely and extensively than the others but recovered vigorously after the first year, reaching the same extent of recovery as the other forest types. The two alluvial forest types showed a negligible fire impact, likely due to their sheltered locations. In the second year, all the impacted forest types studied showed further, almost equal, recovery. In the third year of recovery, there was almost no detectable change and therefore no more notable vegetative growth. Our field data revealed that the dNBR can probably only measure the general vegetation present and not tree recovery via epicormic shooting and coppicing. Eucalypt foliage growth is a critical resource for the koala, so field verification seems necessary unless more-accurate remote sensing methods such as hyperspectral imagery can be implemented.

Full text

Citation: Johnson, D.C.; Srivastava,
S.K.; Shapcott, A. Forest Fire Severity
and Koala Habitat Recovery
Assessment Using Pre- and Post-Burn
Multitemporal Sentinel-2 Msi Data.
Forests 2024,15, 1991. https://
doi.org/10.3390/f15111991
Academic Editor: John N. Williams
Received: 19 September 2024
Revised: 20 October 2024
Accepted: 31 October 2024
Published: 11 November 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Forest Fire Severity and Koala Habitat Recovery Assessment
Using Pre- and Post-Burn Multitemporal Sentinel-2 Msi Data
Derek Campbell Johnson * , Sanjeev Kumar Srivastava and Alison Shapcott
School of Science Technology and Engineering, Centre for Bioinnovation, University of the Sunshine Coast,
Maroochydore, QLD 4558, Australia; ssrivast@usc.edu.au (S.K.S.); ashapcot@usc.edu.au (A.S.)
*Correspondence: derek.johnson@research.usc.edu.au; Tel.: +61-437-770-068
Abstract: Habitat loss due to wildfire is an increasing problem internationally for threatened animal
species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endan-
gered in most of its range, and large areas of forest were burnt by widespread wildfires in Australia
in 2019/2020, mostly areas dominated by eucalypts, which provide koala habitats. We studied
the impact of fire and three subsequent years of recovery on a property in South-East Queensland,
Australia. A classified Differenced Normalised Burn Ratio (dNBR) calculated from pre- and post-burn
Sentinel-2 scenes encompassing the local study area was used to assess regional impact of fire on
koala-habitat forest types. The geometrically structured composite burn index (GeoCBI), a field-based
assessment, was used to classify fire severity impact. To detect lower levels of forest recovery, a
manual classification of the multitemporal dNBR was used, enabling the direct comparison of images
between recovery years. In our regional study area, the most suitable koala habitat occupied only
about 2%, and about 10% of that was burnt by wildfire. From the five koala habitat forest types
studied, one upland type was burnt more severely and extensively than the others but recovered
vigorously after the first year, reaching the same extent of recovery as the other forest types. The
two alluvial forest types showed a negligible fire impact, likely due to their sheltered locations. In
the second year, all the impacted forest types studied showed further, almost equal, recovery. In
the third year of recovery, there was almost no detectable change and therefore no more notable
vegetative growth. Our field data revealed that the dNBR can probably only measure the general
vegetation present and not tree recovery via epicormic shooting and coppicing. Eucalypt foliage
growth is a critical resource for the koala, so field verification seems necessary unless more-accurate
remote sensing methods such as hyperspectral imagery can be implemented.
Keywords: forest; wildfire; koala; habitat; Sentinel-2; dNBR
1. Introduction
Habitat loss due to anthropogenic impacts and climate-induced effects including wild-
fires is becoming an increasingly serious problem internationally for threatened animal
species [
1
,
2
]. Tree-dependent and arboreal animals are at risk of displacement. Exam-
ples include the African Forest Elephant (Loxodonta cyclotis) [
3
]; the Woodland Caribou in
Canada [
2
], the Mount Graham Red Squirrel in the USA (Tamiasciurus hudsonicus grahamen-
sis) [
4
] if fires increase in scale, the Giant Noctule in Europe (Nyctalus lasiopterus) [
5
], and
in Australia, the Koala (Phascolartos cinereus) [
6
]. Koalas are a threatened species mainly
because of habitat loss from clearing and fire, but also because of drought, predators and
disease [
3
]. Although koalas are arboreal, they are not as mobile as some other arboreal
animals when they need to escape from a wildfire. Birds are able to fly to safety, and
some Australian arboreal mammals can glide between trees, such as the Greater Glider
(Petauroides volans), while some mammals can move between tree canopies, such as some
monkeys [
7
]. The koala, however, usually has to climb down from a tree and move across
the ground to move to other trees [
8
]. Dense shrub layers and undergrowth can make
Forests 2024,15, 1991. https://doi.org/10.3390/f15111991 https://www.mdpi.com/journal/forests

Forests 2024,15, 1991 2 of 29
traversal difficult and expose koalas to predators, particularly dogs. This probably makes
them more vulnerable to fires than many other arboreal species.
Large areas of Australian forests were burnt by widespread wildfires in Australia in
2019/2020 [
9
–
11
]. These fires were preceded by drought [
12
], which likely increased the
flammability of these forests [
13
]. Most of these forests and woodlands are dominated by
eucalyptus trees, which provide habitats for the koala (Phascolarctos cinereus); hence, these
fires potentially impacted large areas of their habitats, a phenomenon that occurs across
much of Eastern Australia [
14
]. Shabani et al. [
15
] found that nearly 40% of the total koala
habitat in Australia has a high or very-high fire susceptibility rating and predicted that
this figure will increase to nearly 45% by 2070 due to climate change. Eucalypt forests can
burn at frequent intervals (for example, annually) depending on climatic conditions and
anthropogenic activities [
16
]. If these fires increase in intensity and frequency, then the
koala will be at increased risk.
The recovery responses of eucalypt communities have the potential to significantly
affect the suitability of habitats for koala populations [
17
]. Many eucalypts can recover by
means of epicormic shooting on the stems and/or coppicing from the base of the trunk,
which are both parts that are edible for koalas. If the fire intensity is too severe, then this
will result in top kill [
16
], but the root system can survive and subsequently be coppiced.
Although the most widespread severe fires of 2019/2020 in Australia occurred in the states
of New South Wales (NSW) and Western Australia, severe fires were also widespread
in Victoria and Queensland [
13
]. The koala habitat in South-East Queensland (SEQ) was
therefore also impacted by the fire in 2019/20 [
18
], and the recovery of that habitat was
found to vary with fire severity [
18
]. Koalas are known to utilise a variety of different
eucalypt-dominated forest types in Queensland, Australia; these fine-scale differences
are mapped as Regional Ecosystems (REs; Queensland Department of Environment and
Science) [
19
]. Each RE is defined by a descriptive code consisting of three numbers, which
denote biogeographical region, broad geomorphological unit (land zone) [
20
], and unique
vegetation type, respectively (e.g., 12.8.16; Table 1).The post-fire recovery of eucalypt forests
has been found to vary with forest type, and this is influenced by the varying post-fire
responses of the constituent tree species [
18
,
20
–
22
]. The efficacy of these responses will
affect habitat availability for koalas. Koala habitat mapping [14] has been used to identify
the primary koala habitat REs (Supplementary Table S1).
Table 1. Regional Ecosystems observed within the study area that are koala habitats and were
analysed in this study. * BVG = Broad Vegetation Group at 1:1,000,000 map scale [
19
]. Upland REs are
listed first, followed by alluvial REs. Sample numbers are from the total of 88 plotless sample sites.
Regional
Ecosystem
Code
Forest Type
Analysis Code Short Description (QDES 2023b) and
Comments
Common Names
of Dominants Landscape BVG1 m * Number of
Samples
12.12.12 IPR
Eucalyptus tereticornis, Corymbia intermedia, E.
crebra +/−Lophostemon
suaveolens—woodlands on Mesozoic to
Proterozoic igneous rocks.
ironbark and pink
bloodwood granite ridges 9 g 9
12.12.23 GBS
Eucalyptus tereticornis subsp. tereticornis or E.
tereticornis subsp. basaltica +/−E.
eugenioides—woodlands on crests, upper
slopes, and elevated valleys and plains on
Mesozoic to Proterozoic igneous rocks.
stringybark and
mountain blue gum
granite hills 9 g 66
12.8.16 IBM Eucalyptus crebra +/−E. melliodora, E.
tereticornis—woodlands on Cainozoic
igneous rocks.
blue gum and
ironbark basalt hills 11 a 8
12.3.3 BFA Eucalyptus tereticornis—woodlands and open
forests on alluvial plains. Grow away from
the coast. blue gum alluvial 16 c 5
12.3.11 BCF
The only RE outside of the local study area
that is also classified as a primary koala
habitat (AKF 2023) and therefore included in
this analysis. Eucalyptus tereticornis +/−
Eucalyptus siderophloia,Corymbia
intermedia—open forests on alluvial plains
usually near the coast.
blue gum with or
without ironbark
and pink
bloodwood
alluvial 16 c 0

Forests 2024,15, 1991 3 of 29
The mapping of koala habitats while also incorporating fire resilience and vulnerability
is becoming increasingly necessary due to climate change and the increasing frequency
and severity of wildfires [
17
], which may destroy koala habitats temporarily or perma-
nently
[23,24]
. Knowledge of where these koala habitat areas with fire vulnerability and
fire resilience are located will assist in the planning of future habitat management.
The extent and severity of fires can be mapped using remote sensing data acquired
from Sentinel-2 satellites [
25
]. To investigate the extent of and damage to koala habitats,
the spectral bands of satellite imagery can be manipulated to identify burnt areas using
indices such as the Normalised Burn Ratio and calibrated with field data such as the Com-
posite Burn Index [
26
]. Sentinel-2 MSI satellite data [
27
] are useful for analysing localised
fire impacts due to their relatively high spatial (10–20 m) and temporal (5 days) resolu-
tions [
28
]. By extrapolating findings from a local study area (LSA), regional assessments
can be made. Regional vegetation fire studies using Sentinel-2 data have been successful
worldwide [29–31]
. A 230 Ha koala habitat study area [
18
] in SEQ was extensively burnt
by a severe wildfire in mid-November 2019. Regional Ecosystem (RE) mapping (Queens-
land Department of Environment and Science) [
32
] provides information on described
forest types and other vegetation types in Queensland [
19
] and can therefore be used in
conjunction with Sentinel-2 data to expand a local fire study to a regional scale. Each
Sentinel-2 scene covers about 12,000 km
2
, so one scene covering a great expanse of SEQ can
be classified according to the results from a local fire study, and this can in turn be applied
to each of the relevant koala habitat REs.
This study identifies the distribution and extent of koala habitats in a large part of SEQ
and quantifies how much of each koala habitat forest type was burnt, how badly each was
burnt, and how well each one recovered. This study was conducted to identify an effective
way of mapping wildfires’ impacts on koala habitats and subsequent recovery at a regional
scale. This study asks the following question: How do koala habitat forest types respond to
different fire severities at a regional scale? The following specific questions were addressed:
(a) How much of each koala habitat forest type was burnt on a regional scale, and how
severely were they burnt? (b) Where are the fire-free refugia for koalas in this study area?
(c) Do some koala habitat forest types take longer to recover than others in this region?
2. Methods
2.1. Local Study Area and Forest Types
The local on-ground survey area (LSA) [
18
] was a 230 Ha property about 10 km
south-east of Crows Nest, Queensland, Australia, located within SEQ (Figure 1) and the
SEQ bioregion. The LSA has several native eucalypt forest types, comprising remnant
vegetation on steep hills, and several small creeks [
18
]. A survey of the LSA found that the
types of forests, which were all eucalypt forests (Supplementary Figure S1), were generally
consistent with RE classifications [
19
]. Koala habitat mapping is available for the Australian
distribution of the koala (Koala Habitat Atlas; KHA) [
14
] and based on the constituent tree
species of the vegetation-mapping units produced by state government authorities. It is
classified according to the known tree preferences of koalas. In Queensland, the KHA is
based on RE mapping [
32
] at 1:25,000 scale for SEQ. It includes a variety of vegetation types
classified by floristics and structure, including forests, woodlands, wetlands, rainforest, etc.
Three REs in SEQ have been identified as ‘Primary’ koala habitats by the Australian Koala
Foundation [
14
]. Two ‘Primary’ Koala habitats are found in the LSA; they are mnemonically
coded as GBS (mountain blue gum and stringybark on granite; RE 12.12.23) and BFA (blue
gum on alluvial flats) (Table 1). There are also two REs present in the LSA classified as
‘Secondary A’ [
12
], also constituting valuable koala habitats. These are IPR (ironbark and
pink bloodwood on granite ridges; RE 12.12.12) and IBM (ironbark and blue gum on
microgranite; RE 12.8.16) (Table 1). The third ‘Secondary A’ habitat is very limited in extent
and devoid of koala food trees (as defined by AKF) [
12
], and this RE was excluded from
this study. The sixth RE present in the LSA is a wetland and was also excluded.

Forests 2024,15, 1991 4 of 29
Forests 2024, 15, x FOR PEER REVIEW 5 of 33
2. Methods
2.1. Local Study Area and Forest Types
The local on-ground survey area (LSA) [18] was a 230 Ha property about 10 km
south-east of Crows Nest, Queensland, Australia, located within SEQ (Figure 1) and the
SEQ bioregion. The LSA has several native eucalypt forest types, comprising remnant
vegetation on steep hills, and several small creeks [18]. A survey of the LSA found that
the types of forests, which were all eucalypt forests (Supplementary Figure S1), were gen-
erally consistent with RE classifications [19]. Koala habitat mapping is available for the
Australian distribution of the koala (Koala Habitat Atlas; KHA) [14] and based on the
constituent tree species of the vegetation-mapping units produced by state government
authorities. It is classified according to the known tree preferences of koalas. In Queens-
land, the KHA is based on RE mapping [32] at 1:25,000 scale for SEQ. It includes a variety
of vegetation types classified by floristics and structure, including forests, woodlands,
wetlands, rainforest, etc. Three REs in SEQ have been identified as ‘Primary’ koala habi-
tats by the Australian Koala Foundation [14]. Two ‘Primary’ Koala habitats are found in
the LSA; they are mnemonically coded as GBS (mountain blue gum and stringybark on
granite; RE 12.12.23) and BFA (blue gum on alluvial flats) (Table 1). There are also two
REs present in the LSA classified as ‘Secondary A’ [12], also constituting valuable koala
habitats. These are IPR (ironbark and pink bloodwood on granite ridges; RE 12.12.12) and
IBM (ironbark and blue gum on microgranite; RE 12.8.16) (Table 1). The third ‘Secondary
A’ habitat is very limited in extent and devoid of koala food trees (as defined by AKF)
[12], and this RE was excluded from this study. The sixth RE present in the LSA is a wet-
land and was also excluded.
Figure 1. Map showing the regional study extent and the location of the local study area (LSA)
therein (centre panel and right-hand legend and inset). The forest types comprising 70% or greater
of the vegetation in an area are as follows: GBS = grey gum, mountain blue gum, and stringybark;
IPR = ironbark
on ridges; IBM = ironbark and mountain blue gum on microgranite; BFA = blue gum
flats on alluvium inland; BCF = blue gum flats on alluvium closer to the coast. The upper left-hand
panel shows the LSA with the locations of the ‘Plotless sites’ used to identify forest types (Regional
Ecosystems) and collect the GeoCBI samples. Raw GeoCBI continuous values range from 0 to 3 and
were derived from GeoCBI values from the sites, but they were reclassified in this study into classes
0–5. The lower left-hand panel shows the nearest towns and road network.
By adding the third bioregional primary koala habitat RE to this study, which was
not observed within the LSA, the complete coverage of all primary koala habitats within a
large part of the SEQ bioregion was achieved. This habitat was BCF (blue gum on alluvial
flats closer to the coast and not within the LSA: RE 12.3.11) (Table 1). The REs in the LSA
were assessed and mapped from field survey sites (Figure 1), and then the RE mapping [
32
]
was used to investigate regional impacts for these REs. Only BCF, the RE of interest outside
the LSA, lacked field data.
The LSA was assessed using 88 plotless sample sites (Figure 1) in a stratified random
arrangement to include all terrain types and all fire severity and recovery types [
18
]. The
LSA was first surveyed in early 2020 to identify the REs within it, validate and revise
existing RE mapping via QDES [
32
], and assess fire impact severity. Revised RE mapping
within the LSA was performed at a finer scale of approximately 1:5000 so that sample sites
could be correctly located and classified [18].
The fire in the LSA occurred in November 2019 (late spring), and the impact survey
was conducted in February 2020 (soon after in late summer). The plotless sites were

Forests 2024,15, 1991 5 of 29
revisited annually to monitor recovery in late 2020 and then in 2021 and 2022. Fire recovery
monitoring for our study was limited to three years, set as the minimum anticipated time to
observe recovery but also to allow the acquisition of adequate data to yield timely research
findings. It is acknowledged that further monitoring could be of additional benefit, but
the results will indicate if benefits decrease beyond this time. The influence of terrain
(slope and aspect) was assessed to determine the consistent impact among similar terrain
classes [
18
]. The criteria for fire severity ratings (FSRs) [
18
] were assigned to a range from
0 to 5 (Table 2, Figure 2) based on post-fire observations in the LSA [
18
] and similar fire
studies in Australia conducted by other researchers [33,34].
Table 2. Criteria for fire severity ratings (FSRs) [18].
Class Fire Impact Severity Criterion
0 none
1 low (<2 m were scorched)
2 moderately low (2–5 m were scorched)
3moderately high (just trunks were scorched at >5 m OR a minority of
crowns were scorched)
4 high (most crowns were scorched)
5 severe (crown foliage is gone; ground is bare)
Forests 2024, 15, x FOR PEER REVIEW 6 of 33
Figure 1. Map showing the regional study extent and the location of the local study area (LSA)
therein (centre panel and right-hand legend and inset). The forest types comprising 70% or greater
of the vegetation in an area are as follows: GBS = grey gum, mountain blue gum, and stringybark;
IPR = ironbark on ridges; IBM = ironbark and mountain blue gum on microgranite; BFA = blue gum
flats on alluvium inland; BCF = blue gum flats on alluvium closer to the coast. The upper left-hand
panel shows the LSA with the locations of the ‘Plotless sites’ used to identify forest types (Regional
Ecosystems) and collect the GeoCBI samples. Raw GeoCBI continuous values range from 0 to 3 and
were derived from GeoCBI values from the sites, but they were reclassified in this study into classes
0–5. The lower left-hand panel shows the nearest towns and road network.
By adding the third bioregional primary koala habitat RE to this study, which was
not observed within the LSA, the complete coverage of all primary koala habitats within
a large part of the SEQ bioregion was achieved. This habitat was BCF (blue gum on allu-
vial flats closer to the coast and not within the LSA: RE 12.3.11) (Table 1). The REs in the
LSA were assessed and mapped from field survey sites (Figure 1), and then the RE map-
ping [32] was used to investigate regional impacts for these REs. Only BCF, the RE of
interest outside the LSA, lacked field data.
The LSA was assessed using 88 plotless sample sites (Figure 1) in a stratified random
arrangement to include all terrain types and all fire severity and recovery types [18]. The
LSA was first surveyed in early 2020 to identify the REs within it, validate and revise
existing RE mapping via QDES [32], and assess fire impact severity. Revised RE mapping
within the LSA was performed at a finer scale of approximately 1:5000 so that sample sites
could be correctly located and classified [18].
The fire in the LSA occurred in November 2019 (late spring), and the impact survey
was conducted in February 2020 (soon after in late summer). The plotless sites were revis-
ited annually to monitor recovery in late 2020 and then in 2021 and 2022. Fire recovery
monitoring for our study was limited to three years, set as the minimum anticipated time
to observe recovery but also to allow the acquisition of adequate data to yield timely re-
search findings. It is acknowledged that further monitoring could be of additional benefit,
but the results will indicate if benefits decrease beyond this time. The influence of terrain
(slope and aspect) was assessed to determine the consistent impact among similar terrain
classes [18]. The criteria for fire severity ratings (FSRs) [18] were assigned to a range from
0 to 5 (Table 2, Figure 2) based on post-fire observations in the LSA [18] and similar fire
studies in Australia conducted by other researchers [33,34].
Table 2. Criteria for fire severity ratings (FSRs) [18].
Class
Fire Impact Severity Criterion
0
none
1
low (<2 m were scorched)
2
moderately low (2–5 m were scorched)
3
moderately high (just trunks were scorched at >5 m OR a minority of
crowns were scorched)
4
high (most crowns were scorched)
5
severe (crown foliage is gone; ground is bare)
Forests 2024, 15, x FOR PEER REVIEW 7 of 33
(0)
(1)
(2)
(3)
(4)
(5)
Figure 2. Typical burn severities of eucalypt forest three months after the fire in the local study area.
Ratings range from 0 to 5. (0) None. (1) Low (<2 m scorch)—note recovery of the ground layer. (2)
Moderate low (2–5 m scorch)—note some epicormic shooting. (3) Moderate high (just trunks
scorched >5 m OR a minority of crowns scorched). (4) High (most crowns scorched)—note recovery
via epicormic shooting. Also note recovery of ground layer, which may look like canopy recovery
from remote sensing. (5) Severe (crown foliage gone, ground bare)—note topkill and recovery via
coppicing.
A more detailed post-fire assessment was made using the GeoCBI (geometrically
structured composite burn index) [25] from detailed field photographs. The GeoCBI is a
modification/improvement of the Composite Burn Index [26], which is an on-ground as-
sessment of the effect of fire on vegetation and soil and measures burn severity on each of
the vegetation strata on a site. The GeoCBI adds a fraction of cover (FCOV) for each stra-
tum and the leaf area index (LAI) for the intermediate and tall-tree strata, and a rating is
applied to each site. In our study, it was not possible to record changes in the leaf area
index (LAI), as pre-burn data were not available. The use of other remote-sensing-based
indices instead of GeoCBI was also considered, including the Burn Area Index (BAI) or
Relativised Burn Ratio (RBR). The BAI is dependent on persistence of charcoal, whose
presence is more typical in Mediterranean areas [35]. The RBR was tested in the Western
United States [36], but unlike our study, the plant communities tested are not eucalypt
forests, so this metric was not preferred, whereas the GeoCBI has been tested successfully
in Australia with eucalypts [37]. The GeoCBI uses a straightforward set of field-collected
criteria, making it suitable for calibration of remotely sensed data, but the leaf area index
(LAI) needs to be measured before and after a fire, so pre-fire measurement may not have
been foreseen in some instances. Goodness-of-fit of the GeoCBI with remotely sensed data
can be tested using regression. Extending the use of the GeoCBI to a regional scale, based
on locally collected data, may have limitations due to regional environmental variation,
but this also applies to other methods such as those using the BAI and RBR. In our study,
a comparison of the FSR and the GeoCBI with the dNBR using regression [38] confirmed
that both methods correlated with the dNBR based on 88 plotless sample sites in the LSA.
For FSR, R = 0659, and for GeoCBI, R = 0.716. GeoCBI values were accordingly categorised
into six classes (0–5) similar to FSR (Table 1). Within the LSA, the GeoCBI values ranged
from 0.0002 to 2.8667, with a mean value of 1.777.
2.2. Regional Context
The regional study area, including the LSA, is located in the southern part of SEQ,
which forms the SEQ bioregion [20]. There are currently 172 Regional Ecosystems (REs;
plant communities) recognised and mapped in this bioregion with mapping [32] and de-
scriptions [19]. The RE mapping covers the whole of Queensland and is publicly available
in digital format at 1:100,000 scale statewide and at 1:25,000 scale for the SEQ bioregion.
At the national scale, RE classes are generalised into Broad Vegetation Groups at
1:1,000,000 scale [39–41], but this scale was too coarse for our study.
Figure 2. Typical burn severities of eucalypt forest three months after the fire in the local study area.
Ratings range from 0 to 5. (0) None. (1) Low (<2 m scorch)—note recovery of the ground layer.
(2) Moderate low (2–5 m scorch)—note some epicormic shooting. (3) Moderate high (just trunks
scorched >5 m OR a minority of crowns scorched). (4) High (most crowns scorched)—note recovery
via epicormic shooting. Also note recovery of ground layer, which may look like canopy recovery
from remote sensing. (5) Severe (crown foliage gone, ground bare)—note topkill and recovery
via coppicing.
A more detailed post-fire assessment was made using the GeoCBI (geometrically
structured composite burn index) [
25
] from detailed field photographs. The GeoCBI is
a modification/improvement of the Composite Burn Index [
26
], which is an on-ground
assessment of the effect of fire on vegetation and soil and measures burn severity on each
of the vegetation strata on a site. The GeoCBI adds a fraction of cover (FCOV) for each

Forests 2024,15, 1991 6 of 29
stratum and the leaf area index (LAI) for the intermediate and tall-tree strata, and a rating
is applied to each site. In our study, it was not possible to record changes in the leaf area
index (LAI), as pre-burn data were not available. The use of other remote-sensing-based
indices instead of GeoCBI was also considered, including the Burn Area Index (BAI) or
Relativised Burn Ratio (RBR). The BAI is dependent on persistence of charcoal, whose
presence is more typical in Mediterranean areas [
35
]. The RBR was tested in the Western
United States [
36
], but unlike our study, the plant communities tested are not eucalypt
forests, so this metric was not preferred, whereas the GeoCBI has been tested successfully
in Australia with eucalypts [
37
]. The GeoCBI uses a straightforward set of field-collected
criteria, making it suitable for calibration of remotely sensed data, but the leaf area index
(LAI) needs to be measured before and after a fire, so pre-fire measurement may not have
been foreseen in some instances. Goodness-of-fit of the GeoCBI with remotely sensed data
can be tested using regression. Extending the use of the GeoCBI to a regional scale, based
on locally collected data, may have limitations due to regional environmental variation,
but this also applies to other methods such as those using the BAI and RBR. In our study,
a comparison of the FSR and the GeoCBI with the dNBR using regression [
38
] confirmed
that both methods correlated with the dNBR based on 88 plotless sample sites in the LSA.
For FSR, R = 0659, and for GeoCBI, R = 0.716. GeoCBI values were accordingly categorised
into six classes (0–5) similar to FSR (Table 1). Within the LSA, the GeoCBI values ranged
from 0.0002 to 2.8667, with a mean value of 1.777.
2.2. Regional Context
The regional study area, including the LSA, is located in the southern part of SEQ,
which forms the SEQ bioregion [
20
]. There are currently 172 Regional Ecosystems (REs;
plant communities) recognised and mapped in this bioregion with mapping [
32
] and
descriptions [
19
]. The RE mapping covers the whole of Queensland and is publicly avail-
able in digital format at 1:100,000 scale statewide and at 1:25,000 scale for the SEQ biore-
gion. At the national scale, RE classes are generalised into Broad Vegetation Groups at
1:1,000,000 scale [39–41], but this scale was too coarse for our study.
The five REs of interest (Table 1) were overlaid on five Sentinel-2 scenes covering a
significant portion of the southern half of the SEQ bioregion. Each scene, or tile, covers
an area of slightly greater than 100 km
×
100 km (12,000 km
2
; scenes overlap). The cov-
erage excluded the state of NSW to the south, for which a different vegetation-mapping
system is used; the Pacific Ocean to the east; and the Brigalow Belt bioregion to the west
(markedly different vegetation types) [
19
,
32
]. The regional area of interest was reduced
to a single Sentinel-2 scene, which included the LSA, and all five of the REs of interest
(
Figure 1
). This area constitutes almost 20% of the SEQ bioregion, which covers approxi-
mately
66,000 km2[20]
. The LSA is located towards the north-west corner of the Sentinel-2
scene, and adjacent scenes were not used because the Brigalow Belt bioregion is located
about
20 km
west, and there were a limited number of REs of interest to the north. The LSA
was far away enough from the Brigalow Belt bioregion to eliminate edge effects, and the
five REs of interest were well distributed across the selected Sentinel-2 scene. A map of the
five Sentinel-2 scene extents, and the REs of interest in relation to these extents, is shown in
Supplementary Figure S2. Within the regional area covered by the Sentinel-2 scene, RE map-
ping was clipped into each RE of interest by using ArcGIS v10.8 [
42
]. RE polygons include
both single (pure, 100%) RE types and mosaics, which are mixed with other RE types as a
proportion (e.g., RE type 1, 70%; RE type 2, 30%). The satellite imagery (to be processed)
was considered reliable for 100% (pure) polygons but less reliable as the proportion of RE
types mixed. Excluding all mosaic polygons would have significantly under-represented
each RE of interest, so a minimum of 70% dominance was used [
41
,
43
,
44
]. This allowed up
to 30% of confounding RE types to occur within mosaics, which could reduce the reliability
of the dNBR but generally increase the inclusion of the RE of interest.

Forests 2024,15, 1991 7 of 29
2.3. Data Sets and Sources
Datasets used in this study (other than field sample sites) were sourced from public
web servers (Table 3). The RE mapping [
32
] was obtained from a geodatabase polygon file
(ArcGIS v10.8) [
42
] with several attributes, and those used were RE and mosaic percentage
of each RE (up to five REs per polygon). Field data included the GeoCBI, Fire Severity
Ratings (FSRs), and the RE mapping revision (forest types), and all were stored as shapefiles
(ArcGIS v10.8) [
42
]. Sentinel-2 MSI data [
27
] (Table 4) was selected for analysis due to
their relatively high temporal and spatial resolutions compared to other earth observation
products. These datasets were analysis-ready data from Digital Earth Australia (DEA) [
45
].
Optical surface reflectance data were standardised using pre-processing models already pro-
cessed by DEA to correct inconsistencies in upper-atmospheric reflectance values, including
aerosol optical thickness and ozone, atmospheric correction coefficients, MODTRAN, and
FMASK [
45
]. Cloud-free images were selected. Inconsistencies in terrain were already
corrected by DEA using nadir-corrected bidirectional-reflectance-distribution-function-
adjusted reflectance (NBAR) with additional terrain illumination correction (NBART) [
45
].
The quality assurance processes are detailed by FrontierSI [
46
]. These corrections were
applied uniformly across all temporal images for a single scene. Queensland Fire Scar Map-
ping [
47
] is a geodatabase [
42
] of data collected based on methods developed by Goodwin
and Collett [
48
] and Hardtke et al. [
49
] (Supplementary Table S2). Fire history mapping [
50
]
was in shapefile format [
42
], based on reports of fire timing (start to finish) and extent, and
used to confirm which fires within the regional study area were burning in between the
pre- and post-fire Sentinel-2 dates in November 2019. Regional koala records for the last
10 years were obtained from the Atlas of Living Australia [51].
Table 3. Datasets used in this study.
Dataset Map Scale Source Description
Regional Ecosystem
mapping 1:25,000
Queensland Herbarium, QLD
Dept. Environment and
Science 2023
Vegetation mapping of
Queensland
Sentinel-2 20 m pixels Geoscience Australia 2023 Analysis-ready data from Digital
Earth Australia
GeoCBI sample sites 1:5000 Johnson and Shapcott, 2024 88 plotless field sites
Fire severity rating (FSR)
sample sites 1:5000 Johnson and Shapcott, 2024 88 plotless field sites
Fire recovery sample sites 1:5000 Johnson and Shapcott, 2024 88 plotless field sites
Fire history mapping
1:100,000 nominal depends on
source
Queensland Parks and
Wildlife Service 2021
Mapping of reported fires in
Queensland 1930 to 2023
Fire scar mapping 20 m pixels Queensland Dept.
Environment and Science 2023
Mapping of fires in Queensland
based on dNBR 2019 to 2020
Table 4. Dates the field data were collected and Sentinel-2 scenes that were analysed.
Event Month and Year Best Image Date (No
Clouds or Smoke) Satellite
Pre-fire (2 years) September 2017 7 September 2017 2B
Pre-fire (1 year) December 2018 21 December 2018 2B
Pre-fire (10 days) November 2019 6 November 2019 2B
Fire (not used) November 2019 16 November 2019
(mid-burn not analysed) 2B
Fire (just after) December 2019 6 December 2019 2B
Recovery Year 1 November 2020 20 November 2020 2B
Recovery Year 2 May 2021 14 May 2021 2A
Recovery Year 3 February 2022 18 February 2022 2A

Forests 2024,15, 1991 8 of 29
2.4. Remote Sensing Data Analysis
The NIR and SWIR2 spectral bands from Sentinel-2 (Table 5) were used for the calcula-
tion of the Normalised Burn Ratio (NBR), the differenced NBR (dNBR), and the Normalised
Difference Vegetation Index (NDVI). The imagery used was a series of scenes from 2017
through to late 2022 (used to provide years before fire), then 10 days before fire in mid-
November 2019, then 20 days post-fire, and then one image per year after 2019. Sentinel-2
datasets free of clouds (with <10% cloud cover, and no clouds over the LSA) were selected
to match dates of field sampling as closely as possible (Table 4). A cloud-free image taken
at a date as close to before the fire as possible was selected, and the timing of images in
the years preceding the fire was intended to be as close as possible to an exact year, with
some variation due to cloud-free-image availability. Following the fire, a cloud-free image
taken as close to immediately after the fire as possible was selected, and then recovery-year
images as close to the field-work sampling dates as possible were selected (these images
were cloud-free).
Table 5. Sentinel-2 band wavelengths. Bands are grouped here in order of resolution (pixel or cell
size, square.) Blue, green, and red are in the visible spectrum. NIR is near infrared. Veg red edge is for
vegetation. Narrow NIR is a narrower bandwidth of NIR. SWIR is shortwave infrared and divided
into two bands. SWIR—cirrus detects cirrus cloud reflection. Sentinel-2A (S2A) and Sentinel-2B (S2B)
orbit on opposite sides of the Earth used to halve the data collection interval, decreasing it from
10 days to 5 days.
Band Number Resolution (m) Name
S2A Central
Wavelength
(nm)
S2A
Bandwidth
(nm)
S2B Central
Wavelength
(nm)
S2B Bandwidth
(nm)
2 10 blue 492.4 66 492.1 66
3 10 green 559.8 36 559.0 36
4 10 red 664.6 31 664.9 31
8 10 NIR 832.8 106 832.9 106
5 20 veg red edge 704.1 15 703.8 16
6 20 veg red edge 740.5 15 739.1 15
7 20 veg red edge 782.8 20 779.7 20
8a 20 narrow NIR 864.7 21 864.0 22
11 20 SWIR 1613.7 91 1610.4 94
12 20 SWIR 2202.4 175 2185.7 185
1 60 coastal aerosol 442.7 21 442.2 21
9 60 water vapour 945.1 20 943.2 21
10 60 SWIR—cirrus 1373.5 31 1376.9 30
Whilst vegetation recovery can be assessed with the NDVI and the Differenced NDVI
(dNDVI), the dNBR was preferred for this study [
52
–
54
] to maintain consistency when
comparing years of recovery to the initial fire impact, which was measured with dNBR [
55
].
We tested our field measurements of fire impact and recovery [
18
] against the Sentinel-2
data (dNBR and NDVI) in order to extrapolate our findings from the LSA (230 Ha) to
a regional scale (Table 1). In our study, the NDVI was preferred over the dNDVI as it
was used as a condition assessment tool, as an independent snapshot in time, and not a
measurement of change. We used the dNBR to measure change.
The NDVI for each year of Sentinel-2 imagery was calculated using ArcGIS v10.8 [
42
].
The NDVI is a normalised ratio of near-infrared (NIR) to red:
NDVI = (NIR −Red)/(NIR + Red) (1)
Sentinel-2 band for NIR = band 8a, and for Red = band 4 (Table 5). Narrow NIR from
band 8a (20 m resolution) was preferred to the broader spectrum infrared from band 8 (10 m
resolution). A narrower spectral resolution for NIR was considered more important for this
analysis than the spatial resolution, the latter of which was adequate for a regional-scale

Forests 2024,15, 1991 9 of 29
study. The NDVI was only used for general comparison of vegetative growth for each year,
including years prior to the fire as a reference.
The differenced NBR (dNBR) calculation for each year of Sentinel-2 imagery was
performed using ArcGIS v10.8 [
42
]. The NBR [
56
] is a normalised ratio of near infrared
(NIR) to shortwave infrared (SWIR):
NBR = (NIR −SWIR)/(NIR + SWIR) (2)
The Sentinel-2 bands for NIR = band 8a, and for SWIR2 = band 12 (Table 5). Narrow
NIR from band 8a (20 m resolution) was preferred to the broader-spectrum infrared from
band 8 (10 m resolution). A narrower spectral resolution for NIR was considered more
important for this analysis than the spatial resolution, the latter of which was adequate for
a regional-scale study. This study was conducted at 20 m resolution so band 8a did not
decrease overall resolution in this study. The differenced NBR (dNBR) is the difference
between two NBRs calculated at different times, before and after a fire:
dNBR = pre-fire NBR −post-fire NBR (3)
Resolution of the NBR was retained at 20 m, as this was sufficiently precise for re-
gional analysis, so no resampling to 10 m resolution was necessary. This also significantly
improved computation speed for repetitive analyses of entire Sentinel-2 scenes and elim-
inated system crashes. A resolution of 20 m is approximately equivalent to the nominal
1:25,000 scale of the base mapping of REs [
32
] in SEQ, where line-work precision is no finer
than 25 m. The NDVI was also processed at 20 m resolution due to the regional scale of
the analysis. Differentiation of small vegetation patches below 20 m resolution was not
considered desirable at regional scale due to the potentially fine-scale stochastic nature of
burn patterns within each patch of vegetation [
57
], including influence of vehicle tracks,
fence lines, rocky outcrops, etc.
The dNBR was classified for fire impact and fire recovery using ArcGIS v10.8 [
42
]:
For fire impact, the GeoCBI was used based on the 88 plotless sites in the local study area
(Supplementary Figures S3 and S4). However, the GeoCBI was inadequately sensitive to
differentiate very-low levels of fire severity (or relatively healthy vegetation) throughout
the region. A different method of classification was therefore required to fully assess
regional recovery (GeoCBI was only used for the dNBR of the year of impact, 2019). For
the years of fire recovery (2020, 2021, 2022) and also impact (2019) for comparison, a
system of manual breaks of the dNBR was used (Table 6). The dNBR, GeoCBI, and FSR
data were explored to determine their distribution, and various categorisation methods
were investigated. Finally, to draw a comparison of dNBR with GeoCBI and FSR on a
temporal scale (corresponding to the year of the 2019 fire and then three subsequent years
of recovery), we categorised the dNBR into 22 incremental classes (manual breaks; ArcGIS
v10.8) [
42
] (Table 6). This was defined as a Comparable Incremental Scale Reference (CISR).
Other methods of classification included in ArcGIS v10.8 [
42
], which were not suitable,
included natural breaks (Jenks), equal interval, quantile, geometric interval, and standard
deviation (ArcGIS v10.8, Supplementary Figure S5) [
42
]. They were unsuitable for three
reasons: Firstly, the classes created did not have a common break point between years,
based on an dNBR of zero, so years could not be directly compared. Scales of dNBR would
also have been different, so high dNBR percentiles from one year might not be the same as
high dNBR percentiles in another year. Secondly, all of those automatic classifications were
unsupervised, so none of the classes could be assigned meaningful fire severities or recovery
statuses (whereas GeoCBI can). Therefore, multi-temporal comparison of categories was
performed based on CISR, as opposed to the use of GeoCBI for the fire impact alone. The
GeoCBI intervals were different to those of the CISR, but its six FSR classes (0–5) could be
aligned with those increments (Supplementary Table S3). This therefore provided some
impact description to the burnt section of the CISR. Thirdly, although some of the other
methods (natural breaks, quantile and standard deviation) could be used to divide the

Forests 2024,15, 1991 10 of 29
distribution of dNBR values into meaningful classes for the year of the fire (2019), those
break values could not be meaningfully transferred to later years of recovery (2020, 2021,
2022) because the distribution of dNBR values in most cases moved outside the bounds
of the classes set for 2019 (note the graphical stepwise explanation in Supplementary
Figure S6). Other methods were also considered, such as k-means clustering and machine
learning-based approaches, but the CISR method produced the most easily interpretable
and intuitive graphical results and was able to be processed within ArcGIS v10.8 [
42
], along
with all other data in our study.
Table 6. Classification scale for dNBR for use with Comparable Incremental Scale Reference (CISR).
Class 1 is a conglomerated class including several unburnt classes. Classes 11 and 12 are central
classes indicative of neutral conditions (not burnt, not recovering). Class 22 is a conglomerated class
including several severely burnt classes. The dNBR value immediately post-burn will show areas
with no change (unburnt or non-vegetated) and high values for burnt areas. The dNBR for subsequent
years provides an estimate of vegetation recovery when compared with immediate post-burn dNBR.
Class dNBR Central
Value Burn Severity Class dNBR Central
Value Burn Severity
1≤−1 Vigorous growth 12 0.1 Neutral or
minimal change
2−0.9 13 0.2 Slightly burnt
3−0.8 14 0.3
4−0.7 15 0.4
5−0.6 16 0.5
6−0.5 17 0.6
7−0.4 18 0.7
8−0.3 19 0.8
9−0.2 20 0.9
10 −0.1 Slight recovery 21 1
11 0 Neutral or
minimal change 22 >1 Severely burnt
False positives from the dNBR were not a problem in this study because the RE
mapping of koala habitat forest types was unlikely to contain significant wet areas such
as wetlands or water bodies. The main source of false positives was agricultural areas
due to changes in cropping between pre-fire and post-fire Sentinel-2 images, which gen-
erated a non-zero dNBR value. For example, a harvested crop paddock would generate
a positive value, and this was observed abundantly in the Lockyer Valley, an extensive
agricultural area between Toowoomba and Brisbane (Supplementary Figure S7). An agricul-
tural mask [58] coincided with most, or possibly all, of these false positives. Nevertheless,
the mapped REs of interest do not coincide with agricultural or cultivated areas.
2.5. GeoCBI Classification
The GeoCBI [
25
] was assigned to each of the 88 plotless sites within the LSA and then
classified to enable comparison of different burn severities for each forest type. The existing
classification tool from the field proforma [
25
] is a Burn Severity Scale ranging from 0 to 3,
but it has seven sub-classes within it. In order to make this scale compatible with previous
research in this LSA conducted by Johnson and Shapcott [
18
] (for which a Fire Severity
Rating was used; Table 2), two of the intermediate sub-classes were combined to form a
0–5 scale with generalised descriptors: 0 = none, 1 = low, 2 = moderate low, 3 = moderate
high, 4 = high, and 5 = severe. This GeoCBI could then be applied regionally to the dNBR.
2.6. Data Validation
A digital elevation model [
59
] was used to test for terrain as a confounding factor
for this study by comparing dNBR values from the Sentinel-2 data with elevation, slope,
and aspect values (terrain) using ArcGIS v10.8 [
42
] and linear regression with SPSS [
38
].

Forests 2024,15, 1991 11 of 29
Weather conditions in the weeks leading up to the fire in mid-November 2019 and for the
remainder of that month were consistently dry, with minimal rainfall [
10
,
18
]. Regional
Ecosystem mapping (QDES 2023a) was ground-truthed in key regional locations for each
RE that occurred within the LSA. In some areas that could not be visited, Google Maps
Street View [
60
] was used. Within the LSA, GeoCBI classes from the 88 plotless sites
correlated with the dNBR, with a Pearson value of 0.716 (c.f. [
37
,
61
]). The GeoCBI-classified
dNBR was therefore accepted as a reliable indicator of wildfire severity for this study [
61
].
Results of burnt area mapping and total burnt areas were compared with Queensland Fire
Scar Mapping [47].
2.7. Analysis
2.7.1. How Much of Each Koala Habitat Forest Type Was Burnt on a Regional Scale, and
How Severely Was Each Type Burnt?
Fire impact was assessed by comparing the areas of fire severity classes (GeoCBI) within
each RE. Total area calculations were obtained from raster analysis (ArcGIS v10.8) [
42
]. Total
areas of each fire severity for each RE were also converted to percentages. These results
provided information on how many koala habitats were affected by fire and how severe
the impact was.
We compared total areas of each fire severity (GeoCBI classes 0–5) within each RE
using the Kruskal–Wallis test in SPSS [
38
]. We also compared the highest fire severity
(GeoCBI classes 4 and 5 combined) between REs. No statistical comparison was needed for
subsequent years (of recovery) using the 2019 GeoCBI because it was not able to differentiate
between areas of low fire severity at the regional scale.
2.7.2. Where Are the Fire-Free Refugia for Koalas in This Study Area?
Koala habitat areas suitable for refuge from fire were identified using the following
criteria: (1) the definition of koala habitat forest type as specified in our study (i.e., REs were
GBS, IPR, IBM, BFA, and BCF); (2) fire severity class 0 = unburnt; (3) proximity to burnt
areas of moderate, high, and severe ratings—classes 2, 3, 4, and 5; (4) proximity to recorded
koala sightings. Only koala habitat forest type areas with a koala sighting recorded less
than 10 years ago were included to increase the likelihood of a koala being present in these
mapped forest types.
Buffers around burnt areas ranged from being directly contiguous with the burnt area
to a maximum distance equivalent to the average area of a koala’s maximum home range.
This was initially set at a buffer of 700 m based on mean home-range area of 39.5 Ha [
62
] in
the event of a koala relocating to the refuge permanently or for the long term. However,
home ranges were not necessarily round; they could be elongated [
63
] to suit resources. The
distance (length) was therefore increased to 4 km (corresponding to a rectangle with a width
of 100 m), which is within known travel distances of koalas [
62
]. This area could represent
a gully, for example. All koala sighting records were buffered by the average radius of this
home range area and therefore set to 350 m. The distances selected were intended to be
practical minima and could be extended for various reasons, such as connectivity, or locally
reduced for reasons such as land-use tenure or drought. Polygons of each criterion were
buffered and then converted to raster in ArcGIS v10.8 [
42
]. All unburnt (fire severity class
0) areas that were overlapped by burnt area buffer were selected as refuges, provided that
at least some koala sighting buffer area overlapped with the burnt area buffer.
2.7.3. Do Some Koala Habitat Forest Types Take Longer to Recover than Others in
This Region?
Fire impacts based on the classified GeoCBI from the LSA were initially intended
to also be used as the reference for fire recovery across the regional study area, but we
found that it was not an effective regional metric when based on data from the LSA, which
was relatively severely burnt. The proportional amounts of fire impact at different levels
of severity in the year of the fire (2019) in the region were easily distinguished, but the
subsequent years of recovery (2020, 2021, 2022) showed that almost all of the region had

Forests 2024,15, 1991 12 of 29
suffered no fire impact or had completely recovered or was in a state of healthy growth
(Table 6). This finding was similar to Queensland Fire Scar Mapping [
47
] and indicated
that a different scaling of fire impact and recovery (CISR, based on the dNBR) needed to be
developed for regional study of fire recovery to account for where fires were minimal or
absent. We calculated the total areas of each fire severity level within each forest type (RE)
and the total percentages of each forest type using ArcGIS v10.8 [
42
]. The classified dNBR
data were analysed with the Bivariate Correlation package in SPSS v28.0.1.1 [
38
]. We used
Spearman’s rank correlation to test between slower and faster recovery.
As an overall assessment of original pre-fire condition for all vegetation and the impact
of fire, followed by overall recovery, the NDVI was calculated for two years prior to the fire
in 2019, then for 2019, and then for three years after the fire, across 88 plotless sites in the
LSA [
18
]. This gave us a baseline from which to determine if vegetation had apparently
returned to its original condition after the fire.
2.7.4. Reliability Assessment of the dNBR and NDVI for Forest Fire Recovery
Reliability of the dNBR for forest fire recovery was tested post hoc because the dNBR
is a well-recognised method of assessing fire impact [
54
,
55
]. The dNBR was preferred over
the NDVI for this, although both methods are suitable for monitoring recovery [
64
]. There
are various ways of improving the reliability of the NBR (and hence the dNBR), such as
reducing false positives from water bodies and clouds [
65
], but distinguishing between
different types of vegetative growth (e.g., epicormic shooting and coppicing) may be more
difficult [
66
]. In the case regarding vegetation recovering from fire, the dNBR can quantify
overall vegetative recovery [
52
,
53
]. There are other indices for burned-area mapping, but
their evaluation requires more detailed field data. For example, the Burned Area Index
for Sentinel-2 (BAIS2) [
67
] requires leaf area index data, which we could not record in
our study. Our study focusses on koala habitats and therefore requires the assessment
of specific types of tree regrowth responses, namely, epicormic shooting, coppicing, and
apparent tree mortality. These responses were recorded at 88 plotless sites within the LSA
over three years [
18
] as a percentage of trees and then compared using the dNBR and
the NDVI to see if these responses could be identified and quantified using regression in
SPSS [38].
3. Results
3.1. How Much of Each Koala Habitat Forest Type Was Burnt on a Regional Scale, and How Severe
Was the Burning?
The total burnt area of the five koala habitat REs studied in the regional area, including
fire severity classes, 1–5, was 2400 Ha (Table 7), which is 10% of the total area of these REs
(wherein those REs occupy >70% of the forest). Some additional area would have been
burnt in REs comprising less than 70%, but this is likely to have been relatively minimal
due to the sparsity of such regions. The five koala habitat REs only occupied 2% of the area
of this region. The remainder of the study area was composed of other REs of probably less
habitat value for koalas, non-remnant (cleared) areas, and water bodies.
The areas of each koala habitat forest type in the regional study area ranged from
approximately 3000 Ha to 7000 Ha (Table 7), with GBS corresponding to 5744 Ha (mid-
range), but it had approximately 10 times more area that was severely burnt in 2019 (class
5; 566 Ha; Table 8; Figure 3) than the other forest types combined (54.7 Ha). It also had
almost four times more area badly burnt in 2019 (class 4; 332 Ha) than the other forest types
combined (89 Ha). The fire impacts on koala habitat forest types in 2019 in the regional
study area were greatest for the forest type (RE) GBS, followed by the REs IBM and IPR
(Table 7; Table 1; Figure 3). There was 2910 Ha of IPR within the regional study area, of
which 161 Ha was burnt, and there was 5848 Ha of IBM within the regional study area,
of which 374 Ha was burnt. In contrast, for the alluvial areas, there was 6759 Ha of BFA
within the regional study area, of which only 11 Ha was burnt, and there was 3199 Ha of
BCF within the regional study area, of which only 1 Ha was burnt. For GBS, at least 15%

Forests 2024,15, 1991 13 of 29
(898 Ha) experienced high-to-severe fire impacts (Table 8), with approximately 30% of the
areas of GBS burnt to some degree (1855 Ha). All other REs studied (IPR, IBM, BFA, and
BCF) were burnt less (only 7% burnt combined, totalling 550 Ha) and suffered a minimal
impact from high-to-severe fire intensities (3%, 140 Ha). The overall impacts on the two
alluvial REs BFA and BCF were negligible. In subsequent years (2020, 2021, 2022), almost
nothing was classified as burnt or unrecovered based on the GeoCBI (Table 8, Figure 3).
Table 7. Summary of areas burnt within each forest type (Regional Ecosystem (RE)) within the
regional study area (with an area of approximately 12,000 km
2
) in 2019. Burnt areas are classes 1–5
and exclude class 0, unburnt. Forest types: GBS = grey gum and mountain blue gum and stringybark
(RE 12.12.23); IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on
microgranite (RE 12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum
flats on alluvium closer to the coast (RE 12.3.11). The REs studied were restricted to those mapped
areas [
32
] of greater than 70% of a particular RE due to the limitations of the dNBR in detecting small
proportions of the RE (when mixed with other REs).
Forest Type GBS IPR IBM BFA BCF Total
RE 12.12.23 12.12.12 12.8.16 12.3.3 12.3.11
Total area in bioregion Ha ˆ 15,000 53,000 33,000 38,000 40,000 179,000
Total study area of RE > 70% * 5744 2910 5848 6769 3199 24,470
Proportion within bioregion % 38.0 5.5 17.7 17.8 8.0 13.7
Burnt study area Ha * 1855 161 374 11 1 2402
Burnt study area % * 32.3 5.5 6.4 0.1 0.03 9.8
ˆ Total area of all of the REs in the whole bioregion (QDES 2023b), including smaller percentages of mapped
patches of remnant vegetation (from 100% to as little as 5% of the patches). * Based on REs constituting 70% or
greater of a mapped patch of remnant vegetation within the regional study area covered by one Sentinel-2 scene
(approximately 12,000 km2).
Table 8. Regional fire severity impact with classes based on the GeoCBI from the local study area
for the year of the fire, 2019, and those same classes applied to recovery in the following years.
The regional study area defined by the Sentinel-2 scene is 1,2056.04 km
2
. Forest types: GBS = grey
gum, mountain blue gum, and stringybark (RE 12.12.23); IPR = ironbark on ridges (RE 12.12.12);
IBM = ironbark
and mountain blue gum on microgranite (RE 12.8.16); BFA = blue gum flats on
alluvium inland (RE 12.3.3); BCF = blue gum flats on alluvium closer to the coast (RE 12.3.11). GeoCBI
burn classes: 0 = none, 1 = low, 2 = moderate low, 3 = moderate high, 4 = high, and 5 = severe. Total
areas for each RE were subject to minor variation in later years. Values are rounded.
Total Ha All GeoCBI
Classes 5744 2910 5848 6769 3199 24,470
2019
GeoCBI class GBS Ha IPR Ha IBM Ha BFA Ha BCF Ha Total Ha
0 3889 2749 5473 6759 3198 22,069
1 377 66 120 2 1 566
2 321 39 86 1 0 448
3 259 23 62 1 0 345
4 332 22 64 2 0 421
5 566 10 41 4 0 621
Total burnt area in Ha
(classes 1–5) 1855 161 374 11 1 2402
2020
GeoCBI class GBS Ha IPR Ha IBM Ha BFA Ha BCF Ha Total Ha
0 5744 2909 5850 6769 3178 24,451
1 0 0 0 0 9 9
2 0 0 0 0 5 5
3 0 0 0 0 5 5
4 0 0 0 0 3 3
5 0 0 0 0 0 0
Total burnt area in Ha
(classes 1–5) 0 0 0 0 21 21

Forests 2024,15, 1991 14 of 29
Table 8. Cont.
Total Ha All GeoCBI
Classes 5744 2910 5848 6769 3199 24,470
2021
GeoCBI class GBS Ha IPR Ha IBM Ha BFA Ha BCF Ha Total Ha
0 5744 2910 5850 6769 3198 24,472
1 0 0 0 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 0
4 0 0 0 0 0 0
5 0 0 0 0 0 0
Total burnt area in Ha
(classes 1–5) 0 0 0 0 0 0
2022
GeoCBI class GBS Ha IPR Ha IBM Ha BFA Ha BCF Ha Total Ha
0 5651 2908 5849 6761 3193 24,362
1 28 1 0 4 1 34
2 24 1 0 1 1 27
3 19 1 0 1 1 21
4 19 1 0 2 1 22
5 3 0 1 2 1 8
Total burnt area in Ha
(classes 1–5) 93 2 1 11 5 113
Forests 2024, 15, x FOR PEER REVIEW 17 of 33
Figure 3. Regional fire severity percentages of total area for each forest type tested (GBS, IPR, IBM,
BFA, BCF) for 2019, the year of the fire. The regional study area defined by the Sentinel-2 scene is
1,2056.04 km2. Forest types: GBS = grey gum, mountain blue gum, and stringybark (RE 12.12.23);
IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on microgranite (RE
12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum flats on alluvium
closer to the coast (RE 12.3.11). GeoCBI burn classes: 0 = none, 1 = low, 2 = moderate low, 3 = mod-
erate high, 4 = high, and 5 = severe.
3.2. Where Are the Fire-Free Refugia for Koalas in This Study Area?
The fire-free refugia for koalas in the regional study area were found to be wide-
spread, with the largest areas being situated around the LSA near Crows Nest (Regional:
Figure 4; Inset: Figure 5) and on the Main Range, a mountain range between Allora and
Boonah. There were several smaller areas throughout the region based on smaller fires
identified.
0
10
20
30
40
50
60
70
80
90
100
GBS IPR IBM BFA BCF
Percent of forest type burnt
012345
Fire severity class (from GeoCBI)
Figure 3. Regional fire severity percentages of total area for each forest type tested (GBS, IPR, IBM,
BFA, BCF) for 2019, the year of the fire. The regional study area defined by the Sentinel-2 scene is

Forests 2024,15, 1991 15 of 29
12,056.04 km
2
. Forest types: GBS = grey gum, mountain blue gum, and stringybark (RE 12.12.23);
IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on microgranite (RE
12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum flats on alluvium closer
to the coast (RE 12.3.11). GeoCBI burn classes: 0 = none, 1 = low, 2 = moderate low, 3 = moderate
high, 4 = high, and 5 = severe.
3.2. Where Are the Fire-Free Refugia for Koalas in This Study Area?
The fire-free refugia for koalas in the regional study area were found to be widespread,
with the largest areas being situated around the LSA near Crows Nest (Regional: Figure 4;
Inset: Figure 5) and on the Main Range, a mountain range between Allora and Boonah.
There were several smaller areas throughout the region based on smaller fires identified.
Forests 2024, 15, x FOR PEER REVIEW 18 of 33
Figure 4. Koala habitat areas suitable for refuge from fire. View covers the entire Sentinel-2 scene.
Criteria are (1) koala habitat forest type; (2) fire severity class 0—unburnt; (3) proximity to burnt
areas with moderate, high, and severe ratings—classes 2, 3, 4, and 5; and (4) proximity to recorded
koala sightings.
Figure 4. Koala habitat areas suitable for refuge from fire. View covers the entire Sentinel-2 scene.
Criteria are (1) koala habitat forest type; (2) fire severity class 0—unburnt; (3) proximity to burnt
areas with moderate, high, and severe ratings—classes 2, 3, 4, and 5; and (4) proximity to recorded
koala sightings.

Forests 2024,15, 1991 16 of 29
Forests 2024, 15, x FOR PEER REVIEW 19 of 33
Figure 5. Koala habitat areas suitable for refuge from fire. View is local study area and its surrounds.
Criteria are (1) koala habitat forest type; (2) fire severity class 0—unburnt; (3) proximity to burnt
areas with moderate, high, and severe ratings—classes 2, 3, 4, and 5; and (4) proximity to recorded
koala sightings.
3.3. General Recovery Trend
The NDVI measurements indicated that vegetation cover returned to previous ex-
tents during the recovery phase after the fires (Figure 6). A significant drop in NDVI val-
ues in 2019 was confirmed (p < 0.001, Kruskal–Wallis H = 330.277).
The mean dNBR values for each forest type studied were all positive (>0) in the year
of the fire (2019) and then in all cases became increasingly negative in progressive years
of recovery (2020, 2021, 2022; Figure 7). The results for all the forest types are similar,
except for GBS, which experienced the greatest fire impact (dNBR > 0.2), and BCF, which
showed the least apparent recovery from fire, with dNBR values for each recovery year
of about −0.3, whereas the other forest types showed dNBR values of about −0.4.
Figure 5. Koala habitat areas suitable for refuge from fire. View is local study area and its surrounds.
Criteria are (1) koala habitat forest type; (2) fire severity class 0—unburnt; (3) proximity to burnt
areas with moderate, high, and severe ratings—classes 2, 3, 4, and 5; and (4) proximity to recorded
koala sightings.
3.3. General Recovery Trend
The NDVI measurements indicated that vegetation cover returned to previous extents
during the recovery phase after the fires (Figure 6). A significant drop in NDVI values in
2019 was confirmed (p< 0.001, Kruskal–Wallis H = 330.277).
The mean dNBR values for each forest type studied were all positive (>0) in the year
of the fire (2019) and then in all cases became increasingly negative in progressive years of
recovery (2020, 2021, 2022; Figure 7). The results for all the forest types are similar, except
for GBS, which experienced the greatest fire impact (dNBR > 0.2), and BCF, which showed
the least apparent recovery from fire, with dNBR values for each recovery year of about
−0.3, whereas the other forest types showed dNBR values of about −0.4.

Forests 2024,15, 1991 17 of 29
Forests 2024, 15, x FOR PEER REVIEW 20 of 33
Figure 6. Normalised Difference Vegetation Index (NDVI) measured for two years prior to the fire
in 2019, and three years after the fire, across 88 plotless sites in the local study area.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
2016 2017 2018 2019 2020 2021 2022 2023
NDVI value
Year
Figure 6. Normalised Difference Vegetation Index (NDVI) measured for two years prior to the fire in
2019, and three years after the fire, across 88 plotless sites in the local study area.
Forests 2024, 15, x FOR PEER REVIEW 20 of 33
Figure 6. Normalised Difference Vegetation Index (NDVI) measured for two years prior to the fire
in 2019, and three years after the fire, across 88 plotless sites in the local study area.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
2016 2017 2018 2019 2020 2021 2022 2023
NDVI value
Year
Figure 7. Mean dNBR values for each forest type in each year. A negative dNBR indicates no burning,
a dNBR of 0 is neutral (with no change between two years), and a dNBR >= 1 in this study indicates
severe burning. Forest types: GBS = grey gum, mountain blue gum, and stringybark (RE 12.12.23);
IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on microgranite
(RE 12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum flats on alluvium
closer to the coast (RE 12.3.11).

Forests 2024,15, 1991 18 of 29
3.4. Do Some Koala Habitat Forest Types Take Longer to Recover than Others in This Region?
The distribution of areas across the 0.1 dNBR increments of fire severity (Figure 8)
shows modes (highest scores) for each RE, and in 2019, they mostly have a slightly pos-
itive dNBR value (zero is neutral; Table 6). Although GBS has the lowest mode (see
Supplementary Tables S4–S7
of results that support the graphical figures), it is bimodal
and has a second peak at class 15, which is a higher fire severity.
Forests 2024, 15, x FOR PEER REVIEW 21 of 33
Figure 7. Mean dNBR values for each forest type in each year. A negative dNBR indicates no burn-
ing, a dNBR of 0 is neutral (with no change between two years), and a dNBR > = 1 in this study
indicates severe burning. Forest types: GBS = grey gum, mountain blue gum, and stringybark (RE
12.12.23); IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on mi-
crogranite (RE 12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum flats
on alluvium closer to the coast (RE 12.3.11).
3.4. Do Some Koala Habitat Forest Types Take Longer to Recover Than Others in This Region?
The distribution of areas across the 0.1 dNBR increments of fire severity (Figure 8)
shows modes (highest scores) for each RE, and in 2019, they mostly have a slightly positive
dNBR value (zero is neutral; Table 6). Although GBS has the lowest mode (see Supple-
mentary Table S4–S7 of results that support the graphical figures), it is bimodal and has a
second peak at class 15, which is a higher fire severity.
Figure 8. Burn severity classes for koala habitat forest types (Regional Ecosystems) at the regional
scale across one whole Sentinel-2 scene (covering an area of 100 km × 100 km) in 2019, immediately
after the fire. Negative dNBR indicates unburnt, a dNBR of 0 means neutral (with no change be-
tween two years), and dNBR ≥ 1 in this study indicates severely burnt. Forest types: GBS = grey
gum, mountain blue gum, and stringybark (RE 12.12.23); IPR = ironbark on ridges (RE 12.12.12);
IBM = ironbark and mountain blue gum on