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Identifying Conifer Tree vs. Deciduous Shrub and Tree Regeneration Trajectories in a Space-for-Time Boreal Peatland Fire Chronosequence Using Multispectral Lidar

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Wildland fires and anthropogenic disturbances can cause changes in vegetation species composition and structure in boreal peatlands. These could potentially alter regeneration trajectories following severe fire or through cumulative impacts of climate-mediated drying, fire, and/or anthropogenic disturbance. We used lidar-derived point cloud metrics, and site-specific locational attributes to assess trajectories of post-disturbance vegetation regeneration in boreal peatlands south of Fort McMurray, Alberta, Canada using a space-for-time-chronosequence. The objectives were to (a) develop methods to identify conifer trees vs. deciduous shrubs and trees using multi-spectral lidar data, (b) quantify the proportional coverage of shrubs and trees to determine environmental conditions driving shrub regeneration, and (c) determine the spatial variations in shrub and tree heights as an indicator of cumulative growth since the fire. The results show that the use of lidar-derived structural metrics predicted areas of deciduous shrub establishment (92% accuracy) and classification of deciduous and conifer trees (71% accuracy). Burned bogs and fens were more prone to shrub regeneration up to and including 38 years after the fire. The transition from deciduous to conifer trees occurred approximately 30 years post-fire. These results improve the understanding of environmental conditions that are sensitive to disturbance and impacts of disturbance on northern peatlands within a changing climate.
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Citation: Enayetullah, H.; Chasmer,
L.; Hopkinson, C.; Thompson, D.;
Cobbaert, D. Identifying Conifer Tree
vs. Deciduous Shrub and Tree
Regeneration Trajectories in a
Space-for-Time Boreal Peatland Fire
Chronosequence Using Multispectral
Lidar. Atmosphere 2022,13, 112.
https://doi.org/10.3390/
atmos13010112
Academic Editors: Wenxin Zhang
and Anna Dabros
Received: 28 November 2021
Accepted: 9 January 2022
Published: 11 January 2022
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4.0/).
atmosphere
Article
Identifying Conifer Tree vs. Deciduous Shrub and Tree
Regeneration Trajectories in a Space-for-Time Boreal Peatland
Fire Chronosequence Using Multispectral Lidar
Humaira Enayetullah 1, * , Laura Chasmer 1, * , Christopher Hopkinson 1, Dan Thompson 2
and Danielle Cobbaert 3
1Department of Geography and Environment, University of Lethbridge, 4401 University Drive, Lethbridge,
AB T1K 3E3, Canada; c.hopkinson@uleth.ca
2Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street, Sault Ste. Marie, ON P6A 2E5,
Canada; daniel.thompson@NRCan-RNCan.gc.ca
3Alberta Environment and Parks, 9th Floor, 9888 Jasper Avenue, Edmonton, AB T5J 5C6, Canada;
Danielle.Cobbaert@gov.ab.ca
*Correspondence: humaira.enayetullah@uleth.ca (H.E.); laura.chasmer@uleth.ca (L.C.)
Abstract:
Wildland fires and anthropogenic disturbances can cause changes in vegetation species
composition and structure in boreal peatlands. These could potentially alter regeneration trajecto-
ries following severe fire or through cumulative impacts of climate-mediated drying, fire, and/or
anthropogenic disturbance. We used lidar-derived point cloud metrics, and site-specific locational
attributes to assess trajectories of post-disturbance vegetation regeneration in boreal peatlands south
of Fort McMurray, Alberta, Canada using a space-for-time-chronosequence. The objectives were to
(a) develop methods to identify conifer trees vs. deciduous shrubs and trees using multi-spectral
lidar data, (b) quantify the proportional coverage of shrubs and trees to determine environmental
conditions driving shrub regeneration, and (c) determine the spatial variations in shrub and tree
heights as an indicator of cumulative growth since the fire. The results show that the use of lidar-
derived structural metrics predicted areas of deciduous shrub establishment (92% accuracy) and
classification of deciduous and conifer trees (71% accuracy). Burned bogs and fens were more prone
to shrub regeneration up to and including 38 years after the fire. The transition from deciduous to
conifer trees occurred approximately 30 years post-fire. These results improve the understanding of
environmental conditions that are sensitive to disturbance and impacts of disturbance on northern
peatlands within a changing climate.
Keywords:
remote sensing; machine learning; vegetation classification; climate change; wildland fire
1. Introduction
Peatlands constitute 12% of Canada’s landscape and are characterized by the slow
decomposition of soil organic matter (i.e., peat), having a minimum depth of 40 cm [
1
,
2
].
They play a globally important role as climate regulators due to the gradual sequestration
and storage of atmospheric carbon dioxide (CO
2
) over centuries and their ability to mediate
greenhouse gas fluxes [
3
]. It is estimated that northern peatlands store 10 to 16% of
terrestrial detrital carbon, with significant amounts found within the Boreal and Subarctic
zones of Canada [2].
Wildland fires are currently one of the major disturbances impacting northern peat-
lands [
4
]. It is estimated that fires from 2006 to 2015 burned approximately 200,000 hectares
annually in Alberta [
5
], releasing large amounts of CO
2
and a broad range of pollutants into
the atmosphere [
6
]. In response to the increasing fire frequency, shifts in forests from mixed-
wood and conifer to deciduous tree and shrub species have been observed in parts of the
Arctic-Boreal region [
7
,
8
]. Moreover, permafrost plateaus in northern Canada, dominated
Atmosphere 2022,13, 112. https://doi.org/10.3390/atmos13010112 https://www.mdpi.com/journal/atmosphere
Atmosphere 2022,13, 112 2 of 20
by black spruce trees, may transition into treeless bogs and fens as a result of the changing
climate [
9
]. In the future, climate models predict a warmer, drier climate with increased
fire severity. This is important for the sensitivity of peatland succession in the next few
decades. Peatlands in the Boreal Plains ecozone of Alberta, for example, exist within a
sub-humid climate where evapotranspiration exceeds precipitation during most years [
10
].
Some boreal peatland complexes in western Canada may be close to reaching their hydro-
climatic moisture limit due to changes in hydrology [
2
,
11
]. As a result of climatic drying,
peatlands are also becoming more susceptible to wildland fires as their deep soil carbon
stores transition from moist organic matter to drier fuels for fire [
11
13
]. Enhanced drying
could also predispose these ecosystems to more shrubby and more flammable fuel types,
observed in [
13
] and reviewed in [
14
]. The potential for wildland fire in peatlands is also
exacerbated by the lengthening of the fire season combined with increased fuel availability,
enhancing both the area burned by wildland fire [
13
] and the depth of the burn [
15
]. The
combined impacts of drying on successional trajectories, including peatland shrubification
and more severe fires could result in a decline in boreal species such as black spruce, and
a possible transition to younger deciduous species. Such transitions in peatland vascular
tree/shrub species may have significant implications for the climate–carbon system by
altering carbon dynamics across broad peatland-forest complexes [16,17].
In addition to wildfire and a changing climate, peatlands in Alberta’s oil sands region
are also affected by human development such as draining for resource extraction, including
peat harvesting, oil and gas exploration and mining, and other land disturbances [
18
,
19
].
The oil sands and natural gas sectors contributed $105 billion to the Canadian economy in
2020 [
20
] but their development is associated with substantial forest loss and fragmentation
due to mining, roads, well pads, exploratory seismic lines, and infrastructure [21].
There is a lack of understanding of how peatland ecosystems recover from wildfires
during recent decades of climate warming and anthropogenic disturbances. Ecosystem
responses to stressors can take many years to become evident, and it is difficult to character-
ize cumulative effects due to a lack of baseline data. Shifts in vegetation species, age, and
structure are especially difficult to quantify across large and often remote areas of boreal
peatlands and forests using field measurements. Remote sensing provides an opportunity
to quantify the impacts of disturbances on peatlands across larger, spatially continuous
areas and a greater number of peatlands for which many environmental characteristics,
peatland attributes, and proximal influences can be examined. Remotely sensed data can
also be examined across ‘space-for-time’ chronosequence which captures differences in
structure and productivity with years since disturbance or fire (e.g., [
22
]). Lidar data (terres-
trial, airborne, spaceborne) can be used to quantify structural attributes, such as vegetation
height, canopy cover, understory, etc., because of the ranging nature of the technology and
the rapidity with which laser pulses are emitted, reflected, and recorded [
23
,
24
]. Moreover,
several studies have used lidar technologies for quantifying vegetation structures in peat-
lands (e.g., [
25
,
26
]). In peatlands, conifer and deciduous vegetation may be differentiated
within lidar data based on differences in radiation scattering of leaves vs. needles, along
with typically round (deciduous) vs. conical (conifer) shapes, and the vertical distribution
of foliage within the canopy [
27
29
]. Based on the distribution characteristics of return
intensity between species, these characteristics may be used to distinguish between conifer
and deciduous, and potentially, different species within these [27,29,30].
In this study, we used airborne multi-spectral lidar to classify the proportion of conifer
tree vs. deciduous shrub and tree species within peatlands using a supervised machine
learning approach. The goal of this study was to quantify spatial and temporal variations
in conifer vs. deciduous shrub/tree communities following wildfire using a space-for-time-
fire chronosequence of 5, 18, 30, and 38 years post-fire. The objectives of this research were
to (a) determine the utility of multi-spectral lidar for identifying deciduous vs. conifer
trees, shrubs, and herbaceous ground cover species; (b) quantify proportional coverage of
post-fire shrub vs. tree regeneration within peatlands; and (c) determine the approximate
Atmosphere 2022,13, 112 3 of 20
timing of dominance of shrub vs. tree species determined from growth rate within the
fire chronosequence.
2. Materials and Methods
2.1. Study Area
The study was conducted ~230 km south of Fort McMurray near Wandering River,
Alberta (112
12
0
31” W, 55
37
0
46” N) and is a part of the Central Mixedwood subregion
of the Boreal Plains ecozone (Figure 1). The climate of this region is sub-humid and
includes strong seasonal variations with short, warm summers and long, cold winters [
31
].
Air temperature and precipitation decrease throughout the subregion along a northward
latitudinal gradient [
32
]; however, this spatial reduction in moisture is not found within
the small area examined here. Field data were collected in August 2020, which was one of
the wettest years over the last 100 years, with cumulative precipitation of 308 mm in the
Wandering River in July 2020 [33].
Atmosphere 2022, 13, x FOR PEER REVIEW 3 of 20
were to (a) determine the utility of multi-spectral lidar for identifying deciduous vs. coni-
fer trees, shrubs, and herbaceous ground cover species; (b) quantify proportional coverage
of post-fire shrub vs. tree regeneration within peatlands; and (c) determine the approxi-
mate timing of dominance of shrub vs. tree species determined from growth rate within
the fire chronosequence.
2. Materials and Methods
2.1. Study Area
The study was conducted ~230 km south of Fort McMurray near Wandering River,
Alberta (112°1231” W, 55°3746N) and is a part of the Central Mixedwood subregion of
the Boreal Plains ecozone (Figure 1). The climate of this region is sub-humid and includes
strong seasonal variations with short, warm summers and long, cold winters [31]. Air
temperature and precipitation decrease throughout the subregion along a northward lat-
itudinal gradient [32]; however, this spatial reduction in moisture is not found within the
small area examined here. Field data were collected in August 2020, which was one of the
wettest years over the last 100 years, with cumulative precipitation of 308 mm in the Wan-
dering River in July 2020 [33].
Figure 1. Location of 5 years, 18 years, 30 years, and 38 years since fire chronosequence near Fort
McMurray, Alberta surveyed using multi-spectral lidar. The region is within the Boreal Plains
ecozone. Areas that have not been burned in the recent history of scientific observation (since ~1930)
are found in between the fire scars, predominantly between 1982 and 2002 fires in the western lidar
polygon.
Vegetation in the region consists of deciduous tree species including, but not limited
to, Populus tremuloides Michx (trembling aspen), Populus balsamifera Lyall (balsam poplar),
and Betula papyrifera Marshall (paper birch). Coniferous tree species include Picea mariana
Kuntze, and P. glauca (Moench) Voss (black and white spruce), and Pinus banksiana Lamb.
(jack pine). Shrub species include Prunus virginiana L. (chokecherry), Alnus crispa Pursh
(green alder), Amelanchier alnifolia Nutt (Saskatoon berry) and Symphoricarpos albus K.
Figure 1.
Location of 5 years, 18 years, 30 years, and 38 years since fire chronosequence near Fort
McMurray, Alberta surveyed using multi-spectral lidar. The region is within the Boreal Plains ecozone.
Areas that have not been burned in the recent history of scientific observation (since ~1930) are found
in between the fire scars, predominantly between 1982 and 2002 fires in the western lidar polygon.
Vegetation in the region consists of deciduous tree species including, but not limited
to, Populus tremuloides Michx (trembling aspen), Populus balsamifera Lyall (balsam poplar),
and Betula papyrifera Marshall (paper birch). Coniferous tree species include Picea mariana
Kuntze, and P. glauca (Moench) Voss (black and white spruce), and Pinus banksiana Lamb.
(jack pine). Shrub species include Prunus virginiana L. (chokecherry), Alnus crispa Pursh
(green alder), Amelanchier alnifolia Nutt (Saskatoon berry) and Symphoricarpos albus K. Koch
(snowberry), with a variety of herbaceous and moss species. More than 50% of the area was
dominated by poorly drained fens and bogs [
32
] with treed peatlands containing mostly
black spruce. Peatland shrub species include, but are not limited to, Ledum groenlandicum
Oeder (Labrador tea), Betula pumila L. (bog birch), and Salix spp. L. (willow species).
Atmosphere 2022,13, 112 4 of 20
The surficial geology consists of mostly glacial moraines, stagnant ice moraines, and
glaciofluvial deposits [34].
The study areas have a low density of seismic lines, 1.59 km/km
2
[
35
], and were
burned by fires in 1982, 1990, 2002, and 2015 (Figure 1). The older fires in 1982 and 1990
burned 36,996 and 9451 hectares, respectively [
36
], while fires in 2002 and 2015 burned
more than 236,662 and 9883 hectares, respectively, and include some unburned islands and
partially burned areas.
2.2. Field Data Collection
For validation purposes, shrub and tree heights and species were measured along four
transects (three unburned and one burned). The selection of species along seismic lines was
used so that laser pulse interactions with the tall trees could be minimized and the plots
were homogenous. Species measurements were collected at approximately 4 m intervals
along the center of each seismic line and were geographically located using a Garmin Mon-
tana 650 handheld GPS (n= 202). The Garmin Montana was a Wide Area Augmentation
System (WAAS) enabled with a positional accuracy between 0.4 and 1.0 m [
37
]. Areas
adjacent to seismic lines were dominated by black spruce, Labrador tea, and Sphagnum
moss species (Figure 2).
Atmosphere 2022, 13, x FOR PEER REVIEW 4 of 20
Koch (snowberry), with a variety of herbaceous and moss species. More than 50% of the
area was dominated by poorly drained fens and bogs [32] with treed peatlands containing
mostly black spruce. Peatland shrub species include, but are not limited to, Ledum groen-
landicum Oeder (Labrador tea), Betula pumila L. (bog birch), and Salix spp. L. (willow spe-
cies). The surficial geology consists of mostly glacial moraines, stagnant ice moraines, and
glaciofluvial deposits [34].
The study areas have a low density of seismic lines, 1.59 km/km2 [35], and were
burned by fires in 1982, 1990, 2002, and 2015 (Figure 1). The older fires in 1982 and 1990
burned 36,996 and 9451 hectares, respectively [36], while fires in 2002 and 2015 burned
more than 236,662 and 9883 hectares, respectively, and include some unburned islands
and partially burned areas.
2.2. Field Data Collection
For validation purposes, shrub and tree heights and species were measured along
four transects (three unburned and one burned). The selection of species along seismic
lines was used so that laser pulse interactions with the tall trees could be minimized and
the plots were homogenous. Species measurements were collected at approximately 4 m
intervals along the center of each seismic line and were geographically located using a
Garmin Montana 650 handheld GPS (n = 202). The Garmin Montana was a Wide Area
Augmentation System (WAAS) enabled with a positional accuracy between 0.4 and 1.0 m
[37]. Areas adjacent to seismic lines were dominated by black spruce, Labrador tea, and
Sphagnum moss species (Figure 2).
Figure 2. Examples of seismic line transects for shrub and tree height validation through (a) burned
peatland and (b) forest looking into an unburned treed peatland.
2.3. Lidar Data Collection
The fire chronosequence and an older, unburned (in recent history) reference area
between burn scars was surveyed on 2 August 2020, by the University of Lethbridge AR-
TeMiS Lab using the Titan multi-spectral airborne lidar system (Teledyne Optech Inc.,
Toronto, ON, Canada). The Titan scanner has three channels (C1, C2, and C3) with laser
pulse emission wavelengths of 1550 nm (shortwave infrared, C1), 1064 nm (near infrared,
C2), and 532 nm (green, C3), respectively [30]. Survey characteristics included a mean
flight altitude of 1000 m above the ground surface and pulse repetition frequencies (PRF)
of 100 KHz per wavelength. The survey covered an area of 220 km2 with a lateral strip
overlap of 50% and an average point density of 6.2 points per square meter.
2.4. Supplementary Geospatial Data
In addition to the lidar surveyed areas, we also used additional geospatial layers,
including the Human Footprint Inventory for the Oil Sands Monitoring Region circa 2019
[35]. The Human Footprint Inventory (herein, HFI) includes classified seismic lines and
other linear disturbances such as pipelines and roads. Peatlands were expertly identified
and manually delineated into bogs and fens using the lidar-derived digital elevation
Figure 2.
Examples of seismic line transects for shrub and tree height validation through (
a
) burned
peatland and (b) forest looking into an unburned treed peatland.
2.3. Lidar Data Collection
The fire chronosequence and an older, unburned (in recent history) reference area
between burn scars was surveyed on 2 August 2020, by the University of Lethbridge
ARTeMiS Lab using the Titan multi-spectral airborne lidar system (Teledyne Optech Inc.,
Toronto, ON, Canada). The Titan scanner has three channels (C1, C2, and C3) with laser
pulse emission wavelengths of 1550 nm (shortwave infrared, C1), 1064 nm (near infrared,
C2), and 532 nm (green, C3), respectively [
30
]. Survey characteristics included a mean
flight altitude of 1000 m above the ground surface and pulse repetition frequencies (PRF)
of 100 KHz per wavelength. The survey covered an area of 220 km
2
with a lateral strip
overlap of 50% and an average point density of 6.2 points per square meter.
2.4. Supplementary Geospatial Data
In addition to the lidar surveyed areas, we also used additional geospatial layers,
including the Human Footprint Inventory for the Oil Sands Monitoring Region circa
2019 [
35
]. The Human Footprint Inventory (herein, HFI) includes classified seismic lines and
other linear disturbances such as pipelines and roads. Peatlands were expertly identified
and manually delineated into bogs and fens using the lidar-derived digital elevation
model (DEM), Landsat data (pre-2002 to determine pre-fire peatland characteristics for
some polygons), and high spatial-resolution World Imagery available through ArcGIS
Pro (ESRI, Toronto, ON, Canada), acquired during approximately maximum foliage cover
collected within the last 3–5 years. The DEM was used to identify the lagg found at the
Atmosphere 2022,13, 112 5 of 20
transition between bogs and forest, thereby enhancing identification accuracy compared
with fens. Peatlands were divided into the following categories for comparative analysis:
burned + seismic
line fen; burned + seismic line bog; seismic line only fen; seismic line
only bog; reference bog; reference fen. A total of 76 bogs (21 unburned, 42 burned, and
13 reference) and 79 fens (22 unburned, 43 burned, and 14 reference) were delineated
throughout the two-study area lidar polygons containing the burn chronosequences and
unburned areas between them (Figure 1).
Shrub and tree locations (n= 4000 in total, n= 800 per each fire chronosequence and
unburned area) were manually delineated throughout two study areas within classified
peatlands, transition areas, and uplands using (a) high resolution imagery (30 cm pixel
resolution) and (b) the lidar derived 99th percentile (2 m resolution) of vegetation heights,
subdivided into the following categories: deciduous shrubs (0.5–3 m), deciduous trees
(>3 m), and conifer trees (>3 m). All vegetation with heights greater than 3 m were
classified as a “tree”, as defined by the Alberta Wetland Classification System [
38
]. Manually
identified conifer/deciduous trees vs. deciduous shrubs were identified in areas that were
more than 100 m from seismic lines to reduce the potential for anthropogenic impacts on
vegetation establishment [
39
,
40
]. Seismic lines in the study area were determined using the
HFI layer [35] for the Oil Sands Monitoring Region.
2.5. Data Analysis
2.5.1. Derivation of Geospatial Layers from Lidar
Lidar point cloud processing and raster analyses were carried out using TerraScan
(TerraSolid, Espoo, Finland), LAStools (RapidLasso, Gilching, Germany) and ArcGIS Pro
(ESRI, Toronto, ON, Canada). Lidar data returns were classified into ground and non-
ground returns using the last of many echoes and only echoes in TerraScan. A DEM was
generated at 2 m resolution using triangulation with linear interpolation in LAStools. The
DEM was also used to calculate Topographic Position Index (TPI), indicating low lying areas
and hills in the landscape. TPI was optimized and calculated using a circular search radius
of 50 m to capture generalized variations in topography exceeding microtopographic
variability. The DEM was also resampled to 5 m cell resolution to calculate the local
average slope and aspect, similarly, characterizing the more generalized variations in
microtopography, but not at the hillslope level. Numerous vegetation structural metrics
were calculated based on the distribution of the point cloud, including percent cover,
interquartile range, and maximum height within 2 m resolution cells, along with average
laser pulse return intensity. All structural metrics were generated using all returns from
all three channels with a height cutoff of >0.5 m above the ground surface (to ignore
groundcover), while intensity metrics were derived using all returns from C1 and C2 only
(described in Table 1). In addition to these, the Normalized Difference InfraRed index
(NDIR) was calculated using C1 and C2 average intensities and was used along with other
layers as explanatory rasters to predict areas of conifer or deciduous post-fire regeneration.
The NDIR was used to provide a measure of vegetation moisture conditions [41].
2.5.2. Extracting Lidar Derived Metrics to Field Data
Lidar-derived vegetation and topographic structural and intensity metrics were ex-
tracted for field locations to identify deciduous and conifer tree species, ignoring the
ground cover, using 44 out of the 202 homogenous field points with lidar returns (the
remainder were removed due to a mixture of vegetation species). Lidar-derived percent
cover, interquartile range, and NDIR were extracted for each species and compared at 2 m
grid resolution. Moreover, average C1 (1550 nm) and C2 (1064 nm) lidar return intensities
were examined to identify any differences in return intensity between species (associated
with foliage scattering properties).
Atmosphere 2022,13, 112 6 of 20
Table 1.
Lidar metrics derived for identifying shrubs and trees using return height above ground
greater than 0.5 m. Lidar metrics in bold were used in the final model based on their importance.
Other metrics were also generated but were not included in the forest-based classification (described
below) because they were highly auto-correlated (Pearson’s correlation
0.60). All metrics generated
had a cell resolution of 2 m
2
, except for slope and aspect, which had a resolution of 5 m
2
, resampled
to 2 m
2
for the random forest classification. C1 = 1550 nm (shortwave infrared, SWIR); C2 = 1064 nm
(near infrared, NIR); C3 = 532 nm (green).
Type Lidar Metric Description
C123_Max_hgt Maximum height of all returns from all channels with
heights > 0.5 m from ground
C123_Min_hgt Minimum height of all returns with heights > 0.5 m from ground
C123_IQR_hgt
Interquartile range of height using all returns from all channels
with heights > 0.5 m from ground.
IQR = p75 height p25 height
C123_Ske_hgt Skewness of height values using all returns with heights > 0.5 m
from ground
C123_Kur_hgt Kurtosis of height values using all returns with heights > 0.5 m
from ground
Vegetation Structural Metrics
C123_Cover Percent cover using all returns from all channels with
heights > 0.5 m from ground
C1_Min_int
C1 minimum intensity of returns with
heights > 0.5 m
from ground
C1_Max_int
C1 maximum intensity of returns with heights > 0.5 m from ground
C1_Ske_int
C1 skewness of intensity values using returns with heights > 0.5 m
from ground
C1_Kur_int C1 kurtosis of intensity values using returns with heights > 0.5 m
from ground
NDIR_C1_C2
Normalized Difference InfraRed index using returns with
heights > 0.5 m from ground calculated using the formula
(C1 C2)/(C1 + C2)
C2_Min_int
C2 minimum intensity of returns with heights > 0.5 m from ground
C2_Max_int
C2 maximum intensity of returns with heights > 0.5 m from ground
C2_Ske_int
C2 skewness of intensity values using returns with heights > 0.5 m
from ground
Vegetation Laser Return Intensity
C2_Kur_int C2 kurtosis of intensity values using returns with heights > 0.5 m
from ground
Environmental/Topographic
Slope 5 m resolution slope generated based on LasTool
generated DEM
Aspect 5 m resolution aspect generated based on LasTool
generated DEM
TPI Topographic position index generated using Jenness
topographic position tool having a search radius of 50 m
2.5.3. Random Forest
Forest-based classification and regression were performed in ArcGIS Pro (Version 2.7)
following the original methods described in [
42
]. Random forest is based on a supervised
machine learning algorithm that creates multiple decision trees used to create, in this case,
a prediction raster of conifer vs. deciduous distribution based on structural and intensity
metrics (Table 1). Random forest was used because it is non-parametric and does not
rely on the user to have a priori knowledge of the ecological drivers or characteristics of
the prediction/classification outputs [
43
,
44
]. Since the prediction of areas of conifer vs.
deciduous regeneration was categorical, the model was based on classification trees. The
model was first trained using 100, 500, 1500, and 2500 trees including 70% of the data that
were manually delineated as shrubs vs. trees (n= 800 per each fire chronosequence) for
training and 30% for validation. The default number of trees for the forest-based regression
in ArcGIS Pro is 500; however, increasing the number of trees typically improves model
accuracy [
45
,
46
]. In this study, 1500 trees were used after iterating from 100 to 2500 trees,
identified by determining the threshold of the maximum accuracy, such that the maximum
Atmosphere 2022,13, 112 7 of 20
accuracy shows little improvement after 1500 trees. The number of features that was
randomly selected for each tree was three.
In addition to these, the structural metrics of height and intensity metrics from 1550 nm
and 1064 nm laser wavelengths were used as descriptors of conifer vs. deciduous along
with environmental drivers of slope, aspect, and topographic position index (TPI), which
were used as explanatory variables in the classification (Table 1). The model was first
trained using all variables listed in Table 1. To reduce autocorrelation and model over-
parameterization, explanatory rasters were decreased to the greatest explanatory power
using the most important six variables described in Table 1. Variable importance was
calculated based on the Gini coefficient, which was used to eliminate variables with lower
importance to create a small subset of important variables that predicted most of the
variability in the data. These were used for the final classification [
43
]. After training,
the model was used to create a prediction raster that classified areas of deciduous shrubs,
deciduous trees, and conifers in the study areas. The model reported a sensitivity and
accuracy value for each category, which were used to assess model performance. Sensitivity
measured the number of times the observed category was correctly predicted, and the
diagnosis was calculated using a confusion matrix [46].
Using the results of random forest classification, the proportional cover of shrubs and
trees in peatlands was calculated by dividing the classified pixel area by the total peatland
area. Pixels within 25 m of seismic lines were not included in the proportional cover of
shrubs and trees to reduce the proximal effect of seismic lines on peatlands according
to the results of [
39
]. The cumulative vegetation growth since the fire was calculated by
averaging the 99th percentile of vegetation height per cell and resampling this to 5 m
using the bilinear method in ArcGIS Pro to reduce noise in the vertical data and normalize
change across a larger area, resulting in a more generalized height metric. Post-fire standing
dead trees/snags were removed by thresholding based on height as these can indicate tall,
non-living vegetation in lidar data that are confused with living vegetation. Vegetation
greater than 3 m were removed from the 5-year chronosequence, assuming that these were
burned stems [
22
,
47
,
48
]. Similar stems were not observed in the older chronosequence sites.
2.5.4. Statistical Analysis
Statistical analyses and comparisons between the post-fire chronosequence datasets
were performed in SPSS Version 26 (SPSS Inc., Chicago, IL, USA). Field data were used
to identify species differences in vegetation structure and intensity observed using lidar
data, within 1
×
1 m areas that had low to no species mixture >0.5 m. The field data were
not normally distributed and were compared using the Kruskal–Wallis test. Manually
delineated shrub and tree locations/classified information were used to determine the
prediction/validation layers within the forest-based regressions, applied separately to each
burn area and recently unburned peatlands. A Pearson’s correlation matrix of the lidar data
derivatives was used to identify and remove correlating variables with correlations greater
than 60%. For example, average height (Pearson’s correlation > 0.96) and standard deviation
of height (Pearson’s correlation > 0.86) were highly correlated with maximum height
and were removed from the final model. Moreover, maximum height and interquartile
range (IQR, which is the difference between the 25th and the 75th percentiles in the lidar
point cloud distribution) had a Pearson’s correlation = 0.67. Thus, maximum height
was not included in the final model. Six variables were identified based on importance
(Table 1) to reduce model overfitting. The lidar derived variables were tested for normality
using the Kolmogorov–Smirnov and Shapiro–Wilk tests, indicating that data were non-
parametric. The difference between proportional cover of deciduous shrubs, deciduous
trees, and conifer trees in each post-fire year and mean post-fire vegetation height were not
normally distributed and compared using Kruskal–Wallis one way ANOVA. The research
methodology used is shown in Figure 3.
Atmosphere 2022,13, 112 8 of 20
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Figure 3. Flowchart of research methodology. The flow chart shows the input data and steps taken
to develop the tree and shrub classification.
3. Results
3.1. Differences in Classified Conifer and Deciduous Trees/Shrubs Using Lidar Metrics
Field measurements indicate differences in vegetation structure also observed in li-
dar data. We found that some deciduous species such as alder and aspen were taller, had
greater percent foliage cover, and greater variability in the range of IQR, indicating differ-
ences in the distribution of laser returns across the broadest part of the canopy envelope
(Figure 4). Moreover, the IQR of height and percent cover of foliage were significantly
different between black spruce and alder (p < 0.05) and black spruce and aspen (p < 0.05).
This illustrates a broad distribution of returns within deciduous canopies, compared with
early regeneration black spruce, which had a narrower distribution of returns within the
canopy. In comparison, we found that other deciduous species such as willow and birch
had reduced IQR ranging from 0 to 5 m associated with the positioning of returns higher
in the canopy compared with black spruce and alder (Figure 4a). Significant differences
in IQR were found between birch (distribution of returns at the top of the canopy, low
IQR range) and alder/aspen (distribution of returns throughout the canopy, p < 0.05). Wil-
low had the lowest proportional canopy cover, similar to black spruce and birch to a lesser
extent (Figure 4c), while alder and aspen had the greatest cover in the study areas exam-
ined. These were significantly greater than willow (p < 0.05). Based on the average reflec-
tance of all returns within grid cells there was no significant difference observed between
conifers and deciduous species (p > 0.05) in the ratio of the difference between NIR and
SWIR (NDIR) (Figure 4b). Average laser return intensities of all returns for individual
channels were also observed, where higher SWIR average intensities were observed for
alder, aspen, and birch compared to conifers such as black spruce (Figure 5), which tend
to increase scattering of returns/loss of energy through the canopy. Despite the differences
between the averages, the variability in NIR and SWIR across individual species was also
not significantly different (p > 0.05), possibly due to other influencing factors including
vegetation structure and height, which can impact the number of returns in the canopy.
Figure 3.
Flowchart of research methodology. The flow chart shows the input data and steps taken to
develop the tree and shrub classification.
3. Results
3.1. Differences in Classified Conifer and Deciduous Trees/Shrubs Using Lidar Metrics
Field measurements indicate differences in vegetation structure also observed in lidar
data. We found that some deciduous species such as alder and aspen were taller, had greater
percent foliage cover, and greater variability in the range of IQR, indicating differences in
the distribution of laser returns across the broadest part of the canopy envelope (Figure 4).
Moreover, the IQR of height and percent cover of foliage were significantly different
between black spruce and alder (p< 0.05) and black spruce and aspen (p< 0.05). This
illustrates a broad distribution of returns within deciduous canopies, compared with early
regeneration black spruce, which had a narrower distribution of returns within the canopy.
In comparison, we found that other deciduous species such as willow and birch had
reduced IQR ranging from 0 to 5 m associated with the positioning of returns higher in
the canopy compared with black spruce and alder (Figure 4a). Significant differences
in IQR were found between birch (distribution of returns at the top of the canopy, low
IQR range) and alder/aspen (distribution of returns throughout the canopy, p< 0.05).
Willow had the lowest proportional canopy cover, similar to black spruce and birch to a
lesser extent (Figure 4c), while alder and aspen had the greatest cover in the study areas
examined. These were significantly greater than willow (p< 0.05). Based on the average
reflectance of all returns within grid cells there was no significant difference observed
between conifers and deciduous species (p> 0.05) in the ratio of the difference between NIR
and SWIR (NDIR) (Figure 4b). Average laser return intensities of all returns for individual
channels were also observed, where higher SWIR average intensities were observed for
alder, aspen, and birch compared to conifers such as black spruce (Figure 5), which tend to
increase scattering of returns/loss of energy through the canopy. Despite the differences
between the averages, the variability in NIR and SWIR across individual species was also
not significantly different (p> 0.05), possibly due to other influencing factors including
vegetation structure and height, which can impact the number of returns in the canopy.
Atmosphere 2022,13, 112 9 of 20
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Figure 4. Box plot of (a) IQR of height, (b) NDIR, and (c) percent cover used to separate between
species. The values are derived from lidar data for deciduous and conifer species identified in un-
mixed plots in the field (n = 44).
Figure 4.
Box plot of (
a
) IQR of height, (
b
) NDIR, and (
c
) percent cover used to separate between
species. The values are derived from lidar data for deciduous and conifer species identified in
unmixed plots in the field (n= 44).
Atmosphere 2022,13, 112 10 of 20
Atmosphere 2022, 13, x FOR PEER REVIEW 10 of 20
Figure 5. Average SWIR (shortwave infrared, 1550 nm) and NIR (near infrared, 1064 nm) intensi-
ties for field-identified, unmixed pixels with deciduous and conifer species using all returns of
SWIR and NIR > 0.5 m (n = 44).
3.2. Classification of Conifer vs. Deciduous Shrub/Tree in Post-Fire Peatlands Using Lidar
Forest-based regression parameterised using manually identified deciduous shrubs,
deciduous trees, and conifer trees showed that the structural metric of the interquartile
range (IQR) of height (the difference between the 25th and the 75th
percentiles) was the
most important variable for classifying shrub vs. trees, followed by intensity-based metric
NDIR and percent cover ranked third. Environmental characteristics associated with local
elevation (TPI, slope, and aspect) were less important (Table 2). Structural vegetation met-
rics had higher importance in burned peatlands and could be used to identify shrubs in
the absence of a tree canopy. The IQR of height was consistently the most important var-
iable for describing shrub vs. tree cover (importance ~20% in 5, 18, and 30 YSF), while
NDIR and percent cover varied depending on years since fire (Table 2). Separability was
greater between variables in burned peatlands compared with unburned ones but was
reduced in recently (in the last ~90 years) unburned reference peatlands. Moreover, the
structural variables had reduced importance in the unburned site (p > 0.05) because the
3D point cloud structural metrics provided little discriminatory power between estab-
lished deciduous and conifer canopy. NDIR had the highest importance in unburned sites.
Finally, with regards to the establishment of shrubs vs. trees, we found that environmental
drivers were similar between burned and unburned sites (p > 0.05) (Table 2). The combi-
nation of derivatives in Table 2 provided the most accurate prediction and validation clas-
sification compared with field/manually identified deciduous vs. conifers in the areas ex-
amined.
Table 2. Variable importance for each fire chronosequence and unburned area generated using for-
est-based classification, where YSF refers to years since fire. Here red represents vegetation struc-
tural metrics, yellow represents vegetation laser intensity returns, and green represents environ-
mental/topographic variables.
Variable Importance (%) 5 YSF 18 YSF 30 YSF 38 YSF Unburned
IQR height 19.8 19.7 19.9 18.7 18.5
NDIR 17.1 15.6 16.7 15.5 17.9
Percent Cover 15.5 17.1 17.2 18.6 16.0
Aspect 14.9 16.5 15.5 15.5 16.4
TPI 16.6 16.2 15.9 16.1 15.8
Figure 5.
Average SWIR (shortwave infrared, 1550 nm) and NIR (near infrared, 1064 nm) intensities
for field-identified, unmixed pixels with deciduous and conifer species using all returns of SWIR and
NIR > 0.5 m (n= 44).
3.2. Classification of Conifer vs. Deciduous Shrub/Tree in Post-Fire Peatlands Using Lidar
Forest-based regression parameterised using manually identified deciduous shrubs,
deciduous trees, and conifer trees showed that the structural metric of the interquartile
range (IQR) of height (the difference between the 25th and the 75th percentiles) was the
most important variable for classifying shrub vs. trees, followed by intensity-based metric
NDIR and percent cover ranked third. Environmental characteristics associated with local
elevation (TPI, slope, and aspect) were less important (Table 2). Structural vegetation
metrics had higher importance in burned peatlands and could be used to identify shrubs
in the absence of a tree canopy. The IQR of height was consistently the most important
variable for describing shrub vs. tree cover (importance ~20% in 5, 18, and 30 YSF), while
NDIR and percent cover varied depending on years since fire (Table 2). Separability was
greater between variables in burned peatlands compared with unburned ones but was
reduced in recently (in the last ~90 years) unburned reference peatlands. Moreover, the
structural variables had reduced importance in the unburned site (p> 0.05) because the 3D
point cloud structural metrics provided little discriminatory power between established
deciduous and conifer canopy. NDIR had the highest importance in unburned sites. Finally,
with regards to the establishment of shrubs vs. trees, we found that environmental drivers
were similar between burned and unburned sites (p> 0.05) (Table 2). The combination of
derivatives in Table 2provided the most accurate prediction and validation classification
compared with field/manually identified deciduous vs. conifers in the areas examined.
The forest-based classification model predicted the spatial distribution of deciduous
shrubs, deciduous trees, and conifers to an accuracy of > 95% compared with the training
data (Table 3). However, the ability to predict the presence of conifer and deciduous
trees in the validation data extracted from the lidar data was reduced (accuracy = 71%
for both conifer and deciduous trees) compared with deciduous shrubs (accuracy = 92%).
The greatest confusion between training and validation data occurred in areas of mixed
deciduous and conifer trees with closed canopies (Table 4). Here we find that deciduous
shrubs had the lowest commission error and were mostly correctly identified (omission
error of 15%). The highest rate of confusion was associated with a commission error of 33%
(conifer trees), while the model had an omission error of 27% and 36% for deciduous and
conifer trees, respectively (Table 4).
Atmosphere 2022,13, 112 11 of 20
Table 2.
Variable importance for each fire chronosequence and unburned area generated using
forest-based classification, where YSF refers to years since fire. Here red represents vegetation
structural metrics, yellow represents vegetation laser intensity returns, and green represents environ-
mental/topographic variables.
Variable
Importance
(%)
5 YSF 18 YSF 30 YSF 38 YSF Unburned
IQR height 19.8 19.7 19.9 18.7 18.5
NDIR 17.1 15.6 16.7 15.5 17.9
Percent Cover 15.5 17.1 17.2 18.6 16.0
Aspect 14.9 16.5 15.5 15.5 16.4
TPI 16.6 16.2 15.9 16.1 15.8
Slope 16.1 15.0 14.8 16.7 15.3
Vegetation structural metric
Vegetation laser return intensity
Environmental/Topographic
Table 3.
Model performance for the study area. Sensitivity is the percentage of time each observed
category was correctly predicted, while accuracy takes into consideration how well each category
was predicted and how often it was miscategorised.
Training Data Sensitivity (%) Accuracy (%)
Deciduous shrubs 1.00 0.98
Deciduous trees 0.95 0.96
Conifer trees 0.96 0.96
Validation Data Sensitivity (%) Accuracy (%)
Deciduous shrubs 0.92 0.92
Deciduous trees 0.66 0.71
Conifer trees 0.62 0.71
Table 4.
Confusion matrix of forest-based regression between deciduous shrubs, deciduous trees,
and conifers within burned and recently unburned peatlands.
Deciduous
Shrubs
Deciduous
Trees
Conifer
Trees
Commission
Error
Omission
Error
Deciduous
shrubs 150 5 7 0.07 0.15
Deciduous
trees 12 239 102 0.32 0.27
Conifer trees 14 84 197 0.33 0.36
Numbers in bold represents the number of correctly identified groups compared with validation.
Using forest-based classification, a prediction raster was generated with a pixel res-
olution of 2 m. The classification predicted more deciduous shrub presence in the areas
burnt in 5- and 18-year since fire (YSF) compared to the older burned sites and recently
unburned peatlands (Figure 6). Most deciduous trees were found throughout the 30- and
38-year sites within the chronosequence, while unburned areas were predicted to have
greater areas of peatland containing conifers (Figure 6). Using the topographic metrics,
we found that deciduous shrubs in peatlands were found in areas with shallow slopes
(0–3
) in both burned and unburned sites determined using the random forest classification.
Approximately 1% of flat to shallow slope areas in unburned peatlands had deciduous
shrubs vs. 6% area coverage of conifers. Similarly, burned shallow slope areas within
peatlands had 1–2% area coverage of deciduous shrubs, with a decrease in conifer trees of
1–2%. Comparatively, upland and transition areas with north-facing steep slopes (>25
)
were dominated by mixedwood stands of conifer and deciduous trees while south-facing
slopes were dominated by deciduous trees (Figure 6).
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Atmosphere 2022, 13, x FOR PEER REVIEW 12 of 20
Figure 6. Prediction raster generated for (a) western lidar polygon including vegetation regenera-
tion 18 YSF, 38 YSF, and recently unburned peatlands and (b) eastern lidar polygon including veg-
etation regeneration 5 YSF and 30 YSF. No data areas shaded in grey represent vegetation height
less than 0.5 m. These low vegetation areas are typically representing ground cover, Labrador tea,
and herbaceous species.
3.3. Proportion of Conifer and Deciduous Shrubs and/or Trees in Post-Fire Peatlands
Bogs had an overall higher proportional coverage of deciduous shrubs, deciduous
trees, and conifer trees (Figure 7), while higher areas of coverage of deciduous shrubs
were found in fens in the burned sites. Bogs and fens that have not been burned within
Figure 6.
Prediction raster generated for (
a
) western lidar polygon including vegetation regeneration
18 YSF, 38 YSF, and recently unburned peatlands and (
b
) eastern lidar polygon including vegetation
regeneration 5 YSF and 30 YSF. No data areas shaded in grey represent vegetation height less
than 0.5 m. These low vegetation areas are typically representing ground cover, Labrador tea, and
herbaceous species.
Atmosphere 2022,13, 112 13 of 20
3.3. Proportion of Conifer and Deciduous Shrubs and/or Trees in Post-Fire Peatlands
Bogs had an overall higher proportional coverage of deciduous shrubs, deciduous
trees, and conifer trees (Figure 7), while higher areas of coverage of deciduous shrubs were
found in fens in the burned sites. Bogs and fens that have not been burned within recent
scientific records (reference sites) had higher proportional cover of conifers compared
to deciduous species, ranging from 6.6% in fens to 11.3% in bogs, overall. The highest
proportion of conifers was found in reference peatlands (average = 10.5% in fens, and
average = 11.5% in bogs) that had not been disturbed by natural and/or anthropogenic
disturbances (Figure 7). In the 30 years since fire (YSF), bogs had the highest proportional
area coverage of deciduous trees, which decreased significantly after that period (Figure 7a,
p< 0.05). Differences in proportional area coverage of deciduous shrubs and conifers were
also significant (p< 0.05) within all post fire years except between the 30 YSF and 38 YSF.
Figure 7.
Proportional cover of wetland conifer vs. deciduous species per fire chronosequence, in
(a) bogs and (b) fens.
On the other hand, fens were more prone to post-fire shrub regeneration, having the
highest proportional cover of deciduous shrubs (average = 4.6%) observed at 38 years since
fire. The difference in proportional cover of deciduous shrubs was, at each period since fire
Atmosphere 2022,13, 112 14 of 20
within peatlands (5 YSF, 30 YSF, 38 YSF, and unburned fens (p< 0.05)), associated with a
gradual increase in the proportion of shrubs in the years following fire. Shrubs were also
greater following fire at 38 YSF than unburned sites that had been anthropogenically dis-
turbed and undisturbed reference sites. Interestingly, the proportional cover of deciduous
trees was significantly greater at 30 YSF compared to 38 YSF (Figure 7b, p< 0.05); however,
similar differences did not occur in conifers. Differences between years in deciduous cover
may be associated with other influencing environmental factors, such as surficial geology.
3.4. Cumulative Growth of Conifers vs. Deciduous Tree and Shrubs in the Years Since Fire
In the years since fire, we found that the cumulative growth of shrubs and trees within
bogs and fens has increased over time, resulting in a chronosequence-based growth curve
(Figure 8). Vegetation within both bogs and fens appeared to follow a logistic growth
curve with significantly taller vegetation in unburned bogs compared to fens (p< 0.05).
Moreover, the vegetation height in bogs and fens was statistically significant between
all YSF, except between the 30 YSF and 38 YSF (p> 0.05). The regeneration growth rate
of vegetation was also slower in fens than bogs. We also observed shorter vegetation
heights in the 38 YSF (average = 3.6 m, stdev
±
1.8) compared to the 30 YSF (
average = 3.8
,
stdev ±1.7
) suggesting poor growth conditions, possibly due to other environmental
factors not examined here.
Atmosphere 2022, 13, x FOR PEER REVIEW 14 of 20
deciduous trees was significantly greater at 30 YSF compared to 38 YSF (Figure 7b, p <
0.05); however, similar differences did not occur in conifers. Differences between years in
deciduous cover may be associated with other influencing environmental factors, such as
surficial geology.
3.4. Cumulative Growth of Conifers vs. Deciduous Tree and Shrubs in the Years since Fire
In the years since fire, we found that the cumulative growth of shrubs and trees
within bogs and fens has increased over time, resulting in a chronosequence-based growth
curve (Figure 8). Vegetation within both bogs and fens appeared to follow a logistic
growth curve with significantly taller vegetation in unburned bogs compared to fens (p <
0.05). Moreover, the vegetation height in bogs and fens was statistically significant be-
tween all YSF, except between the 30 YSF and 38 YSF (p > 0.05). The regeneration growth
rate of vegetation was also slower in fens than bogs. We also observed shorter vegetation
heights in the 38 YSF (average = 3.6 m, stdev ± 1.8) compared to the 30 YSF (average = 3.8,
stdev ± 1.7) suggesting poor growth conditions, possibly due to other environmental fac-
tors not examined here.
Figure 8. Average vegetation height (at 99th percentile of lidar returns) with stand age for boreal
peatland fire chronosequence (R
2
= 0.96 for bogs, R
2
= 0.98 for fens). Unburned peatlands are plotted
with an arbitrary stand age of 90 ± 10 years, though there may be many more years since the last
fire, but not determined in the field and not included in the recent history of scientific observation
(since ~1930).
4. Discussion
4.1. Use of Lidar to Identify Conifers and Deciduous Trees and Shrubs
In this study, we demonstrated the use of high spatial resolution lidar data and ma-
chine learning to identify conifer and deciduous shrubs/trees in a post-fire peatland
chronosequence. Lidar-based point cloud structural and intensity metrics were used to
predict areas of conifer and deciduous regrowth with relatively high accuracy compared
to manually identified trees and shrubs in high spatial resolution optical imagery (Table
3, Figure 6). The interquartile range (IQR) of the point cloud distribution between the 25th
and the 75th percentile provided the greatest descriptor of shrub vs. trees, especially in
the burned chronosequence where sites were recently disturbed by fire over the last 5 to
38 years (Table 2). IQR may be an indicator of the volume of biomass and, therefore, dif-
ferences in the distribution of this biomass between conifers/deciduous shrubs and trees
([49], Figure 4). The intensity-based Normalized Difference Infrared Index (NDIR) is
Figure 8.
Average vegetation height (at 99th percentile of lidar returns) with stand age for boreal
peatland fire chronosequence (R
2
= 0.96 for bogs, R
2
= 0.98 for fens). Unburned peatlands are plotted
with an arbitrary stand age of 90
±
10 years, though there may be many more years since the last
fire, but not determined in the field and not included in the recent history of scientific observation
(since ~1930).
4. Discussion
4.1. Use of Lidar to Identify Conifers and Deciduous Trees and Shrubs
In this study, we demonstrated the use of high spatial resolution lidar data and
machine learning to identify conifer and deciduous shrubs/trees in a post-fire peatland
chronosequence. Lidar-based point cloud structural and intensity metrics were used to
predict areas of conifer and deciduous regrowth with relatively high accuracy compared to
manually identified trees and shrubs in high spatial resolution optical imagery (Table 3,
Figure 6). The interquartile range (IQR) of the point cloud distribution between the 25th
and the 75th percentile provided the greatest descriptor of shrub vs. trees, especially in
the burned chronosequence where sites were recently disturbed by fire over the last 5
Atmosphere 2022,13, 112 15 of 20
to 38 years (Table 2). IQR may be an indicator of the volume of biomass and, therefore,
differences in the distribution of this biomass between conifers/deciduous shrubs and
trees ([
49
], Figure 4). The intensity-based Normalized Difference Infrared Index (NDIR) is
ranked second as it is sensitive to reflectance differences between leaf vs. needle foliage
(Table 2). Despite the importance of NDIR in the random forest model, at the species level,
we found no significant differences in NDIR when compared with field data. This is likely
due to the spatial resolution of cells and low point density, while larger cell sizes often
include a mixture of species/deciduous and conifers, which can skew return intensities
resulting in weakness in the NDIR as a discriminatory variable. Budei et al. [
29
] found
that NDIR ratios capture differences in chlorophyll (NIR) and water content (SWIR) of
the leaves, thus providing greater separability. Moreover, deciduous species had higher
NDIR ratios than conifer species because broadleaf species have a spherical crown structure
and larger cross-sectional area, which provides greater reflectance in NIR [
29
,
30
]. While
differences exist between conifer and deciduous vegetation in more recently burned sites,
the structural and intensity metrics were not significantly different in unburned areas where
late successional species dominate.
Through the random forest classification, we also found that the model resulted
in reduced sensitivity of identification of deciduous trees vs. conifers trees, which may
be due to overlapping crowns (Table 4, [
29
]). Our results produced an overall validation
accuracy of 71% for both deciduous trees and conifers (Table 3, Figure 6, based on structural,
intensity, and topographic inputs). Similarly, Li et al. [
28
] found that lidar point clouds
were influenced by the compactness of the tree crown and the vertical distribution of the
tree crown, increasing errors when separating aspen and jack pine.
In addition to structural metrics, topographic derivatives may be used to indicate
where trees and shrubs grow following fires (Table 2, Figure 6). While these are not
direct measures of energy receipt and moisture, they are influencing factors of areas that
receive more (south-facing) or less (north-facing) energy and greater (low-lying, closer to
water table) and higher (further from water table) topo-environmental conditions that may
enhance or deter growth. For example, Thompson et al. [
13
] found that in shrub-dominated
peatlands, a water table decline below 40 cm can cause surface vegetation to dry, making the
ecosystem more flammable. Hence, these structural, environmental, and intensity-based
variables can be determined from multi-spectral lidar to better understand the range and
combination of environmental drivers impacting boreal peatland vegetation succession.
4.2. Changes in Proportional Coverage of Conifers and Deciduous Tree/Shrubs
In this study, we found higher proportional coverage of deciduous shrubs in the early
years post-fire overall (Figure 7). Similarly, Alexander et al. [
48
] found that deciduous trees
and shrubs in interior Alaska had high aboveground net primary productivity and high
deciduous snag biomass with years since fire, while black spruce stands had slower rates of
biomass accumulation. Bogs are typically hydrologically disconnected [
10
,
50
], while others
found increased depth to water table in bogs and poor fens due to climate-mediated drying,
providing ideal conditions for shrubs to grow [
10
,
51
]. However, in older burned bogs
(38 YSF), we observed an increase in proportional coverage of conifers to 4.1% indicating
suitable microsites for black spruce establishment [52].
There is also a lag in vegetation response to wildfires, creating younger forests with
more deciduous-dominated species over time, which are less flammable [
7
,
53
,
54
]. For
example, it takes about two to three years following fire for shrubs to colonize [
55
], while de-
ciduous species dominate peatlands and other boreal ecosystems including pre-disturbance
forest ecosystems in the first 10–20 years of succession ([
56
]; Figure 7). Deciduous tree
species typically die off during the intermediate stages of stand succession due to density-
dependent factors and shifts in dominance towards conifers are also observed in older
post-fire years and unburned peatlands (Figure 7). Unburned peatlands typically have
more heterogeneous eco-hydrological conditions, with large hydrological differences occur-
ring between peatland margins and centers [
10
,
57
]. Unburned bogs in both areas surveyed
Atmosphere 2022,13, 112 16 of 20
by lidar had the greatest proportional cover of conifers, possibly because moist bog sites
underlaid with mineral soils beneath Sphagnum mosses provided suitable microsites for
conifer seedling establishment [
22
,
52
,
54
]. Thus, differences in site hydrology (either spatial
or temporal) can result in differences in the relative deciduous and conifer proportion.
Here, we found that it is approximately between 18- and 30-years post-fire where there is a
transition from deciduous trees to conifer trees observed. After 30 years, we observed a
reduction in the proportional cover of deciduous trees in bogs and fens (Figure 7).
4.3. Spatial Variation in Vegetation Height in Bogs and Fens
Growth curves have been applied to understand the biomass accumulation with stand
age. The cumulative growth curve follows the different stages of boreal mixedwood succes-
sion: stand initiation dominated by deadwood, stem exclusion, and canopy transmission
during the intermediate stages and gap dynamics in old growth [
58
,
59
]. Here, we used a
growth curve to determine variations in vegetation growth in bogs and fens. Although
bogs and fens have a similar range of vegetation heights within the first five years since
fire (Figure 8), we found a greater rate of cumulative growth of woody vegetation in the
later stages of stand succession in bogs compared to fens with time since fire. This may
be associated with lifecycle influences on bog species regeneration, especially where bogs
are dominated by black spruce, which have semi-serotinous cones and a half-life from
4.4 to 16.2 years [
22
,
59
]. Fires often do not burn all available biomass entirely, with some
residual leaves/needles, deadwood, snags, and mosses remaining. These act as a preferred
seedbed for conifer seedling establishment, often requiring 5–10 years for post-fire estab-
lishment [
52
,
53
]. On the other hand, fens may be more sensitive to changes in hydrology
due to drainage, road disturbance, or sustained drought, where a moderate drop in water
table can make it difficult for Sphagnum spp. to colonize, resulting in enhanced broadleaf
species establishment [
60
,
61
]. Deciduous seedling recruitment occurs in the first decade
post-fire, through reproduction via root suckers (aspen) and seed stump sprouts (birch),
which are shade-intolerant [
59
]. However, deciduous species typically experience thinning
in the second decade of stand development, resulting in density-dependent mortality and
replacement by conifers [
22
,
53
,
62
]. Hence, bogs outcompete fens in terms of vegetation
regeneration (Figure 8), and, over time, the growth rate decreases in mature stands to the
point of stabilization.
4.4. Use of Remote Sensing and Possible Limitations
Remote sensing of wetlands provides a broad range of information that could be used
to enhance field measurements. Although field sampling provides insights and measure-
ments of the complex environmental and physical processes that are occurring, it can be
labor-intensive and expensive [
63
]. Cumulative impacts of natural and anthropogenic
disturbances can be estimated using remotely sensed data when calibrated/validated using
field measurements. These datasets provide an opportunity to estimate (optical) or sample
(lidar) vegetation structural characteristics beyond plots via classification and change detec-
tion methods [
63
]. Active remote sensing using lidar provides better estimates of structural
attributes such as tree height. Lidar can be used for individual tree segmentation [
64
]
and separating shrub canopy from ground layers [
65
] at accuracies that are comparable or
potentially better than field structural mensuration methods.
In this study, we used a combination of lidar and field measurements to identify trees
and shrubs. However, despite the collection of 202 species along seismic line transects,
there were not enough field points to run forest-based classification to genus level, as
random forest requires hundreds to thousands of measurements to parameterise the full
distribution of the variability of shrub and tree characteristics. To reduce the implications of
pixel mixtures on structure/intensity metrics, only homogenous plots were used, reducing
the sample size to 44 micro-plot locations. Despite the data density reduction, these plots
were useful for identifying differences in structural variability between species observed
in Figures 4and 5using lidar data. We observed some variability in the 3D structure in
Atmosphere 2022,13, 112 17 of 20
deciduous (aspen, alder, willow, and birch) and conifer species (black spruce) based on the
influences of branching structures and foliage on the point cloud distribution [
28
]. To train
the random forest model, 4000 manually delineated image-based locations of deciduous
vs. conifers were used. Despite this, we expect that there may be some misidentification of
shrubs and trees from high spatial resolution imagery. For example, it was more difficult to
identify deciduous shrubs due to the potential impact of shadows, which occlude shrubs,
such as overstory canopies. One way to improve this study would be to use an object-
oriented classification, which could include structural attributes such as tree crown shape
and crown area to height ratio to better distinguish between conifer and deciduous shrubs
and trees [
29
,
66
]. Though this could enhance uncertainty when applied to early post-
fire vegetation regeneration as small stature individuals may be missed within objects
and require division of structures within pixels similar to spectral unmixing. Moreover,
vegetation height was used to measure post-fire cumulative growth; however, other metrics
could also be used, including allometrically derived biomass [
49
] and/or foliage or total
area coverage [
22
]. Although the cumulative vegetation growth in peatlands follows a
logistic curve to maturity, there remains some uncertainty that could be improved by adding
intermediate chronosequence data or YSF to the curve (e.g., between 18 and 30 years).
5. Conclusions
This study explored the use of airborne multi-spectral lidar data to identify deciduous
shrubs/trees and conifers in a space-for-time post-fire chronosequence boreal peatland
environment using forest-based classification. Using a supervised machine learning model,
deciduous shrubs were classified to 92% accuracy, while deciduous trees and conifers
were classified to 71% accuracy for both, respectively, compared with validation data. The
structural variables of the interquartile range (IQR) followed by NDIR and percent cover
provided more discriminatory power than topographic metrics, particularly in the burned
areas. The results of the forest-based classification illustrated high shrubification in recently
burned peatlands, particularly in the 5 and 18 YSF, while conifers dominate the unburned
peatlands and uplands. We also found that there was more shrubification in fens compared
to bogs, though there was some evidence to suggest a transition between 18 and 30-year
post-fire from deciduous to conifer tree species. These results indicate that as regenerating
forests age, they become more flammable because the fuel content shifts from deciduous to
conifer, providing more fuels for wildland fire. Early post-fire deciduous regeneration in
peatlands may reduce the movement of fire across the landscape. Deciduous shrubification
associated with climatic change in more mature peatland ecosystems could have mixed
implications for fire, by reducing flammability (due to the moisture content of leaves)
but also drying out peat through evaporative losses. A greater understanding of climate-
mediated changes in post-fire peatlands will be critically important for assessing peatland
resilience and fire behaviour in the future.
Author Contributions:
Conceptualization, H.E., L.C., C.H., D.T. and D.C.; methodology, H.E., L.C.,
C.H., D.T. and D.C.; software, L.C. and C.H.; validation, H.E.; formal analysis, H.E.; writing—original
draft preparation, H.E.; writing—review and editing, L.C., C.H., D.T. and D.C.; supervision, L.C.;
project administration, L.C. and D.C.; funding acquisition, L.C.; C.H. and D.C. All authors have read
and agreed to the published version of the manuscript.
Funding:
This research was funded by the NSERC Discovery Grant (grant number 2017-04492) to
Laura Chasmer, Alberta Environment and Parks (grant number 18GRAEM24) to Laura Chasmer
and Danielle Cobbaert, NSERC Canada Wildfire (grant number RES0049086) to Laura Chasmer and
Christopher Hopkinson, and the Oil Sands Monitoring Program. The Titan lidar was purchased
with a grant to Christopher Hopkinson from Western Economic Diversification Canada (grant
number 000015316). Field equipment was purchased using Canadian Foundation for Innovation
(grant number 32436) to Christopher Hopkinson and Laura Chasmer. Funding for this project was
also provided to Humaira Enayetullah by Alberta Graduate Excellence Scholarship, and Northern
Scientific Training Program (NSTP).
Atmosphere 2022,13, 112 18 of 20
Institutional Review Board Statement: Not Applicable.
Informed Consent Statement: Not Applicable.
Data Availability Statement:
Lidar data can be requested through c.hopkinson@uleth.ca and
laura.chasmer@uleth.ca, along with delineated wetlands. Geospatial data layers can be downloaded
from ABMI (https://abmi.ca/home/data-analytics/da-top/da-product-overview/Data- Archive/
Land-Cover.html accessed on 27 November 2021).
Acknowledgments:
For assistance in the field and data collection, the authors would like to thank
Chinyere Ottah and Nick Cuthbertson (University of Lethbridge). For airborne survey, lidar data
pre-processing, and technical support we would like to thank Maxim Okhrimenko and Celeste Barnes
(University of Lethbridge).
Conflicts of Interest: The authors declare no conflict of interest.
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... and Alnus spp. growing on seismic lines and intersections with well pads [37,38]. Vegetation species and canopy heights were determined/measured within 202 × 1 m 2 plots, collected at 4 m intervals at the centre of seismic lines. ...
... Vegetation species and canopy heights were determined/measured within 202 × 1 m 2 plots, collected at 4 m intervals at the centre of seismic lines. Field measurements were used in a previous study [38] to validate lidar-based vegetation structure. ...
... A total of 155 peatlands (sample size, n = 76 bogs, and n = 79 fens) were identified. The variations in vegetation heights (across cells, described below) and classification into deciduous shrubs and conifer or deciduous trees [38] within peatlands were examined. Within classified peatland shapes, peatland form