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In the managed boreal forest, harvesting has become a disturbance as important as fire. To assess whether forest recovery following both types of disturbance is similar, we compared post-disturbance revegetation rates of forests in 22 fire events and 14 harvested agglomerations (harvested areas over 5-10 years in the same vicinity) in the western boreal forest of Quebec. Pre-disturbance conditions were first compared in terms of vegetation cover types and surficial deposit types using an ordination technique. Post-disturbance changes over 30 years in land cover types were characterized by vectors of succession in an ordination. Four post-disturbance stages were identified from the 48 land thematic classes in the Landsat images: "S0" stand initiation phase; "S1" early regeneration phase; "S2" stem exclusion phase; and "S3" the coniferous forest. Analyses suggest that fire occurs in both productive and unproductive forests, which is not the case for harvesting. Revegetation rates (i.e., rapidity with which forest cover is re-established) appeared to be more advanced in harvested agglomerations when compared with entire fire events. However, when considering only the productive forest fraction of each fire, the revegetation rates are comparable between the fire events and the harvested agglomerations. The S0 is practically absent from harvested agglomerations, which is not the case in the fire events. The difference in revegetation rates between the two disturbance types could therefore be attributed mostly to the fact that fire also occurs in unproductive forest, a factor that has to be taken into account in such comparisons.
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Forests 2015, 6, 4105-4134; doi:10.3390/f6114105
forests
ISSN 1999-4907
www.mdpi.com/journal/forests
Article
Monitoring Forest Recovery Following Wildfire and Harvest in
Boreal Forests Using Satellite Imagery
Amar Madoui
1,
*, Sylvie Gauthier
2
, Alain Leduc
1
, Yves Bergeron
3
and Osvaldo Valeria
3
1
Centre d’étude de la forêt, Université du Québec à Montréal, C.P. 8888, Succursale, Centre-ville,
Montréal, QC H3C 3P8, Canada; E-Mail: Alain.Leduc@uqam.ca
2
Ressources naturelles Canada, Service canadien des forêts, Centre de foresterie des Laurentides,
1055 du PEPS, C.P. 10380, Station Sainte-Foy, Québec, QC G1V 4C7, Canada;
E-Mail: Sylvie.Gauthier@canada.ca
3
Forest Research Institute, Industrial Chair NSERC-UQAT-UQAM in Sustainable Forest
Management, Université du Québec en Abitibi-Témiscamingue, 445 boulevard de l’Université,
Rouyn-Noranda, QC J9X 5E4, Canada; E-Mails: Yves.Bergeron@uqat.ca (Y.B.);
Osvaldo.Valeria@uqat.ca (O.V.)
* Author to whom correspondence should be addressed; E-Mail: amar.madoui@gmail.com
Tel.: +514-987-3000 (poste 6872); Fax: +514-987-4647.
Academic Editors: Joanne C. White and Eric J. Jokela
Received: 29 July 2015 / Accepted: 12 November 2015 / Published: 18 November 2015
Abstract: In the managed boreal forest, harvesting has become a disturbance as important
as fire. To assess whether forest recovery following both types of disturbance is similar, we
compared post-disturbance revegetation rates of forests in 22 fire events and 14 harvested
agglomerations (harvested areas over 510 years in the same vicinity) in the western boreal
forest of Quebec. Pre-disturbance conditions were first compared in terms of vegetation
cover types and surficial deposit types using an ordination technique. Post-disturbance
changes over 30 years in land cover types were characterized by vectors of succession in
an ordination. Four post-disturbance stages were identified from the 48 land thematic
classes in the Landsat images: S0 stand initiation phase; S1 early regeneration phase;
S2 stem exclusion phase; and S3 the coniferous forest. Analyses suggest that fire
occurs in both productive and unproductive forests, which is not the case for harvesting.
Revegetation rates (i.e., rapidity with which forest cover is re-established) appeared to be
more advanced in harvested agglomerations when compared with entire fire events.
However, when considering only the productive forest fraction of each fire, the
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Forests 2015, 6 4106
revegetation rates are comparable between the fire events and the harvested
agglomerations. The S0 is practically absent from harvested agglomerations, which is not
the case in the fire events. The difference in revegetation rates between the two disturbance
types could therefore be attributed mostly to the fact that fire also occurs in unproductive
forest, a factor that has to be taken into account in such comparisons.
Keywords: Black spruce-feathermoss; disturbance; post-disturbance recovery;
revegetation rate; succession; time since disturbance.
1. Introduction
Natural disturbances such as fire or insect outbreaks play an important role in the dynamics of
boreal forests and contribute to their maintenance and renewal [1]. Of these natural disturbances, fire
remains the most important in terms of the sheer magnitude of the area that is affected [2,3] and this
disturbance is responsible for shaping the boreal forest [4,5]. However, forest harvesting in Eastern
Canadian boreal forest has gradually increased and, in some regions, even replaced fire in terms of
area disturbed per year as the predominant agent of disturbance in this territory [69]. In the Eastern
Canadian Shield of Quebec, 19,915 km
2
(0.02%) were affected by fire between 1990 and 2008, in
comparison with 51,953 km
2
(0.49%) of logged forest [9]. At the regional scale, 205,635 ha were
burned whereas 413,054 ha were logged in the region west of Lac Saint-Jean between 1973 and 1997 [8].
Moreover, the resilience of the boreal forest and its underlying dynamics may differ considerably
between fire and harvesting [10,11]. Currently, there is a wide range of arguments that have contrasted
the beneficial and detrimental effects of both types of disturbance on forest recovery.
Post-fire forest recovery in the boreal coniferous forests, i.e., the capacity of the forest to regenerate
and to maintain its productivity over the long-term, has been extensively studied for many
years [3,4,12]. In burned areas, pre-disturbance species composition and fire severity are factors that
will determine the composition of post-disturbance regeneration. Some species of the boreal forest
(Pinus banksiana Lambert, Picea mariana (Miller) BSP, and Populus tremuloides Michx.) are
well adapted to fire and can colonize burned stands in the first few years following a burn [13,14].
As post-fire regeneration densities of these species are strongly influenced by their basal areas in the
pre-fire stands [1416], a return to the pre-fire vegetation composition is usually rapid when the basal
area is sufficient. This is not necessarily observed with traditional clear-cutting where mixed stands
with a predominance of deciduous trees will dominate the post-harvest stands [17]. However, post-fire
regeneration failures have been observed in Northern Quebec [18,19]. When time intervals between
fires are short, there is a lack of regeneration because the stands did not have time to rebuild their areal
seed banks between the two episodes [18,20]. Similarly, under certain biophysical limiting conditions,
a full restocking of sites after fire may not be reached [21,22].
The type of surficial deposit, drainage characteristics, and the topography of a territory can
influence both fire and harvesting post-disturbance forest composition [2327]. Harvesting with
protection of the regeneration and soils (CPRS in Quebec, i.e. CLAAG, Careful Logging Around
Advanced Growth) has been used since the mid-1990s, and could favor a rapid return of stands to their
Forests 2015, 6 4107
pre-harvest compositions by maintaining the pre-established regeneration, which then becomes a major
component of the regeneration after harvesting [23,28]. Lecomte et al. [25] and Lafleur et al. [29]
observed, however, that following a low severity fire, sparse regeneration and poor growth can result
from CLAAG in paludified sites throughout a stand’s history.
Actually, most of the published work on landscape analyses and monitoring forest over larger areas
comes from, among all available remote sensing imagery, Landsat imagery [3032], because images
cover large areas (185 × 185 km) with a high spatial resolution (30 m), quality, and relatively short
time frequency. The use of satellite imagery allows for the comparison of the revegetation rate
(i.e., rapidity with which forest cover will rebuild) among different types of disturbances such as fire and
harvesting [3335] and it is considered as the most promising method to measure and evaluate
landscape vegetation cover [3638] and landscape metrics analysis [39]. The main objective of this
study was to compare forest recovery following fire or harvesting in the black spruce-feather moss
subdomain of Western Quebec using satellite data in terms of rate and composition. More specifically,
(1) we compared the pre-disturbance conditions in terms of vegetation composition and surficial
deposit characteristics; (2) we analyzed the post-disturbance recovery after fire or harvesting using
succession vectors that describe changes in vegetation composition throughout time; (3) we evaluated
the rate of revegetation over the first 30 years following the disturbance by comparing Landsat
imagery taken at different times since the disturbance (between 1985 and 2005); and (4) we compared
the post-disturbance land cover composition of fire events and harvested agglomerations. Our first
hypothesis was that the vegetation cover types affected by fires vary (from forested to non-forested)
and occur on different surficial deposit types, while those affected by harvest are exclusively forested
and are mainly located on productive surficial deposits. Our second hypothesis predicted that post-fire
and post-harvest revegetation rates are different in young post-disturbance stages but similar in
advanced ones. Finally, the third hypothesis suggested that composition is dominated by softwoods in
post-fire events and by hardwoods after harvesting.
2. Study Area
Our study area was located within the black spruce-feather moss subdomain [40] of Western
Quebec. The study area extended from the James Bay Lowlands in the west to the Canadian Shield to
the east (80° W to 74° W), and from 49° N to 51° N, which covers 9218 km
2
(Figure 1). It concerns
the four ecological regions 6a, 6b, 6c, and 6d. Ecological regions 6a and 6b belong to the James Bay
Lowlands. All 6b and the western part of 6a were dominated by poorly drained organic soils (>60%),
whereas clay soils were more common in the eastern part of 6a. Regions 6c and 6d fall on the
Canadian Shield and were characterized by thick glacial till and, to a lesser degree, organic soils
mostly occurring in the northern part. According to [41], the poorly drained organic soils constitute the
dominant type of deposit in the west, while thick glacial till predominates in the east (Figure 1). Mean
annual precipitation varies between 700 and 900 mm, while mean annual temperature varies between
2.5°C and C. Topographic relief is relatively uniform and flat in the west and is characterized by
plains with a few scattered rocky hills. It is more rugged in the east, consisting mostly of hills. Major
bodies of water (e.g., lakes) in the west are few and smaller compared with those in the east, and are
more uniformly shaped or circular. In the west, wetlands are more abundant, forming large
Forests 2015, 6 4108
peatlands [42], which would explain less pressure on forests by industrial activities in this part of
the territory.
Figure 1. Study area and location of fire event and harvested agglomeration that were
sampled. Fire event 1: burned areas where pre-burned mature forest covers <40%; fire
event 2: burned areas where pre-burned mature forest covers >40% and for which the
provided time since fire (TSF) is mainly between 14 and 19 years.
Fires are scattered throughout the territory whereas harvested zones are concentrated in the southern
part of the study area, where wetlands are less abundant [42], and extend gradually towards
the northeast.
Forests 2015, 6 4109
3. Materials and Methodology
Two main data sources were used to compare harvested agglomerations and fire events undergoing
post-disturbance recovery, the forest inventory SIFORT (Spatial information on Forest Composition
based on Tessellate) database and a classified multi-temporal mosaic developed from Landsat TM
(Thematic Mapper) imagery. SIFORT [43] is a geospatial database that divided an area into tiles of
15 s in longitude by 15 s in latitude (approximately 14 ha) where information for each grid centroid is
sampled from the forest inventory maps produced by the MFFPQ (Ministère des Forêts, de la Faune et
des Parcs du Québec) using air photos at 1/5000 scale [43]. It provides information on stand origin,
composition, age, height, cover density, surficial deposit, and drainage for each of the three decadal
inventories. It is a geo-referenced database grouping data collected from the last three decadal forest
inventories (SIFORT 1, 1969; SIFORT 2, 1985 and SIFORT 3, 1994). We have used these databases
to identify stand vegetation cover types prior to fire or harvesting disturbances to identify surficial
deposit types and to define the time since harvesting in the harvested agglomerations.
The multi-temporal Landsat imagery mosaics were already classified by the remote sensing team of
UQAT (Université du Québec en Abitibi-Temiscamingue) and LFC (Laurentian Forestry Centre of the
Canadian Forest Service, Quebec). These mosaics were previously used to measure and monitor the
state of the forest over a large area (11.6 million ha) of boreal forest in eastern Canada using several
criteria (based on a combination of land thematic classes) for a 20-year period (19852005) [31].
Kappa validation was assessed using 53,000 fine-resolution geo-referenced digital aerial color photo
frames and temporal change error was also performed. A modal filter (90 m by 90 m) was used to
reduce classification between classes and size. These mosaics show an overall accuracy varying
between 62 and 83% for 1985, 1995, 2000, and 2005 [31,44]. Landsat images were collected during
the peak growing season and top-of-atmosphere reflectance correction was used. To report and
monitor the state of the forest, a hybrid method (supervised and unsupervised techniques) was used
and 48 thematic classes based on Canada’s National Forest Inventory were identified [45] for each
period 1985, 1995, 2000, and 2005. In the current work, we used this product (48 thematic classes) to
construct more robust groupings (with less classes), allowing the characterization and comparison
of the post-disturbance recovery within burned and harvested areas over time (see next sections). The
classified images were first processed with Majority Filter in ArcGIS 9.3 (ESRI Inc., Redlands, CA,
USA), with a 3 × 3 moving window (pixels 30 m), to eliminate isolated pixels resulting from
classification errors of diverse origins. This operation also minimized errors that could occur when
overlaying two successive satellite images. As the methods involved many different steps, the overall
approach is described in Figure 2. To avoid problems related to pseudo-replication, large N, and spatial
auto-correlation, all the analyses were conducted on a per disturbance event basis. This implied,
however, that the time since disturbance was more complex to define for the harvested agglomerations
(harvested areas over 510 years in the same vicinity, see Section 3.2 for details).
Forests 2015, 6 4110
Figure 2. Summary of the methodology steps followed in this study.
3.1. Fire Event Selection
Fires were first identified and dated using the spatially explicit database provided by the MFFPQ.
The following criteria were used to select post-disturbance areas: (1) multiple satellite mosaics of the
same area over time were available; (2) fire events were not truncated and there was no cloud
coverage; (3) data from SIFORT were available to determine the pre-fire vegetation and site
conditions; and (4) the selected fires had not been salvage-logged. In total, 22 fires with sizes varying
between 2000 ha and 52,000 ha were retained for this study (Table 1). The post-fire satellite image
dates provided time since fire (TSF), which varied from three months to 29 years. At least two scenes
were acquired for each fire, while for some fires (fl5, f7, and f13), three post-fire scenes were
available. In total, 48 post-fire scenes were obtained for the selected fires, of which 21, 21, and six had
a TSF of 0 to 10 years, 11 to 20 years, and 21 to 29 years, respectively.
Forests 2015, 6 4111
Table 1. Area, year of disturbance, and time since disturbance (TSD) for each image
period for the 22 fire events and 14 harvested agglomerations. For the harvested
agglomerations, the 10th and 90th percentiles (in area) are presented in parentheses after
the year of disturbance. nd, no data; bh, before harvest.
Number
Area (ha)
TSD
Image 1985
Image 1995
Image 2000
Image 2005
Fires
f1
2,026
nd
nd
4
9
f2
2,486
nd
nd
13
18
f4
3,162
nd
nd
24
29
f5
3,271
nd
nd
4
9
f7
4,243
0.25
nd
14
19
f9
5,175
nd
nd
3
8
f10
5,383
nd
nd
14
19
f13
5,703
0.25
9
14
19
f14
5,853
nd
nd
12
17
f15
6,129
nd
15
20
25
f17
6,973
nd
nd
4
9
f18
7,376
4
9
nd
nd
f20
10,177
nd
nd
4
9
f21
10,373
nd
nd
5
10
f22
11,220
nd
nd
14
19
f26
20,152
nd
nd
14
19
f27
21,262
nd
nd
4
9
f28
20,914
nd
nd
24
29
f29
33,325
nd
nd
14
19
f30
36,325
nd
nd
17
22
f32
42,753
nd
nd
3
8
f33
51,882
nd
nd
14
19
Harvest
c1
15,932
9
19
24
29
c2
27,152
7
17
22
27
c3
48,121
7
17
22
27
c4
7,626
5
15
20
25
c5
16,923
9
19
24
29
c6
12,595
4
14
19
24
c7
96,574
7
17
22
27
c8
43,823
bh
7
12
17
c9
4,165
0
10
15
20
c10
14,379
bh
9
14
19
c11
11,878
bh
5
10
15
c12
47,982
bh
6
11
16
c13
34,146
bh
8
13
18
c14
35,403
bh
7
12
17
Forests 2015, 6 4112
3.2. Delineation of Harvested Agglomerations
The harvested agglomerations that result from an agglomeration of harvested blocks over time were
digitized by drawing polygons on the satellite imagery with ArcGIS 9.3. These areas were easily
identified on the images by their regular geometric configurations. To ensure that these were authentic
harvested agglomerations, SIFORT data containing logging dates were superimposed on the satellite
images. It should be noted that the boundaries/edges of these harvested agglomerations were
delineated subjectively on the images by remaining within the boundaries of the same
harvested agglomeration.
While a fire event is produced over a relatively short period of time, i.e., a few days to several
weeks, a harvested agglomeration can result from several years of harvesting. Consequently, when
comparing post-fire and post-harvest forest revegetation, the latter will exhibit a spread of ages over
time. To minimize this effect, using the SIFORT database, we retained only agglomerations that took
less than 10 years to be created (Table 1). To estimate mean TSH (time since the last harvest) for each
harvested agglomeration, we calculated the mean logging date, weighted by the surface area harvested
each year for each harvesting agglomeration from the SIFORT database (Table 1).
Because of their spatial concentration and agglomeration, the number of harvested agglomerations
that we could select was limited. In total, 14 harvested agglomerations (numbered c1, c2, c3, etc.) were
chosen, with sizes varying between 4000 ha and 97,000 ha. Considering the SIFORT minimal
resolution of 14 ha, that means we have between 285 and 6928 points to describe pre-disturbance
conditions for each selected harvested agglomeration. Once harvested agglomerations had been
chosen, we proceeded with extracting information from the classified satellite images. We were able to
generate 50 scenes for 14 harvested agglomerations that corresponded to at least three post-disturbance
time periods for each harvested agglomeration. The mean TSH dates varied between six months and
29 years, with 16 scenes of 010 years, 20 of 1120 years, and 12 of 2029 years (Table 1).
3.3. Pre-Disturbance States
As knowing the composition of the vegetation cover types that existed prior to the disturbance is
important in explaining changes that take place afterwards, we used the SIFORT database to describe
both vegetation composition and surficial deposits. One variable, describing the vegetation cover types
before disturbance (combining the land class codes, stand composition types, disturbance origin, and
age class), was created for each fire event and each harvest agglomeration. In order to standardize
information codes available in SIFORT 1, 2, and 3, 11 vegetation cover types were defined (Appendix
Table A1): bare humid, bare dry, open water, recently disturbed, deciduous regeneration, and conifer
regeneration, with mature forest covers of shade-intolerant deciduous species, shade-intolerant
deciduous species with conifers, jack pine, black spruce, and balsam fir. The surficial deposits and
drainage class corresponding to the disturbed areas were also extracted from the SIFORT database.
Surficial deposits and drainage were grouped in seven classes: rocky outcrops, tills, Cochrane tills,
sand types, mesic clay, sub-hydric clays, and poorly drained and organic soils (Org) (Appendix
Table A2).
Forests 2015, 6 4113
As a first step, principal component analyses (PCA) were conducted in JMP 7.0.1 (Version 7.0.1,
SAS Institute 2008, Cary, NC, USA) to assess whether fire events and harvested agglomerations were
produced under (1) similar vegetation cover types and (2) similar surficial deposits and drainage
conditions. According to [46], the PCA is an ideal technique for data with approximately linear
relationships among variables. Its objective is to reduce a data set with n objects and p variables to a
smaller number of synthetic variables that represent most of the information in the original data
set. The structure of the ordination is based solely on the matrix of correlations among variables.
Vegetation type or surficial deposit proportions were expressed for each fire event and
harvested agglomeration.
3.4. Post-Disturbance Changes
We evaluated the post-disturbance changes using three different analyses. First, we looked at
succession changes using the land cover type of the classified satellite images. Secondly, we assessed
the rapidity of forest recovery by looking at four forest development stages that correspond to a
recovery gradient of forests. Finally, we evaluated whether forest composition differs among
disturbance types using composition information from the images.
3.4.1. Post-Disturbance Successional Pathways
In order to compare the post-disturbance recovery between a fire event and harvested
agglomeration, we first produced a PCA ordination using the 48 post-fire and 50 post-harvest scenes
and the land cover types of the satellite images (Appendix Table A3) as the vegetation descriptors. The
successional vectors of change were created by joining the information of the same scene for
successive time periods (corresponding to time since disturbance, TSD).
3.4.2. Rate of Forest Revegetation
To estimate the rates of forest revegetation of the disturbed areas, the 48 land thematic classes of
satellite images were grouped based on the nature of cover type, stand cover density (open cover types
vs. closed cover types), stand cover composition, and TDS abundance profile into four development
post-disturbance stages (Appendix Table A3). The Stage 0 (S0: stand initiation phase) groups cover
type still showed traces of recent disturbances such as burn or post-fire regeneration and were mostly
abundant in the first 10 years after disturbance; regeneration Stage 1 (S1: early regeneration phase),
representing the early stage of development, was composed of land cover types that corresponded
mostly to low height shrub vegetation and also occurred mainly during the first 10 years after
disturbance; Stage 2 (S2: stem exclusion phase) grouped young mixed forest cover types mostly
characterized by mixed regeneration and open mixed cover with a deciduous component. This stage
appears more frequently in the Landsat images of more than 10 years post-disturbance and can last up
to 30 years post-disturbance a time where coniferous species start to dominate the canopy. Lastly,
Stage 3 (S3: the coniferous forest) grouped coniferous forest cover types typically observed in black
spruce-feather moss forest in which the canopy is dominated by black spruce and jack pine and
corresponded to either residual habitats or revegetation generally established around 30 years
Forests 2015, 6 4114
following disturbance. The shaded, cloudy snow and ice classes, together with rocky outcrops and
urban environment cover land classes, were grouped as ‘other and excluded from the analyses. This
classification results from the metadata associated with the image classification and was confirmed by
observation of successive images from the same scene for which TSD was known.
Forest revegetation rates were obtained from the overlap of two sequential classified satellite
images of the same territory, corresponding to different times since the disturbance. For example, for a
fire event that was disturbed in 1986, by superimposing the images of 2000 and 2005, we assess
transition (changes of the stage) for all cells composing a fire. As forest succession did not follow a
gradual process or evolution, the transitions from the initial stage to any of the successive ones were
then pooled in three types of changes depending on the initial stage: from S0 to S1, S2, or S3; from S1
to S2 or S3; or from S2 to S3. The same exercise was applied for all the fire events and harvested
agglomerations and the results were then reported over a fixed period of time (i.e., five years) for
comparison purposes. The observed rates of revegetation in the fire events can therefore be compared
with the harvested agglomerations. Arcsine-square root-transformation of the rate of revegetation (as a
proportion) was performed prior to the statistical analysis, where a t-test was applied to determine how
the mean rates of forest revegetation for fire events compared with the harvested ones.
To provide the most pairwise comparisons of forest revegetation after fire and forest harvesting, we
compared the harvested agglomerations only with the fires that had burned mature forests. To do so, an
analysis was conducted with ArcGIS 9.3 to identify the burned areas that had originated from mature
forest cover available in the SIFORT database before the fire. In order to perform comparisons
between post-fire and post-harvest, Landsat images taken between 10 and 29 years after disturbance
were used.
3.4.3. Post-Disturbance Composition
In order to assess if post-disturbance compositions were similar, three groups were created
according to the TSD for all harvested agglomerations and only the fire events that had burned at least
40% of the mature forest cover. Group 1 corresponded to a TSD of 013 years of age, group 2 to a
TSD of 1420 years of age, and group 3 consisted of TSD > 20 years of age. The 48 land thematic
classes from Landsat were also grouped into four land cover types: (1) unproductive and non-forested
land cover types, (2) recently disturbed, (3) coniferous, and (4) deciduous (Appendix Table A4). The
unproductive and non-forested land cover types were excluded from these analyses.
4. Results
4.1. Pre-Disturbance States
The vegetation cover types clearly separated the pre-fire events from the pre-harvested
agglomerations on the PCA ordination (Figure 3a). The pre-fire events are situated on the left side of
axis 2, while the pre-harvested agglomerations are on the right. Overall, three fire events (f17, f27, and
f33) and four harvested agglomerations (c4, c7, c10, and c11) overlapped near the center of the graph.
This distinction between the two disturbances showed that vegetation cover types that are affected by
fire and logging differ considerably. In fact, harvesting took place solely in mature deciduous and
Forests 2015, 6 4115
coniferous stands of dense and open mixed cover or, in other words, commercial forest (Figure 3b).
The fires, in contrast, occurred in a variety of vegetation cover types that included mature forest cover
(coniferous), low shrubs, and non-forested areas (woodlands, mosses, wetlands) (Figure 3b). Similarly,
surficial deposits that were associated with certain vegetation cover types differed between the
harvested agglomerations and burned events (Figure 3c). Harvested agglomerations were situated
mainly on hydric (HClay) and mesic (MClay) clays, while fires occurred on a range of surficial
deposits, including well- or excessively well-drained rocky outcrops, tills, and sandy soils (Figure 3d).
Figure 3. Cont.
Forests 2015, 6 4116
Figure 3. Results of two PCAs based on pre-disturbance vegetation cover types (a and b)
and the types of surficial deposits (c and d) on which the two disturbances occurred for 22
fire events and 14 harvested agglomerations. Data derived from the SIFORT database.
Each cover type or surficial deposit is described as its relative proportion (%) within its
disturbed area. (a and c) Scores. The letters preceding numbers represent type of
disturbance (f = fires; c = harvests), (b and d) Loadings. RegC (coniferous regeneration),
RegD (deciduous regeneration), Dist (disturbance), ID (mature forest covers of
shade-intolerant deciduous), BH (bare humid), BD (bare dry), JP (mature forest covers of
jack pine), H
2
O (water), DeC (mature forest covers of shade-intolerant deciduous),
BF (mature forest covers of balsam fir), BS (mature forest covers of black spruce),
Roc (rocky outcrops), Til (tills), CTil (cochrane tills), Sand (sand types), MClay (mesic
clay), HClay (sub-hydric clay), Org (organic soils).
Forests 2015, 6 4117
4.2. Post-Disturbance Changes
4.2.1. Post-Disturbance Successional Pathways
The PCA ordination of post-disturbance land cover types shows the same distinction as the
pre-disturbance one: for the most part, harvested agglomerations are distinct from the fire events
(Appendix Figure A1a). The harvested agglomerations appear mostly to the right of the first axis while
the fire events are on the left. Some older fire events are found on the right side with the harvests, such
as f18, f4, f22, and f30. In addition, certain young harvested agglomerations are found on the left side
with the fires, such as c4, c7, c8, c10, and c12. The harvested agglomerations are mostly linked to
deciduous cover types (Appendix Figure A1b).
To control for time since disturbance, we compared six fire events (five fires that were 14 to
19 years old and one fire that was 24 to 29 years old) with 14 harvested agglomerations of similar age.
The PCA shows that the harvested agglomerations are distributed according to a TSD gradient
expressed by axis 1, separating the young harvests (to the left) from the old harvests (to the right)
(Figure 4a,b). In the young harvests, we encounter an abundance of recently disturbed covers (harvests
and post-harvest regeneration) compared with old harvests that were dominated by open deciduous
species and dense mixedwood stands. In the six fire events, all post-fire stages are located in the lower
left-hand region of the ordination, in close proximity to young harvests dominated by coniferous
woodlands with moss, and moss and rock ground cover. The successional vector lengths of the six
burns (in bold) are shorter and more diverse in direction than those of the harvests (Figure 4a).
However, the harvest trajectory is longer and shows two patterns of recovery (evolutionary series): one
generating mixed regeneration, while the second generates more open and humid areas, which are less
forested (Figure 4b).
Figure 4. Cont.
Forests 2015, 6 4118
Figure 4. PCA showing successional pathways of post-disturbance land cover types of fire
events and harvested agglomerations. Each land cover type is described using its relative
proportion within the disturbed area. (a) Disturbance scores (fire events and harvested
agglomerations). The digit represents the TSD (time since disturbance). The fire events
(in bold) with pre-burned mature forest covers >40%. (b) Loadings of land cover types.
CfRg (coniferous regeneration), Herb (perennial crops, pasture, fallow, grassland), DcRg
(deciduous regeneration), DyDc (dense young deciduous), OmDc (open mixed deciduous
tendency), Exl (exposed land), OCfmo (open coniferous with moss), DeDc (dense
deciduous), MCfmo (medium coniferous cover with moss), DmDc (dense mixed deciduous
tendency), MoRoc (moss and rock), BpBrHpHr (burn, post-burn regeneration, harvest, and
post-harvest regeneration), Cwmo (coniferous woodland with moss), DmCf (dense mixed
deciduous with coniferous tendency), ODc (open deciduous), DmCf (dense mixed
deciduous with coniferous tendency), OmDcCf (open mixed deciduous and coniferous
tendency), LSh (low shrubs), WlTr (wetland with tall shrubs and trees), MRg (mixed
regeneration), Lic (lichens).
4.2.2. Post-Disturbance Forest Revegetation Rates
Among the 22 fires, only six fires presented at least 40% of mature forest cover in the fire event
(f7, f10, f13, f28, f29, and f33) and a time since disturbance similar to what is observed in harvested
agglomeration (Table 2). Comparison of forest revegetation rate was done between these six fire
events (five that were 14 to 19 years old and one that was 24 to 29 years old) and nine harvested
agglomerations (all 11 to 25 years old) of similar age (Table 3). When the entire area of each fire event
is considered (Table 3a), the six fires show a lower revegetation rate compared with harvested
agglomerations when starting from S0 (0.45 ± 0.15 vs. 0.76 ± 0.16) and S1 (0.58 ± 0.12 vs.
0.66 ± 0.11) (Table 3c). Note, however, that the S0 maturation stage appears rarely after harvesting
(cover less than 10% of harvested area) since advanced regeneration was protected in harvesting
operations. When starting from S2, in contrast, revegetation rates are very similar between the harvests
Forests 2015, 6 4119
and fires (0.11 ± 0.07 vs. 0.11 ± 0.06). The S2 maturation stage also appears relatively rare for both
disturbance origins because of the young age of the compared areas (less than 20 years for most of
them). When considering only the mature forest portion of the fire events, the rate of revegetation from
S1 (0.64 ± 0.1), the most important initial stage for both disturbance origins, appears more similar to
the rate that was calculated for the harvested ones (0.66 ± 0.11) (Table 3b,c).
Table 2. Amount of mature cover (%) in pre-disturbed areas. The “mature” forest covers
were considered when black spruce (BS), jack pine (JP), shade-intolerant deciduous
species (Fi), shade-intolerant deciduous species with conifers (DeC), and balsam fir (BF)
were present. In bold, fire events with >40% pre-disturbance mature forest cover and for
which TSF (time since the last fire) is estimated between 14 and 19 years. *TSF was
estimated to be between 24 and 29 years.
No fire
Mature cover (%)
No harvest
Mature cover (%)
f28*
75.7
c10
88.3
f10
75.3
c04
83.7
f33
72.1
c07
83.1
f21
67.7
c11
82.5
f27
67.7
c02
80.3
f17
65.7
c03
77.6
f1
61.4
c08
77.5
f29
54.3
c09
76.6
f5
50.8
c12
76.5
f13
48.7
c14
72.3
f7
48.4
c15
72.3
f20
37.5
c13
71.1
f26
26.2
c06
60.5
f9
22.1
c01
58.3
f4
17.9
f30
16.9
f14
14.5
f2
14.1
f22
12.9
f18
12.4
f15
11.2
f32
10.7
Forests 2015, 6 4120
Table 3. Comparison of revegetation rate (over a five-year period) between burned (14 to
29 years old) (a) for the entire area of fire events, (b) for the fires that burned only in the
mature forested portion, and (c) harvested (10 to 25 years old) agglomerations. Means and
standard deviations (SD) are included for each type of change.
(a)
Fires
Period
Forest revegetation rate (over a five-year period) for each type of change
S0 to S1-S2-S3
S1 to S2-S3
S2 to S3
f10
1419
0.37
0.47
0.17
f33
1419
0.43
0.60
0.21
f28
2429
0.76
0.41
0.04
f29
1419
0.40
0.67
0.11
f7
1419
0.34
0.74
0.03
f13
1419
0.41
0.57
0.12
Mean
0.45
0.58
0.11
SD
0.15
0.12
0.07
(b)
Fires
Period
Revegetation rate (over a five-year period) for each type of change
S0 to S1-S2-S3
S1 to S2-S3
S2 to S3
f10
1419
0.37
0.47
0.17
f33
1419
0.46
0.66
0.21
f28
24––29
0.74
0.46
0.04
f29
1419
0.40
0.73
0.10
f7
1419
0.33
0.72
0.01
f13
1419
0.43
0.68
0.11
Mean
0.46
0.64
0.11
SD
0.14
0.10
0.08
(c)
Harvest
Period
Revegetation rate (over a five-year period) for each type of change
S0 to S1-S2-S3
S1 to S2-S3
S2 to S3
c4
2025
0.53
0.80
0.03
c6
1924
0.53
0.62
0.04
c8
1217
0.91
0.76
0.15
c9
1520
0.92
0.65
0.09
c10
1419
0.80
0.53
0.08
c11
1015
0.74
0.61
0.07
c12
1116
0.94
0.60
0.18
c13
1318
0.66
0.52
0.17
c14
1217
0.85
0.81
0.21
Mean
0.76
0.66
0.11
SD
0.16
0.11
0.07
In terms of statistical inference, the revegetation rate of the S0 initial stage appears faster after
harvesting than after fire (p = 0.001 for S0 initial stage). This difference is significant when we
consider only the mature forest portion of the pre-fire event or the entire burned area. For the S1 initial
Forests 2015, 6 4121
stage, the difference appears quasi-significant when we compare the revegetation rate of the entire area
of the fire event to the harvested agglomeration (p = 0.107), and becomes non-significant if we
consider only the mature forest portion of the fire event (p = 0.488). As expected by the simple
comparison of their mean values, the revegetation rate of the S2 initial stage shows no significant
difference between the fire event and harvested agglomeration.
4.2.3. Post-Disturbance Composition
Cover composition comparisons were made using fires that had at least 40% of mature forest prior
to the disturbance. Land cover composition for areas aged between 0 and 13 years following fire was
largely dominated by the recently disturbed class (68%), followed by coniferous (19%) and broad-leaf
tree species (13%) (Figure 5). Following harvest, broad-leaf vegetation dominates (45%), followed by
conifers (36%), with the recently disturbed class representing only 19%.
Figure 5. Comparison of cover composition between fire events and harvested
agglomerations in three TSD classes. Only fires that had burned at least 40% of mature
forest cover prior to the disturbance have been analyzed.
For fire events that were aged 14 to 20 years following the fire, the recently disturbed class is also
dominant (42%), followed by broad-leaf tree species (35%) and conifers (23%). Following harvest,
broad-leaf tree species remain more dominant (57%), followed by conifers (39%). The recently
disturbed class decreases substantially to 4% of the harvested area.
In post-fire events exceeding 20 years of age, the recently disturbed class decreases (21%), and
broad-leaf tree species (44%) and conifers (35%) increase in the burned area. Similarly, the recently
disturbed class decreases in harvested stands until 2%, leading to dominance by broad-leaf tree species
(60%) and conifers (38%).
Forests 2015, 6 4122
5. Discussion
Although many studies have used satellite imagery to characterize revegetation dynamics after
major disturbances (e.g., [4749]), to our knowledge, none have compared post-fire and post-harvest
revegetation on a large scale in the boreal forest. The use of temporal satellite images has limitations
related to classification accuracy due to a complexity of spectral characteristics of the Earth’s
surface [50]. Spectral response is influenced by factors such as the species mixture, canopy closure,
and understory contribution [51]. These factors produced a broad range of spectral values and textures
related to one stand structure instead of its composition. For example, [52] found low accuracy
classifying from Landsat images of mixed stands, and [53] found that stand age and height influence
the overall canopy and understory reflectance values. Adding textural information during the
classification process can improve classification accuracy by 12% or more [54]. Among remote
sensing analysis methods, geographic object-based image analysis is considering a promising
approach [55,56]. This approach integrates image segmentation and classification and radiometric and
textural image attributes that reduce the level of subjectivity by the analyst as used in
Enhancement-Classification Method (ECM) [31].
We trust that these weaknesses are not major factors in our analysis, however. First, our analysis
was made on part of the eastern boreal forest only, and concerned areas that had recently been
disturbed only, reducing the variability in forest types as compared to larger areas. The ordination
performed on all land cover types also showed a clear discrimination between fire events and harvested
agglomerations, suggesting that post-disturbance vegetation dynamics follow different pathways
depending on the disturbance origin. Furthermore, succession rates were assessed on differences
between highly contrasted land cover types that characterize canopy closure over the first 30 years in
black spruce-feather moss forest (see Appendix Table A3). For instance, Stage 1 (S1: early
regeneration) and Stage 2 (S2: stem exclusion) are grouping several of the 48 thematic classes and
have spectral values associated with shrub vegetation or mixed forest cover types, respectively. These
highly contrasted classes constitute a robust grouping. Finally, results reported in this study were
mainly based on a comparison between fire events and harvested agglomerations. As land cover types
that constitute each of our successional stages appeared in both disturbances, it would be surprising
that the highlighted differences could result from an unbalanced distribution in error rates between the
two disturbance types.
Concerning the pre-disturbance state, the environments in which fire and harvesting occur may
differ over space and through time. Fires are not “very selective”, occurring in the spring and summer
more or less randomly within the landscape [5759]. Logging operations take place in the mature
forest fraction of the landscape, and tend to be conducted on productive sites. Our results show that
fires occurred in a higher diversity of environments without any noticeable distinction in the vegetation
cover, thereby confirming the random nature of fires in the boreal forest. Madoui et al. [60] also
showed that fires can propagate in non-productive forested areas (open wetlands) under extreme
meteorological conditions. In fact, surficial deposits and landscape configuration strongly contribute to
how succession takes place [20,6163]. It is evident that differences in forest cover composition and
the surficial deposits of harvested or fire events could explain the results that we obtained.
Forests 2015, 6 4123
Lafleur et al. [29] found that stocking after harvest is affected more strongly by soil type than by
harvesting method.
Considering that post-disturbance succession is largely influenced by the severity of the disturbance
and pre-disturbance vegetation [14,62], the non-productive forested character of the vegetation cover
prior to the fire could explain some apparent regeneration failures seen after fires. As harvesting occurs
almost exclusively in productive forest, this type of problem is less likely to be observed in these
landscapes, although the survival of pre-established regeneration could be influenced not only by
harvesting but also by the changes that the site undergoes after logging [64].
5.1. Post-Disturbance Recovery
The differences in land cover dynamics between post-fire and post-harvest disturbances could be
attributable predominantly to variation in the biological legacies left behind after these types of
disturbances [65]. In comparing the different scenes taken after fire and harvesting during the 30 years
following the disturbance using successional vectors, we noticed that fire events show little change in
their land cover types. The harvests show more change in vegetation composition and their
successional pathways appear to be much longer. Even though harvested agglomerations appear at a
more advanced stage of recovery than burned areas of a similar age, harvesting favors the
establishment of an immature deciduous stage that could delay the return of coniferous cover.
These differences between post-fire and post-harvest forest recovery are best illustrated through
ordination, which includes harvests and only the fires that occurred mostly in productive forests
(Figure 4). The short successional vectors of fires suggest that their vegetation covers undergo little
change. In fact, according to [66], the short successional vectors of fires reflect a re-establishment of
forest stands by the same species, especially the relatively mono-specific tendency of jack pine. The
revegetation rate of the forest cover in fire events occurs differently when compared with the mature
logged areas. From one standpoint, the harvested agglomerations consist of very few stand initiation
areas (stage 0) in comparison with fire events of a similar age. The fires, especially when severe, return
the ecosystem to its initial stage of development by burning the humus layer, thereby exposing the
mineral soil and destroying competing vegetation. Logging operations protect the humus layer and
understory vegetation [11], which explains why S0 is practically absent from harvested
agglomerations. This advanced recovery in logged areas is also seen in S1 but to a lesser extent. This
can be explained by the fact that the fires extend over heterogeneous environments (productive and
unproductive vegetation cover) such as wetlands and lichen ground covers, among others. The surge in
regeneration in environments with low productivity would then be weaker than in forested sites. Forest
harvesting occurs only in the productive forest fraction of the harvest agglomeration, in which sites
with low productivity are absent. This is consistent with what the analysis of the forested fraction
suggests for fire events; it shows that revegetation rates are similar to those of the harvested
agglomerations when considering the transition from S1 and from S2. These results suggest that the
limitations of post-fire revegetation could be attributed to the state of the vegetation cover prior to fire
more than to the effect of fire per se.
Forests 2015, 6 4124
5.2. Post-Disturbance Composition
In the first 13 years following fire, burned areas differed in land cover composition from harvested
ones. The recently burned class dominates the post-fire events, whereas the broad-leaf and coniferous
classes dominate the harvested agglomerations. In fact, fire events begin regenerating on soils devoid
of vegetation (primary stage of succession), whereas harvested agglomerations already contain
established vegetation, which explained the abundance of conifers. In early stages of succession,
harvested stands that were harvested using CLAAG contain a relatively larger coniferous component
than burned stands do, due to the protection of advanced coniferous regeneration. When examining fire
events and harvested agglomerations in this age class (0 to 13 years old), we observe that half (50%) of
the fire events that were sampled could be dated between four or five years following disturbance,
compared with only 19% in the harvested agglomerations. This response could explain the dominance
of the recently disturbed stage following fires, representing the period prior to conifer establishment.
At this stage, low shrubs and post-fire regeneration are dominant.
Between 14 and 20 years following disturbance, fire events are still in the process of recovery,
which would explain the dominance of the recently burned class. Conversely, this component is
negligible in harvested stands. At this age stage, the deciduous component dominates harvested stands.
This has been observed and supported by several authors; indeed, Harvey and Bergeron [17] found
that, following harvest, a significant reduction in conifer density was observed, which led to a mixed
species composition or dominance by hardwoods.
At an older age (>20 years), vegetation closure is more pronounced in harvested versus burned
stands. In harvested agglomerations, we did not observe any large changes in stand composition,
except in the recently disturbed class, which disappears in favor of an increase in the hardwood
component. In contrast, the coniferous component of burned stands increases at the expense of the
recently disturbed class. Additionally, small conifers that may have been hidden by low shrubs in early
stages of succession begin to emerge as the canopy closes. This especially applies to black spruce,
which exhibits slow juvenile growth rates. Thus, it may take several years to meet or exceed the height
of shrubs [67], which would permit detection on satellite imagery.
6. Conclusion
Our work showed that forest recovery after fire and harvesting appears to be different both because
succession does not start at the same development stage, and because fire occurs in environments that
are more heterogeneous than harvesting. It is recognized in the literature that, in the case of a severe
fire, the forest ecosystem would reinitiate succession, whereas after harvest, the same ecosystem is
already in advance from a successional viewpoint due to the low impact of the disturbance on the
understory. During the first 20 years following a disturbance, the fire events evolve slowly, while the
harvest agglomerations display a much faster succession. The short succession vectors of fires reflect a
re-establishment of forest stands with the same species composition. However, the harvested
agglomeration trajectories are longer and generally show two patterns of recovery, in which one results
in mixed regeneration while the other tendency shows the onset of open landscapes. Although the
differences exist early after the disturbances, we cannot assess if differences in future stand
Forests 2015, 6 4125
development over the long-term would remain, as we do not have post-disturbance stands older than
30 years. We showed, however, that the post-disturbance vegetation cover observed in fires that mostly
burned in mature forested fractions (i.e. pre-fire productive forest) achieved closure just as quickly as
that observed in the harvested agglomerations. This suggests that the perceived difference in the
rapidity of canopy closure after fire compared with that following harvesting is partly attributable to
the fact that fires burn in heterogeneous environments in which areas are less productive than those
affected by harvesting. Therefore, the recovery problems that are often attributed to a direct effect of
the last fire event could be better explained by pre-disturbance conditions. Our results therefore
suggest that these factors need to be considered when comparing the forest recovery after fire
or harvesting.
Acknowledgements
The original data set was obtained from the MFFPQ (Ministère des Forêts, de la Faune et des Parcs
du Québec) and Natural Resources Canada, and we especially thank Julie Fortin (Direction de
l’environnement et de la protection des forêts) for providing provincial fire data and André Beaudoin
(Canadian Forest Service) for providing satellite imagery. We also thank lanie Desrochers for her
assistance with GIS, Steve Cumming and Nicolas Mansuy for useful comments, and William F.J.
Parsons and Pameal Cheers for English editing. We are most grateful to the Centre d’étude de la forêt
(CEF) and Canadian Forest Service staff for logistical support. Le Fonds quécois de la recherche sur
la nature et les technologies de Québec (FQRNT) and Université du Québec à Montal (UQAM)
(Bourse d’excellence) provided financial support. We finally thank the reviewers and associate editors
for their comments.
Conflicts of Interest
The authors declare no conflict of interest.
Forests 2015, 6 4126
Appendixes
Table A1. Grouping of the vegetation cover types based on SIFORT.
Original code*
New code
Designation
DH
BH
Bare humid
ME 90
DS
BD
Bare dry
EA
H2O
Water
E 90
BS
Black spruce
BB 90
ID
Shade-intolerant deciduous
FI 90
TR 90
BBR 90
DeC
Shade-intolerant deciduous with coniferous
FIPG 90
FIR 90
TRR 90
BR
Dist
Disturbance
CH
BR 10
CT10
EPG 90
JP
Jack pine
PG 90
PGE 90
FI 30
RegD
Deciduous regeneration
FIPG 30
FIR 30
FIR30
E 30
RegC
Coniferous regeneration
EPG 30
PG 30
PGE 30
S 30
S 90
BF
Balsam fir
*DH, Bare humid; ME, Larch; DS, Bare dry; EA, Water; E, Black spruce; BB, Paper birch; FI, Shade
intolerant deciduous; TR, Trembling aspen; BBR, Paper birch, conifers; FIPG, Shade-intolerant deciduous,
jack pine; FIR, Shade-intolerant deciduous, conifers; TRR, Trembling aspen, conifers; BR, Burned; CH,
Windthrow; CT, Clearcut; EPG, Spruce, jack pine; PG, Jack pine; PGE, Jack pine, spruce; FIPG,
Shade-intolerant deciduous, jack pine; S, Balsam fir. The numbers 10, 30, and 90 correspond to the ages.
(Source: SIFORT).
Forests 2015, 6 4127
Table A2. Grouping of the surficial deposits based on SIFORT.
Surficial deposit codes
Designation
R; RLA; R7; R7T; RAA; RS, M1A; 1AR
Rocky outcrop
1AM;
Till
1AA; 1AAM
Cochrane till
2A; 2AE; 2AK; 2BE; 3AN, 4GS, 5S, 6S, 9S
Sand type
5A; 4A
Mesic clay
4GA4
Sub-hydric clay
7E; 7T
Poorly drained and organic soil
Table A3. Successional stages based on land cover types from satellite imagery and mean
relative occupancy (%) of each land cover type by disturbance type.
EOSD
Class
Successional
stages
Designation
Code
Fire
event
s
Harvested
agglomerati
ons
1
Others
Shadow
Shd
1,5
0.3
2
Clouds
Cld
2.4
1.3
9
Unproductive
and non-
forested
land types
Water
H2O
1.9
2.3
5
Exposed land
ExL
0.7
1.1
19
Lichens
Lic
4.0
0.3
20
Moss and rock
MoRoc
1.6
0.4
24
Wetland with herbs
HbWl
0.8
0.4
18
Herb (perennial crops, pasture,
fallow) grassland
Herb
-
0.9
31
Coniferous woodland with lichen
CwLi
0.3
0.1
32
Coniferous woodland with moss
Cwmo
2.3
1.5
45
Coniferous woodland with shrubs
CwSh
0.4
2.8
7
Stand
initiation
Stage 0
Burn
B
36.8
0.2
8
Harvested
H
-
3.8
15
Post-fire regeneration
PBr
20.9
1.1
16
Post-harvest regeneration
PHr
5.8
4.0
14
Low shrubs
LSh
4.0
1.0
10
Early
regeneration
Stage 1
Tall shrubs
TSh
1.0
1.0
11
Coniferous regeneration
CfRg
0.5
3.3
12
Deciduous regeneration
DcRg
0.2
4.5
48
Dense young deciduous
DyDc
-
1.6
40
Open mixed coniferous tendency
OmCf
0.7
3.4
41
Open mixed deciduous and
coniferous tendency
OmDcCf
0.2
0.7
26
Young coniferous
YCf
-
0.4
23
Wetland with shrubs
WlSh
1.9
2.5
13
Stem
exclusion
Stage 2
Mixed regeneration
MRg
5.5
23.0
33
Dense deciduous
DeDc
-
3.0
34
Open deciduous
ODc
0.6
3.4
36
Dense mixed deciduous tendency
DmDc
-
4.4
38
Dense mixed deciduous with
coniferous tendency
DmDcCf
0.1
0.5
39
Open mixed deciduous tendency
OmDc
1.4
4.1
Forests 2015, 6 4128
Table A3. Cont.
EOSD
Class
Successional
stages
Designation
Code
Fire
event
s
Harvested
agglomerati
ons
22
Coniferous
stands
Stage 3
Wetland with tall shrubs and trees
WlTr
4.5
3.7
25
Dense coniferous mature
DCfmat
0.8
1.6
27
Medium coniferous cover with moss
MCfmo
2.2
1.7
28
Medium coniferous cover with
lichen
MCfli
0.3
0.2
29
Open coniferous with lichen
OCfli
0.5
0.3
30
Open coniferous with moss
OCfmo
3.7
4.5
37
Dense mixed coniferous tendency
DmCf
0.6
10.7
Table A4. Post-disturbance types of land cover composition used.
Unproductive and non-
forested land types
Recently
disturbed
Coniferous
Deciduous
Water
Burn
Tall shrubs
Mixed regeneration
Exposed land
Harvest
Coniferous regeneration
Dense deciduous
Lichens
Post-fire
regeneration
Open mixed coniferous
tendency
Open deciduous
Moss and rock
Post-harvest
regeneration
Young coniferous
Dense mixed deciduous
tendency
Wetland with herbs
Low shrubs
Wetland with shrubs
Dense mixed deciduous
with coniferous tendency
Herb (perennial crops,
pasture, fallow,
grassland)
Wetland with tall shrubs
and trees
Open mixed deciduous
tendency
Coniferous woodland
with lichen
Dense coniferous
mature
Deciduous regeneration
Coniferous woodland
with moss
Medium coniferous
cover with moss
Dense young deciduous
Coniferous woodland
with shrubs
Medium coniferous
cover with lichen
Open mixed deciduous and
coniferous tendency
Open coniferous with
lichen
Open coniferous with
moss
Dense mixed coniferous
tendency
Forests 2015, 6 4129
Figure A1. PCA showing the post-disturbance of the land cover types from images of 22
fire events and 14 harvested agglomerations. Each cover type was described using its
relative proportion in the disturbed area in the PCA (a) Scores: f = fires; c = harvests. First
digit = number of disturbances; the second one = time since the last disturbance. (b)
Loadings (land cover types). MCfmo (medium coniferous cover with moss), DCfmat
(dense coniferous mature), OmDc (open mixed deciduous tendency), OCfmo
(open coniferous with moss), Cwmo (coniferous woodland with moss), OmDcCf
(open mixed deciduous and coniferous tendency), LSh (low shrubs), DmDcCf
(dense mixed deciduous and coniferous tendency), OmCf (open mixed coniferous
tendency), ODc (oOpen deciduous), DmDf (dense mixed coniferous tendency), H2O
(water), BpBrHpHr (burn, post-burn, harvest and post-harvest), DmDc (dense mixed
Forests 2015, 6 4130
deciduous tendency), CwLi (coniferous woodland with lichen), MRg (mixed regeneration),
CfRg (coniferous regeneration), DeDc (dense deciduous), MoRoc (moss and rock), ExL
(exposed land), DyDc (dense young deciduous), TSh (tall shrubs), Herb (perennial crops,
pasture, fallow, grassland), Lic (lichens), DcRg (deciduous regeneration), WiSh (wetland
with shrubs). Note that the recent disturbances of fires and harvests (BpBrHpHr) deviate
from the rest of the disturbed types.
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Interest in northern forests is increasing worldwide for both timber production and climate change mitigation. Studies exploring forest productivity at an early age after fire and its determining factors are greatly needed. We studied forest productivity, defined as the combined quality of stocking and growth, of 116 10-to 30-year-old postfire sites. The sites were spread over a 90 000 km2 area north of the Quebec commercial forestry limit and were dominated by Picea mariana (Mill.) B.S.P. and Pinus banksiana Lamb. Seventy-two percent of our sites were classified as unproductive, mainly because of poor growth. Because growth was mostly determined by climatic factors, afforestation alone may not be sufficient to increase stand productivity in our study area. In addition, our results suggest that P. banksiana on dry sites may be less resilient to fire than previously thought, presumably because of poor site quality and climate. Overall, this is one of the first studies to explore productivity issues at an early age in natural northern forests, and the analysis scheme that defines forest productivity as the result of growth and stocking could provide a useful tool to identify similar issues elsewhere. © 2015, National Research Council of Canada. All right reserved.
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Emulation silviculture is the use of silvicultural techniques that try to imitate natural disturbances such as wildfire. Emulation silviculture is becoming increasingly popular in Canada because it may help circumvent the political and environmental difficulties associated with intensive forest harvesting practices. In this review we summarize empirical evidence that illustrates disparities between forest harvesting and wildfire. As a rule, harvesting and wildfire affect biodiversity in different ways, which vary a great deal among ecosystem types, harvesting practices, and scale of disturbance. The scales of disturbance are different in that patch sizes created by logging are a small subset of the range of those of wildfire. In particular, typical forestry does not result in the large numbers of small disturbances and the small number of extremely large disturbances created by wildfires. Moreover, the frequency of timber harvesting is generally different from typical fire return intervals. The latter varies widely, with stand-replacing fires occurring in the range of 20 to 500 years in Canada. In contrast, harvest frequencies are dictated primarily by the rotational age at merchantable size, which typically ranges from 40 to 100 years. Forest harvesting does not maintain the natural stand-age distributions associated with wildfire in many regions, especially in the oldest age classes. The occurrence of fire on the landscape is largely a function of stand age and flammability, slope, aspect, valley orientation, and the location of a timely ignition event. These factors result in a complex mosaic of stand types and ages on the landscape. Timber harvesting does not generally emulate these ecological influences. The shape of cut blocks does not follow the general ellipse pattern of wind driven fires, nor do harvested stands have the ragged edges and unburned patches typically found in stand-replacing fires. Wildfire also leaves large numbers of snags and abundant coarse woody debris, while some types of harvesting typically leave few standing trees and not much large debris. Successional pathways following logging and fire often differ. Harvesting tends to favor angiosperm trees and results in less dominance by conifers. Also, understory species richness and cover do not always recover to the pre-harvest condition during the rotation periods used in typical logging, especially in eastern Canada and in old-growth forests. As well, animal species that depend on conifers or old-growth forests are affected negatively by forest harvesting in ways that may not occur after wildfire. The road networks developed for timber extraction cause erosion, reduce the areas available for reforestation, fragment the landscape for some species and ecological functions, and allow easier access by humans, whereas there is no such equivalency in a fire-disturbed forest.
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Ecosystem theories must encompass the conclusions now emerging from studies of fire and vegetation in fire-dependent northern conifer forests. Such forests comprise more than half the present forest area of North America, including most of the forests that have never been altered through logging or land clearing. Furthermore, vast areas are still influenced by natural lightning-fire regimes, and it is possible to study directly the role of fire in controlling vegetation mosaics and ecosystem function.
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Disturbance plays an important role in the distributional range of species by affecting their colonization potential and persistence. Short disturbance intervals have been linked to reduced seedbank sizes of some species, but the effects of long intervals are largely unknown. To explore the potential existence of seedbank sizes that may also be limited by long disturbance intervals, we studied an area in boreal eastern North America where time since fire (TSF) coincides with an increase in environmental stress (accumulating organic matter measured as depth of the soil organic layer (SOL)). Along a chronosequence dating back about 710 years, we counted the number of seeds cone-1 of black spruce (Picea mariana) and then estimated the number of seeds tree-1 and site-1 by upscaling. Younger sites [TSF 60–150 years] with mature first regeneration trees had average-sized seedbanks for black spruce [12.0–17.9 (105) seeds ha-1], whereas subsequent pulse trees that established in SOL depths greater than 35 cm showed highly reduced seed numbers. Sites with second- to fourth-regeneration pulse individuals [TSF c. 350–710 years] had exceptionally small seedbanks of 0.90 (105) and 0.46 (105) seeds ha-1, respectively. Radial tree growth rate showed a similarly negative response to SOL depth and could potentially be used as an indicator of seed output in plant species. Because the decline in seedbank size was possibly caused by more general environmental stress factors such as reduced nutrient availability, we suggest exploring whether other examples of ecosystems exist where long time since disturbance may lead to reduced seedbank sizes.
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Although the concept of forest ecosystem management based on natural disturbance has generated a great deal of interest, few concrete examples exist of FEM principles being put into application. Silvicultural practices that emulate natural disturbances are proposed with examples from the principal vegetation zones of Quebec. With the exception of the large-scale use of careful logging to protect advanced regeneration in ecosystems generally controlled by fire, stand-level silvicultural practices currently used are reasonably similar to natural disturbances, although important differences exist. In contrast, at the forest-level, even-aged management, as is currently practised, rarely permits adequate reproduction of the variety of age classes, stand types, and structural components normally found in the boreal forest. A model that allows an even-aged management approach inspired by natural dynamics is proposed.
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Post-fire regrowth is an important component of carbon dynamics in Canada’s boreal forests, yet observations of structural development following fire are lacking across this remote and expansive region. Here, we used Landsat time-series data (1985-2010) to detect high-severity fires in the Boreal Shield West ecozone of Canada, and assessed post-fire structure for > 600 burned patches (>13,000 ha) using airborne light detection and ranging (lidar) data acquired in 2010. We stratified burned areas into patches of dense (> 50% canopy cover) and open (20-50% canopy cover) forest based on a classification of pre-fire Landsat imagery, and used these patches to establish a 25-year chronosequence of structural development for each class. While structural attributes were similar between dense and open patches during the first ten years since fire (YSF), canopy cover (cover above 2m) and stand height (75th height percentile) were significantly higher (p < 0.001) for dense patches by the end of the chronosequence (20-25 YSF), suggesting that differences in site productivity were driving patches towards pre-disturbance structure. Our results suggest that growing space remained in stands at the end of the chronosequence, and therefore stem exclusion was not yet reached, as canopy cover was significantly lower (p < 0.001) for patches at 20-25 YSF (mean = 41.9% for dense, 18.6% for open) compared to patches with no recorded burns (mean = 63.3% for dense, 38.6% for open). The lasting impact of high-severity fire on structure was further confirmed by estimates of stand height, which were approximately half as tall for patches 20-25 YSF (4.9 m for dense, 4.2 m for open) compared to patches with no recorded burns (9.8 m for dense, 7.7 m for open). Additionally, we assessed the structural complexity of burned stands using measures of canopy roughness (i.e., rumple) and the distribution shape of lidar returns (i.e., skewness and kurtosis), which provided evidence of young, even-aged structure once a new overstory was formed. As forest inventories are not routinely conducted across Canada’s northern boreal, the fusion of Landsat time-series and airborne lidar data provides powerful means for assessing changes in forest structure following disturbance over this large forested area.