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We evaluated how historical storm events have shaped the current forest landscape in three Pyrenean subalpine forests (NE Spain). For this purpose we related forest damage estimations obtained from multi-temporal aerial photographic comparisons to the current forest typology generated from airborne LiDAR data, and we examined the role of past natural disturbance on the current spatial distribution of forest structural types. We found six forest structural types in the landscape: early regeneration (T1 and T2), young even-aged stands (T3), uneven-aged stands (T4) and adult stands (T5 and T6). All of the types were related to the timing and severity of past storms, with early-regeneration structures being found in areas markedly affected in recent times, and adult stands predominating in those areas that had suffered lowest damage levels within the study period. In general, landscapes where high or medium levels of damage were recurrent also presented higher levels of spatial heterogeneity, whereas the opposite pattern was found in the less markedly affected landscape, characterized by the presence of large regular patches. Our results show the critical role that storm regimes in terms of timing and severity of past storms can play in shaping current forest structure and future dynamics in subalpine forests. The knowledge gained could be used to help define alternative forest management strategies oriented toward the enhancement of landscape heterogeneity as a measure to face future environmental uncertainty.
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J. Mt. Sci. (
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Abstract:
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(CTFC), Ctra
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words: St
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u
cture; Airbo
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enean; Suba
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t
roductio
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Natural
m
ponent of
r
opean fores
c
ades, their
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eased (Doll
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tinue in the
a
mics i
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a
ta and
A
003-2611-917
6
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00-0002-5
0
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2-5327-569
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; e-mail: aria
d
2
; e-mail: dol
o
mail: lluis.col
l
M
orunys, Km 2.
E
n
tjuïc, Bacelona
n
S, et al. (2015
)
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tographs. Jou
r
e
nt, CAS and S
p
DOI: 1
0
g
e re
g
ular
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hat storm r
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ast stor
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structure a
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s. The kno
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fine altern
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ted towar
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rm re
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F
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lp
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disturbance
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the functio
t (Schelhaas
impact on
2000) and
t
future due t
o
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the P
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erial
P
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; e-mail:
a
0
40-712X; e-
m
5
; e-mail: san
t
d
na.just@icgc
.
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rs.cabre@icg
c
l
@ctfc.es
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S-
2
5280, Sols
o
08038, Spain
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Assessing pos
t
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nal of Mount
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http://j
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r.gonzal
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iago.martin
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na, Spain
t
-storm forest
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in Science 12(
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erlin Heidelbe
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s.imde.ac.cn
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-014-3327-3
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esults show
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). DOI:
r
g 2015
J. Mt. Sci. (2015) 12(4): 841-853
842
both land-use and climate changes (Seidl et al.
2011). Natural disturbances are often perceived as
source of distress for forest managers and society.
However, they are an integral part of forest
ecosystems and a key factor driving forest
dynamics and landscape heterogeneity (Oliver and
Larson 1990; Vallecillo et al. 2009). Compared to
other factors that modulate landscape
configuration, such as physiographic or climatic
conditions, disturbances are the one with a more
sudden nature, i.e. they change drastically and in a
short period of time the current shape and future
spatial and temporal dynamics pathways (Forman
1995; Harcombe et al. 2004). In mountainous
areas, where the occurrence of different types of
natural disturbances is frequent, a better
understanding of their regime, and their effects on
forested ecosystems would improve the prediction
of the future evolution of these ecosystems and
their associated services (He and Mladenoff 1999;
Mailly et al. 2000; Brotons et al. 2013), allowing
the definition of adapted management regimes.
Wind and snow storms are among the most
relevant and globally extended disturbances in
mountainous areas. The effects of storms on forest
structure and landscape configuration have been
intensively studied worldwide, as for example in
North America (Canham et al. 2001), New Zealand
(Moore et al. 2013), Finland (Jalkanen and Mattila
2000), or in Switzerland (Dobbertin 2002). In
contrast, in Southern Europe the role and
consequences of storms in the dynamics of forest
ecosystems have been widely overlooked. In these
areas most attention has been paid on forest fires,
which are the most common disturbance and main
cause of tree mortality (Alexandrian et al. 2000).
However, in mountainous areas important
damages associated to storms have been pointed
out (Martín-Alcón et al. 2010). In general, storm
damage is highly dependent on fine-scale factors
such as local climatic conditions (Quine 2000), the
topographical position and soil properties of the
stand (Ruel 2000; Mayer et al. 2005) and the stand
structure and composition (Mason 2002; Jactel et
al. 2009).
At larger scales, assessing the variability and
spatial configuration of forest landscapes
represents an important challenge, especially on
areas where active disturbances regimes and
abrupt topography. However, increasing the spatial
extent of ground-based ecological studies, in which
information is gathered in the field, is often limited
by myriad practical considerations such as
financial cost or the rigor of data collection (Rich et
al. 2010). In this context, the use of remote sensing
tools has been proved to be an affordable and
rigorous means to characterize spatial
heterogeneity in forest ecosystems, given that they
allow the provision of spatially continuous
information. In this regard, multispectral satellite
images (Wang et al. 2010; Negrón-Juárez et al.
2014), aerial photographs (Pearson 2010; Boucher
and Grondin 2012; Kane et al. 2013) and airborne
LiDAR (Laser imaging Detection and Ranging)
(Rich et al. 2010; Hayes and Robeson 2011) have
shown their potential to identify the occurrence
and impact of different types of disturbance.
Moreover, the information generated from LiDAR
can be also used to identify patterns and changes of
the forest structure at landscape level (Vepakomma
et al. 2010) and subsequently quantify the spatial
characteristics of the forest landscape by selecting
and estimating adequate landscape metrics (e.g.
number and size of patches, diversity of fragments
etc.) (McGarigal and Marks 1995). These indices
have been found relevant to explain some of the
ecological or physical processes influenced by the
configuration of such landscape (Leitão et al. 2006),
or to analyze the impact of past events on the
resulting landscape spatial patterns. For example,
heterogeneity and diversity at the landscape scale
are recognized as desirable attributes to reduce
damages associated to disturbances and long-term
environmental change. On this regard, identifying
the allocation, spatial arrangement and
connectivity of the different forest structures and
succession stages is a critical step for planning
sustainable and adaptive forest management
(O'Hara 1996; Puettmann et al. 2013). Therefore,
advancing in the knowledge of how forest
successional stages and its spatial distribution in
the landscape are conditioned by the type,
recurrence and intensity of disturbances may be of
critical importance for defining adequate
management objectives for heterogeneity at
multiple scales as part of complex adaptive systems,
and adapting forest management plans to future
disturbances.
With this ultimate objective, we have studied
the structural response of Pyrenean subalpine
J. Mt. Sci. (2015) 12(4): 841-853
843
forests dominated by Pinus uncinata to different
degrees of damage associated to recurrent storm
events. For this purpose, we have: (1) determined
stand-level forest typologies from LiDAR derived
data (2) identified how the timing and damage of
storms influence the actual state of the forest,
regarding the abundance of the previously defined
forest typologies across the landscapes; and (3)
examined how the current spatial configuration of
the studied landscapes relates to the effects of past
natural disturbances.
1 Methods
The procedure used to obtain landscape-level
information on forest structural attributes and
their evolution over time based on LiDAR data and
damage estimations is outlined in Figure 1. It
comprises (i) acquisition of LiDAR data and
transformation of the raw data into individual tree
metrics, (ii) conversion of the tree-level data into
stand-level forest structural variables,
(iii) definition of forest types, (iv) identification of
historical storm damage by comparison of aerial
photographs, (v) combination of forest types and
storm damage to study the relationship between
historical damage and current forest structure, and
(vi) assessment of the spatial patterns for the three
landscapes.
1.1 Study area
The study area was located in the
north-eastern part of the Iberian
Peninsula (Figure 2), in the eastern
Pyrenees (mean altitude 1800 m a.s.l,
precipitation above 900 mm and
mean annual temperature below 9ºC).
Three different forest landscapes,
dominated by mountain pine (Pinus
uncinata Ram.), were selected for the
study: L1 (54 ha), L2 (69 ha), and L3
(85 ha). The selection of these
landscapes was based on a previous
survey of forest areas affected by
storms in 1973 and 1982 (Martín-
Alcón and Coll 2008) and was
motivated by the importance of
mountain pine in the region
(dominating approximately 64,000 ha), and the
challenge that storm damage entails for the
management of these forests (Martín-Alcón et al.
2010).
1.2 Converting LiDAR data into tree-level
attributes
LiDAR data were provided by the Cartographic
and Geological Institute of Catalonia (ICGC). Data
were acquired from a specific flight conducted for
this study in October 2011, using an ALS50 II laser
scanner mounted on a Cessna Caravan 208B
aircraft. The LiDAR flight plan provided a
minimum first return point density of 6 pulses per
square metre and each pulse could receive a
maximum of 4 returns, generating a point cloud of
returned laser pulses. The processing of the raw
LiDAR data consisted of an initial calibration and
adjustment of the point cloud, obtaining a
georeferenced cloud in terms of the x, y and z
coordinates, followed by an automatic classification
to differentiate ground from non-ground points
with the TerraScan ground classification routine
(Terrasolid 2012). Ground routine classified
ground points by iteratively building a triangulated
surface model. The routine started by selecting
some local low points that were confident hits on
the ground. It was assumed that any 40 by 40
Figure 1 Flowchart of the method.
J. Mt. Sci. (2015) 12(4): 841-853
844
meter area would have at least one hit on the
ground and that the lowest point would be a
ground hit. The routine built an initial model and
started molding the model upwards by iteratively
adding new laser points to it. Each added point
made the model following the ground surface more
closely. Iteration parameters of angle (4º) and
distance (1 m) determined how close a point must
be to a triangle plane for being accepted as a
ground point and added to the model. LiDAR point
coordinates were adjusted according to the
methodology proposed by Kornus and Ruiz (2003).
Then a manual edition of the classified cloud was
done with the purpose to refine the automatic
classification and extract wires and towers from
power line infrastructures. After manual edition, a
new automatic classification was done in order to
extract building roofs, leaving only vegetation
points in the non-ground returns. Finally, non-
ground returns were classified in different
vegetation strata, and were then height-normalized
by replacing the elevation with its vertical distance
to a triangular irregular model (TIN) generated
from ground returns, obtaining a canopy height
model. A predefined height of 0.15 m above the
previously defined ground height was selected and
used as a threshold to separate woody and shrubby
vegetation from herbaceous vegetation and soil.
Once the canopy height model defined, tree
detection was implemented using standard
hydraulic modelling from ArcGis 9.3 Hydrology
Tools, which detects local minimums that are
linked to the position of the trees and basins which
are similar to canopy areas. In fact this
segmentation founds aggregation of trees around a
dominant tree, not individual trees. Tree height
was obtained by computing the maximum height
value of the canopy height model within each small
aggregation of trees.
Additionally, the relationship between the
crown area obtained from LiDAR data and the
expected crown area according to Ameztegui et al.
(2012) was used to identify and split groups of
single trees that were wrongly classified from
LiDAR. Finally, based on the tree height and crown
area of the trees, allometric relations for mountain
pine (Gracia et al. 2004; Ameztegui et al. 2012)
were used to estimate the diameter at breast height
(DBH) of each single tree.
1.3 Estimating stand structural attributes
Based on the size of the trees, stand-level
variables were estimated across all the landscapes
studied. These stand variables were calculated by
creating a regular grid of 3325 squares measuring
25 by 25 meters (0.0625 ha), and aggregating the
tree-level information for each square. A first group
of variables described the main stand
characteristics in terms of tree canopy cover
(TCC, %), tree density (N, stems·ha-1 with DBH
greater than 7.5 cm), tree recruitment (Nm,
stems·ha-1 with DBH between 2.5 and 7.5 cm),
basal area (G, m2·ha-1), mean tree height (HM, cm),
dominant tree height (H0, cm), and dominant
diameter (D0, cm), which were estimated with the
mean of the 20% largest trees, and mean diameter
(DM, cm). A second group of variables described
the distribution of basal area and stocking density
according to tree size: fine wood (FW: with DBH
between 7.5 and 22.4 cm), medium wood (MW:
with DBH between 22.5 and 32.4 cm), and thick
wood (TW: with DBH greater than 32.5 cm). A
variable reflecting the stand’s structural
irregularity was also defined by estimating the
relative difference between dominant height and
mean height (RD_H).
1.4 Forest structural typology
Based on the previously estimated attributes, a
subset of seven stand-level variables was chosen as
the basis for defining a structural typology for our
Figure 2 Location of the study area and the three
analyzed landscapes (L1, L2 and L3).
J. Mt. Sci. (2015) 12(4): 841-853
845
mountain pine-dominated forest landscapes. The
variables were chosen according to previous
studies (Reque and Bravo 2008; Martín-Alcón et al.
2012), as meaningful and non-redundant in terms
of information: tree canopy cover, basal area, mean
diameter, tree recruitment, and the percentages of
fine wood and thick wood, and the relative
difference between dominant height and mean
height. The different structural types were
determined using Ward's hierarchical clustering
method with squared Euclidean distance as the
similarity metric (Ward 1963). The number of
groups was selected according to the cut-off point
of the hierarchical dendrogram, when similarity
measure made a sudden jump (Hair et al. 2006).
Finally, the statistical significance of the structural
typology was evaluated using non-parametric tests
(Kruskal-Wallis and Mood’s median) (Basterra et
al. 2012; Montealegre et al. 2014).
1.5 Assessing storm damage
In order to evaluate the damage caused to the
forest cover during the storms of 1973 and 1982,
aerial photographs from 1956, 1977 and 1996 (the
closest in time available for the two storm periods)
were collected, geographically corrected, and
compared for each period and landscape. Non-
supervised classification was used to classify the
aerial photographs to differentiate between
vegetation and bare land. Once the vegetation
cover was defined for each year (i.e. 1956, 1977 and
1996), the amount of storm damage was estimated
for each 25 m × 25 m grid plot within the
landscapes by identifying the pixels that had
reduced their vegetation coverage between the
years compared. In this way, the percentage of
pixels per grid plot changing from “vegetation
covered” to “bare land” from 1956 to 1977 defined
the amount of damage that occurred during the
1973 storm, and the change between 1977 and 1996
defined the amount of damage that occurred
during the 1982 storm. Finally, each of the 3325
plots within the 208 ha occupied by the three
landscapes was classified according to tree canopy
cover change into: high damage (change greater
than 66.6%), medium damage (change between
33.3% and 66.6%), and low damage (change less
than 33.3%). Undamaged plots (i.e. plots showing
nil or positive cover change) were considered as
low damage. Additionally, management plans from
the landscapes studied were examined to ensure
that no management operations had been
implemented after the storms, other than salvage
logging of fallen or severely damaged trees.
1.6 Relating historical storm damage and
current forest structure
Once the forest typology and damage level
were estimated and mapped across the three
different landscapes, the two sources of
information were plotted in order to infer the role
of past storm damage (considering its occurrence
date and damage level) in determining current
forest typology. For this purpose, the current
abundance of the forest types within each
landscape were related to level of damage and time
since the disturbance took place with the use of
observed and estimated frequencies of forest types.
Estimated values were obtained by multiplying the
marginal frequencies for the typologies and
damage of each landscape, and then dividing by the
total number of observations in each landscape.
Additionally, chi-square tests (Cochran 1954) were
performed for each landscape, in order to
determine whether the abundance of the different
forest types was independent of the occurrence and
severity of past storms.
1.7 Spatial analysis
An estimation of different landscape metrics
was implemented based on the defined forest
typology and its spatial distribution, with the aim
of identifying the impact of disturbances on
shaping the spatial configuration of the forest
landscapes. The different landscape metrics were
obtained using the Patch Analyst extension of
ArcGIS (Rempel et al. 2012) and include: number
of patches (NumP) and mean patch size (MPS) to
characterize the size and abundance of forest
patches, and mean shape index (MSI) as an
indicator of patch form. The spatial arrangement of
the landscape was assessed using Shannon’s
diversity index (SDI), which evaluates landscape
heterogeneity from the diversity of fragments, and
Shannon’s evenness index (SEI), which reflects the
homogeneity of the landscape.
J. Mt. Sci. (2015) 12(4): 841-853
846
2 Results
2.1 Structural characterization of forest
types and distribution across the
landscapes
Six structural types were defined from a subset
of stand variables, and were used to classify the
3325 plots in our three landscapes (Figure 3).
Kruskal-Wallis non-parametric comparison tests
on multiple independent samples (P < 0.01) and
Mood’s median test (P < 0.01) confirmed the
independence between groups for the six structural
types obtained. Forest types T1 and T2
corresponded to stands on early regeneration
stages with dominancy of FW (Table 1). The main
difference between these types was the higher level
of tree recruitment and the presence of some
surviving large-size trees in T2, whereas T1 was
characterized by the abundance of young trees over
7.5 cm in DBH. Forest type T3 matched young
even-aged forest stands, with most of the trees
being between 15 and 20 cm in DBH, but
accompanied by a limited number of larger trees
(Table 1). Type T4 corresponded to uneven-aged
stands with the highest RD_H and a fairly
balanced occupancy of FW, MW and TW classes.
Finally, types T5 and T6 corresponded to adult
forest stands with limited presence of regeneration
(Table 1), the main difference between these two
types being that T5 presented a higher degree of
structural irregularity with shared dominance of
MW and TW, whereas T6 was characterized by an
overwhelming dominance of large trees classified
as TW, combining mature and fully even-aged
stands.
Once the types were defined, their distribution
across the three different landscapes was mapped
(Figure 4, Table 2). This revealed that most of the
area in the analyzed landscapes corresponded to T5,
T6, T4 and T3. By contrast, T1 was present in only
7% of the area, and T2 in not more than 1%. In
landscape L1, the most representative type was T3,
with 43% of the plots, followed by T4, T5 and T1,
meaning that this landscape was mainly
characterized by the dominance of young stands.
Landscape L2 was characterized by the dominance
of more mature structures (types T6 and T5), and
lower presence of the remaining forest types.
Finally, the most representative types were in
landscapes L3, T5, T4 and T6 , being present over
35%, 32% and 21% of the area respectively. Other
types such as T3 and T1 also had a limited presence
on this landscape, suggesting that landscape L3
occupied an intermediate position in between L1,
Figure 3 Graphical description of different forest types.
Table 1 Summary of the stand variables defining the forest types
T TCC G DMN NmFW MW TW RD_H HM H0
T1 34.8 6.0 12.0 425.9 350.3 87.3 3.1 0.0 0.1 6.98 8.15
T2 43.4 3.5 14.7 192.1 1875.8 51.5 26.6 6.9 0.1 7.68 8.51
T3 74.0 21.9 16.7 952.7 28.9 82.0 15.3 2.7 0.2 8.99 10.81
T4 44.1 15.7 20.7 402.6 152.4 29.0 28.6 42.4 0.4 10.14 14.51
T5 67.9 28.0 29.5 439.3 22.5 13.9 30.8 55.3 0.3 12.96 16.33
T6 89.4 41.4 43.1 283.9 5.9 0.6 6.1 93.2 0.1 16.86 19.13
Notes: TCC: tree canopy cover (%); G: basal area (m
2·ha-1); DM: mean diameter (cm); N: stocking density
(stems·ha-1); Nm: recruitment (stems·ha-1); FW: basal area of fine wood (%); MW: basal area of medium wood (%);
TW: basal area of thick wood (%); RD_H: relative difference in heights; HM: mean height (m); H0: dominant
Height (m); T: structural type.
J. Mt. Sci. (2015) 12(4): 841-853
847
younger, and L2, more mature.
2.2 Damage assessment and
relationship between forest types
and storm damage
The comparison of aerial photographs
from 1956, 1977 and 1996 allowed an
assessment of the changes in forest cover
between the two periods (Figure 5, Table 3).
During the first period (1956 to 1977), 7% of
the total area was affected by high damage,
19% by medium damage, and 74% by low
damage. During the second period (1977 to
1996), 3% of plots were affected by high
damage, 36% by medium damage, and 61%
by low damage. When the damage
assessment was implemented separately for
each landscape, it was observed that during
the 1973 storm landscape L1 suffered the
highest damage, landscape L2 remained
unaffected, and landscape L3 suffered from
significant damage over approximately 20%
of its area. During the 1982 storm, landscape
L1 was the least affected of the three,
landscapes L2 and L3 suffered from medium
storm damage over 40% of their area, and
the presence of highly damaged stands was
identified on 3% and 6% of the area
respectively.
The influence of storm damage on
defining the current forest state was assessed
from the relationship between the different
levels of damage, the period when the
damage occurred, and the relative
abundance of the forest types over the
different landscapes (Figure 6). This
evaluation showed that the highest levels of
damage occurred during the first period,
translated by a higher presence of type T3
and to a lesser extent of T1. Medium levels of
damage during this period led to types T3,
T4, T5 or T1. Lower levels of damage during
this first period were usually associated with
a higher variation in the level of damage
observed during the second one. Plots
showing high and medium levels of damage
during the second period appeared
associated with the presence of types T4, T1
and T3, whereas forests that were mildly or
Figure 4 Spatial distribution of the forest.
Figure 5 Spatial distribution of the damage for the periods 1956
–1977 and 1977–1996.
J. Mt. Sci. (2015) 12(4): 841-853
848
not affected by storm damage during these two
periods were characterized by the current
dominance of types T5 and T6.
The observed and
expected frequencies (Table 4)
showed the relationship
between forest types and
historical storm damage.
Higher levels of influence were
found for types T5 and T6,
which were located in areas
that had suffered the lowest
damage levels in the study
period. On the other hand,
when high damage had
occurred recently, types T1
and T2 were the most
distinctive. However, when
high damage occurred in the
first period followed by no
high damage, T3 was the more
representative type, whereas
type T4 appeared when
landscapes were affected by
medium damages in one of the
periods, usually following or
followed by a period of low
damage. Finally, the
homogeneity test gave values
of χ2 equal to 149, 328 and 343,
respectively for each landscape,
all significantly higher (P <
0.05) than the corresponding
chi-square table value (56);
this confirmed that the abundance of the different
forest types was clearly dependent on the temporal
pattern and damage level of past storms.
2.3 Spatial analysis
The assessment of landscape metrics revealed
the critical role of the disturbances on the spatial
distribution of the types across the landscapes
(Table 5). Those landscapes where high or medium
levels of damage were common (L1 and L3)
presented (independently of the time when damage
occurred) a more complex pattern, and higher
levels of spatial heterogeneity, with smaller patch
sizes, higher MSI, SDI and SEI. By contrast, the
least affected landscape (L2) enclosed, on average,
the largest and most regular patches, the lowest
SDI and a SEI closer to zero, so that it can be
considered the most homogeneous of the three
landscapes. The size of the landscapes seemed to
Table 2 Relative abundance of the forest types
within each landscape in percentage of the
landscape occupied (Unit: %)
Structural type
Landscape T1 T2 T3 T4 T5 T6
L1 12 0 43 2
7
17 1
L2 5 1 5 9 25 55
L3 4 0
7
32 35 21
Total
7
1 18 22 26 26
Table 3 Relative abundance of the damage
levels per landscape and storm occurrence
period (Unit: %)
1st period 2nd period
L1 L2 L3 Total L1 L2 L3 Total
High 19 0 1
7
0 3 6 3
Medium 38 2 18 19 15 46 4
7
36
Low 43 98 81 74 85 51 4
7
61
Table 4 Observed versus expected frequencies of the forest types
(Obs/Exp), per damage combination and landscape
1#2#T1 T2 T3 T4 T5 T6
Landscape 1
L 28/34 0/1 86/119 84/75 78/47 1/2
L M13/11 0/0 24/40 41/25 14/16 1/1
H 3/0 0/0 0/1 0/1 0/1 0/0
L 33/36 1/1 133/125 84/79 38/49 3/2
M M 8/4 0/0 10/13 10/8 3/5 0/0
H 0/0 0/0 0/0 0/0 0/0 0/0
L 21/20 2/1 116/70 13/44 12/28 0/1
H M0/0 0/0 0/0 0/0 0/0 0/0
H 0/0 0/0 0/0 0/0 0/0 0/0
Landscape 2
L 22/25 2/7 33/28 36/47 152/133 292/297
L M13/23 5/7 22/26 46/43 104/123 309/276
H 12/1 8/0 2/2 5/3 2/
7
0/16
L 1/1 0/0 0/1 5/2 11/5 4/12
M M 0/0 0/0 0/0 2/0 2/1 1/3
H 0/0 0/0 0/0 0/0 0/0 0/0
L 2/0 0/0 1/0 1/0 0/1 0/2
H M0/0 0/0 0/0 0/0 0/0 0/0
H 0/0 0/0 0/0 0/0 0/0 0/0
Landscape 3
L 4/19 0/1 15/29 69/144 210/158 146/94
L M19/25 0/1 38/38 216/185 177/203 123/121
H 20/4 2/0 6/6 55/28 4/31 1/19
L 8/8 0/0 20/12 62/59 77/65 15/38
M M 7/3 0/0 9/5 37/23 13/25 4/15
H 0/0 0/0 0/0 0/0 0/0 0/0
L 1/1 0/0 3/1 3/4 5/4 0/3
H M0/0 0/0 0/0 1/0 0/0 0/0
H 0/0 0/0 0/0 0/0 0/0 0/0
Notes: 1#: 1st period; 2#: 2nd period; L: Low; M: Medium; H: High.
J. Mt. Sci. (2015) 12(4): 841-853
849
be the most important feature defining the number
of patches per landscape, so that that no clear
relation between this metric and the level of
damage could be inferred.
3 Discussion
In recent years, the
comparison of aerial photographs
and the analysis of LiDAR data
have been successfully used to
assess, respectively, vegetation
cover changes associated with
natural disturbances (e.g. Foster et
al. 1999; Haire and McGarigal
2010), and forest successional
patterns (Zimble et al. 2003;
Falkowski et al. 2009). In this
study we combined the two
methods to analyze post-
disturbance successional pathways,
and in particular the role of the
temporal pattern and damage level
associated with storms on forest
structure and landscape
configuration (Kwak et al. 2010;
Vepakomma et al. 2010). The
results of our study show how
forest development is clearly
influenced by its particular history
of disturbances, their associated
damage, and the time since the
disturbance took place (Figure 7).
Although the disturbance
recurrence on a specific site can
modify successional pathways, the
current state of the forest is mostly
defined by the most severe
disturbance. Disturbances causing small levels of
damage were found to have a relatively low impact
on the forest development in terms of structural
attributes, with mature forest structures (T5 and
T6) being the most common in areas without
significant levels of damage. By contrast, those
forests affected by higher levels of damage usually
regressed towards earlier stages of development
(T1), or transitional stages (T3) if the disturbance
took place early enough to let the forest evolve
from the previous early stages of development. For
T2 (the least common of the types defined), it was
difficult to identify the factors determining its
origin. This type, dominated by trees in an early
stage of development and a small number of
Figure 6 Relative abundance of the forest types per damage level and
landscape.
Table 5 Landscape configuration variables of
forest calculated with the Patch Analyst
program (Unit for plots: %)
L
s Plots NoP MPS MSI SDI SEI
L1 26 112 0.47 1.36 1.32 0.74
L2 33 119 0.57 1.29 1.23 0.69
L3 41 178 0.47 1.37 1.38 0.77
Notes: Ls: Landscape; NoP: number of patches;
MPS: mean patch size; MSI: mean shape index; SDI:
Shannon’s diversity index; SEI: Shannon’s evenness
index.
J. Mt. Sci. (2015) 12(4): 841-853
850
remnants of mature reservoir
individuals, could originate from a
high, but non-complete, damage
disturbance, but also from
recurrent disturbances causing
medium damage, or by a medium
damage disturbance causing a
destabilization or debilitation of
the surviving trees with
subsequent delayed tree mortality.
The results of the landscape
spatial configuration analyses
agreed in general with those
reported in similar studies
regarding the marked relationship
between landscape heterogeneity
and the occurrence and damage of
natural disturbances (Lindemann
and Baker 2001; Hayes and
Robeson 2011). As observed by
Mori and Lertzman (2011) in their study on
subalpine Canadian forests, we found highest
landscape heterogeneity and patch richness in
areas where the impact of storms in terms of forest
damage was highest (L1 and L3).
On the other hand, landscapes suffering low
damage by storm disturbances (L2) showed in
general relatively untouched forests and a higher
spatial homogeneity. It is noteworthy that all the
storms studied caused local adverse effects rather
than severe damage over large areas. In the latter
case, more homogeneous landscapes would be
expected in the disturbed areas.
A better understanding of the impact of
natural disturbances on forest landscapes,
combined with proper assessments of the future
evolution of the forest according to these
disturbances, should allow significant
improvements in current forest management and
planning strategies. Ability to quantify forest
damage and changes in forest structure should
permit a better estimate of the future evolution of
associated services (wood products, scenic beauty,
biodiversity and others) and reduce uncertainty
when forest management is oriented toward
maximizing their production (von Gadow 2000;
Gonzalez et al 2005). Also, better comprehension
of the storm regimes and their consequences on
important attributes such as landscape
heterogeneity could be used to implement new
forest management models oriented toward
reducing the impact of human interventions (see
for example Bergeron et al. (2002) and Kamimura
and Shiraishi (2007) on close-to-nature
management) or new management strategies
aimed at enhancing resilient forest attributes to
face future disturbances and long-term
environmental changes (Bolte et al. 2009;
Puettmann et al. 2009; Stephens et al. 2010).
Studies analyzing the influence of disturbances
on forest evolution are often focused on the impact
of a single large disturbance (Batista and Platt
2003; Kupfer et al. 2008; Wang et al. 2010; Allen
et al. 2012). However, forests in the Pyrenees
seldom suffer from damage linked to large extreme
disturbances, these being important in only a few
dispersed specific locations (López-Moreno et al.
2008; Muntán et al. 2009). This characteristic
disturbance regime provides the opportunity to
compare similar forest systems (located not far
from each other) that have suffered storms that
took place at different times and caused different
levels of damage (Martín-Alcón and Coll 2008),
and where no post-disturbance management
operations modified the natural evolution of the
forest. However, our study presented some
limitations. First, a limited set of historical aerial
photographs were available for the study area.
These photos were obtained some years after the
storm events and so may underestimate its
Figure 7 Potential forest successional pathways.
J. Mt. Sci. (2015) 12(4): 841-853
851
associated damage, since the development of some
regeneration in the first years following the
disturbance event would be expected. Another
limitation arising from our study is that although
high resolution data was found to be extremely
useful for defining the current forest structure, it
was not accurate enough to provide information on
standing deadwood, an important variable to
define a stand’s structural heterogeneity (Pesonen
et al. 2008; Martín-Alcón et al. 2012). Finally, we
note that using LiDAR multi-temporal data from
well-differentiated years, instead of data from a
single LiDAR flight, would enhance our knowledge
of the evolution of forest and its dynamics after
natural disturbances (St-Onge and Vepakomma
2004; Vepakomma et al. 2011).
In conclusion, we have evaluated the utility of
combining LiDAR data, aerial photography and
spatial pattern analysis to assess forest
successional stages after natural disturbances. The
definition of three degrees of damage associated
with historic storms on two study periods made it
possible to infer successional pathways on
mountain forests affected by recurrent storms. The
combination of forest structural typology with
damage estimation and their temporal and spatial
distribution explained the evolution of forests since
the storm took place, and how landscape
heterogeneity naturally emerges as a consequence
of this type of disturbance regime. This is relevant
to the future definition of forest management and
planning strategies.
Acknowledgements
Financial support for this study was provided
by the Spanish Ministry of Economy and
Competitiveness through the project RESILFOR
(AGL2012-40039-C02-01). LC and JRGO were
both supported by Ramón y Cajal contracts (RYC-
2009-04985 and RYC-2011-08983). The Research
General Direction of the Generalitat de Catalunya
provided SMA with support through a pre-doctoral
grant (FI-DGR from AGAUR). Francesc Cano
helped us find the most suitable forests for this study.
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... Alternative data sources have been used to model changes in species distributions, such as forest maps (Benito Garzón et al., 2008), National Forest Inventories (NFIs) (Hernández et al., 2014) or remote sensing techniques such as aerial photographs and satellite images (Blázquez-Casado et al., 2015;Wittmann et al., 2002). NFIs provide estimates of forest resources, growth, mortality, forest health and other valuable information (McRoberts and Tomppo, 2007). ...
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To gain understanding of patterns in forest structure and their causes, we mapped the distribution of three canopy cover classes and measured change in one of them over 40 years using aerial photographs for the 500 ha Neskowin Crest Research Natural Area (Lincoln and Tillamook Counties, Oregon). One class (fine texture, trees of uniform crown diameter and height) covered about half the area; it was identified as second growth originating after a large regional fire in 1845. The other major class (coarse texture, trees of variable crown diameter and height), occupying about 35% of the area, was unburned or partially burned in 1845. The third class (openings with down stems visible on the ground) was blowdown patches. The blowdown patches were very small in 1953; they grew incrementally, and by 1994 had coalesced into a large patch occupying about 15% of the area. A long-term windstorm susceptibility model developed for southeast Alaska identified the region where the blowdown patch occurred as being very susceptible to maritime windstorm disturbance. This correspondence between predicted susceptibility to damage and actual blowdown supports the hypothesis that windstorm effects may be strongly constrained by topography. The results also suggest that blowdown in storm-susceptible topographic settings can be the result of multiple windstorm events over time, rather than a single event. The resulting forest is a mosaic of large multi-aged chronic-disturbance patches embedded in a matrix structured by fine-scale patch processes. A consequence of a constraint on blowdown is that at the scale of hundreds of hectares biomass may not fluctuate strongly over time unless stand-destroying fires occur.
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With an average of 50 000 fires and 600 000 ha burnt, forest fires in the Mediterranean basin represent a significant part of all fires that occur in the world. Several sources estimate the total annual cost of firefighting and safety devices in the region to be more than US$1 billion (Le Houerou, 1987). Despite the efforts made, particularly in the countries of southern Europe, the phenomenon is far from stabilizing and even appears to be increasing significantly in most of the 23 countries studied. Nowadays, forest statistics are better kept than 20 years ago. Some of the increases observed might in fact be due only to an improvement in the quality of the data.