Introduction
It is estimated that forests account for half of the global terrestrial net
primary productivity and act as important sinks of atmospheric CO2
(Bonan, 2008). Forests in the Northern Hemisphere contribute significantly to
this sink, with the mid- and high-latitude ecosystems as major contributors
(Goodale et al., 2002; Kurz et al., 2008b). The high-latitude forests are
predicted to be among the ecosystems that are most strongly influenced by
climate change (Kurz et al., 2008b); a warmer climate is likely to increase
forest productivity (e.g. Nemani et al., 2003; Boisvenue and Running, 2006),
and result in higher uptake of CO2 from the atmosphere. On the other
hand, it is projected that the impact of forest disturbances will increase
with a warmer climate (Seidl et al., 2014), and there are indications that
disturbances such as wind, fires, and insect outbreaks have led to
saturation of the carbon sink in European forests (Nabuurs et al., 2013). One
important forest disturbance agent is insects; it is projected that the
temporal and spatial dynamics, as well as the intensities and ranges of
insect herbivore outbreaks, will be influenced by global warming
(Vanhanen et al., 2007; Battisti 2008; Jepsen et al., 2008; Netherer and Schopf, 2010).
These insect outbreaks can severely disturb forest ecosystems, and have a
strong impact on carbon dynamics (Kurz et al., 2008a; Jepsen et al., 2009;
Heliasz et al., 2011). Quantitative effects of insect outbreaks on the carbon
balance are, however, not well known (Clark et al., 2010; Schäfer et al.,
2010; Hicke et al., 2012), and insect outbreaks are generally excluded in
large scale carbon modelling, which may result in overestimation of forests'
ability to act as carbon sinks (Kurz et al., 2008b; Hicke et al., 2012).
Consequently, it is important to develop methods both to monitor the spatial
extent of insect outbreaks and to quantify the impact of these outbreaks on
the carbon balance.
One alternative to estimate the impact on forest productivity is modelling:
the impact of a large-scale outbreak of the mountain pine beetle
(Dendroctonus ponderosae Hopkins) in British Columbia, Canada, was
studied with a forest ecosystem model by Kurz et al. (2008a). The
impact on the carbon balance of gypsy moth (Lymantria dispar L.)
defoliation in New Jersey, USA, was also modelled with both a canopy assimilation
model (Schäfer et al., 2010) and a terrestrial biosphere model (Medvigy
et al., 2012). The impact of spruce budworm (Choristoneura fumiferana Clem.) outbreaks in eastern Canada were modelled by Dymond
et al. (2010), and Landry et al. (2016) developed a Marauding Insect Module
(MIM) in the Integrated Biosphere Simulator (IBIS) that enables simulation
of insect outbreak for three insect functional types. Another alternative to
quantify the influence of an insect outbreak on the carbon balance is to
apply eddy covariance (EC) measurements: Brown et al. (2010, 2012) studied
how a mountain pine beetle outbreak influenced net ecosystem productivity
(NEP) in British Columbia, Canada; Clark et al. (2010, 2014) studied
differences in net ecosystem exchange (NEE) between undisturbed years and years with severe
defoliation by the gypsy moth in New Jersey, USA; and Heliasz et al. (2011)
estimated the reduction in NEE during the growing
season due to outbreaks of autumnal moth (Epirrita autumnata
Borkhausen) and winter moth (Operophtera brumata L.) in northern
Sweden in 2004. Even though not explicitly studied, there was gypsy moth
defoliation of holm oak (Quercus ilex L.) present in a time series
of EC measurements in southern France (Allard et al., 2008). These methods
generate valuable data on the impact of insect defoliation on the carbon
balance; however, to quantify the total regional impact, data on the extent
of defoliation events are required.
To generate wall-to-wall estimates of the disturbance effect on the carbon
balance, remotely sensed data from satellites can be used. Several studies
have demonstrated that satellite based remote sensing techniques can be
applied to detect insect disturbances with high accuracy; see, for example, Wulder et
al. (2006), Adelabu et al. (2012), and Rullan-Silva et al. (2013) for reviews.
In this paper we study outbreaks of autumnal moth and winter moth in
subarctic mountain birch (Betula pubescens ssp. czerepanovii N.I.
Orlova) forests in northern Sweden. These outbreaks often cover large areas,
but are often followed by within-season recovery of the foliage in parts of
the outbreak areas, which in combination with cloudy conditions can limit
the possibility to map the outbreaks with remote sensing methods.
Nevertheless, outbreaks of autumnal and winter moth have been mapped in
northern Fennoscandia with high accuracy with Landsat data (Tømmervik et al., 2001;
Babst et al., 2010). The low temporal resolution of Landsat (16 days
revisit time) can, however, be a limitation; as an example, only
fractions of the area included in this study were visible in Landsat data
during the peak of a severe outbreak in 2013. An alternative to Landsat data
is coarse spatial resolution data from, for example, the Moderate Resolution Imaging
Spectroradiometer (MODIS) sensor, which provides data with high (daily)
temporal resolution and a spatial resolution of 250 m × 250 m or
coarser. MODIS-derived normalized difference vegetation index (NDVI) has
been used to map autumnal and winter moth outbreaks with high accuracy in
northern Fennoscandia (Jepsen et al., 2009), and Olsson et al. (2016b)
developed a method for near-real-time monitoring of insect-induced
defoliation that facilitates monitoring of refoliation later in the growing
season.
Furthermore, there is a large body of research demonstrating that vegetation
primary productivity can be estimated with remotely sensed data and a light
use efficiency (LUE) approach (e.g. Prince, 1991; Ruimy et al., 1994; Running
et al., 2004; Xiao et al., 2004; Wu et al., 2010; McCallum et al., 2013; Gamon
2015). The LUE concept was introduced by Monteith (1972) and Monteith and
Moss (1977), suggesting that the primary productivity of plants has a strong
linear relationship to the absorbed amount of photosynthetically active
radiation (APAR), i.e. solar radiation in the spectral range 400–700 nm
that is absorbed by the plant canopy. Since near-linear relationships
between satellite-derived vegetation indices and the fraction absorbed PAR
(fAPAR) have been established (e.g. Asrar et al., 1984; Sellers, 1987; Goward
and Huemmrich, 1992; Myneni and Williams, 1994; Olofsson and Eklundh, 2007),
it is possible to create a LUE model driven by remote sensing data. Such a
LUE model could be applied for large-area estimates of the impact of forest
disturbance on the uptake component of the carbon balance. Bright et al. (2013)
utilized Landsat data to map bark beetle damage in northern Colorado,
USA, and MODIS GPP data, which are based on a LUE model, to quantify the
impact of the damage on GPP. However, to the knowledge of the authors, no
previous study has utilized remote sensing data and developed a LUE model to
monitor and quantify the impact of defoliating insects' outbreak on GPP.
In this study we utilized EC measured GPP to develop a LUE model, driven by
MODIS-derived NDVI, to quantify the regional impact on GPP of insect-induced
defoliation, and to map the spatial extent of the defoliation. Our main
study objective was to compare GPP for years with insect damage (2004, 2012
and 2013) with GPP for years without insect damage (2007, 2009, 2010, 2011
and 2014) in the birch forest of a subarctic valley of northern Sweden. The
analysis was achieved with two methods: (1) finding GPP for undisturbed
forest and estimate the impact of an insect outbreak with a common reduction
factor derived from EC data, and (2) by applying a LUE model for both
undisturbed and defoliated pixels and computing the differences.
Materials and methods
Study area
The study area was the mountain birch (Betula pubescens ssp.
Czerepanovii N.I. Orlova) forests in a valley south-west of the village of Abisko (68.35∘ N, 18.82∘ E), and along Lake
Torneträsk, as illustrated in Fig. 1 (green). The area is located in the
subarctic zone in northern Sweden with Lake Torneträsk at an altitude of
345 m a.s.l. and with the highest mountains reaching 1700 m a.s.l.
(Interact, 2016). These birch forests are infested by the autumnal moth
(Epirrita autumnata Borkhausen) and the winter moth
(Operophtera brumata L.) in time intervals of 9–10 years (Bylund,
1995; Tenow et al., 2007). The first reported outbreaks by the autumnal moth
in northern Fennoscandia are from the mid-1800s, and the winter moth has been
observed in the northern parts of Fennoscandia since late 1800 (Tenow, 1972).
These insect outbreaks strongly influence the birch forests (Ammunét et
al., 2015): severe defoliation events may result in stem mortality, requiring
decades of recovery (e.g. Tenow, 1996; Tenow and Bylund, 2000; Jepsen et al.,
2013), and understorey vegetation can shift into more grass-dominated
communities (Karlsen et al., 2013; Jepsen et al., 2013). Root-associated
fungal communities can change (Saravesi et al., 2015), as can chemical
and physical properties of the soil (Kaukonen et al., 2013). A warmer
climate, especially a lower frequency of years with extremely cold winters,
as reported by Callaghan et al. (2010), strongly influences birch moth
populations (Babst et al., 2010). The autumnal moth outbreaks have expanded
into colder, more continental regions, and the winter moth has reached
further to the north-east into areas where the autumnal moth previously
dominated (Jepsen et al., 2008). The latest outbreaks in the study area
occurred in 2004, with a documented reduction in carbon sink strength of
89 % at an EC tower located in birch forest (Heliasz et al., 2011), and
in 2012 and 2013 (Bengt Landström, county administrative board of
Norrbotten, personal communication, 31 October 2013). These outbreak events
were included in this study.
The studied birch forest (green) along the south-west part of Lake
Torneträsk and in the valley to the south-west of the village of Abisko.
The locations of the eddy covariance (EC) tower used to obtain GPP and the
spectral tower used to obtain fAPAR data are also shown. Reference system is
SWEREF99 TM and latitude and longitude are in WGS84. Source of background
map: Lantmäteriet (Dnr: I2014/00579).
Data
Remote sensing data and smoothing of time series
We used two Terra/MODIS satellite data products with 8-day temporal
resolution: (1) MOD09Q1 version 5, surface reflectance in the red and near-infrared (NIR) bands, including quality assurance (QA) information, with
250 m × 250 m spatial resolution, used mainly to derive NDVI (LPDAAC
2016a); and (2) MOD09A1 version 5, surface reflectance, as well as QA data,
with 500 m × 500 m spatial resolution (LPDAAC, 2016b), utilized due
to the product's more comprehensive QA data.
NDVI was computed from the MODIS data as (Rouse et al., 1973; Tucker, 1979)
NDVI=(NIR-red)/(NIR+red),
where red is reflectance in the red wavelength band, and
NIR is reflectance in the near-infrared wavelength band. We created
time series for the period 2000–2014 for all pixels in the study area and
processed in TIMESAT ver. 3.2. TIMESAT is a software package used to reduce
the influence of noise by fitting smoothed functions to time series of data
(Jönsson and Eklundh, 2002, 2004). In this study we applied the same
fittings and weights as in Olsson et al. (2016b): Double logistic functions
were used to smooth the raw NDVI data and QA data from both MOD09Q1 and the
more comprehensive QA flags in MOD09A1 were utilized to estimate the quality
of the NDVI observations. In this study we use the term NDVIDL
to refer to the smoothed time series of NDVI.
Fraction of canopy-absorbed PAR and relationships with NDVI
The fraction of PAR absorbed by the canopy (fAPARcanopy) was
measured at a spectral tower located in birch forest north-west from Abisko
(Fig. 1, black star). fAPARcanopy was obtained using
the four-component method, i.e. measurements of incoming PAR above canopy,
the total reflected PAR above the canopy, the transmitted PAR below the
canopy, and the reflected PAR by the understorey vegetation and ground below
the canopy. See Eklundh et al. (2011) for detailed information about the
estimation of fAPARcanopy. All PAR sensors were calibrated at
the field site following the procedure by Jin and Eklundh (2015), and
fAPARcanopy at solar noon time was calculated and used in the
final analysis. fAPARcanopy data were available for the years
2010 and 2011.
Average fAPARcanopy over 8-day periods, coinciding with
the MODIS 8-day periods, were computed, and an ordinary least squares
(OLS) regression was performed to find the relationship between
fAPARcanopy and NDVIDL Myneni and Williams (1994).
The linear equation derived was used in the LUE model to obtain fAPAR
from the double logistic fitted NDVI.
Eddy covariance and meteorological data
The EC tower is situated in the eastern part of the study area (Fig. 1, black
triangle) and located near the crossing point of four nominal MODIS pixels
with 250 m × 250 m spatial resolution (Fig. 2). Vegetation in the
four pixels is similar, with some open mires in the north-east pixel and a
paved road crossing the two southernmost pixels. The tower's footprint is
estimated to be about 200 m long, which is slightly smaller than a MODIS
pixel. The prevailing wind directions are from the west and from the east;
hence, the main footprint of the EC tower is to the west and east from the
tower, where vegetation is most homogeneous. Time series of NDVI were
extracted and mean values and standard deviations were computed for the four
MODIS pixels to study whether there were any larger deviations in the pixels'
NDVI signals. In Fig. 3, mean NDVI and standard deviation for the four pixels
in the period 2010–2014 are shown. The low standard deviations indicate that
there are minor differences in the NDVI signal between the pixels during the
main growing season for both raw NDVI and
NDVIDL both for years without disturbance and for outbreak
years. Hence, we assume that a varying footprint of the EC tower due to
varying wind directions and stability will have a limited influence on the
EC measurements.
The location of the eddy covariance (EC) tower (yellow triangle)
near the crossing point of four nominal MODIS pixels with
250 × 250 m spatial resolution (white lines). Reference system:
SWEREF99 TM. Lantmäteriet (Dnr: I2014/00579).
The EC measurements were made 8 m above ground, 3.3 m above canopy, using a
three-dimensional sonic anemometer (Metek USA-1; METEK GmbH, Germany) and an
open-path infrared gas analyzer (Licor 7500; LI-COR Inc., USA). The system
was operated with a frequency of 20 Hz, and data were recorded by a data
logger (CR1000; Campbell Scientific, Inc., USA). Additional measurements of
air temperature (Vaisala WXT510; Vaisala, Finland) and incoming
photosynthetic flux density (PPFD; JYP 1000, SDEC, France), used for flux
partitioning and gap filling, were made at the tower. Data were obtained each
year during the period 1 May to 30 September, which is from before the start
of the growing season (Karlsson et al., 2003) until the late growing season;
during the years included in this study GPP was approaching zero by the last
week of September. For the years 2004 and 2013, temperature and PAR were
obtained from Abisko Scientific Research Station (ANS,
2015); comparisons between data from ANS and the EC tower
showed small differences for the years when data were available from both
sources.
Mean (black) and standard deviation (grey) of NDVI 2010–2014 for
the four pixels around the eddy covariance (EC) tower. Panel (a) is
raw NDVI and (b) is NDVI fitted with double logistic functions in
TIMESAT (NDVIDL). Years 2012 and 2013 are those with insect
outbreak. In 2013 the birch forest was refoliating later in the growing
season. There is small secondary peak in raw NDVI (a) appearing each
year. This peak appears during the winter, when there is no vegetation in the
study area, and is hence removed from the smoothed data (b).
EC flux calculations were done with the EddyPro software ver. 5.2.1 (LI-COR
Inc., USA). Gaps caused by bad weather conditions, bad EC measuring
conditions, or short breaks in instrument functioning were filled with
the “Eddy covariance
gap-filling & flux-partitioning tool”
(http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/). The main reasons for
removing data were precipitation, as we used an open-path gas analyser, and
atmospheric conditions that did not fulfil the turbulence conditions
required. We considered the gap-filling approach suitable also for defoliated
years since the gap-filling function is created based on data from short time
windows, usually 7 days, and hence adjusts the fitting parameters for
changing ecosystem conditions. A model from the same website was used to
partition NEE into GPP and ecosystem respiration (Reco). It was
assumed that night-time NEE is equal to night-time Reco.
Accordingly, the accepted night-time data were fitted to the Lloyd and
Taylor (1994) model based on air temperature. This model was also used to
estimate Reco during daytime conditions. GPP was estimated as the
residual after subtracting Reco from the measured NEE. Details
about gap filling and flux partitioning are described in Reichstein et
al. (2005).
Land cover and elevation data
Land cover data were obtained from the Swedish mapping, cadastral, and land
registration authority (Lantmäteriet; Dnr: I2014/00579). These land cover
data are based on a classification of Landsat TM data, were updated in the
year 2000 as a part of the CORINE land cover project, and have a spatial
resolution of 25 m × 25 m (Lantmäteriet, 2010). Birch forests
in the study area were identified by extracting all pixels with broadleaved
forest. Since birch is the dominating tree species with only a few sporadic
individuals of other species (Sonesson and Lundberg, 1974), all forests were
considered to be birch. These data were used to calculate the fraction forest
cover per MODIS pixel.
Elevation data were obtained from Lantmäteriet (National survey of
Sweden) as a digital elevation model (DEM) with 50 m × 50 m
spatial resolution (Lantmäteriet; Dnr: I2014/00579). Mean elevation for
each MODIS pixel was computed as the average altitude of all DEM pixels
covered by a MODIS pixel. To adjust for altitudinal differences in
temperatures across the study area, a mean summer temperature gradient of
0.5 ∘C per 100 m (Josefsson, 1990; Holmgren and Tjus, 1996) was
applied to the temperature data from the EC tower.
Light use efficiency model
A LUE model with mean values of daily GPP in 8-day intervals
(GPPlue) (g C m-2 day-1), corresponding to the time
interval of the MODIS data, was developed as
GPPlue=ε×fAPAR8day×PAR8day,
where ε (g C MJ-1) is the light use efficiency,
fAPAR8day is fAPAR for a MODIS 8-day period derived from
NDVIDL, and PAR8day (MJ m-2 day-1) is
mean daily PAR measured at the EC tower over the 8-day period. The light use
efficiency varies between vegetation types, and variability in meteorological
conditions is accounted for through reductions factors for temperature and
vapour pressure deficit (e.g. Field et al., 1995; Prince and Goward, 1995;
Potter et al., 1999; Turner et al., 2003). In this study the light use
efficiency was computed as
ε=εmax×f8day,
where εmax (g C MJ-1) (see Sect. 2.3.3) is the
maximum efficiency applied in the model and f8day is a
reduction factor. We assumed that accounting for temperature only is
sufficient in our study region, which is supported by Bergh et al. (1998) and
Lagergren et al. (2005). Two models were created to describe
f8day, as in Lagergren et al. (2005): one model for the first
part of the growing season and one model for the second part of the growing
season.
First part of the growing season
During the first part of the growing season, covering May to late June,
f8day depended on growing degree days (GDDs) and frost events,
where GDD was computed with a base temperature of 5 ∘C, following
Senn et al.'s (1992) method applied to mountain birch development in northern
Finland:
GDDt=GDDt-1,Tmean8≤5GDDt-1+Tmean8-5,Tmean8>5
where Tmean8 (∘C) is the mean temperature for a
MODIS 8-day period. The reduction factor was computed as
f8day=1,GDDt≥GDDthres1-GDDthres-SGDDGDDthres+SGDD,GDDt<GDDthres,
where GDDthres (see
Sect. 2.3.3) is a threshold applied to decide when temperature and frost
events no longer influence
ε, in a similar fashion to Bergh et al. (1998) and
Lagergren et al. (2005). SGGD is a reduction factor influenced
by GDD and frost events and computed as
SGDD=GDDt1+Pfrost,
where Pfrost is a reduction factor controlled by frost events
and computed as
Pfrost=0,Tmin8≥-30.05×(-3-Tmin8)5,-8≤Tmin8<-30.05,Tmin8<-8,
where Tmin8 (∘C) is the lowest temperature during a
MODIS 8-day period.
Second part of the growing season
In the second part of the growing season, covering late June to September,
f8day is controlled by mean temperature only as
f8day=1,Tmean8≥TthresTmean8Tthres,Tmean8<Tthres,
where Tthres (∘C) (see Sect. 2.3.3) is a temperature
factor for controlling the influence of the 8-day mean temperature during
the second part of the growing season.
LUE model optimization
The LUE model was optimized to find three factors: (1) the GDD threshold
(GDDthres), (2) the temperature factor (Tthres),
and (3) the period to change from the first to the second seasonal model.
These were found by minimizing the root mean square error (RMSE) and
maximizing R2, based on GPPlue and daily mean values of
GPP from the EC tower over MODIS 8-day periods (GPPEC). To
compute εmax, the mean value of the light use
efficiency for all MODIS periods with maximum efficiency, i.e.
f8day= 1, was calculated, where the efficiency was computed
as
εmax=GPPECfAPAR8day×PAR8day,
where GPPEC was derived from the EC tower. Two
εmax values were computed: one including data
from the 5 years (2007, 2009, 2010, 2011 and 2014) with undisturbed birch
forest, and one (εmax,def) for the year 2012
with insect defoliation. No data were available from 2008 due to equipment
failure, and in 2013 the measurements were disturbed by larvae climbing the
equipment.
LUE model uncertainty
A Monte Carlo approach was applied to evaluate the uncertainty of the LUE
model by creating sets with 100 parameter values each for
εmax and slope and intercept derived from the OLS
regression between fAPARcanopy and NDVIDL. The
standard deviation of εmax was estimated from all MODIS
periods with maximum efficiency, as described in Sect. 2.3.3, and a 95 %
confidence interval for the regression line was estimated. The different sets
of parameters were created randomly from a uniform distribution, and the
Monte Carlo simulation was run for all possible combinations of parameter
values for the 5 years with undisturbed forests and over 15 sets of 100 MODIS
pixels with birch forest. Mean and standard deviation of LUE modelled GPP
were estimated from these simulations.
Identifying MODIS pixels with defoliated birch forest
Defoliated MODIS pixels were identified for the 3 years with insect
outbreaks with a near-real-time monitoring method based on Kalman filtering
and cumulative sums (Olsson et al., 2016b). The method identifies a seasonal
trajectory of NDVI representing birch forest during a year without
disturbances, called stable season. A Kalman filter (Kalman, 1960)
is applied to the raw NDVI observations from the year of study and
deviations from the stable season are computed. A cumulative sum (CUSUM)
filter (Page, 1954) is applied to these deviations, and a pixel is classified
as defoliated when the cumulative sum of deviations reaches a given
threshold. In a near-real-time application the stable season can only be
derived from years prior to the year of study. In this study we modified the
method so that the stable season was derived from all years with available
data. For high detection accuracy, the method requires that a MODIS pixel is
covered by at least 50 % forest. Hence, based on the land cover data from
Lantmäteriet, forest in pixels with lower forest cover was excluded,
resulting in 100 km2 of the in total 125 km2 birch forest in the
study area being included; the mean forest cover was 80 % per MODIS pixel.
The method detected 74 % of the defoliated sampling areas in the study
area with a misclassification of undisturbed areas of 39 % (Olsson et al.,
2016b).
Annual GPP loss due to insect defoliation
GPP for years without insect defoliation was estimated for all pixels by
applying the LUE model and computing the mean value for the 5 years without
insect outbreak, and with data available from the EC tower. The 8-day average
of incoming PAR (PAR8day), measured at the EC tower, was
assumed to be valid for all pixels in the study area, which was also
suggested by comparisons between PAR measured at the EC tower and ANS.
Two methods were applied to study the reduction in annual GPP due to the
insect outbreaks: (1) a method based on a reduction factor derived from the
EC data from 2012, when the birch forest in the footprint of the tower was
severely defoliated and no refoliation occurred. This reduction factor was
applied to all pixels in the study area and (2) a method where the LUE
model was applied to all defoliated pixels with εmax,def
computed for defoliated growing seasons, and where the loss
in GPP was computed as the difference between undisturbed and defoliated
years.
Correlation between ground-measured fraction of absorbed PAR by the
canopy (fAPARcanopy) and MODIS-derived NDVI smoothed with a
double logistic function in TIMESAT (NDVIDL) in 8-day intervals.
Only NDVIDL values ≥ 0.4 were included in the OLS
regression resulting in the black line. R2= 0.81 and N= 29.
Method 1 – GPP reduction factor
The fraction of the measured annual GPP at the EC tower that was lost due to
the insect outbreak in 2012 was computed as
GPPredfact=1-GPPdefoliated/GPPundisturbed,
where GPPredfact is the reduction factor and
GPPdefoliated is annual GPP from the EC tower in 2012.
GPPundisturbed is GPP from the tower representing a year
without disturbances and computed as the mean of annual GPP for the 5 years
without disturbances.
The reduction in annual GPP was computed for each pixel by applying the
reduction factor to GPP for undisturbed years and multiplying with the area
forest cover in the pixel. The same reduction factor was applied to all
years with insect defoliation. The total impact of the defoliation was
computed as the sum of GPP loss for all defoliated pixels in the study area,
and for each year with insect outbreak.
Influence on RMSE and R2 of GDDthres (a),
Tthres (b), and the optimal period to change from the
first to the second f8day model (c). RMSE is computed
from mean of daily GPP over 8-day periods.
Light use efficiency (ε), NDVI fitted with double
logistic functions (NDVIDL) scaled ×2 (green), and PAR
(orange) for the 6 years with data from the EC tower. Black lines with error
bars and black circles are the light use efficiency values included when
εmax and εmax,def were computed
for undisturbed and defoliated years, respectively. The error bars are
symmetric and 1 standard deviation higher or lower than the mean values.
Method 2 – LUE model for defoliated pixels
The LUE model, modified to model growing season with defoliation, was
applied to all defoliated pixels in the study area to estimate annual GPP
for each year with defoliation. Derivation of εmax,def was done with the same method as εmax, but only data from 1 year with insect outbreak (2012) were
available to estimate εmax,def and to evaluate
the performance of the defoliation LUE model. For each year with insect
outbreak, the regional reduction in GPP was computed by summing, over all
pixels identified as defoliated, the difference between GPP for years
without outbreak and GPP for this specific outbreak year.
Influence of refoliation
We also studied how recovering foliage later in the growing season
influenced the two methods. The assumption was that recovering foliage would
result in slightly higher NDVIDL values, which would
enable Method 2 to capture the refoliation and, hence, estimate GPP losses
more accurately. All pixels that were detected as defoliated were classified
as refoliated or non-refoliated with the defoliation monitoring method. The
differences between GPP loss derived with methods 1 and 2 were
computed as GPP loss method 1 minus GPP loss method 2. Finally, the
mean differences for refoliated and non-refoliated pixels were derived.
Discussion
This study has shown a substantial setback in GPP caused by insect
defoliation in a subarctic deciduous forest in northern Fennoscandia. At the
EC tower, GPP decreased by 260 g C m-2 yr-1 (59 %) during the
outbreak in 2012 compared to the mean GPP of undisturbed years. In the
entire study area annual mean values of decrease in GPP ranged from
190 ± 67 to 270 ± 95 ± g C m-2 yr-1. The total
decrease in regional GPP due to the three insect defoliation events studied
here was estimated to be 45 ± 14 Gg C, which is of the same magnitude
as the average annual regional GPP of 41 ± 12 Gg C yr-1 for single
years with no disturbances. During the most severe outbreak year (2012), the
annual regional GPP loss was nearly 50 % (20 Gg C yr-1), with 76 %
of the 100 km2 birch forests in the study area defoliated. In this
study we have estimated the impact on GPP only but we noted that during the
outbreak in 2012 the decrease in Reco was larger than the decrease in
GPP during the growing season around the EC tower. Respiration is affected
by insect outbreaks in two ways: (1) autotrophic respiration is reduced as
defoliated trees cannot photosynthesize and (2) heterotrophic respiration
increases when dead larvae decompose. The amount of carbon respired by
larvae is likely to be the same as the amount of carbon in the eaten leaves,
so we should only observe a shift of respiration in time. In addition,
larvae transport nutrients from trees to fungi and bacteria living in soil,
which further increase respiration. The increase in heterotrophic
respiration did not offset decrease in autotrophic respiration, and
Reco for the outbreak year decreased in comparison to non-disturbed
years. This study also highlights the advantage of combining EC data and
remote sensing data by applying data from an EC tower to calibrate a LUE
model and applying satellite data to estimate the impact on GPP over larger
areas. EC measurement alone cannot be extrapolated with high accuracy if the
spatial and temporal extent of an outbreak is unknown, and the LUE model
could not be developed without EC data. The combination facilitates
wall-to-wall mapping of forest disturbances and quantitative estimates of
the impacts on primary productivity.
There are, however, limitations in the study that must be considered. One
major challenge is to establish baseline conditions for GPP in areas with
reoccurring insect outbreaks, as in Abisko. As a comparison, Olsson et
al. (2016a) tested a defoliation detection method on the outbreak in Abisko
in 2013 and achieved the highest detection accuracies when the baseline
conditions were based on the 6 years with highest NDVI values in the period
2000–2012. In this study the annual GPP for years without disturbances was
estimated as the mean of the 5 years without insect outbreak and with
available EC data. It is likely that some of these years were influenced by
the insect outbreak in 2012. The 2 years prior to when the insect
populations reached outbreak levels (2010 and 2011) had lower annual GPP than
the years 2007 and 2009 (Table 1), and it is likely that GPP in 2014 was
influenced by the insect defoliation in 2012 and 2013. Heliasz (2012)
suggested that GPP reaches pre-outbreak levels 2–3 years after an outbreak,
and Hoogesteger and Karlsson (1992) showed that leaf area index (LAI) returned to
pre-defoliation levels 2 years after 100 % artificial defoliation even
though tree ring width was lower than normal at least 3 years after the
experiment. For the birch forests it may take decades to fully recover from
severe outbreaks (Tenow and Bylund, 2000). To get an indication of the
potential influence on GPP by insect defoliation for the non-outbreak years
we modelled GPP based on PAR for the years with data available from the EC
tower and compared with EC-derived GPP (see Supplement). The result showed
that measured GPP at the EC tower, and GPP modelled with PAR data, were
similar in 2007 and 2009. In the 2 years prior to the outbreak (2010 and
2011), measured GPP was lower than PAR modelled GPP, indicating that there
were signs of defoliation by growing larval population. Also in 2014 when the
birch forests were recovering, measured GPP was lower than PAR modelled GPP.
During the insect outbreak in 2012 measured annual GPP was
290 g C m-2 yr-1 lower than PAR modelled GPP, which is larger
than the decrease of 260 g C m-2 yr-1 applied in this study. In
addition, we ran the LUE model with meteorological data from ANS for the year
2008 to fill the gap in the time series with measured GPP and to study how
well it agreed with the years 2007 and 2009. According to the LUE model,
annual GPP at the EC tower was 440 g C m-2 yr-1 in 2008, which
agrees with the GPP value for undisturbed years of
440 g C m-2 yr-1 applied in the study. However, since years
that are influenced by pre-outbreak defoliation as well as a recovery year
are included as undisturbed years, it is likely that the baseline GPP applied
in this study is lower than GPP for undisturbed conditions. This is also
indicated by the larger difference between PAR modelled and measured GPP in
2012 and suggests that the estimated decreases in GPP due to insect outbreaks
in this study are on the lower side.
Another limitation is the assumption that no other factors than insect
outbreaks influence annual GPP, even though it is likely that also
meteorological conditions influence GPP. The comparison between EC-derived
GPP and PAR modelled GPP suggests that only 2 years with EC data represent
undisturbed forest; hence, the amount of data from the EC tower is too small
to study correlations between EC-derived GPP and meteorological variables.
Instead we studied correlations between NDVI and meteorological data from
ANS, where we used the mean of the highest NDVIDL value of
each year derived from 200 MODIS pixels with birch forest. To minimize the
influence of insect-induced defoliation we excluded the outbreak years and
years prior to and after outbreaks. No linear correlations between PAR and
GPP were found. There were, however, negative correlations between
temperature and seasonal maximums of NDVIDL, with the
strongest correlation between NDVI and the mean temperature in May–June.
The influence of temperature on NDVI was weak, and due to the estimated
uncertainties of the LUE model of 30 % we did not include these
correlations in the analysis. However, with data from the EC tower available
for more years it would be a potential improvement of the method to include
meteorological data when estimating the decrease in annual GPP.
There are also uncertainties in the LUE model. The relationship between
fAPAR8day and NDVIDL (Eq. 11) was estimated from
two growing seasons without disturbances. Due to larvae disrupting the PAR
sensors there were no fAPAR data available from the outbreak years; hence,
Eq. (11) was used also for defoliation events. Furthermore, the relationship
was derived from fAPAR obtained from the upper canopy, which may not be
representative of the entire forest, since the relationship between
fAPAR8day and NDVIDL is likely to vary with
understorey and forest densities in the study area. The relationship is also
likely to vary with varying understorey responses due to defoliation, which
may influence the estimated decreases in annual GPP. Accounting for these uncertainties would require more data on the fAPAR and NDVI relationship as well as more detailed land cover data, which would make the model more complex.
Hence, we assume this limitation to be acceptable, and since the aim of the
study was to estimate the influence of defoliation of the birch trees, we
considered fAPARcanopy to be the most suitable variable. Another
potential limitation is that the LUE model developed for years with
defoliation seems to underestimate GPP for values lower than about
1.5 g C m-2 day-1 (Fig. 8). However, for the outbreak year with
available EC data (2012) the underestimated values from the LUE model are
mainly due to a cold spring that resulted in a large reduction factor
(f8day). During the main growing season LUE modelled and
EC-derived GPP agrees well, which increases confidence in the modelling.
It may also seem surprising that the difference in NDVIDL was
comparably low in relation to the difference in light use efficiency. It is,
however, known that NDVI saturates for high LAI and that small changes in
NDVI can be associated with large changes in LAI (e.g. Myneni et al., 2002).
The light use efficiency, on the other hand, can decrease substantially with
lower LAI since more leaves will operate in the light-saturated portion of
the photosynthesis (e.g. Medlyn, 1998). There are also uncertainties in how
well the EC tower footprint represents the entire study area. Heliasz (2012)
utilized a permanent EC tower as reference and a mobile EC tower to study
variability in carbon exchange in the birch forests around Abisko and
concluded that there were only minor differences in GPP at seven sites
during the peak growing season in 2008 and 2009. Hence, we consider the EC
tower footprint to be representative for the study area.
The accuracy of the defoliation detection method also influences the results
of the study. The method missed 26 % of the defoliated MODIS pixels and
misclassified 39 % of the undisturbed pixels as defoliated in the
evaluation data used by Olsson et al. (2016b). This implies that the
defoliated areas in 2004 and 2013 were slightly overestimated, while the
defoliated area in 2012 is likely underestimated, though the impact on the
total numbers is likely small. It should also be considered that 20 % of
the forests in the study area were excluded since they are located in MODIS
pixels with < 50 % forests cover. Thus, the current estimate of a
total reduction in GPP may be conservative.
A limitation with the developed LUE model for large-area estimates is that it
includes observed meteorological data (temperature and PAR). An alternative
for running the model over larger areas would be to use modelled
meteorological data (Olofsson et al., 2007; Schubert et al., 2010). There are
also uncertainties related to the temperature data utilized. The gradient
applied to model mean temperatures depending on altitude is likely to give
accurate estimates in the study area. However, minimum temperatures are more
uncertain since cold air can drain downhill and accumulate in valleys and low
areas, rather than decrease with altitude. Altogether, since the EC tower is
located on a small ridge in the lower, flat parts of the study area, we
anticipate that the temperatures there are not substantially lower than the
area in general. We compared with lowest daily temperature from Abisko
research station, which is located near the spectral tower 10 km to the west
(Fig. 1), and at a slightly higher altitude than the EC tower. For all
periods with frost events during the early season, i.e. when the lowest
temperature influences f8day, the mean value of absolute
differences, with the coldest temperatures at the research station, was only
0.4 ∘C. With these small temperature differences and since frost
events only influence GPP in the early growing season, the impact on annual
GPP was considered minor.
The defoliation detection methods used in this study gives a time series of
smoothed NDVI that captures the timing of the defoliation event as well as
the potential refoliation. The LUE model, on the other hand, utilizes NDVI
smoothed with double logistic functions. These functions do not capture the
typical seasonal trajectory for years with refoliation. This is illustrated
in Fig. 3, where raw NDVI stays around 0.6 during the entire growing season
in 2012, when there was no refoliation around the EC tower. In 2013, when
there was substantial refoliation around the EC tower, raw NDVI stays around
0.6 during June, but it increases to pre-outbreak levels in early July, when
refoliation occurs. In 2004 the raw NDVI values has a pattern similar to that in
2013 with low values (around 0.6) until early August, when refoliation
results in a later season peak in NDVI. This seasonal development of raw
NDVI agrees well with GPP for the limited period with available EC data in
the outbreak year 2004. NDVIDL does not capture this
trajectory, with sharply increasing NDVI values that level off and start
increasing again later in the season. However, even though the actual timing
of the defoliation is not captured during years with refoliation the total
growing season GPP is well modelled. A new version of TIMESAT, currently
developed and tested, will also capture more detailed seasonal trajectories
with smooth fitting of curves. These new curve-fitting methods have a
potential to improve the performance of the LUE model.
We applied two methods to quantify the impacts on GPP to study which methods
performed better for refoliating birch forests. The assumption was that
Method 2 would be more adaptive and adjust for differences in defoliation
intensities between MODIS pixels. Since the level of defoliation, as well as
understorey responses to the defoliation, is likely to influence
NDVIDL, which in turn will influence fAPAR, it was
anticipated that a method based on a LUE model to derive GPP during
defoliation events would capture variability in defoliation levels and
understorey responses between MODIS pixels. Method 1, on the other hand, with
a common reduction factor, does not account for local differences between
pixels and is similar to upscaling the local conditions at the EC tower,
even though the method has the advantage that annual GPP for each pixel is
derived with a LUE model, and hence should be more accurate than assuming
that GPP for all MODIS pixels is identical to GPP at the EC tower. For the
years 2004 and 2012, the two methods resulted in similar estimates of the
GPP loss with slightly larger decrease in GPP for Method 2. In 2013, the
difference between the methods was larger with the highest decrease in
annual GPP for Method 1. One possible explanation for the smaller decrease
in annual GPP according to Method 2 for the year 2013 is that the growing
season seems to have been shorter and that refoliation started earlier and
was stronger in 2013 compared to 2004; this is indicated by the seasonal
developments of NDVI. It should also be noted that higher NDVI might be due
to increasing growth of understorey grasses favoured by the changed light
conditions due to defoliation (Karlsen et al., 2013) rather than recovering
birch.
The impact of insect outbreaks on the carbon balance has been quantified in
earlier studies: Heliasz et al. (2011) studied the impact on NEE of the
autumnal moth and winter moth outbreak in Abisko in 2004, but these
measurements started on 2 July, which was around 10 days after the larvae
reached peak densities, which most likely resulted in an underestimated
reduction in NEE. To facilitate a comparison between the outbreak years 2004
and 2012, we computed GPP for the period 2 July to 30 September for all
years with EC data. This indicated that the two outbreak years had a similar
impact on the carbon balance during the period studied with a GPP loss of
210 g C m-2 yr-1 in 2004 and 200 g C m-2 yr-1 in 2012
compared to years without disturbance. Furthermore, the loss of 200 g C m-2 yr-1
in the year 2012 and for the same time period as
studied in the year 2004, compared to the GPP loss of 260 g C m-2 yr-1
for the entire growing season in 2012, suggests that the impact on
NEE was underestimated by Heliasz et al. (2011). Clark et al. (2010) found
the highest difference in NEE between undisturbed years and years with
severe defoliation by the gypsy moth in New Jersey, USA, to be 266–480 g C m-2 yr-1,
and Clark et al. (2014) found that midday NEE during
complete defoliation was 14 % of pre-defoliation rates.
Allard et al. (2008) noted that cumulative NEE was lower during a year with insect
defoliation compared to years without disturbances; however, the low NEE
value might to a large extent have been caused by a dry spring. Brown et al. (2010)
found that a mountain pine beetle outbreak turned a forest into a
carbon source; no pre-outbreak EC data were available to quantify the impact
on NEP, but recovery after the outbreak was faster than anticipated (Brown
et al., 2012). It should be noted that the mountain pine beetle feeds within
the phloem and directly kills trees, while the moth species discussed above
are defoliators that usually only kill trees in cases of severe and repeated
outbreaks (Hicke et al., 2012). Modelling studies have also found that
forests have changed from sinks into sources of carbon, in some cases for
extended periods (Kurz et al., 2008a; Dymond et al., 2010; Schäfer et al.,
2010; Medvigy et al., 2012). However, to our knowledge, this is the first
study that has utilized remote sensing data and developed a LUE model
calibrated with EC data to both quantify and map the spatial extent of the
impact of defoliating insects' outbreaks on GPP.
The results of this study could help to reduce uncertainties in the impact
of insect outbreaks on primary productivity as well as to improve carbon
budgets by including insect-induced defoliation. For the mountain birch
forests in this study the estimated reduction in annual GPP, compared to
years without disturbances, was 50 % when there was limited refoliation in
the study area. For years with widespread refoliation, the annual GPP losses
were about one-third of GPP for years without disturbances. In addition, the
spatial and temporal mapping of insect defoliation provided by remote
sensing is important for accurate simulation of the carbon dynamics.
Furthermore, the outbreak area included in this study is only a fraction of
the 10 000 km2 estimated to have been severely defoliated in northern
Fennoscandia during the period 2000–2008 (Jepsen et al., 2009). Assuming
that the conditions were similar over northern Fennoscandia, the insect
defoliation over these vast areas would result in a potential total regional
GPP loss for the time period of the magnitude 2–3 Tg C. Models not
accounting for such recurring disturbance events would seriously
overestimate the ability of these forests to absorb atmospheric CO2.