Forest canopy structure is strongly influenced by environmental factors and
disturbance, and in turn influences key ecosystem processes including
productivity, evapotranspiration and habitat availability. In tropical
forests increasingly modified by human activities, the interplay between
environmental factors and disturbance legacies on forest canopy structure
across landscapes is practically unexplored. We used airborne laser scanning
(ALS) data to measure the canopy of old-growth and selectively logged peat
swamp forest across a peat dome in Central Kalimantan, Indonesia, and
quantified how canopy structure metrics varied with peat depth and under
logging. Several million canopy gaps in different height cross-sections of
the canopy were measured in 100 plots of 1 km
The structure of forest canopies is a determinant of fundamental ecological processes governing productivity, nutrient cycling and turnover across tropical landscapes (Asner et al., 1998; Brokaw, 1982; Denslow, 1987; Kellner et al., 2009; Prescott, 2002; Vitousek and Denslow, 1986). For example, the interception and processing of light, and thus primary production, is affected not only by total leaf area but also by the layering, positioning and angle of leaves within the canopy (Asner et al., 1998; Ellsworth and Reich, 1993; Montgomery and Chazdon, 2001; Stark et al., 2012); evapotranspiration is also affected by the internal length of hydraulic pathways and roughness of the canopy (Costa and Foley, 1997; Malhi et al., 2002). Canopies provide habitats for epiphytes and a multitude of vertebrates and invertebrates, sometimes strongly dependent on micro-climate controlled by canopy structure (Bergen et al., 2009; Palminteri et al., 2012; Simonson et al., 2014; Vierling et al., 2008). Yet, the complex environmental drivers and spatial disturbance and recovery patterns leading to the observed variety of three-dimensional canopy organization across landscapes remain poorly understood. In particular, in human-modified tropical forests the interplay between environmental factors and disturbance legacies on forest canopy structure is practically unexplored. In the biodiversity hotspot of Borneo, more than 30 % of forest cover has been lost over the past 40 years, 46 % of remaining forests have been selectively logged (Gaveau et al., 2014), and further tracks of old-growth forest are earmarked for concessionary selective logging (Abood et al., 2014; Gaveau et al., 2014) and/or are affected by illegal logging (Curran et al., 2004; Englhart et al., 2013.).
Borneo's tropical peat domes are natural laboratories for exploring changes in forest canopy structure with environment. Peat domes form by accumulation of organic matter over millennia; peat dome complexes can span up to 60 km in diameter, with peat depths reaching up to 20 m in the centre of the dome (Ashton, 2014). Trees become shorter, more narrowly stemmed, and more densely packed towards the centre of the domes (Anderson, 1961; Bruenig and Droste, 1995; Bunyavejchewin, 1995; Page et al., 1999; Whitmore, 1975), where there is a greater accumulation of peat, decreased nutrient availability (Page et al., 1999) and protracted substrate anoxia (Hoekman, 2007; Page et al., 1999; Wösten et al., 2008). Yet, this current understanding of forest structural changes is based on very few field studies (Anderson, 1961; Bruenig and Droste, 1995; Bunyavejchewin, 1995; Page et al., 1999; Whitmore, 1975). Further progress is impeded by access to these remote locations, which are difficult to traverse by foot. While many ecological studies have focused on plant community shifts in environments gradually changing from moist and fertile to dry and nutrient-poor, the ecology of plant communities in increasingly waterlogged and nutrient-poor conditions is much less well studied (Coomes et al., 2013).
The influence of current and past human disturbance can no longer be ignored when studying environmental gradients across tropical forest landscapes. At least 20 % of tropical forests worldwide have been disturbed by selective logging for economically valuable timber (Asner et al., 2009). Logged forests have more open canopies (Asner et al., 2004b) and networks of logging routes (Andersen et al., 2013; Asner et al., 2004b; Gaveau et al., 2014) that allow continuous human access (Laurance et al., 2009) with negative impacts on biodiversity (Burivalova et al., 2014). Set against a backdrop of rapid deforestation (Hansen et al., 2013), selectively logged forests are increasingly important for conservation of biodiversity and ecosystem services (Edwards et al., 2014; Laurance and Edwards, 2014; Putz et al., 2012). Optical satellite studies have had limited power in measuring logging effects as they lack information about the intricate three-dimensional structure of canopies, and only recently have researchers used satellite radar data to delineate degraded forests (e.g. Schlund et al., 2014). Airborne laser scanning (ALS) has opened new avenues for canopy research, as it provides detailed information on canopy height, layers and the location of canopy gaps over entire landscapes (Drake et al., 2002; Dubayah et al., 2010; Kellner and Asner, 2009; Lefsky et al., 2002). Here we define canopy gap as an opening in the forest canopy, which can result from tree fall or from the organization of crowns and can reach to different heights above-ground. Previous studies have used ALS to analyse the variation in gap sizes in different forest types within landscapes (Asner et al., 2013, 2014; Boyd et al., 2013; Espírito-Santo et al., 2014; Kellner and Asner, 2009; Kellner et al., 2011) and the impacts of logging on above-ground biomass (Andersen et al., 2013; d'Oliveira et al., 2012; Englhart et al., 2013; Kronseder et al., 2012; but see Weishampel et al., 2012). Changes in canopy structure along continuous environmental gradients within landscapes and the potentially long-term impact of logging on canopy structure remain to be studied.
We quantified landscape-scale changes in canopy structure across a peat swamp
forest (PSF) in Central Kalimantan, Indonesian Borneo, using an ALS survey of
750 km
Our study site (ca. 750 km
Map of old-growth (light grey), selectively logged forest (red)
and non-forest (dark grey) within the 750 km
Much of the area was selectively logged from 1980 to 2000 (Englhart et
al., 2013; Gaveau et al., 2014), and an agricultural development project
destroyed most of the southern section of the peatland between 1996 and 1999
(the Mega Rice Project, see Aldhous, 2004). Selective illegal timber
extraction has persisted despite the area becoming legally protected for
conservation (
We mapped forest cover and human-made linear features corresponding to logging routes (i.e. light railways, trails and canals) using Landsat satellite imagery from 1994 to 2013 processed with CLASlite, a freely available software that performs spectral un-mixing on satellite images (Supplement). CLASlite renders sub-pixel fractional cover information that enabled the identification of logging routes characterized by high fractions of soil or dead vegetation (Asner, 2009). Our local logging route map is similar to the Borneo-wide map of Gaveau et al. (2014), except that we have included additional logging routes resulting from illegal timber extraction after 2000. Forested areas within 500 m of a logging route were classified as selectively logged; the rationale being that mean canopy height maps (measured from ALS) indicate a recovery of canopy height after 500 m. Furthermore, logging operations were reported to extend to 500 m from railways in PSF (Franke et al., 2012) (Supplement). Forest within 5 km of the Kapuas river could not be classified as “old-growth” because local villagers have traditional land rights in that area, and make use of the forests (KFCP, 2009). Since 54 % of that area was interspersed with logging routes, it was classified as “logged”.
ALS data were collected during the dry season of 2011 (15 August to
14 October) with an Optech Orion M200 laser scanner at maximum half scan
angle of 11
A total of 100 virtual plots of 1
List and description of canopy structure metrics used in this study.
Within each plot, canopy height was extracted from 10 000 random selected pixels (to optimize computing time and provide a representative sample) of the CHM, from which the canopy top height (99th quantile of height) was calculated. We identified the height of the band containing the highest percentage of ALS returns in the vertical frequency distributions of returns (see above, 0–1 m voxels excluded to avoid ground returns), as a proxy for maximum canopy volume (Asner et al., 2008, 2014). The canopy shape parameter is given by the ratio of the height of maximum canopy volume to canopy top height (Asner et al., 2014).
To identify canopy gaps, we took horizontal cross-sections of the CHM in 1 m
increments from 2 up to 12 m above-ground (following Kellner and Asner,
2009) and recorded agglomerations of empty pixels surrounded by full pixels.
For example, agglomerates of empty pixels in the 5 m height layer indicate
gaps extending to
Detection of canopy gaps of a forest using airborne laser scanning
(ALS)
The gap size frequency distribution (GSFD) describes the relationship between
the frequency and area of gaps (Fig. 2c–e). Recent studies using ALS to
detect canopy gaps have fitted a power law to describe the GSFD (Asner et
al., 2013; Boyd et al., 2013; Espírito-Santo et al., 2014; Kellner and
Asner, 2009; Kellner et al., 2011; Lobo and Dalling, 2013). In such a power
law, the probability of gap size
We used a hierarchical Bayesian model with random plot effect to estimate
parameters
Peat depth is the main environmental gradient determining forest physiognomy
on peat domes (Page et al., 1999). In the research area, peat depth could not
be estimated directly from the DEM because the mineral bedrock increases in
elevation from south to north (6 to 32 m a.s.l.; source: FetchClimate, 2012). We
disposed of an independent data set of more than 300 peat depth measurements
across the study area and measured canopy top height from ALS (99th quantile
of height) within a 100 m neighbourhood. We first tested for the effect of
logging on canopy top height in this independent data set by fitting
generalized linear models containing peat depth and additive or
multiplicative effects of logging as a factor (yes, no). No significant
logging effect was detected. We found that canopy top height was closely
related to peat depth (
Logging was first included as a categorical variable (i.e. logged vs. unlogged) in regression models, and we also calculated a basic “logging pressure index” (LPI) for each logged plot. Since no official logging records were available, we approximated logging pressure by the density of logging routes detected in historical satellite images (see “Study area”). In the “new routes LPI”, the density of logging routes was weighted according to the year those logging routes were first detected: old logging routes received a smaller weight than newer logging routes as we assumed that forest recovery was greater, and logging impact was smaller, along older routes. Different weightings were explored (Supplement). In contrast, the “cumulative LPI” weighted all roads equally. The “new routes” approach assumes that most logging disturbance is happening at logging frontiers while the “cumulative” approach assumes that all existing routes are used at any given time.
Because forest structure is generally closely related to peat depth in tropical PSF (Page et al., 1999), we needed to compare logged and old-growth plots found on similar peat depths to assess the impact of logging on canopy structure correctly. This motivated us to use a matching approach which selected and weighted plots in order to achieve logged and old-growth plot samples comparable in terms of peat depth. Matching on peat depth to the nearest metre was performed in R using the “exact matching” option in the MatchIt package (Ho et al., 2011), yielding a selection of 47 old-growth and 30 logged plots out of the 100 plots described in the “Study area” section. The 23 logged plots that were not matched were mostly on shallow peats around the edge of the peat dome, where hardly any old-growth forest remains. We further restricted the statistical comparison between logged and unlogged plots to peat depths from 6 to 12 m where both treatments were more evenly represented and outlying weight values were avoided; this left us with 45 old-growth and 18 logged matched plots. Since variable numbers of logged and unlogged plots were matched for a given peat depth, the matching algorithm provided weights to be used in weighted regressions. No comparison between old-growth and logged plots was possible on peats shallower than 6 m because those areas were dominated by logged forest only.
We tested the effect of peat depth and logging as explanatory variables of
canopy height metrics (canopy top height, canopy shape) and gap metrics (mean
gap area, gap fraction in all 2 to 12 m CHM cross-sections, and
To test whether logging pressure had an effect on forest structure within
logged regions of the forest, generalized linear models were fit to canopy
structure metrics of logged plots, using peat depth and “logging pressure
index” (LPI) as explanatory variables. Note that LPIs did not significantly
co-vary with peat depth (
Along the whole peat depth gradient and in both old-growth and logged plots, canopy top height decreased by 1 m for each metre of added peat depth (Fig. 3a, Supplement). In an independent data set of more than 300 peat depth measurements and associated canopy top height measurements, canopy top height was not affected by logging (Supplement), suggesting that some large trees (presumably of low commercial value) were left within the plots. The fact that canopy top height was unaffected by logging meant that we could infer peat depth from canopy top height in plots where this information was missing (Supplement). The canopy shape, derived from the complete ALS point cloud, did not change along the peat depth gradient in old-growth forest (grey line, Fig. 3b) suggesting that the height of the main canopy volume decreased in parallel to canopy top height (Fig. 3a).
Changes in
Canopy gap metrics of old-growth forest also significantly changed along the
peat depth gradient. Gap metrics in cross-sections around 8 m above-ground
were the most responsive to peat depth and logging effects. The canopy
vertical profiles (Fig. 3c) reveal that gaps at 8 m above-ground are clearly
located below the bulk of the canopy volume and thus are more likely to have
been created by tree mortality rather than just being open spaces between
crowns. We hence use the 8 m cross-section to illustrate findings and give
full details for all cross-sections in Tables S3 and S4. The mean gap size
and gap fraction of old-growth forests decreased with increasing peat depth
(grey lines in Fig. 4a–b) in the 8 m height cross-section. The GSFD scaling
coefficient (
Changes in
Canopy top height accounted for a large proportion of the variation in canopy
gap metrics along the peat dome (recalling that peat depth is negatively
related with canopy top height and mean gap area) and was linearly related to
mean gap size (Fig. 5a,
Selective logging altered both canopy height and canopy gap sizes along the
peat dome, especially for higher cross-sections (model M2 or M3 selected). As
already described, logging did not influence canopy top height (Fig. 3a).
However a marked decrease of canopy shape was observed (Fig. 3b), indicating
the removal of canopy volume in logged plots. In the 8 m cross-section,
logged plots had larger gaps, a higher gap fraction and a higher proportion
of large gaps (smaller
Because of unequal effects on canopy top height and gaps, we no longer
observed the tight relationships (marked decrease in
There was limited evidence that logging route density within logged areas had
an influence on canopy structure. The logging pressure indices (LPI) did not
explain differences in canopy shape parameter, gap fraction,
Logging had a constant effect on canopy shape across the peat dome (Fig. 3b;
model M2 selected), but had differing effects on canopy gap metrics except
Major changes in canopy structure across the tropical peat swamp forest landscape closely followed the peat depth gradient. The canopy structure of selectively logged forests remained altered after concessionary logging had ended, although structural recovery depended strongly on peat depth. As such, the landscape-scale relationship between forest height and canopy gap structure was lost in selectively logged forests.
We observed a strong decrease in canopy top height (from about 34 m to 23 m) with peat depth, consistent with field observations (Anderson, 1961; Page et al., 1999; Whitmore, 1975) and ALS results from other Southeast Asian peat domes (Kronseder et al., 2012; Boehm et al., 2013), although for unknown reasons the neighbouring Sebangau peat dome bears tall forest (45 m) on deep peat (Page et al., 1999). PSF exhibit limited height development in comparison to neighbouring lowland dipterocarp forests, where emergent trees typically reach up to 60 m in height (Ashton et al., 1992). The canopy vertical profile revealed that the emergent layer is lost with increasing peat depth. Emergent trees are sometimes lost on nutrient-poorer soils (Whitmore, 1975; Kapos et al., 1990; Paoli et al., 2008; but see Ashton et al., 1992) and shallow rooting depth as a result of substrate waterlogging is likely to limit tree height development (Crawford et al., 2003). Similar patterns are observed in flooded vs. terra firma neotropical forest types (Asner et al., 2013; Boyd et al., 2013; Coomes and Grubb, 1996).
Recent applications of airborne laser scanning (ALS) have identified
power-law GSFDs in the neotropics (Asner et al., 2013, 2014; Boyd et al.,
2013; Espírito-Santo et al., 2014; Kellner and Asner, 2009; Kellner et
al., 2009; Lobo and Dalling, 2013) and Hawaii (Kellner and Asner, 2009;
Kellner et al., 2011). Our analysis of an Indomalayan tropical PSF landscape finds a very wide range of scaling exponents
Changes in the vertical forest structure along the peat dome were associated
with a decrease in mean gap size, gap area fraction and the proportion of
large gaps. We know of only limited evidence from three field-based studies
(Bruenig and Droste, 1995; Kapos et al., 1990; Schaik and Mirmanto, 1985) and
one ALS-based study (Kellner et al., 2011) reporting lower gap fractions and
smaller average gap sizes in nutrient-poor soils than in higher fertility
conditions. These gap patterns may arise from both changes in the
organization of crowns in the canopy as well as from changing disturbance
patterns along the edaphic gradient. First, smaller gap sizes may be due to a
loss of large emergent trees and even canopies filled with small crowns on
nutrient-poor substrate (Kapos et al., 1990; Paoli et al., 2008). These
shorter trees will additionally create smaller canopy openings when dying
(Numata et al., 2006). Accordingly, we found a close link of mean gap size
and
Environmental gradients are natural laboratories to explore environmental controls over forest structure using ALS. Changes in forest canopy structure along the peat depth gradient are similar to those observed along a substrate age gradient in Hawaii where nutrient limitation switches from N to P over time, with highest resource availability at intermediate soil ages (Kellner et al., 2011): along both gradients the forests are tallest where nutrients are most plentiful within the landscape, and the taller forests have more structured canopies (emergent layer and main canopy) and large canopy gaps. Canopy height decreases with altitude along an Amazon-to-Andes elevation gradient (Asner et al., 2014), but the changes in canopy structure are quite distinct from those observed in the peat swamp and soil chronosequence: the shorter forests here are sparse in trees, and dominated with a dense fern and bamboo understory, the latter having very open canopies with most canopy volume close to the ground and high proportions of large gaps. The use of different definitions of canopy gaps renders comparison of results difficult (Lobo and Dalling, 2014). While GSFD coefficients are insensitive to plot size, especially in forests dominated by small gaps such as PSF, they vary widely with different height thresholds and spatial resolution of the canopy model (Lobo and Dalling, 2014). We chose a small minimum gap size and different height thresholds following the majority of studies recently published (Kellner and Asner, 2009; Kellner et al., 2011; Asner et al., 2013; Boyd et al., 2013; Lobo and Dalling, 2013). If a consensus is found, combining ALS-derived forest structure measurements with ground data of major environmental drivers could open new avenues for researchers to explore ecological processes, e.g. disturbance dynamics, at spatial scales at which such processes take place, rather than being confined to small-scale plot studies.
Anthropogenic disturbance events such as selective concessionary and illegal logging leave long-lasting legacies of altered dynamics, carbon stocks and species composition in tropical forests often visible more than 20 years after activities have stopped (Numata et al., 2006; Sist and Nguyen-Thé, 2002; Slik et al., 2002). Consistent with this, we detected alteration of forest canopy structure 11 years after selective concessionary logging had stopped and, interestingly, recovery was modulated by environmental conditions along the peat dome.
Logged forests harboured an altered vertical structure and larger gaps, a
higher gap fraction and lower
Canopy structure in logged sites did not generally relate to the “logging pressure index” (LPI), except that larger gaps close to the ground were found in areas with dense logging route networks. This effect did not vary with the age of logging routes, which suggests that existing logging routes have slow structural recovery or continue to be used for illegal timber harvesting. Usually, canopy recovery depends strongly on time since logging and on logging intensity (Asner et al., 2004b, 2006; Sist et al., 1998). Logging infrastructure and routes, used here to infer the presence and timing of logging, might however not always be a good predictor of logging effect severity (Asner et al., 2004b). PSF on deep peat was deemed unsuitable for commercial logging operations due to low density of poles and fragility of the system (Bruenig and Droste, 1995). Yet we detected concessionary logging railways on deep peat in our study area, and we are developing new techniques to better monitor illegal logging (unpublished data). Subsequent ALS research should preferably be carried out in logging concessions where timing and intensity of logging are well documented (see e.g. Andersen et al., 2013; d'Oliveira et al., 2012). Since the logging pressure was relatively homogenous along the peat depth gradient and canopy structure did not respond to variation in logging pressure, we can interpret observed differences in canopy gap patterns between logged and old-growth plots as mostly related to inherent differential forest recovery rates along the peat dome.
Canopy structural responses to selective logging were influenced by peat
depth; a likely explanation is slower recovery rates of forests growing on
nutrient-depleted and waterlogged substrates in the centre of peat domes. Gap
metrics were most sensitive to differential recovery across the peat dome. In
particular, a clear segregation in GSFD scaling exponent
The ability of ALS to measure gaps reaching down to different layers of the forest vertical profile provides unique information on canopy gaps at different recovery stages (Boyd et al., 2013). Such gaps are hard to detect using optical satellite imagery as these data do not allow vertical penetration. For instance, Franke et al. (2012) report that canopy disturbance of PSF from selective logging and small logging trails became invisible in RapidEye satellite images with 5 m spatial resolution only a year after they were active, likely due to leaf cover rather than biomass recovery (Asner et al., 2004a).
The absence of pervasive logging damage close to the ground (2 m to about 5 m above-ground) indicates that regrowth, either by saplings, resprouting of damaged trees or by lateral filling, has occurred to a certain degree across the studied peat swamp, which is positive news for conservation and rehabilitation endeavours in the area (BOS Foundation, 2008). Tropical PSF stabilize deep peat deposits beneath them (Moore et al., 2013) acting as globally important carbon stores whose conservation is key to climate change mitigation (Murdiyarso et al., 2010; Page et al., 2002, 2011). However, concessionary and illegal logging remain widespread (Miettinen et al. 2012; Abood et al. 2014; Gaveau et al. 2014). The links between logging disturbance and peat stability remain to be addressed. In any case, open canopies after logging lead to higher light penetration (Numata et al., 2006), drier and warmer understory conditions (Hardwick et al., 2015) making deadwood in logged forests more prone to fire (Siegert et al., 2001) – a major issue in tropical peatlands (Page et al., 2002). Our study demonstrates that ALS can provide improved assessments of logging legacies in different tropical forest types, underpinning effective and adapted management and conservation plans.
We are grateful to the Indonesia-Australia Forests and Carbon Partnership and (the no longer operating) Kalimantan Forests and Climate Partnership for sharing the ALS and peat depth data. This research was carried out in collaboration with the Governments of Australia and Indonesia, but the analysis and findings of this paper represent the views of the authors and do not necessarily represent the views of those Governments. We thank G. Vaglio Laurin, and the reviewers M. Disney, F. Espirito-Santo, M. Hayashi and R. Hill for useful comments on an earlier version of the manuscript. We are grateful to A. Tanentzap for help with the RStan code and R. Kent and M. Dalponte for technical advice. B. Wedeux is funded by an AFR PhD Fellowship (1098188) from the Fonds National de la Recherche, Luxembourg. Edited by: A. Ito