Ecological Informatics 38 (2017) 50–61

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Ecological Informatics

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Regional mapping of vegetation structure for biodiversity monitoringusing airborne lidar data

Xuan Guo a,⁎, Nicholas C. Coops a, Piotr Tompalski a, Scott E. Nielsen b, Christopher W. Bater c, J. John Stadt c

a Department of Forest Resource Management, 2424 Main Mall, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canadab Department of Renewable Resources, Faculty of Agricultural, Life, and Environmental Sciences, University of Alberta, Edmonton, Alberta, T6G 2H1, Canadac Forest Management Branch, Forestry Division, Alberta Agriculture and Forestry, 9920-108 Street NW, Edmonton, Alberta, T5K 2M4, Canada

⁎ Corresponding author.E-mail address: ariaguo.ubc@gmail.com (X. Guo).

http://dx.doi.org/10.1016/j.ecoinf.2017.01.0051574-9541/© 2017 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 13 July 2016Received in revised form 25 January 2017Accepted 29 January 2017Available online 1 February 2017

Vegetation structure is identified as an important biodiversity indicator providing the physical environment thatgenerates, supports, andmaintains forest biodiversity. Airborne lidar systems (light detection and ranging) havethe capacity to accuratelymeasure three-dimensional vegetation structure, andhavebeenwidely used inwildlifehabitat mapping and species distribution modeling. Large–area structural inventories using lidar-derived vari-ables that characterize generic habitat structure have rarely beendone, yetwould be helpful for guiding biodiver-sity monitoring and conservation assessments of species at regional levels. This study provides a novel approachfor processing regional-scale lidar data into categorical classes representing natural groupings of habitat struc-ture. We applied cluster analysis on six lidar-derived habitat-related variables to classify vegetation structureinto eight classes for the forested areas of ten natural subregions in boreal and foothill forests in Alberta, Canada.Structure classes were compared across different natural subregions and under anthropogenic/non-anthropo-genic disturbance regimes. We found that the Lower Foothills Natural Subregions had themost complex vegeta-tion structure, andwildfirewas themost prevalent disturbance agent for all classes except for the rarest class (i.e.stands with high standard deviations of height and low canopy cover)whichwasmore heavily altered by timberharvesting. This data product provides continuous, regional mapping of vegetation structure directly measuredfrom lidar, with a spatial resolution (30 m) relatively finer than what was provided by polygon-based forest in-ventories. This vegetation structure classification and its associated spatial distribution address the fundamentalissue of habitat structure in biodiversity monitoring. It can serve as a base layer used together with species andland cover data for forest resources planning, species distribution and animalmovementmodeling, aswell as pri-oritization of conservation efforts on critical habitat structures.

© 2017 Elsevier B.V. All rights reserved.

Keywords:Vegetation structureBiodiversityLidarWildlife habitatCluster analysis

1. Introduction

Vegetation structure is considered an important component inwild-life and forest management (Noss, 1990; McCleary and Mowat, 2002;Lindenmayer et al., 2006). For example, forest canopies mediate micro-climate, provide perching, nesting, foraging, and covering habitats formany animal species, and influence food quality, diversity and accessi-bility (Hamer and Herrero, 1987; Johnson et al., 2002). Forest structureis also inextricably affected by disturbance regimes, especially wildfire,harvesting, and road development, which may favour certain specieswhile discouraging others (Tews et al., 2004; Devictor et al., 2008;Boutin et al., 2009; Desrochers et al., 2012). Forest horizontal and verti-cal structures have therefore been identified as essential biodiversity in-dicators across a broad range of forest ecosystems around the world(Ozanne et al., 2003; Chirici et al., 2011; Gao et al., 2014). In general, for-est species diversity is positively associated with vegetation structural

diversity because different vertical strata and structural heterogeneityof forest stand provide ecological niches for species of various habitatspecializations (MacArthur, 1958; Hunter, 1999; Culbert et al., 2013).This linkage between forest biodiversity and forest structure is a centralassumption in ecosystem-based management approaches to forestry,where forestmanagers attempt tomaintain the diversity of forest struc-tural attributes at both landscape and stand scales in order to maintainforest biodiversity (Hunter, 1993).

MacArthur (1972) identified productivity, climatic stability, andhabitat structure as three primary drivers of biodiversity whose effectscan be reflected in three aspects: composition, structure, and function(Franklin et al., 1981; Noss, 1990). Spectral information acquired fromoptical remote sensing data has been widely used to assess composi-tional and functional components of biodiversity over broad spatialscales (Cohen and Goward, 2004; Duro et al., 2007; Coops et al., 2008;Schuster et al., 2015). Habitat classifications based on land cover types(Wessels et al., 2000; Franklin et al., 2001; McDermid et al., 2009;Riggio et al., 2013), and habitat suitability indices derived from vegeta-tion productivity and seasonality (Nilsen et al., 2005; Coops et al., 2008)

51X. Guo et al. / Ecological Informatics 38 (2017) 50–61

have contributed significantly to species distributionmodels and animalmovement studies. As opposed to correlations with spectral indices, thestructural component of biodiversity has principally been assessedthrough forest resource inventories that require labor-intensive fieldsurveys and/or aerial photo interpretation (Fensham et al., 2002; Hydeet al., 2006; Clawges et al., 2008; Nijland et al., 2015b). In addition, re-source inventories submitted by multiple forest management stake-holders may lack consistency in interpretation standards, updateschedule, and aerial coverage when collectively used for large-areahabitat mapping (McDermid et al., 2009). Moreover, vegetation heightestimates from photo interpretation are reported at the polygon levelwhere the within-polygon variations in height and structure are notreadily assessed (Culbert et al., 2013). More detailed, fine-scalemapping of vegetation structure is needed to allow a broader range ofbiodiversity values to be included in forest management planning.

Fig. 1. Boreal and foothills forest and associated natural subregio

Airborne lidar (light detection and ranging) is an active remote sens-ing technology that can accurately measure three-dimensional vegeta-tion structure (Lim et al., 2003). Lidar-derived canopy height, canopyheight variation, and canopy cover metrics have been used widely inforest ecological studies to determine or predict a number of importantforest attributes, including: forest vertical layering and overall architec-ture (Maltamo et al., 2005); forest successional stages (Falkowski et al.,2009); vegetation strata and forest genera (Morsdorf et al., 2010; Kim etal., 2011); tree species abundance (Ewijk et al., 2014); forest volume,biomass and carbon storage (Zald et al., 2014); vegetation regeneration;and response after timber harvesting (Nijland et al., 2015b).

Although lidar technology cannot directlymeasure forest biodiversi-ty, previous studies have examined the hypothesis that vegetationstructure is an important indicator of species diversity as postulatedby MacArthur and MacArthur (1961) and Erdelen (1984). Species

ns within the Government of Alberta's lidar data coverage.

Table 1Six selected lidar variables.

Lidar metrics Explanation Reference

SD of height Standard deviation ofvegetation height

Clawges et al., 2008

Canopy cover % of total first returns above1.37 m

Vogeler et al., 2014

Canopy height density -1.37 to 5 m

% of first return between 1.37and 5 m

Bergen et al., 2009

Canopy height density - 5to 10 m

% of first return between 5 and10 m

Næsset &Gobakken, 2008

Canopy height density - 10to 20 m

% of first return between 10and 20 m

Næsset &Gobakken, 2008

Canopy height density - 20to 30 m

% of first return between 20and 30 m

Næsset &Gobakken, 2008

52 X. Guo et al. / Ecological Informatics 38 (2017) 50–61

distribution and habitat models built on lidar-derived structural vari-ables indicate strong relationships between bird species occurence,richness, and canopy height and cover metrics (Wulder et al., 2008;Graf et al., 2009; Goetz et al., 2010; Hovick et al., 2014; Hill andHinsley, 2015). For example, positive correlations have been found be-tween avian species richness and foliage height diversity (Clawges etal., 2008; Bergen et al., 2009), as well as canopy height (Goetz et al.2007). Coops et al. (2016) found that adding variables correlated withvegetation structure to existing bird richness models improved modelpredictive power dramatically. Lidar-derived structural metrics havealso been used to examine habitat suitability for plant, bird and mam-mal species, including grizzly bears (Ursus arctos) and african lions(Panthera leo) (Bergen et al., 2009; Mueller et al.,2009; Lucas et al.,2010; Simonson et al., 2012; Jung et al., 2012; Gao et al., 2014; Nijlandet al., 2015a; Davies et al., 2016).

Biodiversity monitoring programs focused on a limited list of indica-tor species may not provide an adequate measure for biodiversity as awhole due to the fact that these species may not represent the fullrange of trophic levels and habitat specializations (Marcot et al., 1994;Boutin et al., 2009; Marchese 2015). Furthermore, current lidar-basedspecies habitat studies with fine-spatial resolution data tend to focuson small study sites, which may in turn restrict the generality of theanalyses to broad-scale, multispecies, habitat-based monitoring(Boutin et al., 2009; Zellweger et al., 2013; Vogeler et al., 2014). In con-trast, habitat monitoring projects at regional or national levels com-monly use generalized variables from remote sensing data of coarserspatial resolution (McDermid et al., 2005).

Broad-scale, multispecies, and multi-dimensional biodiversity mon-itoring is difficult and costly (Gaston 1996, Green et al., 2005). A habitat-based approach may better lend itself to the conservation of plant andwildlife species diversity than species-specific monitoring of biodiversi-ty. Thereforewe propose to examine vegetation structure across a broadarea as an indicator to infer overall habitat potential. Vegetation struc-ture is a major driver of forest species diversity by representing thephysical context that generates, supports, and maintains biodiversity(Moritz, 2001; Marchese, 2015). Noss (1990) also emphasized that atop-down assessment of biodiversity status should start with a broad-scale inventory of vegetation composition, habitat structure, and their

Table 2Pearson's correlation coefficients for six lidar variables (p value = 0.05).

SD of height Canopy cover

SD of height 1.00Canopy Cover 0.49 1.00Canopy Height Density - 1.37 to 5 m −0.34 0.02Canopy Height Density - 5 to 10 m 0.05 0.52Canopy Height Density - 10 to 20 m 0.47 0.68Canopy Height Density - 20 to 30 m 0.53 0.36

landscape patterns. Without a comprehensive understanding of whattypes of habitat structure exist over the landscape, biodiversity moni-toring based solely on relationships between species diversity, environ-mental gradients, vegetation composition, and productivity developedfrom localized studies could be biased and incomplete.

In this study, we utilized a large-area discrete-return lidar datasetcovering themanaged forests of Alberta, Canada, to develop an invento-ry of vegetation structure that efficiently synthesizes forest vertical var-iation into distinct categorical classes for use in conservation andmanagement activities. To do so, we selected six structure-relatedlidar metrics that measure canopy height, cover, and height variationat a 30 m spatial resolution (i.e. grid cell size) covering the study area(the majority of Alberta's forests). Second, we applied cluster analysisto classify forest structure into eight classes and used discriminate func-tion analysis to contrast variable importance within and between iden-tified classes. Finally, we interpreted and compared forest attributes ofspecies and age composition, wetland presence, and the effects of dis-turbance (both anthropogenic and non-anthropogenic) regimes in thederived classes.

2. Materials and methods

2.1. Alberta natural regions and subregions

Alberta's landscape is divided into six natural regions based on theinfluence of climate, topography, and geology (Natural RegionsCommittee, 2006). In this study, we focused on the Boreal Forest andFoothills Natural Regions, which together cover N44 million ha ofthe provincial land base (68%), and support Alberta's biodiversity byproviding critical seasonal andpermanent habitats to numerouswildlifespecies (Brandt, 2009).

The Boreal Forest Natural Region (Fig. 1) is the largest natural regionin Alberta. This region is influenced by short summers and long, coldwinters, with deciduous, mixedwood and coniferous forest intertwinedwith extensive wetlands (Natural Regions Committee, 2006). Eight nat-ural subregions occur within this region, with elevations ranging from150m to 1100m above sea level. Upland forests are composed of trem-bling aspen (Populus tremuloides), balsam poplar (Populus balsamifera),white spruce (Picea glauca), and jack pine (Pinus banksiana), with morepine-dominant stands in areas with higher elevation. Treed wetlandsare characterized by black spruce (Picea mariana) and western larch(Larix occidentalis), with both bogs and fens being common across thelandscape.

Prominent wildlife species of the Boreal Forest Natural Region in-clude moose (Alces alces), caribou (Rangifer tarandus), black bear(Ursus americanus), wolf (Canis lupus), beaver (Castor canadensis),snowshoe hare (Lepus americanus), and many migratory birds such aswhooping crane (Grus americana). About 110 rare plant species werereported in this natural region across a range of upland and wetlandecosystems (Natural Regions Committee, 2006). Major land uses inthis region include agricultural cultivation, oil and gas extraction andsignificant harvesting of timber.

Canopy height density

1.37 to 5 m 5 to 10 m 10 to 20 m 20 to 30 m

1.000.14 1.00−0.49 0.06 1.00−0.30 −0.22 0.19 1.00

Fig. 2. Cluster dendrogram from hierarchical clustering process with eight classes identified in red box.

53X. Guo et al. / Ecological Informatics 38 (2017) 50–61

The Foothills Natural Region (Fig. 1) extends east from the flank ofthe Rocky Mountains, north from the Bow River Valley (51.176°N,115.570°W) to just south of Grande Prairie (55.170°N, 118.799°W), cov-ering 10% of the province. The region is divided into two natural subre-gions characterized by a cool, moist climate and long growing seasons,with gently undulating to rolling terrain ranging from 700 m to1700m above sea level into the RockyMountains. Dominant vegetationcover in the Lower Foothills Natural Subregion is mixedwood forest,consisting of trembling aspen, white spruce, lodgepole pine (Pinuscontorta) and balsam popular, whereas the Upper Foothills NaturalSubregion is more dominated by uniform lodgepole pine and blackspruce stands.

Variable topography, surface and groundwater regimes create di-verse plant andwildlife communities in the Foothills region. This regionprovides critical habitats for grizzly bear, woodland caribou (Rangifertarandus caribou), many song bird species and about 80 rare plant spe-cies (Natural Regions Committee, 2006). However, the Foothills NaturalRegion is heavily managed for commercial timber production and rap-idly expanding oil and gas explorationwhich have created a fragmentedlandscape across the region (Linke et al. 2005).

Fig. 3. Difference between identified structure classes for each lidar-de

2.2. Lidar data and derived metrics

The Government of Alberta, Canada has acquired airborne discrete-return lidar data over N33 million hectares of forested area in the prov-ince, covering 75% of Alberta foothills and boreal regions (Fig. 1). Thedata used in this study were acquired between 2003 and 2014, withmore than half of the data obtained in 2007 and 2008. The pulse densityranges from1 to 4 returns perm2. These datawere collected bymultiplecontractors with a scan angle of b25° from nadir and a vertical accuracyof no larger than 30 cm root mean square error (Alberta Environmentand Sustainable Resource Development, 2013). Bare-Earth products de-rived from these data were used to normalize the point elevations toheight above ground level. A suite of forest canopy metrics were gener-ated using a 30 m grid based on normalized first returns of the lidarpoint cloud data using a combination of software packages specializedfor lidar data processing, including FUSION (McGaughey, 2015) andLAStools (Isenburg, 2016).

Based on existing literature, we selected six lidar-based metricsthat were commonly related to forest biodiversity (Table 1). Asheight-related metrics were among the most widely used lidar metrics

rived variable (boxplot represent mean ± 1 standard deviation).

Table3

Confus

ionmatrixof

thetest

datasetfrom

discriminatefunc

tion

analysis.

Class

Description

12

34

56

78

TotalC

ount

User'saccu

racy

1Sh

ort,med

ium

cano

pyco

verstan

d29

,914

3533

203

1225

2838

1118

1468

040

,299

0.74

2Sh

ort,op

encano

pystan

d35

8695

,782

12,660

114

830

574

011

4,08

60.84

3Verysh

ort,de

nsecano

pyco

verstan

d14

730

31,761

00

137

880

37,023

0.86

4Verytall,

complex

stan

d41

30

020

,709

398

1192

013

8124

,093

0.86

5Verytall,

open

cano

pystan

d0

00

019

750

00

1975

16

Tall,

dens

ecano

pyco

verstan

d59

460

8833

000

63,765

3338

576

,442

0.84

7Sh

ort,closed

cano

pystan

d20

30

860

010

827

,222

027

,619

0.99

8Verytall,

closed

cano

pystan

d0

00

1881

03

013

,797

15,681

0.88

TotalC

ount

41,535

99,315

44,798

27,116

6694

66,187

36,390

15,183

337,21

8Ave

rage

Accuracy:

84%

Prod

ucer'saccu

racy

0.72

0.96

0.71

0.76

0.3

0.96

0.75

0.91

54 X. Guo et al. / Ecological Informatics 38 (2017) 50–61

for habitat monitoring and assessments, we chose four height strata(1.37m to 5m, 5m to 10m, 10m to 20m, 20m to 30m) and calculatedcanopy height density (percentage of lidar returns) within each heightstratum. A suite ofmetrics on canopy height density can revealmore de-tails about canopy height distribution along the vertical dimension thandirect lidar height measurements, thus we used a combination of cano-py height metrics in this study. Short shrubs below 1.37 m were notconsidered. Previous studies have found strong correlations betweenbird species diversity and the proportional lidar returns near the forestfloor (b5 m) (Clawges et al., 2008; Bergen et al., 2009; Mueller et al.,2009). Tall shrub and tree saplings below 5mprovide important habitatfor many bird and mammal species. Therefore, canopy height densitybetween 1.37 m to 5 m was used. The other three height strata usedin this study represented the range of lower (5–10 m), middle (10–20 m) and upper (20–30 m) canopy height distribtuions in Albertawhich might be utilized by wildlife species with different habitatneeds (Coops et al., 2016).

Beyond canopy height density, standard deviation (SD) of heighthas also been used as an important predicator of bird richness andabundance (Bergen et al., 2009; Mueller et al., 2009; Culbert et al.,2013; Vogeler et al., 2014). Forest stands with low standard devia-tions of height tend to have less structural diversity than standswith high standard deviations of height, therefore providing lessniche habitat for plant and wildlife species (August 1983). Canopyheterogeneity has also been used as an indicator of the presence oflarge snags for cavity-nesting species (Martinuzzi et al., 2009; Bateret al., 2009).

Canopy cover was measured as the total percentage of lidar returnsabove a minimum tree height threshold (1.37 m in this study). Opencanopy stands with strong light penetration near ground level are asso-ciated with high exposure to solar radiation in shrub and understorylayers. These stands generally have high volume of understoreyplant biomass and plant species diversity which provide importanthabitat for understory and shrub nesting birds species and arthropodcommunities (Blakely and Didham, 2010). In contrast, medium toclosed-canopy stands help maintain a suitable moisture content forlichen growth which could provide critical winter habitat for caribouand other ungulate speices (Kershaw, 1977; Apps et al., 2001; Coopset al., 2010).

2.3. Disturbance and vegetation inventory plots

Three data sourceswere used in the study to interpret vegetation at-tributes and disturbance regimes for lidar-based structure classification.The Alberta historical wildfire database is a polygon-based GIS (geo-graphic information system) layer with spatial information of largefires (N200 ha) since 1961 (Alberta Environmental Protection, 1991).We used in our analyses wildfire information from this database be-tween 1980 and 2010. An anthropogenic disturbances databasewas de-rived from a nationwide inventory of polygonal disturbance typesacross the Canadian boreal ecosystem produced through the interpreta-tion of 2008–2010 Landsat images at 1:50,000 viewing scale (Pasher etal., 2013). Cutblocks, agricultural development, oil and gas exploration,and urban settlementwere themajor disturbance types recorded in thedatabase. The Alberta Biodiversity Monitoring Institute (ABMI) hasestablished a provincial monitoring system with 1656 rectangular per-manent photo plots (3 kmnorth to south, 7 kmwest to east) distributedin a 20 km systematic grid covering 5% of Alberta's land base to evaluateand report biodiversity status in the province (Castilla et al., 2013). Wechose photo plotswith images collected between2008 and 2011 to gen-erally match the year of the anthropogenic disturbance layer in thisanalysis. Within each photo plot, detailed vegetation inventory andland use classification with a minimummapping unit of 2 ha were pro-duced based on high-resolution (0.30m spatial resolution) aerial photointerpretation.

Canopy Height Density 20 - 30 m

Canopy Height Density 10 - 20 m

Canopy Height Density 1.37 - 5 m

Canopy Height Density 5 - 10 m

SD of Height

Total Canopy Cover

Fig. 4. Structure classes plotted against the first two discriminate axes with the strength and direction of each variable indicated.

55X. Guo et al. / Ecological Informatics 38 (2017) 50–61

2.4. Data analysis

Cluster analysis is an exploratory, unsupervised classification tech-nique that agglomerates objects based on perceived intrinsic similarityof the data to identify natural groupings (Kaufman and Rousseeuw,2009). It is used to reveal the general characteristics and underlyingstructure of data. An ideal cluster contains a set of objects which aresimilar to each other within the cluster, but isolated from objects inother clusters. This method has been widely used in multivariate-based ecosystem classifications (Fitterer et al., 2012; Thompson et al.,2016) and ecoregion zoning (Coops et al., 2009). In our analysis, atwo-step clustering analysis (Chiu et al., 2001) was used to accommo-date our large dataset and associated computing time. A k-means clus-tering algorithm was applied as the first step to classify the data intopre-clusters, replacing raw data. These pre-clusters were then groupedusing a commonly used agglomerative hierarchical clustering method(Unweighted Pair Group Method with Arithmetic Mean) based onEuclidean distance.

Before cluster analysis, lidar data pointswere filtered to remove veg-etation heights above 45m (air points) and below 1.37m (which corre-sponds to the height at which stem diameters are measured in thefield). All data were standardized to bewithin the same range bymulti-plying a scalar. Pearson's correlation test (at p-value = 0.05) was per-formed to assess correlated relationship between the six variables. Thek-mean clustering algorithm generated 100 pre-clusters, which werein turn further reduced to form the final structure classes through thehierarchical clustering process. The two-step clustering process wasconducted in R software with the packages “bigmemory” and“biganalytics” (Kane and Emerson, 2010).

To test the uniqueness of the structure classes, a non-parametricKruskal-Wallis rank sum test (Breslow 1970), followed by a Dunn'spost hoc multiple comparison test, was used to examine if the overallclustering was significant for each selected lidar variable, and if thepairwise difference between clusters was significant for each individualvariable. When considering all variables together, a two-sample loca-tion test based on marginal ranks (Sen and Puri, 1971) was used as anon-parametric multivariate statistical test to determine if the cluster-ing results were significant with consideration of all selected variables.Linear discriminate function analysis was applied on 10% of the datato verify the separability of clustering results and assess variable impor-tance from a modeling perspective. The predicted structure classes and

the six variables used in the discriminant function analysis were plottedagainst the first two axes of the discriminate coefficients to contraststructural characteristics within and between structure classes and as-sess the strength of each variable in discriminating these classes.

To interpret the dominant forest features in each structure class,such as dominant species, layering architecture, density class, age com-position, and wetland coverage, we sampled and overlaid 58 ABMIphoto plots (Fig. 1) containing N15.000 polygonswith our classification.To examine the influence of anthropogenic and non-anthropogenic dis-turbance on each of the structure classes, the historical wildfire and an-thropogenic disturbance databases were overlaid with the structureclassification. At the polygon level, the information in the ABMI photoplots and the two disturbance inventories were summarized to eachstructure class by pixel in the overlaid area.

3. Results

Pearson's coefficients for the six lidar variables were significantlylower than 0.7 (p value= 0.05), indicating no significantly strong linearcorrelation between selected variables (Table 2). Fig. 2 shows the den-drogram from the two-step clustering and highlights where the naturalbreaks for eight structure classes were apparent in terms of maximizingthe dissimilarity between an appropriate number of structure classes.Both the Kruskal-Wallis rank sum test and the two-sample locationtest based on marginal ranks indicated a significant difference betweenthe eight classes for each individual variable and for all six variables (pvalue = 0.05). The Dunn's post-hoc test between paired samples wasalso significant for all variables (p value=0.05), except for the standarddeviation of height between classes 4 and 5, and the canopy height den-sity between 20 and 30 m for classes 3 and 7 (Fig. 3). The discriminatefunction analysis on the test dataset indicated an accurate classification(average accuracy: 84%) for all the classes except class 5 which wasoftenmisclassified into class 1 or 2 (Table 3). The eight structure classeswere all well separated when plotted against the first two discriminatecoefficient axes (Fig. 4) which explained 80% of the total variance. Totalcanopy cover and canopy height density between 20 and 30 m werethe most influential variables in terms of discriminatory power amongdifferent structure classes.

The spatial distribution of the eight classes is shown in Fig. 5(1) andan illustration of the stand profiles of the eight classes using samples ofthe lidar point cloud is demonstrated in Fig. 5(2). In Fig. 5(1), different

Fig. 5. The eight structure classes in Alberta: 1) an overviewand zoomed in view (A: transitional area between the Lower Boreal Highlands and CentralMixedwood Subregions; B: riparianzone along Athabasca River; C: gentle slope in transitional area between the Dry Mixedwood to Lower Boral Highlands Subregions); 2) illustration of the stand profile for each structureclass.

56 X. Guo et al. / Ecological Informatics 38 (2017) 50–61

natural subregions exhibit different spatial patterns of structure classifi-cation. For example, black spruce-dominated wetland forest type asso-ciated with structure classes 2 and 3 was more commonly found inthe Upper Boreal Highland Natural Subregion and Central MixedwoodNatural Subregion (A and B), whereas aspen-dominated upland foresttype associated with structure class 8 and 6 was more often in the DryMixedwood Natural Subregion (C). From the stand profiles, it wasclear that four of the eight classes were driven by differences in canopyheight density in each height stratum (classes 3, 6, 7 and 8). And theother four classeswere differentiated by canopy cover and standard de-viation (SD) of tree height (classes 1, 2, 4, and 5).

Short and open canopy stands with low standard deviations (SD) ofheight and low canopy cover (class 2) were the most common standstructure type in all natural subregions, except the Dry Mixedwood,Lower Foothills and Upper Foothills Natural Subregions (Fig. 6). Thisclass was characterized by black spruce-populated wetlands with wild-fire as the most common disturbance agent (Table 4). The multimodalage distribution of this class might indicate cohorts of regenerated up-land forest stand type dominated by aspen species after disturbance(Fig. 7; Table 4). Stands with tall, dense canopy cover and dominantheights between 10 and 20m (class 6)were associatedwith the secondlargest class by aerial extent and were most commonly found in

Fig. 6. Percentage of composition of structure classes in each natural subregions.

57X. Guo et al. / Ecological Informatics 38 (2017) 50–61

the Central and Dry Mixedwood, Lower and Upper Foothills NaturalSubregions, and in the higher elevated areas in the Lower BorealHighlands Natural Subregion (Fig. 6). Based on the ABMI photo plots,this class was characterized by dense, aspen-dominated upland foreststands with uneven age composition (Fig. 7). Two aspen dominatedstructure classes (class 4 and class 5) with high standard deviations ofheight, but contrasting canopy cover, were the most structurally com-plex stands in the study area. In fact, the stand type represented bystructure class 5 accounted for b1% of the study area and was scatteredthroughout the Central and DryMixedwood, Lower and Upper FoothillsNatural Subregions (Fig. 6), with strong associationswith harvesting ac-tivities (40%) and recent wildfires (15%) (Fig. 8).

As indicated from Fig. 5(1), tall and closed-canopy, aspen-dominat-ed, upland stand type (class 8) were spatially concentrated along riverfloodplains (A), at a lower slope position along transitional areas be-tween Dry Mixedwood and Lower Boreal Highland Natural Subregions(B), and between Central Mixedwood and Lower Boreal HighlandNatural Subregions (C). Forest stands with dense canopy cover andvery short vegetation heights ranging from 1.37–5 m (class 3) weremostly black spruce-dominated wetlands with wildfires being a com-mon disturbance agent (Table 4). This stand type was most abundantin Central Mixedwood and Lower Boreal Highlands Natural Subregions(Fig. 6). Closed-canopy stands with dominant tree heights ranging from5 to 10m (class 7)weremost encompassingwith respect to stand struc-ture of a broad range of species and age compositions (Fig. 7). Structureclass 1 associated with stands of short tree height and medium canopycover was mostly aspen-dominated forest type with moderate influ-ence of human andwildfire disturbance (Table 4; Fig. 8).We speculated

Table 4Interpretation of each structure class fromABMI photo plots (species code: AW=trembling aspB = 30–50% canopy closure; C = 50–70% canopy closure. Age composition: the number in the

Class Description Leading species Age co

1 Short, medium canopy cover stand Aw Multim2 Short, open canopy stand Sb Multim3 Very short, dense canopy cover stand Sb Multim4 Very tall, complex stand Aw Unimo5 Very tall, open canopy stand Aw 20006 Tall, dense canopy cover stand Aw Unimo7 Short, closed canopy stand Aw & Sb Unimo8 Very tall, closed canopy stand Aw Unimo

that the multimodal age composition of structure classes 1 and 3 indi-cated cohorts of recently generated forest stands following disturbances(Fig. 7).

4. Discussion

4.1. Structure classification

Canopy height density and canopy cover are the two major factorsdiscriminating different structure classes. The contribution of standarddeviation of height is less significant indicating some degree ofmulticollinearity between canopy cover, canopy height and standarddeviation of height. In general, tall stands tend to havehigh standardde-viations of height. Also, single-layer stands with high canopy cover usu-ally have lower standard deviations in heights than more complexstands. The low predictive accuracy of structure class 5 in discriminantfunction analysis may be due to the extremely low areal coverage ofthis class and large variations in heights.With canopy height distributedevenly along the vertical dimension, this structure class wasmore likelyto be misclassified with other classes whose dominant height is withinthe range of height variations of the class 5 forest structure type.

4.2. Habitat value and associated ecological process

Vegetation height variation in different natural subregions is pre-dominately influenced by elevation, climate regimes and tree speciesdistribution (Hansen et al., 2014). Optimal slope position, soil nutrientsand drainage condition (Little et al., 2002, Zhang et al., 2011) also

en; SB=black spruce. Density class (overstorey/understorey): A=6–30% canopy closure;bracket indicates the year of origin).

mposition Density class Percent wetlands Percent fire

odal (1950,2000) A/- 16% 13%odal (1950,2000) A/- 64% 28%odal (1950,1980) A/B 44% 21%dal (1910) C/A 2% 2%

A/- 5% 17%dal (1950) C/- 4% 2%dal (1950) A/- 18% 18%dal (1910) C/- 1% 1%

Fig. 7. Interpretation of each structure class from ABMI photo plots: a) Year of Origin; b) Species Composition (AW: trembling aspen; BW: white birch; LT: western larch; PB: balsampopular; PJ: Jack Pine; Pl: lodgepole pine; SB: black spruce; SW: white spruce).

58 X. Guo et al. / Ecological Informatics 38 (2017) 50–61

facilitated the formation of spatial clusters of tall and dense forest stands(class 8) across the study area (Fig. 5a) which may provide cover andprotection for certain wildlife species such as great gray owl (Strixnebulosa) and woodland caribou (Servheen and Lyon, 1989; Duncan,1997). Large trees, the presence of which is positively related to treeheight, may form snags and cavities, providing important habitat formany cavity-nesting species (Conner and Adkisson, 1977; Nelson etal., 2005; Jung et al., 2012).

Dense stands with the most complex vegetation structure (class 4)were highly abundant in the Lower Foothills Natural Subregion whererolling terrain and various topographic features were found. Theseareas span a broad range of environmental gradients creating diversemicrohabitats for vegetation structural development (Opedal et al.,2015). Similarly, Hansen et al. (2014) confirmed that mature forestwith fully-developed canopy structures were more likely found inmountainous areas than on flat landscapes in southeastern US. Areas

of rough terrain are often associated with a high level of plant speciesrichness (Opedal et al., 2015) with terrain shading as an important fac-tor affecting the occurrence of individual plant species (Nijland et al.,2014). As structurally complex areas tend to accommodate a higherlevel of biodiversity (Vierling et al., 2008), this structure class (class 4)with its topographic context may be of high interest for biodiversitymonitoring and conservation.

Tall, well-developed, semi-open forest stands were previously re-ported to have higher levels of plant, bird andmammal species diversitycompared to dense, closed-canopy stands (Hobson and Bayne, 2000;Gil-Tena et al., 2007; Smart et al., 2012). Semi-open stands with differ-ent levels of light penetration allow both light-adapted and shade-adapted species to flourish, and also provide a variety of food resourcesand sufficient open space for wildlife species to forage (Nielsen et al.,2004). Intuitively, these findings may indicate that the tall, multi-lay-ered stands with structure class 4 and 5 have more overall habitat

Fig. 8. Percentage of anthropogenic and non-anthropogenic disturbance in each structure class.

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potential than dense, closed-canopy standswith structure class 8. How-ever, with different disturbance regimes altering vegetation structurepattern, further research is need to quantitatively assess the changesof canopy cover, vertical architecture, stand dynamics and landscapepatterns after disturbance on forest biodiversity. In this study, wefound human disturbance increased complex vegetation structure(class 5) which was geographically rare across the landscape.

4.3. Implication of the data product

This study integrates canopy cover, canopy height and height varia-tion into a single classification scheme based on natural variation in ver-tical structure of vegetation in Alberta's boreal and foothills forest. The30 m resolution (grain or cell size) of the structure classification is suf-ficient to reflect subtle differences in vegetation structure, especially infragmented landscapes such as cutblocks and other human disturbedareas (Fig. 9a). Satellite-based optical land cover mapping is relativelyinsensitive to this level of structure variation (Wulder, 1998; Lim etal., 2003; Goetz et al., 2007). As opposed to polygon-based vegetationinventory plots, our approach captures within-polygon variations interms of tree height and canopy gaps which can be reported to reflectfine-scale habitat values (e.g. snags and cavity trees) (Fig. 9b). Further-more, this study follows a consistent and objective classification schemethat does not require costly, labor-intensive manual digitalization andphoto interpretation. In addition, the qualitative structural inventory

Fig. 9. A) Fragmented landscape caused by human disturbance in Lower Foothills natural Subpolygons from ABMI photo plot.

and stratification can serve as a descriptive tool to augment quantitativeresource inventorieswhen accurate estimation of timber attributes (e.g.volume, stocking) is not required for management activities, such aswildlife conservation and forest protection where timber harvesting isnot the top priority (Reque and Bravo, 2008).

The structure classification that we derived has the capacity to helpconservation planners identify areas of high conservation value and in-terest for follow-up remote sensing and ground data collection. Ourclassification of vegetation structure describes habitat-related vegeta-tion features independent of a specificwildlife species, addingflexibilityto wildlife monitoring programs across different taxonomic groups.However, each structure class is likely to encompass a variety of biolog-ical and ecological conditions, making labeling classes with accuratebiodiversity traits difficult. As a result, this classification should be con-sidered as a base layer used to generate a series of value-added dataproducts when integrated with spatial information of wildlife speciesdistribution, land cover types, forest inventories, ecosystem classifica-tion and human disturbance data. Without this additional information,the vegetation structure classification alonemay lack biological and eco-logical relevance. For example, a black spruce-dominated bog couldhave a similar stand structure to that of a burned or harvested aspenstand with vegetation regeneration, but provides different habitatvalues.

The lidar data used in this study is a provincial compilation collectedthrough multiple years and seasons by various data providers using

region; B) Comparison between the structure classes from LiDAR data and the inventory

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different sensors, however following the same data acquisition stan-dards. Wildfire and land use changes during the period of data collec-tion may not be fully represented in our analysis because of temporalmisregistration. However, with the majority of the data collected in2007 and 2008, this discrepancy should be alleviated. Also, time seriesof image products containing information about land cover changes lo-cally or regionally can be overlaid with the classification to update thespatial distribution of vegetation structure to a more contemporary sta-tus. Photogrammetric point cloud data can also be processed to developsimilar structural variables to classify vegetation structure for local areaswith fine-resolution, cost-efficient sensors. This study demonstrateshow regional lidar data can be utilized to classify and map vegetationstructure for large-area wildlife and biodiversity monitoring initiatives.

5. Conclusions

Our approach to classify forest structure using lidar data across themajority of the forested area in Alberta, Canada highlights the maturityof the technology and is indicative of its widespread uptake and utiliza-tion. The eight unique structure classes reflect the general vegetationstructure types in Alberta across different natural subregions. The inclu-sion of information on vegetation structure derived from lidar remotesensing can improve our ability to describe forest biodiversity patternsover that of optical sensor data. When combined with species andland cover information, the derived classification should be very usefulfor forest management planning, biodiversity monitoring and prioriti-zation of conservation programs.

Acknowledgements

This work was funded by the Government of Alberta (GOA:16GRFMB08) and a Natural Sciences and Engineering Research Council(NSERC) (RGPIN 311926-13) Discovery grant to N. Coops. The ALS datawere provided by Alberta Agriculture and Forestry. We would like tothank the anonymous reviewer and the editor for their insightful com-ments on the paper, which helped us improve the work.

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