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All content in this area was uploaded by Tim L Webster
Content may be subject to copyright.
The application of lidar-derived digital elevation
model analysis to geological mapping:
an example from the Fundy Basin,
Nova Scotia, Canada
Tim L. Webster, J. Brendan Murphy, John C. Gosse, and Ian Spooner
Abstract. High-resolution laser altimetry (lidar) is applied to geological problems such as bedrock and surficial mapping
and the relationship to earth surface processes in the Fundy Basin of Nova Scotia. A “bald earth” lidar digital elevation
model (DEM) is used in conjunction with field observations to map three flow units of the Jurassic North Mountain Basalt
(NMB) based on contrasting terrain characteristics (slope, smoothness, and relief). The variable resistance of the flow units
to erosion, documented by shatterbox experiments and down-core fracture density data, has a measurable control on incision
by post-glacial consequent streams. In catchments where till cover is thick, greater surface runoff and weaker infiltration
increase incision by as much as 43% for a given flow unit. Interpretation of field, petrologic, and digital topography data
indicates previously unrecognized craters in the lower flow unit are the result of interaction between partially solidified lava
and surface water or groundwater. Two new sets of surficial landforms have been identified that indicate ice was directed
northwestward into the Bay of Fundy during the late stages of glaciation. Twice as many wave-cut terraces have been
identified in the lidar DEM than previously mapped, and elevations have been extracted and compared with published values
that are related to higher sea-levels at 12–14 ka. This paper demonstrates through a range of examples that the precision and
enhanced resolution of lidar can improve our understanding of how landscapes form and evolve.
Webster et al.193
Résumé. L’altimétrie lidar à haute définition s’applique à des phénomènes d’ordre géologique, tels la cartographie du
sousbassement rocheux ou de la surface terrestre, ainsi qu’au rapport avec les processus à la surface terrestre dans le bassin
du Fundy en Nouvelle Écosse. Un modèle numérique d’élévation lidar « terre nue » est utilisé parallèlement avec des
observations sur le terrain pour dresser la carte de trois écoulements basaltiques du Jurassique à North Mountain selon des
traits caractéristiques divers (la pente, la régularité et le relief). Les différents facteurs de résistance à l’érosion des différents
écoulements exercent un contrôle mesurable sur l’érosion verticale d’écoulements ultérieurs post-glaciaires. Dans des
bassins-versants à couvert morainique épais, un plus fort ruissellement de surface et une plus faible infiltration dans le sol
sont susceptibles d’augmenter l’érosion verticale pour un écoulement donné de jusqu’à 43 %. L’interprétation de données de
terrain, ainsi que des données pétrologiques et topographiques numériques donne à entendre que des cratères dans
l’écoulement le plus bas, jusqu’alors non constatés, proviennent de l’interaction entre la lave partiellement refroidie et
solidifiée et de l’eau : soit l’eau de surface, soit l’eau souterraine. Ont été identifiés deux nouveaux ensembles de relief
surfacique qui donnent à entendre que la glace se serait déplacée vers le nord-ouest pour se verser dans la Baie de Fundy
pendant une phase tardive de l’âge glaciaire. Deux fois plus de terrasses coupées par l’action des vagues ont été constatées
qu’il n’en était antérieurement cartographiées et des altitudes topographiques correspondant à des niveaux plus élévés de la
merilya12ou14quatorze mille ans ont été calculées et comparées aux valeurs publiées. Ce présent travail fait voir, à
travers une série d’exemples, que la précision et la très haute définition du lidar peuvent enrichir notre compréhension des
processus de formation et évolution du paysage.
Introduction
Geological mapping and surface-processes studies in
forested regions are hampered by poor exposure of outcrops,
difficulty of access, incomplete view of landforms, and
deceiving patterns related to tree type or health. In particular,
the quality of maps in forested regions diminishes with distance
from exposures along coasts, streambeds, and peaks. Light
detection and ranging (lidar) can overcome the challenge of the
forest obstacle and is becoming more available in remote
© 2006 CASI 173
Can. J. Remote Sensing, Vol. 32, No. 2, pp. 173–193, 2006
Received 30 September 2005. Accepted 21 February 2006.
T.L. Webster1and J.C. Gosse. Department of Earth Sciences, Dalhousie University, Edzell Castle Circle, Halifax, NS B3H 4R2, Canada.
J.B. Murphy. Department of Earth Sciences, Saint Francis Xavier University, Antigonish, NS B2G 2W5, Canada.
I. Spooner. Department of Geology, Acadia University, Wolfville, NS B4P 2R6, Canada.
1Corresponding author. Present address: Applied Geomatics Research Group, Centre of Geographic Sciences (COGS), Nova Scotia
Community College, 50 Elliot Rd., Lawrencetown, NS B0S 1M0, Canada (e-mail: timothy.webster@nscc.ca).
forested regions of Canada where interpretations of airphotos
and satellite imagery cannot be readily checked in the field.
Many landscapes within the Appalachians are forest covered
and are characterized by ridge-and-valley physiography, with
the central and northern regions having the added
complications of glaciation (Randall et al., 1988). These
landscapes have evolved over millennia and have been
overprinted in some instances by multiple ice sheets that
manifest themselves in subtle landforms commonly obscured
by the forest. In addition to these problems inland, coastal
landscapes have been modified by postglacial fluctuations in
sea-level that are not well constrained.
The purpose of this study is to determine if the enhanced
resolution and precision of lidar can improve our understanding
of how a landscape forms and evolves. We provide examples of
how a “bald earth” lidar digital elevation model (DEM) can be
used to discover new mesoscale (approx. 1 km) landforms,
improve the resolution of bedrock mapping that could never
before be delimited, and conduct surface-processes studies.
We show that high-resolution lidar data from the Fundy
Basin in the Annapolis Valley of Nova Scotia improve the
precision of bedrock and surficial geology mapping of the
North Mountain. The surficial geology of the area had recently
been mapped by Stea and Kennedy (1998) using traditional
methods of interpreting landforms from aerial photographs,
providing an opportunity to test if new landforms can be
identified using lidar. Catchment basins and stream
longitudinal profiles derived from the lidar DEM were used
with drainage divides to determine stream incision depths,
which were related to the different volcanic flow units of the
underlying basalt formation and till thickness. Several
previously unidentified craters are detected in the basalt using
the lidar DEM. Elevations of several existing and newly
discovered wave-cut terraces are extracted and compared with
previous measurements of Stea and Kennedy. We conclude
with an overview of the availability of high-resolution lidar to
the understanding of how landscapes form and evolve.
Study area
The study area is located along a 20 km × 18 km section of
the North Mountain and Annapolis Valley, along the eastern
shore of the Bay of Fundy in Nova Scotia, Canada (Figure 1).
The physiography of the region is typical of the northeastern
Appalachians, with ridges and valleys underlain by a variety of
rock types (Randall et al., 1988). The maximum relief of the
study area is 265 m (elevations ranging from sea level to the top
of the North Mountain ridge). Streams draining into the Bay of
Fundy off the North Mountain erode volcanic strata of the
North Mountain Basalt (NMB), which dips approximately 6° to
the northwest toward the bay (Withjack et al., 1995). These
streams are ephemeral in their upper reaches, with their peak
flows occurring in the spring and fall seasons. Their reaches are
ungraded for the most part and have several knick zones
(Webster, 2005a). The land surface generally slopes from the
crest of the North Mountain toward the bay at 3°–5°. To the
southeast, the NMB forms a cuesta of the Annapolis Valley that
is underlain by weaker sedimentary rocks (Figures 1,2). The
land cover on the North Mountain is influenced by the
thickness of the till cover; farmland (pastures and hayfields)
and mixed forest occur in the east where the till is thickest,
whereas mixed forest cover is extensive in the west where thin
till covers scoured bedrock. There are more roads and other
anthropogenic influences in the east than in the west, where
there is one paved road along the coast and several gravel roads
run north–south associated with forestry operations. The
coastline varies from gently sloping bedrock platforms to
approximately 25 m cliffs that occur in embayments. The
region is characterized as having a modified continental
climate, strongly influenced by the adjacent Atlantic Ocean.
Meteorological records from Environment Canada indicate an
annual mean precipitation of 1127 mm based on records from
1971–2000 and an average daily temperature of 6.8 °C
(http://www.climate.weatheroffice.ec.gc.ca/climate_normals/
results_e.html).
The Annapolis Valley lies within the Mesozoic Fundy Basin
and is predominantly underlain by Triassic sedimentary rocks
(Blomidon and Wolfville formations), flanked by the Jurassic
NMB to the north and Paleozoic rocks of the Meguma terrane
and the South Mountain batholith to the south (Figures 1,2).
The NMB represents the northernmost extent of the Central
Atlantic Magmatic Province (CAMP), which is associated with
basaltic magmatism erupted during the early stages of the
opening of the Atlantic Ocean (Marzoli et al., 1999). The NMB
comprises three flow units with different physical and chemical
characteristics that affect their resistance to erosion (Dostal and
Dupuy, 1984; Papezik et al., 1988; Dostal and Greenough,
1992; Kontak, 2001). The NMB dips gently to the northwest,
forms the southeast limb of a regional syncline (Withjack et al.,
1995), and is crosscut by north- to northeast-trending faults and
fractures that exhibit dextral displacement (Olsen and
Schlische, 1990; Schlische and Ackermann, 1995) (Figure 1).
Hudgins (1960) identified several individual flows that extend
along most of the length of the NMB, and Kontak (2001)
reported that the NMB comprises three flow units. The lower
flow unit (LFU) forms the cuesta of the valley and consists of a
thick (40–150 m), massive single flow that is columnar jointed
(Figure 2). The middle flow unit (MFU) conformably overlies
the LFU and consists of multiple thin flows that are highly
174 © 2006 CASI
Vol. 32, No. 2, April/avril 2006
Figure 1. Location map of the Bay of Fundy, southwest Nova Scotia and the Annapolis Valley. The inset map shows the location of the Bay
of Fundy within eastern North America. The lidar study area is depicted by the dashed black and white polygon in the centre of the map. The
white outline highlights the North Mountain Basalt Formation, which forms the cuesta of the Annapolis Valley (after Keppie, 2000). The
shaded relief map was constructed from a 20 m DEM originally supplied by the Nova Scotia Geomatics Centre, Service Nova Scotia and
Municipal Relations.
© 2006 CASI 175
Canadian Journal of Remote Sensing / Journal canadien de télédétection
176 © 2006 CASI
Vol. 32, No. 2, April/avril 2006
Figure 2. Shaded relief map of the lidar DEM with NMB flow unit boundaries (white lines). LFU, lower flow unit; MFU, middle flow unit; UFU, upper flow unit (Webster et al.,
2006). BF, Blomidon Formation; WF, Wolfville Formation (after Keppie, 2000). The shading angle was from the northwest, with a zenith angle of 45° and a 5× vertical
exaggeration applied.
vesicular and amygdaloidal (Figure 2). Zeolites are most
common in the MFU of the NMB where they occur in
subeconomic quantities as amygdules and also in veins, pipes,
and “bubble trains” (Kontak, 1999; Pe-Piper, 2000; Pe-Piper
and Miller, 2002). The upper flow unit (UFU) conformably
overlies the MFU, outcrops along the shore, and consists of one
or two massive flows (Figure 2).
The study area lies at the margin of the Wisconsinan
Laurentide ice sheet and has been affected by repeated episodes
of glaciation until ca. 12 ka (Stea and Mott, 1998). A major
southwestard ice flow from Prince Edward Island is recorded
by southward-trending striae crossing earlier southeastward-
trending striae and the deposition of the Lawrencetown Till in
the area (Stea et al., 1998). This muddy till unit typically
consists of 20%–30% gravel, 30%–40% sand, and 30%–50%
mud (silt and clay) and has a low permeability (Lewis et al.,
1998). Ice then flowed northwestward over the NMB into the
Bay of Fundy from the Scotian Ice divide across the axis of
Nova Scotia (Stea et al., 1998).
With the late-glacial rise of relative sea-level, ice margins
were probably destabilized, with ice flow increasingly directed
into the Bay of Fundy (Stea et al., 1998). Raised beaches and
deltas dated at 12–14 ka occur along the flanks of the bay (Stea
and Mott, 1998). Between 12.5 and 11 ka, relative sea-level was
at its lowest (–35 m), as sea-level rise had not kept pace with
land isostatic rebound (Stea and Mott, 1998). The relative sea-
level history of the region is complicated and varies spatially
along the Bay of Fundy. The relative sea-level curve reported
for the upper Bay of Fundy region by Amos and Zaitlin (1985)
has relative sea-level at approximately 40 m above present at
14 ka, followed by a decline to –30 m at 7 ka, then increasing to
present levels. Postglacial isostatic adjustments in the region
are ongoing as a result of the removal of ice (Grant 1980).
Impact of lidar on geomorphology
The relationship between stream incision and factors
associated with fluvial erosion such as rock uplift, climate,
base-level changes, and bedrock resistance to erosion (e.g.,
Pazzaglia et al., 1998; Stock and Montgomery, 1999; Kirby and
Whipple, 2001; Stock et al., 2005) is important for the
application of geomorphic transport laws that drive landscape
evolution models (e.g., Dietrich et al., 2003; Pazzaglia, 2003).
The availability of moderate scale (approx. 30–100 m) DEMs
has facilitated quantitative analysis of stream incision and basin
morphometrics (Sklar and Dietrich, 1998; Snyder et al., 2000).
In attempts to relate these factors to incision, many studies use
drainage area as a proxy for stream discharge, which strongly
influences stream power and the ability to incise. Few studies
examine the local hydrological effects of surface and
groundwater interaction on discharge (Tague and Grant, 2004),
however. Variable till cover within a region can affect
infiltration and net discharge between basins of similar
drainage areas. The availability of high-resolution (4 m lidar)
DEMs can facilitate quantitative analysis between incision and
basin morphometrics at sufficiently small scales to allow the
examination of isolated influences on stream evolution (e.g.,
base-level, bedrock resistance, till cover). Previous studies
have considered the variations in the resistance of bedrock to
erosion (Sklar and Dietrich, 2001) when relating stream or
basin morphometry to the fluvial process between regions (Belt
and Paxton, 2005), but the variations of bedrock resistance
within a region (<100 km2) are less constrained, in part due to
the scale of studies (Montgomery and López-Blanco, 2003).
Lidar has recently been used in a variety of geoscience
applications, including the analysis of river networks (Kraus
and Pfeifer, 1998; Gomes Pereira and Wicherson, 1999; Stock
et al., 2005; Webster, 2005a) and the generation of cross
sections across rivers (Charlton et al., 2003), in general
glaciology (Krabill et al., 1995; 2000), in groundwater
monitoring (Harding and Berghoff, 2000), in the investigation
of landslides (McKean and Roering, 2003), and in the mapping
of tectonic fault scarps and geomorphic features (Haugerud et
al., 2003; Webster et al., 2006). Flood and Gutelius (1997) and
Wehr and Lohr (1999) provide a general overview of airborne
laser scanning (lidar) technology and principles.
Various studies have reported on the calibration and
systematic errors of lidar systems (Kilian et al., 1996; Filin,
2001; 2003a; 2003b) and the accuracy of laser altimetry data
(Huising and Gomes Pereira, 1998; Kraus and Pfeifer, 1998;
Ahokas et al., 2003; Artuso et al., 2003; Webster, 2005b;
Webster and Dias, 2006). Classification of the lidar returns into
“ground” and “non-ground” (i.e., vegetation or buildings)
points is important for geomorphic analysis and can affect the
accuracy of derived bald earth DEMs, especially in forested
regions. An overview of the general classification procedure
that exploits automated routines is provided in Hodgson et al.
(2005). Previous studies (Hodgson et al., 2003; Hodgson and
Bresnahan, 2004; Hopkinson et al., 2005) have shown that lidar
ground accuracy changes based on land cover type. Thus,
validation of the lidar data both in open areas and under the
forest canopy is conducted in this study to characterize the error
of the derived DEM.
Methods and results
Lidar survey
Details of the lidar survey and system specification can be
found in Webster (2005b). An Optech ALTM1020 lidar sensor
mounted in a Navajo P31 twin-engine fixed-wing aircraft was
used to survey an area of 350 km2during a 2 week period in
July 2000. The lidar operated at a 5000 Hz laser repetition rate
along with the scanning mirror operating at 15 Hz to direct the
laser pulses across the swath (Figure 3). At a flying altitude of
800 m, the laser beam had a ground footprint diameter of 25 cm
with an average post spacing of about3m(Figure 3). Since a
bald earth DEM was one of the desired outcomes of the survey,
the lidar unit was set to record the last return pulse. This
increased the probability of obtaining a return from the ground
or close to it in forested areas. The lidar provider classified the
point cloud into ground and non-ground points using the
© 2006 CASI 177
Canadian Journal of Remote Sensing / Journal canadien de télédétection
REALM software from Optech Inc. (Toronto, Ont.) prior to
data delivery. The details of the parameters used in this process
were not provided.
In 2000, lidar data were typically delivered in ASCII files
consisting of x,y,zdata. In addition to the typical x,y,zdata
fields, the global positioning system (GPS) time for every laser
shot was also included. This gave us the ability to examine the
lidar data by GPS time or flight line (strip). The elevations were
converted from ellipsoidal (smooth mathematical surface
representing the earth) to orthometric heights above the geoid
(equipotential surface based on the earth’s gravity field) based
on the HT1_01 model available from the Geodetic Survey of
Canada, and both sets of heights were included. The ground
and non-ground lidar point data were delivered in 4 km×4km
tiles based on a Universal Transverse Mercator (UTM) grid.
Lidar processing
Lidar surface construction and analysis
The ability of the laser shots to penetrate the vegetation
canopy has the potential to reveal subtle geomorphic features
on high-resolution lidar-derived DEMs compared to that of the
traditional DEMs derived from photogrammetry. Prior to this
study, the highest resolution DEM available for this area was
20 m, based on1:10000scale mass points and contours
generated photogrammetrically (Figure 4A). A Delaunay-
triangular irregular network (TIN) was constructed from the
different combinations of lidar points, and 2 m resolution grids
were interpolated from the TINs utilizing algorithms available
within ArcGIS™. Colour shaded relief (CSR) maps were
constructed in PCI Geomatica™ by illuminating the surfaces
from the northwest, perpendicular to the strike of the flow
units, at a zenith angle of 45° and with a 5× vertical
exaggeration applied (Figure 4). All of the lidar returns
(ground and non-ground) were used to construct a digital
surface model (DSM) of the study area (Figure 4B). As the
North Mountain is covered with dense forest, the vegetation
obscures the morphology of the ground, and only gross relief
features are visible in the DSM. Most remote sensing
techniques are limited to acquiring information from the top or
within the canopy and must infer the ground elevation, e.g.,
Shuttle Radar Topography Mission. With lidar, however, we
have the capability to highlight subtle geomorphic features
178 © 2006 CASI
Vol. 32, No. 2, April/avril 2006
Figure 3. Lidar system configuration. The main components of the lidar system consist of a
global positioning system (GPS), an inertial measurement unit (IMU), and a laser ranging
system. The Optech ALTM 1020 was used for the survey and was set to acquire the last
returning pulse. The ground post spacing is determined by the pulse repetition rate (5000 Hz),
the scan mirror oscillation rate (15 Hz), the flying height (800 m), and the speed of the aircraft.
under the forest canopy, thus a bald earth DEM was constructed
by interpolating only the ground lidar points (Figure 4C). The
ability to remove the noise of the forest cover from the terrain
provides a potentially valuable tool for geomorphic
investigations, as is evident in Figure 4. Although the CSR
DEM was useful for analyzing the contacts between flow units,
it was limited in highlighting some of the subtle lower relief
landforms. A new map was constructed from the DEM with a
shading azimuth angle of 225° to highlight northwest-trending
landforms that may parallel one of the dominant ice flow
directions in the area. The elevation colour ramp was repeated
to optimize chromostereoscopy three-dimensional (3D)
visualization (see Toutin and Rivard, 1995). The colour ramp is
applied to the elevation range 0–100 m and repeated for
elevations 101–265 m to highlight the low-relief landforms
(Figure 4D). This map was useful for identifying subtle
landforms that occur in the valley floor and along the coast
(Webster, 2005a).
These methods suffer from the common bias of all shaded
relief maps: only features that trend perpendicular to the
illumination direction are highlighted. Paganelli et al. (2003)
used principal component analysis (PCA) on RADARSAT-1
images (standard beam modes S1 and S7 in ascending and
descending orbits) to enhance the interpretability of surface
features for geological applications in northern Alberta. The
PCA analysis reduced redundancy in the RADARSAT-1
imagery and created new component images that enabled a
structural interpretation of the geology to be conducted. In this
study, the PCA technique has been applied to grey-scale shaded
relief images generated from the lidar DEM to enhance subtle
topographic features regardless of their orientation. This
method facilitates the highlighting of all dominant topographic
features regardless of orientation. These relief images were
analyzed using PCA. The first three components (PC1, PC2,
PC3) contain over 99% of the information (e.g., image
variance) and were used to construct a composite image where
components PC1, PC2, and PC3 were projected through the
red, green, and blue colour guns, respectively (Figure 5).
This map (Figure 5) highlights two distinct morphological
characteristics of the NMB in this region. The western region of
the North Mountain study area is characterized by rough
topography with abrupt ridges and narrowly incised valleys,
whereas the eastern region of the study area is characterized by
smooth topography with broadly incised valleys. This reflects
differences in glacial history: areas to the west consist of
glacially scoured bedrock with a thin till veneer, and those to
the east consist of a thicker blanket of Lawrencetown Till (Stea
and Kennedy, 1998) (Figure 5).
Low-permeability till cover can affect the surface water –
groundwater hydrology of a catchment, which can in turn affect
the amount of runoff entering a stream after a rain event. The
amount of water within a stream influences its discharge and
thus its ability to incise. To determine the influence of till cover
on the surface water – groundwater interaction, two catchments
of similar size (Peck and Sabeans basins) with contrasting
amounts of till cover were instrumented to record discharge and
water-chemistry parameters (Figure 5) (Webster, 2005a).
Catchment basin delineation
The 2 m lidar DEM was averaged down toa4mresolution to
reduce disk space requirements and improve processing speed
without a significant reduction in detail and quality. GPS
validation points were overlaid on lidar-derived DEMs at
variable resolution (2, 4, 10, and 20 m) to determine the effect
of cell size on error (e.g., Wolock and Price, 1994; Zhang and
Montgomery, 1994; Goa, 1997; Walker and Willgoose, 1999).
A combination of Rivertools™ and PCI Geomatica™ software
was used to extract the morphometric parameters from the
catchment basins and stream longitudinal profiles (Webster,
2005a). Catchment basin morphometries were calculated for
the main streams draining into the Bay of Fundy from the lidar
DEM using the standard D-8 algorithm (Jenson and Dominque,
1988; Costa-Cabral and Burges, 1994) in Rivertools™. The
sinks (depressions within the DEM treated as errors by the
algorithm) were filled in the DEM to allow continuous
downstream flow and to compute stream flow direction and
catchment basins. Inspection of the resultant catchment basin
boundaries and stream longitudinal profiles indicated that most
catchments had significant sinks that represented the upstream
channel adjacent to the road network. As a culvert could not be
represented in the DEM, a “notch” was cut across the roadbed
and assigned an elevation based on the nearest downstream
elevation to improve the accuracy of the catchment basins and
stream profiles (Webster, 2005a). These modifications to the
DEM allowed the water to “pass through the roadbed” and the
generation of a more correct flow direction and flow
accumulation grid along with the basin boundary (e.g.,
Figure 5).
Stream networks
Longitudinal profiles of trunk streams derived from the DEM
were compared with profiles of trunk streams available from
the1:10000topographic map which were overlaid on the
notched DEM. The trunk streams derived from the DEM are
longer (i.e., more vertices defining a line) but do not extend as
far upstream in the basins as trunk streams from the map
(Figure 6). In part, this is a result of the fractal nature of stream
lengths (Turcotte, 1992), but the greater lengths of the DEM-
derived stream lines is also a consequence of the grid cell origin
of the network compared with the straighter line segments on
the map. Several flat areas resulting from sinks being filled
were observed along the DEM-derived stream long profile even
after the culverts were notched in the DEM (Figure 6). It was
determined that longitudinal profiles obtained using streams
from the topographic map and the notched DEM prior to sinks
being filled were the most representative and were used for the
rest of the analysis.
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180 © 2006 CASI
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Lidar validation
Real-time kinematic (RTK) GPS checkpoints were collected
from a moving vehicle on the road using Leica system 530
dual-frequency receivers for height validation. Baselines up to
a maximum of 12 km were maintained to minimize errors. In
general, the carrier phase real-time differential GPS had a
height precision of 5 cm or better. Traditional topographic
surveys utilizing a Leica total station were conducted under the
forest canopy across a crater structure visible in the lidar DEM
and along a stream longitudinal profile and stream cross
sections.
The validation of the lidar was carried out in the ESRI
ArcGIS™ environment using two techniques: (i) ground
validation points were compared with proximal lidar points
using an automated procedure written in the Arc Macro
Language (AML) (the code is available for download from
Webster and Dias, 2006), and (ii) ground validation points were
compared with the interpolated lidar DEM (Webster, 2005b).
Lidar ground points within2mofRTKGPScheckpoints
acquired along roads were analyzed. The summary statistics
indicated a mean difference in orthometric height (∆z= GPS –
lidar) of 0.03 m, with a standard deviation of 0.14 m and a root
mean square (RMS) error of 0.15 m (Webster, 2005b). A total
of 96.2% of the lidar ground points analyzed were within
30 cm. The distribution of ∆zwith GPS times or flight lines
showed an even distribution either side of the 0 m value, and
there does not appear to be any significant systematic height
bias between strip-flight lines (Webster and Dias, 2006).
The GPS checkpoints were overlain on the DEM, and the
corresponding cell values extracted and compared (∆z= GPS –
lidar DEM). The summary statistics indicate a mean ∆zof
0.05 m, with a standard deviation of 0.20 m and an RMS error
of 0.21 m (Webster, 2005b). The higher positive ∆zvalues
associated with the DEM are a result of interpolation errors and
instances where lidar points along the road have been
misclassified as non-ground, resulting in the DEM surface
being too low. This error usually occurred in areas where the
roadbed is elevated above the surrounding area (Webster,
2005b).
The total station transects under the vegetation canopy across
the crater structure were overlaid on the DEM. The summary
statistics indicated a mean ∆zof –0.12 m, with a standard
deviation of 0.34 m and an RMS error of 0.36 m (Webster,
2005b). In general, the DEM was slightly higher than the
survey heights and was interpreted to be a result of lidar returns
off of shrubs in low-lying areas being classified as ground
(Webster and Dias, 2006).
The results of the total station transect along the longitudinal
profile of Sabeans Brook indicate the DEM has several errors
(Figure 7). The summary statistics indicate a mean ∆zof –0.94 m,
with a standard deviation of 1.26 m and an RMS error of
1.57 m. The survey data match the DEM within 0.5 m in many
places but are too high by a few metres in other places
(Figures 7,8B). When the anomalous sections are removed
from the profile (survey points 20–70 and 160–190) the
summary statistics indicate a mean ∆zof –0.27 m, with a
standard deviation of 0.39 m and an RMS error of 0.47 m.
These results are similar to those observed in the forest
transects across the crater structure. The most significant errors
occur at a bend in the stream where the slope of the cut bank is
steepest (Figure 7). Topographic cross sections are used to
determine if the source of the error is related to a horizontal
offset of the lidar data (Figures 7,8). The cross sections
extended farther than the survey data that are confined to the
immediate banks of the stream channel. Based on cross
sections (CS1 and CS3) north and south of the cut bank, it
appears there is no significant horizontal shift of the lidar data,
since the channels are generally aligned (Figures 8C,8E). At
cross section 2 (CS2), the stream has eroded into the bedrock,
resulting in a narrower channel at this location (Figure 8D).
Field visits confirm that dense deciduous trees overhang the
channel at this location (Figure 7) and are considered to be the
source of the error. In areas of high error, the lidar points
classified as ground appear to be the dense overhanging
vegetation (Figure 8A), and no lidar returns made it to the
streambed. Discontinuities identified on the stream profiles
similar to those highlighted in Figure 8 were investigated and
the profiles were smoothed in areas suspected of having
erroneous elevations for the streambed.
The DEM error in the streambed (1σ= 1.26 m) is the largest
error encountered during the lidar validation procedure and has
important implications for surface process rates (e.g., derived
from incision depths). Other potential sources of uncertainty in
DEM data are a function of grid cell size and local slope (Zhang
et al. 1999). To test the variability of ∆zwith cell size, the GPS
points were overlain on lidar DEMs of 2, 4, 10, and 20 m
resolutions, resulting in standard deviations for ∆zof 0.20, 0.32,
0.29, and 0.64 m, respectively. These results likely do not
reflect the accuracies expected under the canopy in variable-
relief terrain, as the GPS points were acquired on open road
surfaces. The average slope within the catchment basins on the
North Mountain is 6.4°, thus at 4 m cells the expected error is
±0.44 m. Given the magnitude of different sources of error, the
most significant errors arise from low vegetation being
classified as ground and areas of steep slopes.
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Figure 4. Comparison of colour shaded relief lidar surfaces to a conventional DEM. (A–C) Panels were constructed with a shading angle
from the northwest with a zenith angle of 45° and a 5× vertical exaggeration applied. (A) A 20 m DEM derived from1:10000scale mass
points and contours. Original DEM supplied by the Nova Scotia Geomatics Centre, Service Nova Scotia and Municipal Relations. (B)A2m
lidar digital surface model (DSM) constructed from all of the lidar returns. (C)A2mlidarDEMconstructed from ground lidar returns. The
curvilinear ridge (arrow) represents the contact between the UFU (north) and MFU (south). The flows of the MFU are visible as a series of
steps south of the contact (oval). (D) A 10 m averaged lidar DEM with a optimized colour ramp and a shading angle from the southwest with
a zenith angle of 45° and a 5× vertical exaggeration applied.
Bedrock mapping of NMB flow units and craters
Basalt flow units
Although individual basaltic flows were mapped by previous
workers (Hudgins, 1960; Kontak, 2001), emphasis was mostly
confined to coastal exposures, and no maps delineating the
three flow units were compiled for this area. Distinctions in the
field between the two flow units (MFU and LFU) were based
primarily on the amount of vesicles and amygdules present in
the outcrop and stratigraphic position (Kontak, 2001). The
topographic ridge identified in the lidar DEM lies in close
proximity to the contacts between the MFU and UFU observed
in the field, suggesting that this contact has a distinct
topographic signature (Figures 2,4). We can resolve many
individual flows (at least up to five) of the MFU based on subtle
steps visible in the lidar DEM, which suggests that the thinner
amygdaloidal flows are less resistant to erosion than the more
massive basaltic flows in either the LFU or UFU (Figure 4).
The contacts between flow units are clearly visible in the
DEM in the western half of the study area but are obscured by
the thicker till blanket in the east (Figures 2,4,5). Well-
constrained locations of the contacts between the flow units in
the streams were used to construct contact planes between the
units dipping at approximately 6° toward the northwest. These
planes were projected onto the terrain surface along with the
outcrop locations to assess the complexity and consistency of
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Figure 5. Principal components PC1, PC2, and PC3 composite image projected through red, green, and blue colour guns, respectively, derived
from shaded relief maps of the lidar DEM based on the eight cardinal directions. The black outline denotes the deposition of the
Lawrencetown Till (LT). The heavy white double-headed arrow indicates the orientation of streamlined landforms that are related to the last
ice movement and correlate with smooth terrain on the North and South mountains. The white circles denote two of the craters identified in
the DEM. The eastern crater is Mt. Rose and is detailed in Figure 9. The white outlines on the North Mountain represent example drainage
basins from Peck and Sabeans outlets extracted from the lidar DEM. The red triangles denote where these two basins were instrumented to
record stream discharge and water quality parameters.
the geological structures across the study area (Webster et al.,
2006). The intersection of the contact planes and the terrain was
used to locate the flow boundary contacts where the topography
was affected by the till blanket.
Phreatomagmatic craters
Hudgins (1960) and Stevens (1980) speculated that circular
structures identified along the shoreline and inland might
represent source vents for the NMB. A linear sequence of nine
additional craters has been identified in the LFU based on the
lidar DEM. The crater structures are positive relief features
characterized by outer elevated ridges and a lower interior that
sometimes has a higher central core. Transects across two of
these structures were sampled to compare the rocks in the
vicinity of the craters with the typical characteristics of the flow
unit elsewhere. Rock samples were examined petrographically
and analyzed for major and selected trace elements by X-ray
fluorescence at the Nova Scotia Regional Geochemical Centre
at St. Marys University. Analytical methods are given in
Greenough and Dostal (1992). Representative minerals were
analyzed with an electron microprobe at Dalhousie University
using a JEOL 733 Superprobe (Webster et al., 2006).
Petrographic examination of samples collected along transects
across each crater indicate that several contain abundant
mesostasis and glass, interpreted by Kontak et al. (2002) to
represent a quenched residual melt. For example, three of the
samples (mr12, mr13, and mr14) collected near the crater at Mt.
Rose (Figures 5,9) have abundant quenched glass present, but
samples farther from the crater (mr2, mr7, and mr11) have
holocrystalline textures with minimal glass (Figure 9).
Microprobe analysis revealed the presence of epidote in
samples near the craters, indicating low-temperature alteration.
Similar-sized craters (diameter and relief) found in the Roza
Member of the Columbia River Flood Basalts (CRFB) (McKee
and Stradling, 1970; Martin, 1989), which is similar in
thickness to that of the LFU of the NMB, suggest that flow
thickness may be an important factor for the formation of the
craters.
Surficial geology and wave-cut terraces
The contrast in terrain roughness from west to east of the
North Mountain is clearly visible in the lidar DEM (Figure 5).
The rough terrain in the western region correlates with glacially
scoured bedrock, and the smoother terrain in the eastern region
correlates with the deposition of the Lawrencetown Till blanket
(Stea and Kennedy, 1998) (Figure 5). Two previously
unidentified glacial landforms have been mapped in the valley
floor based on the DEM (Figure 5): (i) a set of oval landforms,
with their long axis trending approximately 335° (west of the
white line in Figure 5), where field observations indicate they
are composed of Lawrencetown Till and are draped at their base
by lacustrine clay; and (ii) a set of streamlined landforms
composed of Lawrencetown Till, with their long axis trending
approximately 310° (east of the white line in Figure 5)
(Webster, 2005a). The boundary between these landforms
corresponds to the difference in terrain roughness on the North
and South mountains (Figure 5). These observations are
indicative of the glacial ice dynamics in the region. The
deposition of the till blanket has modified the upper reaches of
the eastern catchments to flow parallel to this direction (east of
the white double-headed arrow in Figure 5), indicating these
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Figure 6. Comparison of stream longitudinal profiles for a typical drainage basin (Gaskill Brook) acquired by
different GIS methods. The mapped stream from the1:10000scale topographic map (thick black line) was overlain
on the lidar DEM prior to filling sinks to generate a profile. The streams extracted from the flow accumulation grid in
Rivertools™ are plotted with the (sinks) filled DEM (thin black line) and the raw (prior to filling sinks) DEM (thin
grey line) for comparison. The mapped stream profile extends farther upstream into the basin and does not have as
many “flat” sections as that of the filled DEM profile. The Rivertools™-generated profiles have more vertices and
thus appear “longer” than the topographic mapped stream profile for a given section. This is a result of the tendency
of the Rivertools™ streams to meander because of the raster nature (flow accumulation grid) of the stream network.
184 © 2006 CASI
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Figure 7. Colour orthophotograph (1992) of Sabeans Brook near the outlet with total station (TS) survey points
colour-coded by ∆z(TS survey – lidar DEM) and cross section locations (CS1, CS2, and CS3). The largest error in
the lidar DEM occurs at the bend in the stream where the bank is steepest. The large sand deposit east of the stream
represents one of the raised beaches.
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Figure 8. (A) Perspective view looking southeast of lidar ground (dark grey points) and non-ground (light grey
points) with total station (TS) survey points (black squares) collected for the Sabeans stream longitudinal profile.
(B) Stream profiles derived from the TS survey (heavy black line) and lidar DEM (thin black line) and the difference
(thin grey line) DZ (TS survey – lidar DEM) on the right yaxis and cross section locations. (C, D, E) Cross sections
CS1, CS2, and CS3, respectively, of the lidar DEM (black dots outside the TS survey and grey triangles for the TS
survey locations) and the TS survey data (black triangles) with the difference in heights (grey dots) associated with
the right yaxis DZ (TS survey – lidar DEM).
streams are still adjusting to the presence of the till blanket.
These reaches then trend in a more northerly flow direction
midway to their outlets, similar to the streams in the scoured
bedrock catchments.
The normalized hydrographs (Tague and Grant, 2004) of the
two catchments (Figure 5) indicated that till cover does
influence surface water – groundwater interaction and that the
till blanket catchment receives overland flow more rapidly and
in greater volume after a precipitation event than the scoured
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Figure 9. Crater structure at Mt. Rose with rock sample locations. The location of this crater is the eastern white
circle in Figure 5. (A–F) Thin sections of the rock samples (mr2, mr7, mr11, mr12, mr13, mr14) under plane-
polarized light. Samples mr2, mr7, and mr11 show a holocrystalline mineral texture, indicating slow cooling
conditions of the lava flow, and are farthest from the crater. Samples mr12, mr13, and mr14 show a mesostasis texture
representing quenched melt, indicating rapid cooling of the lava flow. (G) Shaded relief lidar DEM of the crater
structure and sample locations.
bedrock catchment. These results are consistent with the water
chemistry data that show Sabeans Brook to be turbid and the
specific conductance decreases after a rain event, whereas Peck
Brook is less turbid and the specific conductance increases after
a rain event (Webster, 2005a).
Several new wave-cut terraces have been identified in the
lidar DEM along the coast in the glacial till blanket area
(Figure 10). Surface profiles are extracted from the lidar DEM
and are compared with previously published elevations by Stea
and Kennedy (1998). The terraces identified in the lidar DEM
correspond to elevations near 5, 10, 15, 23, 25, and 35 m above
mean sea-level (Figures 10B,10C,10D). The terraces at levels
15, 23, and 35 m are more pronounced than those near 5, 10,
and 25 m. The terraces appear more developed in the eastern
profiles (Figure 10D) compared with those in the western
profiles (Figure 10B) in this section of the coast (Figure 10).
Landscape metrics for surface processes
Incision depth
To quantify the influence of different lithologies on stream
erosion, stream incision depths were calculated for each
catchment basin by subtracting the stream longitudinal profile
from the average surface profile defined by the elevations of the
drainage divides (Webster, 2005a). Valley cross sections
between drainage divides were extracted to evaluate the
incision depth using a method described in Montgomery
(2002). Sklar and Dietrick (2001) showed that rock strength
was related to the ability to resist erosion by abrasion. The
variability in NMB resistance to erosion by abrasion was tested
in the laboratory using a shatterbox. Drill core of the NMB
(Comeau, 1978) was examined to quantify the fracture density
to determine the susceptibility to erosion by plucking for the
middle and lower flow units.
The stream and surface profiles and incision-depth curves
have been intersected with the flow unit map of the NMB
(Figure 2). The flow unit contacts have been projected through
the profiles to relate the variable lithology in the streambed to
incision depth (Figure 11). In general, the stream incision
depth reaches a maximum within the middle flow unit (MFU)
(Figure 11). Many knick zones occur either within the MFU or
upstream of the contact between the MFU and the LFU.
Incision in the upper flow unit (UFU) and the lower flow unit
(LFU) is similar in three of the four basins studied where both
units occur in the streambed (Webster, 2005a). The variation in
incision depths may be related to several characteristics of the
bedrock lithologies, including the resistance to abrasion and the
degree and spacing of fractures. As indicated by experimental
results with the shatterbox, the MFU is much more susceptible
to erosion by abrasion than the UFU and LFU. Variations in
resistance to abrasion of the MFU are attributed to the variable
concentration of vesicles and zeolite-filled amygdules. Drill-
core analysis indicated that the average fracture density
(number of fractures per metre) of the LFU is 10 compared with
7 of the MFU (Webster, 2005a). The UFU does not occur in the
drill core; however, field observations indicate that secondary
minerals have sealed fractures in this unit. The UFU appears to
act as a cap rock, protecting the MFU from the development
and migration of knick zones. Where the UFU outcrops on the
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Figure 10. Wave-cut beach terraces along the Bay of Fundy. (A)
Grey scale shaded relief map of the lidar DEM with surface profile
locations (TP1, TP2, TP3) across the terraces. Shading angle from
the northwest with a zenith angle of 45° and a 5× vertical
exaggeration applied. (B, C, D) Surface profiles of TP1, TP2, and
TP3, respectively, with arrows denoting possible terrace locations.
Terraces appear more developed in the eastern profile (TP3)
compared to that of the west (TP1).
coast, the bedrock platform is gently sloping and few knick
zones are observed upstream. Where the UFU has been eroded
and the MFU is exposed at or near base level, sea cliffs are
present and knick zones are prevalent upstream (Webster,
2005a).
Discussions and conclusion
This study has demonstrated that the enhanced precision and
resolution of lidar, compared with traditional DEMs, can
improve our understanding of how a landscape forms and
evolves. Validation was required to characterize the error
associated with the lidar DEMs, especially in forested regions.
The results of the lidar validation in open areas are similar to
those reported in other studies (Huising and Gomes Pereira,
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Figure 11. Surface (grey line) and stream longitudinal (blue line) profiles and stream incision curves (red line) with
the North Mountain Basalt flow unit contacts projected through the profiles (black lines). LFU, lower flow unit;
MFU, middle flow unit; UFU, upper flow unit. The vertical arrows denote where the projected flow unit contacts
intersect the streambed. The incision-depth curves are highest where the MFU occurs within the streambed. (A) Peck
Brook profile. (B) Sabeans Brook profile (see Figure 5 for stream locations). The MFU has the deepest incision
depth (red line associated with the right yaxis) of the flow units.
1998; Ahokas et al., 2003; Artuso et al., 2003; Hopkinson et al.,
2005). The lidar DEM is not as accurate under the forest
canopy in areas of dense shrubs as indicated by the crater
validation survey. These results are consistent with those
reported by Hodgson and Bresnahan (2004) and Hopkinson et
al. (2005), who examined lidar ground accuracy in different
land cover types. The DEM error under the forest canopy is
–0.12 ± 0.34mat1
σ, which is slightly higher than the error of
0.11 ± 0.16 m at 1
σreported by Hopkinson et al. (2005) for
ground elevations under tall shrubs (2–5 m) but closer to the
error of 0.06 ± 0.23 m at 1σreported by Hodgson and
Bresnahan (2004).
The highest error found in our DEM was associated with
vegetation overhanging the streambed that had been incorrectly
classified as ground points. When these points are removed
from the analysis, the results are similar to those of the previous
studies. Accurate classification of the lidar point cloud into
ground and non-ground points is important for accurate
geomorphic analysis. Lidar data must be critically examined to
check for such classification errors. The combination of the two
validation techniques, comparing checkpoints with proximal
lidar ground points and with the DEM, can facilitate the
identification of these problems (Webster, 2005b). Because it is
very time consuming to acquire subdecimetre-level precision
height measurements under the forest canopy, however, it is
difficult to constrain this error spatially. This suggests that lidar
surveys should be conducted during leaf-off conditions to
ensure minimum deciduous leaf cover. Of the leaf-off periods
available for this region, spring has the added benefit of
reduced shrub and understory height as a result of flattening by
the winter snow pack.
These errors notwithstanding, the increased precision and
resolution of the lidar DEM have improved our understanding
of how landscapes form, as demonstrated by the identification
of a linear sequence of craters that was previously unknown.
Hudgins (1960) may have identified one of the craters
discussed in this study and proposed that it might represent the
volcanic source of the NMB. Our analysis of the geomorphic,
petrographic, and geochemical data, however, suggests that the
development of the craters resulted from a thick lava flow
(LFU) travelling over water or water-saturated sediments which
caused a phreatomagmatic explosion and the development of
rootless cones (Webster et al., 2006). Erosion has since
removed the cone material, leaving the more resistant quenched
dykes that intruded the radial fracture pattern. Although Kontak
et al. (2002) state that mesostatis is rare in the LFU, we
consistently found it in LFU samples collected near the craters.
The model builds on the interaction of lava and groundwater
proposed by Hodges (1978) for the CRFB craters. Possible
modern analogues occur in Iceland (Thorarinsson, 1953),
where cones are present on the surface of the lava flows. The
model suggests the possible requirement of a thick (>50 m)
flow for their formation by way of comparison with the craters
from the CRFB.
The high-precision lidar was critical in constructing a new
geological map that delineates the three flow units based on
their different topographic expressions. The variations in relief
between the three flow units are attributed to their variable
resistance to erosion. Stream incision is one of the dominant
surface processes that influences landscape evolution. The
resolution of lidar has allowed us to analyze moderate size
(5 km2) catchment basins and isolate factors such as lithology
controlling incision within a region (Montgomery and López-
Blanco, 2003). In this study, stream incision depths are directly
related to variations in bedrock lithologies. Maximum incision
rates have been computed from the depth curves assuming
incision began after deglaciation at 12 ka (Webster, 2005a).
Sklar and Dietrich (2001) have shown in laboratory
experiments that rock resistance to abrasion has a significant
effect on erosion. Whipple et al. (2000) presented qualitative
evidence on the relative efficacy of fluvial erosion processes
and confirmed the strong influence of lithology on erosion and
that joint spacing and fractures control plucking. The factors
controlling erosion of the flow units were confirmed by our
laboratory experiments using a shatterbox, which tested
resistance to abrasion. Fracture densities in drill core were used
to quantify the susceptibility of the flow unit to plucking. The
MFU, the unit most susceptible to erosion by abrasion based on
our shatterbox experiments, has deeper incision depths than the
UFU and LFU, consistent with the predictions of Sklar and
Dietrich. The LFU is susceptible to erosion by plucking as a
result of the high fracture density measured from the drill core,
an interpretation supported by the findings of Whipple et al.
The UFU appears to be the most resistant unit and acts as a cap
rock protecting the MFU from knick zone development. This is
similar to the interpretations of Reneau (2000), who proposed
that a resistant basalt unit isolated the watershed from base
level changes along the Rio Grande.
Lidar has also facilitated the identification of previously
unidentified glacial landforms in the region. The ability to
remove the vegetation and highlight these subtle landforms is
important in areas that have experienced multiple episodes of
glaciation with different ice flow directions. Although not
directly applied in our study, the ability to map these subtle
landforms and determine the dominant ice flow direction is
important for mineral exploration in glaciated terrains where
till sampling is employed to trace anomalies. In our study, the
streamlined landforms are interpreted to represent the last ice
movement across the region and resulted in the redistribution of
Lawrencetown Till on the eastern half of the North Mountain
study area. This has had the affect of smoothing the landscape
of the NMB in this area compared to the scoured bedrock and
changing the flow direction of the upper reaches of streams in
the thicker till covered catchments as evident in the lidar DEM
maps, especially the principal component composite (Figure 5).
Pazzaglia et al. (1998) point out that stream power can
control the shape of the stream longitudinal profile and that
discharge is influenced by the drainage area and infiltration
characteristics of the basin. Many studies have used drainage
area as a proxy for stream discharge (Sklar and Dietrich, 1998;
Snyder et al., 2000; Kirby and Whipple, 2001; Mather et al.,
2002; Finlayson and Montgomery, 2003; Stock et al., 2005) in
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estimating stream power. The results of this study suggest that
the amount of glacial till cover and the fracture density of
bedrock can influence infiltration rates and affect stream
discharge. The variations in the specific conductance of the
water related to runoff or base flow are consistent with the
findings of Hem (1985) and Winter et al. (1998). The low
permeability of the till blanket promotes surface runoff,
whereas the scoured bedrock promotes infiltration and delivers
water to the stream by increased base flow (see also Tague and
Grant, 2004). These results indicate that drainage area may not
be a good proxy for discharge in glaciated terrains without
considering till cover thickness.
This difference in the hydrologic processes between
catchments with variable till thickness also manifests itself in
basin hypsometries (Webster, 2005a). The scoured bedrock
catchments are characterized as having one main stream with
narrow valleys, whereas the thicker till covered catchments
have better developed tributaries and wider and deeper valleys
interpreted to be a result of increased surface runoff. The till
cover clearly has had, and continues to have, an influence on
the evolution of the landscape.
Terraces in the study area at levels of 18, 24, and 42 m above
mean sea-level are interpreted to be associated with the higher
sea-levels at 12–14 ka (Stea and Mott, 1998). These levels are
not in direct agreement with the terraces extracted from the
lidar, which occur at levels 15, 23, and 35 m above mean sea-
level and less pronounced near 5, 10, and 25 m above mean sea-
level. The additional terrace levels resolved with the high-
resolution lidar can add to our understanding of the episodic
nature of relative sea-level and isostatic adjustments within the
region. The variation in terrace heights along the coast suggests
that areas to the west may have rebounded more quickly than
those to the east, consistent with the deglaciation history of the
area, where ice left the western region earlier than the east (Stea
and Mott, 1998). The ability of lidar to quantify these terrace
levels and trace them over large distances can assist in better
defining the past sea-level history of the area.
In conclusion, the enhanced resolution of lidar over
traditional DEMs is providing data with the quality of
airphotos, with the added benefit of penetrating the forest
canopy to measure the ground surface, or close to the ground
surface. This allows for subtle landforms to be identified and
traced over large distances which otherwise may not be
recognized and for the precise measurements of geomorphic
features. As high-resolution lidar becomes increasingly
available over forest-covered regions, new landforms will be
discovered and the accuracy of bedrock and surficial mapping
will be improved. With these improvements to mapping, our
understanding of factors controlling landscape development
will be advanced in different environments in ways similar to
those demonstrated in this study.
Acknowledgements
We would like to thank Ralph Stea and Dan Kontak of the
Nova Scotia Department of Natural Resources for field visits
and discussions and field assistants to TW (Alex Mosher,
Adam Csank, and Daniel Roberts). We would like to thank
Trevor Milne and the students from the Applied Geomatics
Research Group (AGRG) class of 2003–2004 for the total
station survey under the canopy, George Dias for writing the
AML code for the point validation procedure, and Lisa
Markham and Dan Deneau for assisting in parts of the AML.
The lidar data were supplied by the AGRG of the Nova Scotia
Community College (NSCC) and funded by a Canada
Foundation for Innovation research grant from Industry
Canada. TW would like to thank Bob Maher for his
encouragement and management flexibility and acknowledge
financial assistance provided from the Natural Sciences and
Engineering Research Council of Canada (NSERC, via JBM)
and the NSCC toward TW’s Ph.D. and financial support from
ACOA Atlantic Innovation Fund (AIF) grant 1005052 to JCG.
We would like to thank the comments of an anonymous
reviewer and the guest editor of the special issue, Chris
Hopkinson, for their comments, which have improved the
manuscript.
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