A LiDAR-based decision-tree classification of open

Remote Sensing of Environment 164 (2015) 90–102

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Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse

A LiDAR-based decision-tree classification of open water surfaces in an Arctic delta N. Crasto a, C. Hopkinson b,⁎, D.L. Forbes c,d, L. Lesack e, P. Marsh f, I. Spooner g, J.J. van der Sanden h a

KBM Resources Group, 349 Mooney Ave., Thunder Bay, ON P7B 5L5, Canada Department of Geography, University of Lethbridge, 4401 University Dr., Lethbridge, AB T1K 3M4, Canada Geological Survey of Canada, Bedford Institute of Oceanography, 1 Challenger Drive, PO Box 1006, Dartmouth, NS B3Y 4A2, Canada d Department of Geography, Memorial University of Newfoundland, St. John's, NL A1B 3X9, Canada e Department of Geography and Biological Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada f Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON, Canada g Department of Earth and Environmental Science, Acadia University, 12 University Av., Wolfville, NS B4P 2R6, Canada h Canada Centre for Remote Sensing, Natural Resources Canada, 560 Rochester Street, Ottawa, ON, K1A 0E4, Canada b c

a r t i c l e

i n f o

Article history: Received 24 August 2014 Received in revised form 1 April 2015 Accepted 6 April 2015 Available online 28 April 2015 Keywords: LiDAR Open water River channel Lake Floodplain Decision tree Classification Mackenzie Delta Arctic

a b s t r a c t In the Mackenzie Delta, western Arctic Canada, decisions relating to navigation, socio-economics, infrastructure stability, wildlife, vegetation and emergency preparedness are closely related to the delta hydrology. Presented here is a remote sensing decision-tree approach to delineate open-water hydrological features using highresolution LiDAR terrain, intensity and derivative data. The proposed classification scheme exploits the propensity of LiDAR point attributes and data metrics such as point density and standard deviation (of intensity and elevation) to cluster around characteristic response values over water and non-water surfaces. Due to the impracticability of validating an Arctic water surface classification over such a huge and remote area, results of the hierarchical classification were compared to alternative classifications derived from Radarsat-2 and a manually intensive digitisation technique. Open-water features were identified with N95% accuracy when compared to manually interpreted data. The spatially extensive but temporally distinct information on the hydrological setting of the delta thus extracted forms the basis for calculation of time-invariant parameters such as off-channel storage capacity and hydraulic gradients. In situations where LiDAR data are primarily collected in support of terrain-based watershed hydrologic or floodplain hydraulic assessments, contemporaneous water extent and associated level data are valuable in further characterizing terrain hydrological characteristics. © 2015 Elsevier Inc. All rights reserved.

1. Introduction The appropriate classification of open water bodies (lakes and river channels) is an important aspect of remote sensing applications to hydrology (Marsh et al., 2009), water resource monitoring (Sawaya, Olmanson, Heinert, Brezonik, & Bauer, 2003), ecological studies (Maxa & Bolstad, 2009), coastal zone management (Brzank, Heipke, Goepfert, & Soergel, 2008) and infrastructure management (Ferguson, Ryder, Verth, Richardson, & Ray, 2009). Classification of open water bodies using optical multi-spectral satellite remote sensing techniques is well established (e.g. Frazier & Page, 2000; Sawaya et al., 2003). There are many other tested and potential approaches to remote sensing-based water body delineation, including the use of LiDAR and complimentary

⁎ Corresponding author. E-mail addresses: [email protected] (N. Crasto), [email protected] (C. Hopkinson), [email protected] (D.L. Forbes), [email protected] (L. Lesack), [email protected] (P. Marsh), [email protected] (I. Spooner), [email protected] (J.J. van der Sanden).

http://dx.doi.org/10.1016/j.rse.2015.04.011 0034-4257/© 2015 Elsevier Inc. All rights reserved.

data sources to: a) mask the effect of low signal-to-noise ratio over water bodies that introduce artefacts in elevation models; b) provide a tool to map spatial distribution of water surface properties (Hopkinson, Crasto, Marsh, Forbes, & Lesack, 2011); and c) prioritize regions around existing water bodies that are most likely to contain information on important hydrodynamic variables, such as lake sill elevations (Crasto, Hopkinson, Marsh, Forbes, & Spooner, 2012; Marsh & Hey, 1989) and boundary conditions such as riparian vegetation or bank stability (Gomes-Pereira & Wicherson, 1999). With archives of airborne LiDAR data gradually becoming available for public use around the world, and with modern LiDAR sensors now able to simultaneously capture topographic and bathymetric data (e.g. the Aquarius, Optech Inc., Canada, and the Chiroptera, AHAB, Sweden), there is a need to further explore automated LiDAR-based water classification and land/water boundary delineation. The research presented is a sub-component of a broader study investigating river flow hydraulics and modeling, as well as spring flood peak water levels, timing, and storage capacity across the Mackenzie Delta in the Canadian Arctic. The primary objective here is to develop and evaluate a decision-tree LiDAR-based classification of open water,

N. Crasto et al. / Remote Sensing of Environment 164 (2015) 90–102

river channels and lake surfaces in this low relief Arctic delta environment (Fig. 1). 1.1. Landcover classification using LiDAR There are numerous methods with various levels of sophistication to classify spectral data. However, none can be considered a priori superior to others or applicable to all data types. Approaches to classification can be broadly divided into two categories: a) clustering through use of training areas, thresholding or contextual information; and b) edge or boundary detection optionally followed by region extraction (Pal & Pal, 1993). Common examples of clustering techniques include supervised and unsupervised classification (Robertson, 1973; Strahler, 1980), decision-tree classification (Chasmer, Hopkinson, Quinton, Veness, & Baltzer, 2014; Friedl & Brodley, 1997), neural networks and fuzzy logic classification (Mountrakis, Im, & Ogole, 2011).

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Landcover and open-water classifications from LiDAR are distinct to classifications using image spectral response. LiDAR is a ranging system used to record elevation measurements, with a supplemental measure of signal return strength (referred to as intensity) typically in the near infra-red part of the spectrum. The amount of energy reflected from a surface largely determines whether a LiDAR sensor detects a return pulse or not (Adams, 1999). Many LiDAR systems use a laser with a wavelength of 1064 nm, while some systems operate at 1550 nm or other wavelengths. An advantage of a 1064 nm sensor for water is that much of the pulse energy is reflected from the surface instead of being absorbed or transmitted, and therefore is more likely to be detected by the sensor; especially at near-nadir scan positions. At 1550 nm most of the energy is absorbed due to the water absorption coefficient of an incident pulse at 1550 nm being about two orders of magnitude higher than at 1064 nm (Matthies, Bellutta, & McHenry, 2003). Intensity information recorded by most commercially available LiDAR sensors is a

Fig. 1. The Mackenzie Delta study area. Water body image source: Natural Resources Canada, National topographic database base maps, presented under license (geogratis.gc.ca).

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unitless index of the peak amplitude of a LiDAR pulse (Wehr & Lohr, 1999) and can be affected by survey parameters such as beam divergence, pulse repetition frequency (PRF), altitude and atmospheric conditions (Hopkinson, 2007). Methods presented by Song, Han, Yu, and Kim (2002), Charaniya, Manduchi, and Lodha (2004), Goodale, Hopkinson, Colville, and Amirault (2007) and Fras, Attwenger, and Bitenc (2007) simplify the intensity response by assuming similar conditions throughout a dataset either to an empirical fit or to supervised training areas. Goodale et al. (2007) however, introduced a logical classification approach that included intensity response as a qualifier for classes for surfaces with similar terrain characteristics. In contrast, methods presented by Höfle et al. (2009) and Höfle and Pfeifer (2007) require calibration using brightness targets in addition to data on radiative transfer properties of the atmosphere at the time of data acquisition. The primary purpose of the intensity calibration routines suggested by Höfle et al. (2009) is to normalize recorded intensities to baseline range and environmental conditions. While this approach is useful in high-relief environments such as the Austrian Alps (Höfle et al., 2009), classifications in low-relief topography, such as coastal deltas, would likely realize minimal benefit from the added complexity of a radiometric intensity correction. Studies dedicated to landcover classification using LiDAR data have established the viability of using intensity and metrics such as point density and standard deviation of elevation. For example, Chasmer et al. (2014), Goodale et al. (2007), Song et al. (2002) and Fras et al. (2007) outlined classification methods that use intensity information to complement elevation-based classification criteria; Amolins, Zhang, and Dare (2008) incorporated standard deviation of elevation and point density metrics in addition to intensity to classify urban landcover classes; and Charaniya et al. (2004), Arefi and Hahn (2005) and Höfle et al. (2009) used combinations of intensity, elevation and associated LiDAR data metrics to create supervised training areas for subsequent statistical classification routines.

2007). Four major distributaries, the West Channel, Napoiak Channel, Middle Channel and East Channel (Fig. 1) transport the bulk of sediment and discharge of the Mackenzie River system. Like other pan-Arctic rivers, the discharge from the Mackenzie River has important biogeochemical and oceanographic effects that are actively under investigation (Lesack, Marsh, Hicks, & Forbes, 2014). Levees, which occur along the delta channels, are formed by sedimentation during overbank flooding, and are typically the highest elevation features in all regions of the delta. Due to their elevation, levees play an important role in controlling off channel flooding. Due to their location on the delta plain, levees often occur between channels and lakes, and control the flooding hydrology of lakes. Lakes in the Mackenzie Delta are classified as no-closure (always connected to channels), low-closure (typically inundated each year during spring floods, then isolated for the remainder of the season), or high-closure (lying above the 1-year repeat annual peak water level) based on the flooding hydrology of the lakes (Emmerton et al., 2007; Marsh & Hey, 1989). Marsh and Hey (1989) quantified the flood frequency of these lakes, which is controlled by both the lowest elevation of the levee on the floodplain and the sill elevation along channels that connect the lakes to the main channels (Mackay, 1963). Furthermore, Marsh and Hey (1989) and Emmerton et al. (2007) have shown that levee heights that control the flooding hydrology of lakes are in equilibrium with annual peak water levels on record for a given area. Delta lakes constitute an integral component of off-channel storage, acting to detain and attenuate runoff from spring floods while playing a key role in overall land–atmosphere–ocean hydrological and biogeochemical interactions. Recent estimates indicate that approximately half the Mackenzie River system discharge during spring-melt may be contributions from off-channel storage (Emmerton et al., 2007). The application of LiDAR to automated open water classification and water level extraction (Hopkinson et al., 2011) in this lake-dominated landscape has the potential to support storage capacity calculations across the Delta.

1.2. Assumptions

3. Data preparation

Based on the interaction of incident laser pulses with water and the absence of other specular reflective surfaces in a given area (typical of non-urban landscapes), it can be surmised that at typical survey altitudes and sensor configurations (within the normal operational range of a commercial airborne terrestrial LiDAR sensor and given missionspecific needs), open water areas are likely to exhibit the following LiDAR point attribute characteristics: a) returns over still water from non-nadir scan angles are likely to be absent due to reflection away from the LiDAR sensor; b) point clusters over water would be less dense compared to dry surfaces due to lack of returns from higher scan angles; and c) if present, surface waves would alter the variability of the intensity response from a well-defined bimodal distribution over open water to one that is less defined as the presence of surface waves alters the angle of incidence. The proposed method uses data-driven thresholds for intensity and elevation. It exploits the tendency of water and non-water features to cluster around distinct attribute and attribute-derived classes (e.g. Charaniya et al., 2004) that are logically filtered to mask areas of interest (e.g. Goodale et al., 2007).

3.1. Data-acquisition and pre-processing

2. The Mackenzie Delta The Mackenzie Delta in western Arctic Canada (Fig. 1) is a great northern delta dominated by a complex network of lakes and channels; it is located between the Richardson Mountains on the west and the Caribou Hills on the east and extends from Point Separation in the south to the Beaufort Sea in the north, a distance of about 200 km. The Mackenzie Delta covers an area of approximately 13,000 km2; like other Arctic deltas in late-summer, it is characterized by high open water coverage, with over 49,000 lakes and hundreds of channels representing 35% to 40% of the area (Emmerton, Lesack, & Marsh,

LiDAR data were acquired between 11 Aug. 2008 and 16 Aug. 2008 (Hopkinson et al., 2009) when water levels in the delta were approaching annual minimum levels. The sensor used was an Optech ALTM 3100C sensor (λ = 1064 nm; beam divergence = 0.3 mrad) flown between 1000 and 2000 m agl and operated at a pulse repetition frequency (PRF) of 50 kHz with a 50° scanner field of view and 50% flight line sidelap (i.e. 200% coverage of any region on the ground). Range measurements were georeferenced using an Applanix POS/AV positioning and inertial measurement system that was differentially corrected to several GPS base stations in and surrounding the area of interest. The base stations were surveyed using static GPS positioning relative to two Canadian Active Control Stations in Tuktoyuktuk and Inuvik, NWT. To address systematic sensor-related bias, ALTM system components were calibrated using accurately surveyed runway and building control targets prior to and following the main survey campaign (Goulden & Hopkinson, 2010; Katzenbeisser, 2003), with range checks performed at the Inuvik runway before and after each flight. Additionally, a range correction was applied to intensity data per flightline to mitigate the effect of slant range bias, altitude changes and relief using Eq. (1) (Hopkinson, 2007; Luzum, Starek, & Slatton, 2004)

Icor ¼ Iobs

R2obs R2re f

ð1Þ

where, Icor = corrected intensity, Iobs = observed intensity, Robs = observed range and Rref = reference range. The reference range selected


was the logged average flying altitude above ground for each flightline. Pulse-level intensity values were outputted to a unitless 8 bit scale of 1 to 255. A three dimensional adjustment between flightlines to address alignment issues, as outlined by Katzenbeisser (2003), was also performed using proprietary algorithms implemented in the Terramatch© module within the Terrasolid© (Finland) suite of LiDAR data processing software. Corrective systematic offsets for easting, northing, elevation, heading, roll and pitch were calculated for each flightline. For efficient processing, the data were segmented into 2 km × 2 km tiles and exported as ASCII text files with the following attributes: i) 2D UTM grid position coordinates; ii) elevation above the Canadian Gravimetric Geoid model 2005 (CGG2005); iii) intensity; and iv) scan angle. All coordinates were processed relative to NAD83CSRS and projected to UTM Zone 8 N grid coordinates. Elevations were transformed using Eq. (2). H CGG05 ¼ h−NCGG05

ð2Þ

where, HCGG05 = orthometric height above the Canadian Gravimetric Geoid 2005 model (CGG05), h = ellipsoidal height above NAD83CSRS and, NCGG05 = CGG05 geoid undulation. Accurate orthometric elevations were important due to the broader hydrological and hydraulic objectives of the data collection, and given the very low relief deltaic landscape being mapped. 3.2. Water mask validation To test the open-water classification results, comparisons were made to manually digitized open-water masks from 12 sample tiles in the study area (validation sites in Fig. 1). The waterline was interpreted and digitized based on height, data density and if required, intensity characteristics of the LiDAR point cloud. To assess the manual delineation process against a proven independent data source, results over four tiles were compared with a water mask derived from Radarsat-2 image data. Optical satellite-based open water classification is an established technique (e.g. Frazier & Page, 2000; Sawaya et al., 2003) and under appropriate conditions could provide an excellent comparative dataset for the LiDAR-based approach tested here. However, it was not feasible to use optical methods here for two primary reasons: i) for comparisons over surfaces that change through time, temporally coincident data are required. The study period was characterized by cloud cover and rain and it was not possible to task an optical satellite imaging sensor such as Worldview or RapidEye during conditions of low visibility. ii) Lower resolution optical satellite sensors such as Landsat TM or SPOT were unsuited to the task, again due to lack of temporal coincidence but also due to the incompatibility of spatial resolution. Radarsat-2, on the other hand, operates in the microwave part of the electromagnetic spectrum and can thus operate in cloud covered conditions. Furthermore, it has the ability to capture data at a high resolution that is compatible with airborne LiDAR. The utility and limitations of radar images for application to the mapping of water extent are well documented (e.g. Brisco, Short, van der Sanden, Landry, & Raymond, 2009; Reschke, Bartsch, Schlaffer, & Schepaschenko, 2012; and Santoro & Wegmüller, 2014). The Radarsat2 image applied in this study was well suited to the task thanks to its technical specifications and favourable wind conditions at the time of acquisition (11 Aug. 2008, 08:31 local standard time). The technical specifications of the image were as follows: horizontal transmit and vertical receive polarization (HV), nominal spatial resolution 2.6 m in ground range by 2.8 m in azimuth (Ultra-Fine mode), incidence angle range 36.4° to 37.7° (beam 9), nominal scene size 20 km in range by 20 km azimuth, number of looks 1 in range by 1 in azimuth. According to hourly data available from the nearby Inuvik weather station, the image was acquired under light breeze to calm wind conditions below speeds of 2.5 m/s.

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The image processing approach adopted comprised the following steps: image ingest and calibration of pixel values (i.e. backscattered power) to beta nought, speckle reduction by means of 7 by 7 FGAMMA filter, orthorectification with the help of orbital data to the UTM NAD83 coordinate system at a pixel spacing of 3 m by 3 m, conversion of pixel values from the linear to the decibel (dB) scale, thresholding of pixel values to generate a preliminary water mask (backscattered power for water ≤ −19 dB), cleaning of the preliminary water mask by means of a 5 by 5 mode filter, finalize water mask by correcting errors of commission in the town of Inuvik (introduced by e.g. paved areas, bare ground, lawns) and errors of omission in the East Channel at Inuvik (introduced by ships), convert the final water mask from the raster to vector format (shape file). 3.3. Variable selection and gridding The average ground point spacing of the Mackenzie Delta LiDAR dataset was ~1.5 m; therefore a lower grid resolution of two metres as the nearest rounded-up integer was chosen for subsequent processing. Table 1 provides a list of variables selected for classification based on their characteristic response over open water bodies and gridding algorithms implemented (explanations follow). Search radii were selected iteratively by testing different values starting with two metres and increasing in increments of one metre until discernible feature patterns over known open-water areas were observed. Since no channel or lake edge validation data existed, the choice of optimal search radii was a subjective process that relied on transposing intermediate gridded results with raw lidar point data to observe the correspondence along water feature boundaries. Minor misclassifications and noise in initial implementation stages were acceptable, as these are corrected in latter stages of data filtering. Because of the specular reflective nature of water surfaces, recorded intensities of LiDAR pulses over open water are typically high (frequently reaching signal saturation) at near-nadir angles and very low at higher scan angles, where a large number of reflected LiDAR pulses are lost due to reflection away from the sensor. Therefore, intensity (i in Fig. 2) was chosen as a potential classifier for open water; combining the intensity response with scan angle (a in Fig. 2) offers additional control over intensity classification criteria. It should be noted that the incidence angle is a more accurate predictor of intensity response than scan angle. However, the two were considered approximately equal by assuming a horizontal flat surface on the ground and a level airborne platform. This assumption is justified because the water body and delta areas of interest are flat, and during the airborne survey over the Mackenzie Delta roll angles rarely exceeded four degrees. Intensity (i) and scan angle (a) grids were interpolated using the nearest neighbor (NN) algorithm with a five metre search radius (Table 1). NN was chosen for speed of implementation over large ascii datasets and because, in the case of intensity (i), the gridded data only needed to be binned into one of three classes (Fig. 2). NN was appropriate for scan angle (a), as values will increase systematically away from nadir and the pattern is highly predictable; i.e. a more sophisticated interpolation algorithm will not produce any improvement in the result. A digital elevation model (z in Fig. 2) derived from ground-classified LiDAR returns offers further discriminating information on potential water bodies given the LiDAR survey was conducted when water levels were approaching an annual minimum. Because lakes in the middle to southern section of the Mackenzie Delta primarily receive water from spring floods (Marsh & Hey, 1989; Prowse & Beltaos, 2002), it is unlikely that open water will be present during late summer at surface elevations above peak historical water levels on record estimated across the survey polygon (P_zmax in Fig. 2). Furthermore, most open water bodies in a 2 km × 2 km tile in the Mackenzie Delta, with the exception of high-closure lakes described by Marsh and Hey (1989), would be expected to lie within the lower of two elevation bi-clusters of a surface elevation dataset. Digital elevation model (DEM) grids were interpolated

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Table 1 Summary of variables selected for open water classification and their characteristic response over open water. Fig. 2 further illustrates the interaction between these variables.

Attributes

Variable

Response over water

Gridding algorithm

Search radius (m)

Intensity (i) Scan angle (a)

Low at higher scan angles; saturated at near-nadir angles None; but supports segmenting saturated high intensity returns by assuming Scan angle ≈ Incidence angle Local minimum and constrained by upper limit of max peak flood levels on record (Marsh & Hey, 1989) Lower than topographic surfaces at non-nadir incidence angles Low High

Nearest neighbor Nearest neighbor

5 5

Inverse distance weighted (IDW) to a power

5

Density Standard deviation Standard deviation

3 10 10

Elevation (z) Metrics

Point density (pd) SD-Elev (SDz) SD-Intensity (SDi)

using an inverse distance weighted algorithm to a power of two (Shepard, 1968) with a search radius of 5 m. The advantages of IDW over exact interpolators such as irregular triangles-based methods are: i) noise-suppression through smoothing by a weighted average; and ii) IDW does not interpolate beyond the extent of the search radius. Interpolating in areas of large data gaps such as open water bodies degrades the overall quality of the surface through edge artefacts (Franke, 1982). Metrics derived from primary LiDAR point attributes offer additional cues to location of open water over surface representations. Three metrics implemented in this classification routine are: i) point density (pd in Fig. 2); ii) standard deviation of elevation (SDz in Fig. 2); and iii) standard deviation of intensity (SDi in Fig. 2). Due to the specular reflective nature of water surfaces, the overall point density over open-water is expected to be lower than terrain surfaces. Furthermore water surfaces are locally ‘flat’ compared to surfaces displaying vegetation growth and therefore a low local standard deviation of elevation is expected for LiDAR returns over open water. Due to the possible presence of surface waves over large open-water areas and large changes in intensity response due to incidence angle, a large variation in intensity is expected over open water features. Therefore, to avoid roads or barren land being classified as water, the standard deviation

of intensity response was used to discriminate between flat homogeneous terrestrial surfaces and open water. 4. Classification rules Based on observed characteristics of data distributed throughout the study area, the following open water classification rules were proposed (Fig. 2) and evaluated: • Add areas with null returns, low intensity returns away from nadir and high intensity returns from near-nadir observations. • Remove areas with surface elevations above peak water levels from further processing. • Add low lying areas with low point density or, flat areas with highly variable intensity response. 4.1. Areas with null, low and high intensity Large contiguous gaps in LiDAR data are likely to be found over open-water due to specular reflection of incident pulses away from the sensor. For the purpose of using non-null intensity response as a

Fig. 2. Overview of the hierarchical classification workflow. P_zmax = local peak water levels above which open-water is unlikely to be found; P_Imin = low intensity threshold, P_Imax = high intensity threshold.


classifier, three data thresholds specific to each processed tile were estimated as follows: i) an intensity value above which an observation can be classified as ‘high’ (P_imax in Fig. 2); ii) the scan angle (a) below which a high intensity response could be expected over water bodies; and iii) a minimum intensity value below which large contiguous areas could be confidently classified as open water (P_imin in Fig. 2). Over 95% of randomly sampled data registered an intensity response below 150 (N = 9,037,333, 95th percentile = 143). Further analysis indicated that 95% of points with intensities above 150 were attributed to scan angles less than or equal to three degrees from nadir, due to high intensity and some saturated returns from near-nadir scans over open water. Intensity response varies with flying height, sensor settings and ground conditions, so the threshold was adjusted to account for any changes in survey or ground surface conditions; e.g. the ‘South’ transect was flown lower than the ‘Inuvik’ (central) transect, and the ‘North’ transect was flown higher than the other two (2000 m above ground level). Also, lake coverage attributes change from south to north such that size increases while density decreases (Mackay, 1963). Therefore, a data-driven response was tested per tile to classify the 95th to 99th percentiles of intensity values as high and 25th to 30th as low. An anticipated limitation of using a tile-based percentile threshold (as opposed to the whole dataset) is the risk of eliminating areas of interest in relatively homogeneous settings or in tiles that are not completely populated with data. In such areas, relative land-cover proportions do not represent the entire Mackenzie Delta. Therefore, default high and low thresholds were used if an area was found to be too homogeneous to discriminate. An area was flagged as being homogeneous (or containing insufficient data) if the difference between P_imax and P_imin was within 40 intensity units; the threshold of 40 was determined by sampling different types of homogenous areas. It was expected that after the gridding process a threshold using the low intensity value would

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primarily return contiguous open-water areas, while other returns with low intensity would be: a) raised above the threshold during the gridding process due to the prevalence of high intensity data surrounding these returns; b) filtered using other classification criteria; or c) isolated in the final output and therefore generalized during the final stages of classification. 4.2. Areas with surface elevations above peak water levels This criterion is used to mask areas with elevations above which open water is unlikely to be found based on knowledge of the flooding hydrology of lakes in the delta, as described by Marsh and Hey (1989). For example, the two hydrometric stations 10MC003 and 10MC008 within the Inuvik survey polygon (Fig. 1) recorded historical peak water levels at 6.19 m and 6.15 m above CGG05 respectively (Environment Canada, 2014). Because the difference between the two was only 0.04 m, a rounded-up value of 6.2 m was selected as a peak water level threshold throughout the survey polygon. In survey polygons without hydrometric stations such as the North and South survey polygons, the threshold was estimated using the nearest available hydrometric stations followed by visual estimation of the terrain to the nearest rounded-up integer using a coarse resolution DEM (approximately 30 m) of respective survey polygons. 4.3. Low-lying areas with low point density and flat areas with scattered intensity response For clustering, the so-called natural breaks least squares solution originally developed by Jenks (1963) was used for elevation and point density. For standard deviation metrics a modified natural breaks solution that weighted clusters towards approximately equal-area solutions using

Fig. 3. Comparison of a Radarsat-2 derived water mask with manual delineation results over two out of four tiles. A) Open-water areas that were manually digitized from the LiDAR point cloud; B) water mask obtained based on a Radarsat-2 observation on 11-Aug-2008; C) overlay of images from A and B, illustrating areas of difference.

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proprietary methods developed by ESRI (2009) was used. An equal-area weighted method is suited to relatively more continuous datasets such as standard deviation metrics where least-squares breaks may shift based on the presence of extreme values that cannot be predicted. In preliminary tests using intensity alone as a classifier, it was observed that the edges of water features were not well defined. This was primarily due to intensities near the edge of water features being higher than the low intensity threshold (P_imin). A potential reason for relatively high intensity being recorded near the edge of water may be due to different surface characteristics of nearshore features such as saturated sediment and exposed sandbars. On closer examination it was found that a discriminating characteristic of LiDAR response at the edge of water features is the relatively lower observed point density. Therefore, data density values were divided into three clusters: high density—expected from nadir specular reflection response; medium density—expected from terrestrial lambertian reflection response; and low density—response over water where some returns are lost due to specular reflection away from the sensor. Elevation values were also stratified into two classes: high elevation—for floodplains and levees; low elevation—beaches and open water. Therefore, combining the lower two density and elevation attribute clusters should tend towards mapping open water areas. Another area where intensity values may cross over into characteristic terrestrial response values is near the edge of near-nadir scans over open-water. The discriminating derived attribute in these areas is that the intensity response is scattered between values that range from high to low. Such a response however, can also be expected under multiple return forest canopies. Therefore, to extract open-water

areas with scattered intensity response; i.e. high SDi cluster, must be logically combined with a flatness criterion; i.e. low SDz cluster. 4.4. Generalizing results The first step to generalization was completing the classification process by generating a binary raster. Any unclassified areas at this stage were classified as non-open-water areas. Next, the effect of noise was mitigated by filtering regions containing less than 500 cells but not those with null intensities. At 2 m grid resolution, this criterion translates to a ground area approximately equal to 2000 m2. In the context of the hydrologic regime of the delta as a whole, any false negatives arising due to area-based filtering will be negligible (Emmerton et al., 2007). However, this setting is data- and goal-dependent. For the purpose of this project the generalization cell limit was first selected to be arbitrarily high (1000) and later adjusted to 500 based on the performance of classification results under different area thresholds. Finally, gaps in the classification were filled by expanding regions identified as water by two cells and then shrinking the result by the same amount. The expansion fills any one or two celled islands within regions, whereas, the shrinking step simplifies the boundaries that were previously expanded. 5. Classification results No large errors were visible in the manually digitized water mask when compared to a Radarsat-2 water mask (Fig. 3A and B). Minor differences were apparent (Fig. 3C); however, the higher resolution of

Fig. 4. North transect open-water classification results (see Fig. 1 for relative location). A) classification results; B) manually digitized open-water; C) error matrix overlay (Green = error of commission; Red = error of omission).


LiDAR and limitations of the Radarsat-2-based classification due to its reliance on spectral characteristics of targets indicate that results of both methods are in good agreement with each other (N95% overlap between the two masks). Despite the overall good correspondence between LiDAR and Radarsat-2 in Fig. 3, there are some minor discrepancies that warrant further consideration. The Radarsat-2 data sampled at a coarser resolution than the LiDAR and during post-processing a 5 × 5 filter smoothed the mask to remove outliers. Consequently, lower resolution plus data cleaning likely result in a tendency for systematic removal of some small regions of open water, as is observed in some areas. This is particularly evident in the case of some small channels (less than ~10 m wide) that link lakes in floodplain areas. Emergent vegetation in shoreline areas can increase radar backscatter, leading to a possibility of reduced open water extent in Radarsat-2 water masks, while tall onshore vegetation can cause shadowing, thus increasing the apparent extent of open water. Overall, neither influence is thought to present a major problem in this Arctic landscape, where vegetation cover is neither dense nor tall. In regions of high biomass, however, such as the tropics, emergent and riparian vegetation would be expected to reduce the accuracy of the water mask generation process. Aside from small (close to pixel-level) variations between the two water masks, one area in Fig. 3a and b (top row) stands out (536319E, 7575302N) as the LiDAR mask illustrates connectivity between two water bodies, while Radarsat-2 indicates they are separated. Closer scrutiny of the LiDAR point cloud elevation and intensity data indicates that the region linking the two water bodies is an area of saturated sediments with moisture flowing through or across the sediments with no

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appreciable depth. Therefore, from the LiDAR sensor perspective, the surface behaves like open water, as it has specular reflectance properties leading to saturated or zero intensity returns, with no local surface gradient. From the C-band radar backscatter perspective, the saturated sediments demonstrate elevated signal backscatter characteristics relative to typical open water surfaces. Figs. 4–6 present results of the 12 tile locations shown in Fig. 1. Table 2 presents the error matrix (Congalton, 1991) for the tiles tested within each of the transects and demonstrates an overall correspondence between manual and decision tree classification results exceeding 90%. Location 2 in Fig. 4 illustrates a high error of commission. This is possibly related to digitizing errors, ground condition or due to the data in this area being acquired over multiple survey flights. However, no major water body over 2000 m2 was found to be misclassified. Results are visibly improved in the south over the Inuvik transect (Fig. 5). In this area lakes are smaller and levees better defined (Mackay, 1963). However, the data within the south transect presented a challenge (Fig. 6) that can be traced to changes in flying height between different missions over the same survey polygon from approximately 1000 to 2000 m agl. When a large change in flying height occurs, a change in signal intensity is observed. Using Eq. (1), an increase in flying height of 1000 m would translate to a fourfold decrease in intensity (Hopkinson, 2007). A clustering algorithm designed for a single flying height will not account for this change in overlapping areas, and this explains the relatively large omissions in the centre of the channel at Location 3 in Fig. 6. Such errors are easy to identify in QC but it is a limitation of an automated algorithmic approach.

Fig. 5. Inuvik classification results (see Fig. 1 for relative location). A) Classification results; B) manually digitized open-water; C) error matrix overlay (Green = error of commission; Red = error of omission).

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6. Discussion 6.1. Effect of subjectivity associated with manual digitization The visual results (Figs. 4–6) illustrate several small areas of discrepancy between the manual and decision-tree classification around the margins of open water areas. Therefore the role of subjectivity associated with manual digitization of the validation dataset must be considered. Ground-based GPS validation of LiDAR-classified open water in the Mackenzie Delta was impractical due to the large size and remote nature of the area, the temporal dynamics of open water extent and the logistical limitations (time and funds) associated with ground survey work in the Arctic. Without the option to ground-validate features and given the absence of aerial photographs at the time of data collection, manual digitization of open water areas from a LiDAR point cloud was the only option. However, this method is dependent on delineation decisions made by a digitization operator. Selecting a boundary, whether in the field with a GPS receiver or during the digitization process, is susceptible to operator-based subjective decisions over boundary locations. Jahn and Dunne (1997) outline several criteria that influence subjective interpretations, which can occur in remote sensing feature detection; including biases resulting from previous experience or due to the level of vector generalisation during the digitization process (Goodchild, 2001; Sarti, Malladi, & Sethian, 2000). In most channel/bank situations, the boundary between land and water is easy to interpret. However, in areas like beaches where boundaries are sometimes less distinct, digitization subjectivity can lead to uncertainty. Fig. 7 illustrates the effect of

operator-related subjectivity on digitization results on a subset of data from Tile 1. Areas A and B shown in Fig. 7 were digitized by the same operator who digitized the validation dataset at different times, whereas Area C was digitized by a different operator who was provided the same instructions as the previous operator. Areas for the newly digitized datasets A, B and C differed from the original by 15%, 9% and 14% respectively. However, the broader consequences of such uncertainties are dependent on the scale of application (Goodchild, 2001). In this case, the illustrative test areas in Fig. 7 are small compared to most of the channel and lake water bodies in the comparative test areas in Figs. 4, 5 and 6, so these areal percentages are systematically large compared to the uncertainty expected in the study as a whole. Nonetheless, this illustrates the consequences of choices made by an operator during manual digitization and how this can impact remote sensing-based water extent validation in the absence of more reliable ground truth. 6.2. Effect of attribute thresholds on results Although the classification results presented are encouraging, there is potential to improve the decision-making process for determining attribute thresholds, primarily intensity, but also elevation. A modified decision-tree process would be necessary if the classification routine were implemented outside of the Mackenzie Delta. For example, the error of omission in Tile 3 from the Inuvik transect was approximately twice as much as the others including one feature that was marginally above the 2000 m2 threshold of generalization. In other cases most errors of omission were located at the edges of classified features by a

Fig. 6. South transect classification results (see Fig. 1 for locations). A) Classification results; B) manually digitized open-water; C) error matrix overlay (Green = error of commission; Red = error of omission).


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Table 2 Error matrix of classification results. Rows = manually digitized; columns = decision tree classification. See Fig. 1 for approximate locations. ^ = 0.91) North transect (κ Reference (#Cells) Water Land Col Total Err. of Comm.

Water 2072689 132554 2205243 6%

Land 53826 1910695 1964521

Row total 2126515 2043249

Err. of Omiss. 3%

Water Land Col Total Err. of Comm.

Water 1718710 87681 1806391 5%

Land 54924 2308449 2363373

Row total 1773634 2396130

Err. of Omiss. 3%

Water Land Col Total Err. of Comm.

Water 1665524 26882 1692406 2%

Land 20345 2457013 2477358

Row total 1685869 2483895

Err. of Omiss. 1%

^ = 0.93) Inuvik transect (κ Reference (#Cells)

^ = 0.98) South Transect (κ Reference (#Cells)

relatively larger margin than expected due to subjectivity associated with the validation dataset alone. The most likely reason for observed high errors of omission in the Inuvik transect are changes in survey configuration (altitude and PRF) that occurred as a result of changing cloud and weather conditions during the survey period. This observation highlights the importance of constant data acquisition parameters and/or the means to correct the intensity response to any changes in configuration. Intensities over the two flightlines from a lower altitude will be systematically higher than those from higher flying altitudes (Hopkinson, 2007). To compensate, the intensity thresholds for these two flightlines must also be systematically higher than elsewhere in the dataset. Furthermore, to minimize other possible systematic offsets in intensity it follows that thresholds could be customized for each flightline to adapt to intensity distributions based on source power and other factors that may vary between flightlines. Another threshold that can be calculated adaptively on a per-tile basis is the elevation threshold above which open water is unlikely to exist (P_max in Fig. 2). The hydrology of the Mackenzie and the presence of hydrometric stations in the area of interest facilitate the use of elevation thresholds derived from historical flood records. However, if the methods were applied in an area with different flooding hydrology (e.g. where local rainfall inputs exceed upstream channel inflows), an elevation derived from peak flood levels may not produce desired results. In such areas, either an elevation threshold derived adaptively using data with the extents of a single tile, or one from an appropriately defined area based on geomorphology as the spatial domain would be needed. The methods presented, however, can serve as a basis upon which subsequent attribute- or metric-based filters can be implemented. For example, if the classification routine were implemented without P_zmax, the algorithm would try to identify clusters using all data from a 2 km × 2 km tile. The results of the classification routine when implemented using an artificially high surface elevation threshold in the Inuvik transect (P_zmax = 999 m above CGG05), as shown in Fig. 8, visibly indicate that only minor adjustments are required for Tiles 1, 3 and 4. The high false positives in the modified results for Tile 2 are attributed to the high standard deviation of intensity associated with the overlap of flightlines representing different altitudes, ground conditions and atmospheric conditions. This further suggests that alternative spatial domains (e.g. tile vs. flightline vs. entire area) for determining thresholds or additional filtering criteria may be required for open-water classification in areas outside the Mackenzie Delta.

6.3. Limitations of methods The study presents a classification based on a decision-tree that avoids hard-coded thresholds. Working at a deltaic scale in a remote northern region makes it logistically impractical to control for all data and environmental constraints, so an adaptive approach is required in identifying suitable thresholds. Due to the reliance on intensity as one of the metrics used to characterise open water, classifier thresholds need to be calibrated on a case by case basis to accommodate variations resulting from survey configuration (e.g. Hopkinson, 2007) or weather and ground moisture conditions (e.g. Garroway, Hopkinson, & Jamieson, 2011). Other limitations of the presented methodology include: a) LiDAR sensors operating at different wavelengths will

Fig. 7. Effect of operator subjectivity on digitization of comparison test data.

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Fig. 8. Comparison of classification results using altered peak elevation threshold (P_zmax = 999 m above CGG05).

demonstrate differing dropout and intensity characteristics over water; and b) for sensors utilizing waveform digitization instead of return peak amplitude recording will also require some adjustment of the threshold approach advocated in this study. 6.4. Applying methods over larger areas Results indicate that the decision-tree open-water classification approach using natural clusters in data, segmented using data-driven thresholds, was 95% accurate when applied to single tiles and validated

against manually digitized open-water features. Based on reasonable confidence in these results, the tile-based classifications were mosaiced to cover larger areas (Fig. 9). The area shown in Fig. 9 includes sample Tiles 2, 3 and 4 from the Inuvik transect. However, assessing the validity of these results is primarily based on visual cues derived from the underlying shaded-relief representation of a ground-classified DEM. The task of creating a ground validation dataset for such a large area would be laborious and at best would complement results from the four sample tiles that are representative of every independent flightline from the study area.

Fig. 9. Classification results over a larger area derived from mosaicing classification results for several tiles spanning the red box shown inset. Location of hydrometric station is approximate.


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Fig. 10. Interpolated spatial distribution of water levels for a sample area in the Mackenzie Delta, N.W.T. Inset: Water surface and terrain hypsometry.

The mapping of open-water areas is not a singularly practical application of LiDAR, and when considered alone is unlikely to be used as a justification for commissioning LiDAR missions. However, within the context of hydrologic and hydraulic modeling aided by spatially explicit maps of water levels across the Mackenzie Delta (Hopkinson et al., 2011), a contemporaneous water mask can be used to extract water level distribution data as shown in Fig. 10. The utility of LiDAR terrain modeling is well known in scientific literature and industry practice as a support tool for watershed hydrological, floodplain hydraulic and coastal hydrodynamic modeling. Using LiDAR data to further extract a water mask to delineate the boundaries to which water levels must be applied adds another level of utility without the need for additional thematic data sources. Furthermore, as combined terrestrial and bathymetric LiDAR systems become more common place, the methods presented will aid in providing complimentary data streams describing water level, depth and area; i.e. all the necessary parameters to calculate water body storage attributes. 7. Conclusions A simple data driven approach has been presented that classifies open-water areas by exploiting both primary and secondary attributes of LiDAR data such as intensity, elevation and localized noise-levels. An advantage over contemporary methods that achieve the same result is that no elaborate in-situ data from the time of data acquisition in the form of brightness targets and atmospheric conditions is required. In regions where data are obtained from an increasing volume of archived LiDAR surveys, such data-driven approaches can be customized for implementation in different locations. Minor problems were encountered with flightline-dependent signal intensity fluctuations due to operational adjustments in survey altitude during cloudy conditions. While it was not possible to completely address these challenges in this study, it is suggested that they could be remedied by either: a) adapting the procedure to define intensity thresholds on a flightline basis instead of a tile basis; and/or b) normalizing intensity to a given altitude for the entire Delta dataset, instead of on a mission by mission basis. Further research is warranted into the general area of intensity normalisation and the potential classification improvements that this might produce. However, when using standard operational LiDAR data deliverables, the typical user is limited in their capability to perform sophisticated intensity calibration and this remains primarily an area of academic research.

The utility of LiDAR for topographic and vegetation canopy representation is well established but more work is needed to evaluate the utility of LiDAR over channel, lake and wetland surfaces. The semi-automated decision-tree water mask and level approach presented has the potential to be extended and used in floodplain storage calculations or integrated with morphological channel and bank delineation routines to characterize the flood hydrology of the Mackenzie Delta and similar channel-, lake- or wetland-dominated environments. While airborne LiDAR-based monitoring of water extent, level and storage is not feasible in the short-term, integration of these LiDAR products with temporal hydrometric and satellite remote sensing water mask data provide a basis for monitoring changes in lake, wetland and river channel storage.

Acknowledgments The data employed here were acquired and funded through a collaborative research program involving the Applied Geomatics Research Group, Environment Canada, Natural Resources Canada, Aboriginal Affairs and Northern Development Canada, and Simon Fraser University, with support from the Natural Sciences and Engineering Research Council, the Office of Energy Research and Development, ArcticNet and the Government of Canada Program for IPY. Positioning and data validation were supported by the Canadian Geodetic Survey and the Geological Survey of Canada (Natural Resources Canada). The field contributions of J.-C. Lavergne, Gavin Manson, Mark Russell and Cuyler Onclin are acknowledged with thanks. This is contribution number 20140594 of the Earth Sciences Sector, Natural Resources Canada. Canadian Crown Copyright reserved. We would like to also acknowledge the valuable comments and suggestions provided by the anonymous reviewers.

References Adams, M.D. (1999). LiDAR design, use and calibration concepts for correct environmental detection. IEEE Transactions on Robotics and Automatics, 20, 1–9. Amolins, K., Zhang, Y., & Dare, P. (2008). Classification of LiDAR data using standard deviation of elevation and characterisitic point features. 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, vol. 2. (pp. II-871–II-874). Arefi, H., & Hahn, M. (2005). A hierarchical classification procedure for segmenting and classification of airborne LiDAR images. IEEE International Geoscience and Remote Sensing Symposium, 2005, Seoul, South Korea, Vol. 7. (pp. 4950–4953).

102


Brisco, B., Short, N., van der Sanden, J.J., Landry, R., & Raymond, D. (2009). A semiautomated tool for surface water mapping with RADARSAT-1. Canadian Journal of Remote Sensing, 35(4), 336–344. Brzank, A., Heipke, C., Goepfert, J., & Soergel, U. (2008). Aspects of generating precise digital terrain models in the Wadden Sea from LiDAR - water classification and structure line extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 63, 510–528. Charaniya, A.P., Manduchi, R., & Lodha, S.K. (2004). Supervised parametric classification of aerial LiDAR data. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’04), Washington, DC, USA. (pp. 30). Chasmer, L., Hopkinson, C., Quinton, W., Veness, T., & Baltzer, J. (2014). A decision-tree classification for low-lying complex land cover types within the zone of discontinuous permafrost. Remote Sensing of Environment, 143, 73–84. Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. Crasto, N., Hopkinson, C., Marsh, P., Forbes, D.L., & Spooner, I. (2012). Delineation of lakes and channels in the Mackenzie Delta, NWT using airborne LiDAR. Remote Sensing and Hydrology—Proceedings of a Symposium, Jackson Hole, Wyoming, USA, September 2010; IAHS Publication no. 352. (pp. 359–362). Emmerton, C.A., Lesack, L.F.W., & Marsh, P. (2007). Lake abundance, potential water storage and habitat distribution in the Mackenzie River delta, western Canadian Arctic. Water Resources Research, 43(W05419), 1–14. Environment Canada (2014). HYDAT Database, National Water Data Archive. On-line at http://www.wsc.ec.gc.ca/applications/H2O/index-eng.cfm (last accessed 2014-0626) ESRI (2009). ArcGIS 9.3.1 desktop help. (Redlands, CA, USA). Ferguson, N., Ryder, S., Verth, S., Richardson, P., & Ray, R. (2009). Vegetation management through LiDAR derived CADD models: Compliance with NERC reliability standard FAC-003-1. Electrical Transmission and Substation Structures, 2009, 1–12. Franke, R. (1982). Scattered data interpolation: Tests of some methods. Mathematics of Computation, 38, 181–200. Fras, M.K., Attwenger, M., & Bitenc, M. (2007). Land use classification based on the intensity value of the reflected laser beam. Geodetski Vestnik, 51(3), 501–518. Frazier, P.S., & Page, K.J. (2000). Water body detection and delineation with Landsat TM data. Photogrammetric Engineering and Remote Sensing, 66(12), 1461–1468. Friedl, M.A., & Brodley, C.E. (1997). Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3), 399–409. Garroway, K., Hopkinson, C., & Jamieson, R. (2011). Investigating the influence of surface moisture and vegetation cover on airborne lidar intensity data. Canadian Journal of Remote Sensing, 37(3), 275–284. Gomes-Pereira, L.M., & Wicherson, R.J. (1999). Suitability of laser data for deriving geographical information: A case study in the context of management of fluvial zones. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2), 105–114. Goodale, R., Hopkinson, C., Colville, D., & Amirault, D. (2007). Mapping Piping Plover habitat in coastal areas using airborne LiDAR data. Canadian Journal of Remote Sensing, 33(6), 519–533. Goodchild, M.F. (2001). Metrics of scale in remote sensing and GIS. International Journal of Applied Earth Observation and Geoinformation, 3(2), 114-110. Goulden, T., & Hopkinson, C. (2010). The forward propagation of integrated system component errors within airborne LiDAR data. Photogrammetric Engineering and Remote Sensing, 76(5), 589–601. Höfle, B., & Pfeifer, N. (2007). Correction of laser scanning intensity data: Data and model driver approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 415–433. Höfle, B., Vetter, M., Pfeifer, N., Mandlburger, G., & Stotter, J. (2009). Water surface mapping from airborne laser scanning using signal intensity and elevation data. Earth Surface Processes and Landforms, 34(12), 1635–1649. Hopkinson, C. (2007). The influence of flying altitude, beam divergence and pulse repetition frequency on laser pulse return intensity and canopy frequency distribution. Canadian Journal of Remote Sensing, 33(4), 312–324. Hopkinson, C., Crasto, N., Marsh, P., Forbes, D.L., & Lesack, L.F.W. (2011). Investigating the spatial distribution of water levels in the Mackenzie Delta using airborne LiDAR. Hydrological Processes, 25, 2995–3011.

Hopkinson, C., Fox, A., Monette, S., Churchill, J., Crasto, N., & Chasmer, L. (2009). Mackenzie Delta LiDAR Collaborative Research Data Report. Lawrencetown, NS: Applied Geomatics Research Group (Revised 03/2011). Jahn, R.G., & Dunne, B.J. (1997). Science of the subjective. Journal of Scientific Exploration, 11(2), 201–224. Jenks, G.F. (1963). Generalization in statistical mapping. Annals of the Association of American Geographers, 53(1), 15–26. Katzenbeisser, R. (2003). About the calibration of LiDAR sensors. ISPRS Workshop: 3D Reconstruction from Airborne Laser Scanner and InSAR Data, Dresden, Germany, 8-10 October 2003, Vol. 8-10. (pp. 8–10). Lesack, L.F.W., Marsh, P., Hicks, F.E., & Forbes, D.L. (2014). Local spring warming drives earlier river-ice breakup in a large Arctic delta. Geophysical Research Letters, 41. Luzum, B.J., Starek, M., & Slatton, K.C. (2004). Normalizing ALSM Intensities. Rep_2004-07001. Gainesville, FL, USA: University of Florida. Mackay, J.R. (1963). The Mackenzie Delta Area. N.W.T. Geographical Branch Memoir 8, Misc. Report 23Ottawa, ON, Canada: Geological Survey of Canada. Marsh, P., & Hey, M. (1989). The flooding hydrology of Mackenzie Delta lakes near Inuvik, N.W.T., Canada. Arctic, 42(1), 41–49. Marsh, P., Lesack, L., Hicks, F., Roberts, A., Hopkinson, C., Solomon, S., et al. (2009). Hydrology of the Mackenzie Delta: Off-channel water storage and interactions between the Mackenzie Delta and Beaufort Sea. 17th International Northern Research Basins Symposium and Workshop Iqaluit-Pangnirtung-Kuujjuaq, Canada, August 12 to 18, 2009, 217–226. Matthies, L.H., Bellutta, P., & McHenry, M. (2003). Detecting water hazards for autonomous off-road navigation. AeroSense 2003—International Society for Optics and Photonics. (pp. 231–242). Maxa, M., & Bolstad, P. (2009). Mapping northern wetlands with high resolution satellite images and LiDAR. Wetlands, 29(1), 248–260. Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259. Pal, N.R., & Pal, K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277–1294. Prowse, T.D., & Beltaos, S. (2002). Climatic control of river-ice hydrology: A review. Hydrological Processes, 16, 805–822. Reschke, J., Bartsch, A., Schlaffer, S., & Schepaschenko, D. (2012). Capability of C-band SAR for operational wetland monitoring at high latitudes. Remote Sensing, 4(10), 2923–2943. Robertson, T.V. (1973). Extraction and classification of objects in multispectral images. LARS Symposia. (pp. 21). Santoro, M., & Wegmüller, U. (2014). Multi-temporal synthetic aperture radar metrics applied to map open water bodies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3225–3238. Sarti, A., Malladi, R., & Sethian, J.A. (2000). Subjective surfaces: A method for completing missing boundaries. Proceedings of the National Academy of Sciences, 97(12), 6258–6263. Sawaya, K.E., Olmanson, L.G., Heinert, N.J., Brezonik, P.L., & Bauer, M.E. (2003). Extending satellite remote sensing to local scales: Land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment, 88(1), 144–156. Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. 23rd ACM National Conference. (pp. 517–524). Song, J.H., Han, S.H., Yu, K.Y., & Kim, Y.I. (2002). Assessing the possibility of land-cover classification using LiDAR intensity data. PCV'02: Photogrammetric computer vision. ISPRS Symposium, Graz, Austria,;vol. Commission, III. (pp. 1–4). Strahler, A.H. (1980). The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, 10(2), 135–163. Wehr, A., & Lohr, U. (1999). Airborne laser scanning: An introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing, 54, 68–82.