08/01/2012. LiDAR. LiDAR Benefits. LiDAR-BASED DELINEATION OF WETLAND BORDERS. CCFFR-2012 Society for Canadian Limnologists:

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LiDAR CCFFR-2012 Society for Canadian Limnologists: Science for Wetland Policy and Management LiDAR-BASED DELINEATION OF WETLAND BORDERS Distance from laser to ground and back again: Determined as

used to determine the location and elevation of the reflecting surfaces (roofs, leaves, ground, etc). Point cloud data Jae Ogilvie, Hua Kim Wen, and Paul A.

us/divisions/prodmgt/aerial/pages/lidarbasics.aspx LiDAR Benefits Abstract This presentation introduces a GIS-based method designed to sharpen the delineation of wetland borders.

1 LiDAR CCFFR-2012 Society for Canadian Limnologists: Science for Wetland Policy and Management LiDAR-BASED DELINEATION OF WETLAND BORDERS Distance from laser to ground and back again: Determined as laser-pulse travel time taken between emission and return to the air-borne pulse sensor Laser pulse emission rate: up to 500,000 per second Elevation: flying height and position of the aircraft are used to determine the location and elevation of the reflecting surfaces (roofs, leaves, ground, etc). Point cloud data Jae Ogilvie, Hua Kim Wen, and Paul A. Arp Department of Forestry and Environmental Management, University of New Brunswick, NB. January 5-7, Moncton, NB Edited from: LiDAR Benefits Abstract This presentation introduces a GIS-based method designed to sharpen the delineation of wetland borders. This method utilizes LiDAR point-cloud data (1m resolution) in the ESRI environment for systematic digital representations of bare-ground elevation, slope, mean of the standard deviation slope within a 20 m radius, vegetation height above bare ground and depth-to-water (DTW). The criteria for an automatic delineation of wetlands borders are set as follows: DTW < 1m, standard deviation of slope within a 20 m radius < 0.1, and vegetation height < 2 m. These criteria delineate GPS-tracked wetland borders within 4 m 8 times out of 10, conform well to high-resolution surface images, and tend to be more consistent in this regard than image delineated wetland borders. Once the various LiDAR derived data layers are assembled, the results can be further scrutinized through image overlay and by way of line scanning, to obtain a vertical view of the upland-wetland- wetland upland transitions. Applications of this methodology within the context of rural and urban development and related wetland conservation planning are also discussed. Much improved slope, flow channel, wet-areas and wetland mapping Leading innovations towards precision forestry, agriculture and wetland conservation Towards single-tree forest inventory and detailed vegetation classification Many applications in natural resource management, from field operations to policy formulations Edited from: 1

Wet Areas or depth-to-water mapping: the concept Vegetation height Flow Channels Cartographic depth-to-water (DTW) DEM surface 1. Prepare bare-ground DEM surface from LiDAR data (last returns) 2.

Use the wet-areas delineation algorithms to determine the cartographic depth-to-water index (DTW) across the landscape, using all flow channels and shorelines for the DTW = 0 reference 4.

2 Wet Areas or depth-to-water mapping: the concept Vegetation height Flow Channels Cartographic depth-to-water (DTW) DEM surface 1. Prepare bare-ground DEM surface from LiDAR data (last returns) 2. Predict locations of stream channels; decide on flow initiation area (e.g. 4 ha) 3. Use the wet-areas delineation algorithms to determine the cartographic depth-to-water index (DTW) across the landscape, using all flow channels and shorelines for the DTW = 0 reference 4. Subtract DTW from DEM to get the cartographically referenced water table elevation 5. Overlay the first LiDAR returns to obtain vegetation height. LiDAR bare-ground DEM, hill-shaded + cartographic depth-to-water (DTW) 0-1m 15 m drop Hydrological risk show case: Grand Bay NB, Highway 7, LiDAR bare-ground DEM, hill-shaded 2

LiDAR bare-ground DEM, hill-shaded + cartographic depth-to-water

circle), with 2m contours Fir Spruce Fir Spruce Cedar Cedar Larch

Spruce Beaverdam Alder Focusing on the wetland: DEM-base slope

WAM: delineating watershed border (left, yellow line); flood risk

combined with ppt during storm events determines max flow rate at

3 LiDAR bare-ground DEM, hill-shaded + cartographic depth-to-water (DTW) 0-1m + average tree height (moving average per 20 m circle), with 2m contours Fir Spruce Fir Spruce Cedar Cedar Larch Cedar Fir Spruce Cedar Black spruce (raised bog) Fir Spruce Fir Spruce Beaverdam Alder Focusing on the wetland: DEM-base slope derivation plus tree height (2 m intervals) + DTW (0 1 m) Using WAM: delineating watershed border (left, yellow line); flood risk mapping using the filled and unfilled DEM; watershed area combined with ppt during storm events determines max flow rate at culvert location, and matches that with existing or contemplated culvert capacity. 3

LiDAR DEMs: Conformance (%) 80 60 40 Air-photo LiDAR-WAM Prov.

4 100 Probability of wetland border 'x' meters away from DTW=0.5m contour Using provincial DEMs: generally ± 40 m, 8 times out of 10 Using bare-ground LiDAR DEMs: Conformance (%) Air-photo LiDAR-WAM Prov. DEM WAM generally ± 4 m, 8 times out of Nearest distance between GPS track and selected DTW contour or air-photo wetland border, in m 4

5 Another development example 5

Using TRAIL to optimize proposed forest road locations (black) Elevation (m) Elevation

10P7 10P8 10P9 10P10 Proposed 10P11 Alternative 10P12 10P13 10P14 9113m 9472m Road

0 Normalized Distance Elevation (m) Elevation (m) 500 490 480 470 500 490 480 SP1 Road

SP15 S2 S3 S4 S5 S6 S7 S8 S9 7174m 7115m Road 3TRAIL 470 0.0 0.2 0.4 0.6 0.8 1.

6 Using TRAIL to optimize proposed forest road locations (black) Elevation (m) Elevation (m) P1 10P2 10P3 10P4 Road 1AP P5 10P6 10P7 10P8 10P9 10P10 Proposed 10P11 Alternative 10P12 10P13 10P m 9472m Road 1TRAIL Normalized Distance Elevation (m) Elevation (m) SP1 Road 3AP SP2 SP3 SP4 SP5 SP6 SP7 S1 Proposed SP11 SP8 SP9 SP10 SP12 SP13 Alternative SP14 SP15 S2 S3 S4 S5 S6 S7 S8 S9 7174m 7115m Road 3TRAIL Normalized Distance Wet areas mapping: DTW according to municipal drainage infrastructure (blue shading) Wet-areas mapping, across the maritimes region 6

levels: low (green), high (red) Sackville,

7 Sackville, NB LiDAR DEM (1m Resolution) Digitized Mapped Water & Prov. Stream Network New Brunswick: Coastal flooding and cliff erosion risk map Risk levels: low (green), high (red) Sackville, NB Hillshade of LiDAR DEM (1m Resolution) Sackville, NB LiDAR DEM (1m Resolution) Drainage Ditches, Breaches, Dykes & Municipal Infrastructure 7

Marsh: Coastal flooding Inland + coastal

water atlas + 0-7 m coastal flood + 0-1 m

with storm-water management +9.09m +7.78m +8.

8 Sackville, NB LiDAR DEM (1m Resolution) Wet-Areas-Map & Flood Potential Tantramar Marsh: Coastal flooding Inland + coastal flooding, with outline of the Saxby Gale surge Sackville, NB LiDAR DEM (1m Resolution) Trans-Canada Highway Potential Flooding Tantramar Marsh Area Moncton LiDAR DEM + prov. water atlas m coastal flood m wet-areas next to flow channels and shorelines, with storm-water management +9.09m +7.78m +8.03m +8.81m +6.88m +7.16m +7.13m +5.17m 8

wet-areas next to flow channels and shorelines,

of NB flood claims, December 2010 600 500 400 300

associated with flood claim locations St.

, 2010, centered on Charlotte Mall Temporary road

Irving Gas Station Containment berms Oil

9 Moncton LiDAR DEM + prov. water atlas m coastal flood m wet-areas next to flow channels and shorelines, no storm-water drainage ETF DTW project Frequency of NB flood claims, December Depth-to-water (m) associated with flood claim locations St. Stephen: flooding Dec. 13., 2010, centered on Charlotte Mall Temporary road with two tanks Pipeline Northwest Charlotte Mall, Irving Gas Station Containment berms Oil retrieval: Temporary road with two tanks to retrieve spilled oil (drum storage) Milltown Boulevard, coastal flooding (red) 9

practices; insightful rural, urban and industrial wetland planning.

small to regions; provide many high-resolution dimensions for wetland risk assessments and related functional a are complementary to other geospatial

legal verifications. Many thanks to Block Lee Swanson Robert Hughes Nelda Craig Lori Moffat Sabine Dietz NB Env.

10 Spill source. Outlook. LiDAR-based wetland delineation leads to: improved field reconnaissance and detailed feature delineations; better and more informed wetland conservation practices; insightful rural, urban and industrial wetland planning. The wetland data layers so generated are: cartographically correct and numerically stable (+/- 4 m, eight times out of ten); cut across scales, from small to regions; provide many high-resolution dimensions for wetland risk assessments and related functional a are complementary to other geospatial data layers; facilitate communications, and promote wetland impact awareness, from private to regulatory; require ground work for final adjustments and legal verifications. Many thanks to Block Lee Swanson Robert Hughes Nelda Craig Lori Moffat Sabine Dietz NB Env. NB ETF RAC EMO DTO ASRD Calgary, October 2011 Oil spill: extent controlled by (i) hydraulic gradients (white contour lines), (ii) amount of water flow (broad arrows, proportional to upslope area), (iii) natural blocks (beaver dams), (iv) presence of water: oil floats and backs up towards shallow areas with little flow. (v) Shown: hill-shaded DEM; DTW (0 25 cm contour lines <1m with a 4 ha flow initiation threshold 10

Paul A. Arp, Mark Castonguay, Jae Ogilvie, Shane Furze Forest Watershed Research Centre Faculty of Forestry and Env. Management, UNB June 1, 2015

Atlantic Climate Adaptation Solutions Association LiDAR Acquisition in Support of Flood Hazard Mapping: New Brunswick Flood Risk Priority Areas Final Report Paul A. Arp, Mark Castonguay, Jae Ogilvie, Shane