2. Study Area:
Plum Creek Timberland Ownership
Essex County, VT
County = 676 Square Miles
PCT Ownership = 85,190 Acres
(additional 991 acres in neighboring counties included here)
3. Abstract
Goals:
Locate Hydrologic features utilizing LiDAR data on company owned timberlands located
in Northeastern Vermont
o LiDAR Basics (Learn / Understand)
o Understand the importance of hydrology in a forest and why it needs identification &
classification
o Outline the end user needs (Foresters)
o Isolate an Area of Interest (AOI) where field hydrology has been surveyed by foresters
o Exceed current company hydrology layer in accuracy and detail
Perform further useful analysis based on outputs derived from LiDAR data in ArcGIS
10.2
o QA / QC data
o Derive surface and elevation models
o Produce vector line stream features (classified) in adequate detail
o Produce raster models of vegetation canopy, slope, aspect and contour layer
4. LiDAR ASPRS Codes
Light Detection and Ranging (like SONAR
but light pulses through air)
Up to 500,000 pulses per second
Result is the ability to map in very high
resolution 3D space
Pulse returns are recorded in classes and
portrayed as point clouds, each point is a
reflection (return) off of a ground feature
Returns have Horizontal Coordinates (X,
Y) and a vertical plane (Z)
American Society for Photogrammetry
and Remote Sensing (ASPRS) Defines
classification codes for different returns
Classification Value (bits 0-4) Meaning
0 Never classified
1 Unassigned
2 Ground
3 Low Vegetation
4 Medium Vegetation
5 High Vegetation
6 Building
7 Noise
8 Model Key
9 Water
10 Reserved for ASPRS Definition
11 Reserved for ASPRS Definition
12 Overlap
13–31 Reserved for ASPRS Definition
5. Constraints
• Sheer size of datasets can be
huge
• Essex County Vermont = 431,360
acres
• Dataset contains 254 LAS files
• 3,583,771, 446 LAS Points!!!
• Processing can be extensive
• Much of the processing for this
project was cut down to 88 files
and 1,388,031,413 points
• Tiles that intersect tracts
• Smaller AOI of 9 tiles also
created
6. Sensors
Leica ALS 50 Airborne Laser Scanner
o Low Inertia High Speed Scan Mirror
3000 meters elevation
75 Degree field of view
o Vertical Accuracy around 15 cm
o Horizontal Accuracy below 1 meter
o System Controller : drives optical scanner, reads the scan angle, controls GPS timing,
formats outputs for recording on a high speed data logger
o Position and Orientation System
o Galvanometer: compares the scan position from controller with actual position
o Data source : removable hard disk
o Laptop with Operator Interface Software (Diagnostics)
o Capable of detecting multiple targets from a single outbound pulse
o Multiple Return Intensity: Different levels of Forest Canopy along with distance to
sensor are measured
7. Sensors
Optech Airborne LiDAR Terrain Mapper
o Undocumented which specific model used
o Optech’s Orion C300-1 most comparable system to the ALS50
Inertial and virtual referencing
Simultaneous control and flight monitoring
In air point cloud histogram display
Real Time LAS file generator
8. Data• Minimum Point Density:
• Forested areas require more points per square meter than bare earth
• Field of View: Not to exceed 15 degrees off nadir (30 degree scan angle threshold)
• Degree of Overlap (between flight lines): less than 20% can lead to gaps in data
Raw LiDAR data .LAS files were acquired from the Essex County Soil Survey Team (VT)
Data gathered by Sanborn Map Company contracted by US Army Corps of Engineers
between 2005 and 2006
Ground Control: 5 GPS base stations (all NGS control monuments)
Inclement weather forced multiple deployments to complete the area
The full GPS network and tile layout of the project ranged between the following
coordinates:
• N 44°20’ to N 45°00’
• W 71°30’ to W 72°05’
Designed to achieve 1.5 meter (or better) ground spacing resolution
DEM generation for Topographic mapping
Metadata
10. Company Data
County
Tract
Stand
AOI
Internal Land Systems (ILS)
Hydrology
o Derived from the National
Hydrology Dataset
o Field gathering from Foresters
Distinguishing NHD data from
field data (DSL Name field)
o “DSL Hydrology” = NHD data
o “null” = Field gathered data
o Layers are merged “in house”
by regional analyst and
entered into ILS
11. QA/QC
• Ensures the product received matches metadata and specifications
• Can reveal important aspects of data that can affect outcome
• Overlap
• Point Cloud Density and relation to threshold level
• Points that fall outside the norm
• Environmental Noise
• Points below ground level
• Utilizes the LAS Point Statistics as Raster Tool
• Tool will create a raster based on statistical measurements from the LAS files
that are referenced by the LAS dataset
15. QA/QC Results Point File Information:
Provides statistical information regarding the distribution in LAS point clouds
• Point Spacing • Point Count
17. Terrain Dataset
• Begin by creating a new File GDB and
Feature Dataset
• Define the Coordinate System
• NAD 83 UTM Zone 19N, NAVD 88
• The average point spacing must be specified.
• This value is the average distance between
two points in the LAS files.
• This information was ascertained by the
“Point File Information Tool”
• The LAS classifications codes are filtered to
the specified value. In this case value 2 for
ground points (select by attributes tool)
• Statistics are computed on the Point Spacing
field
• Average Point Spacing = 1.62
• The feature dataset will house the new
feature class
18. LAS to Multipoint Tool &
New Terrain Feature Class
• Converts the raw LiDAR data into a multipoint feature
class
• Class 2 (ground) results provide the closest bare earth
representation
• Average point spacing is defined by the Point File
Information Tool and is rounded to 2 meters
• Output is stored on the new feature dataset
• The new Terrain is created based on the multipoint
feature class
• Result: Triangulated Irregular Network (TIN)
representation of the area
• Symbolized to accentuate features
• Time intensive process as there are over 3 billion points
in the dataset (over 5 hours)
19. LAS Dataset
• Creates a reference for the whole set of
.LAS files
• Subset (AOI) created as well
• A new LAS Dataset is created in the Arc
Catalog
• Files are added to the dataset via the
“LAS Dataset Properties” window
• Add files button
• Map to LiDAR files
• Run Statistics, examples:
• Point Spacing
• Number of Returns
• Scan Angles
• Intensity
• Set X, Y and Z coordinates for the Dataset
20. LAS Dataset
• Newly Created LAS Dataset shows tiles that represent the area of data coverage
• Can also be viewed as a TIN representation at scales of 1:8000 or smaller
• The LAS Dataset Toolbar was utilized to filter down to just ground returns, all other returns are displayed as
colorless areas
• Further analysis uses linear interpolation to fill those areas
21. Hydrologic DEM
• Digital Elevation Models (DEM’s) are created from each dataset
• Based solely on ground return data
• These will provide representation of ground terrain, water paths,
depressions and drainage
• Crucial in forest management planning to avoid streams and wet areas and
certain times of the year
• Different methods are employed to convert the datasets, but the results
are similar
• DEM’s from each dataset are created for the whole area but an AOI was
developed to speed up processing and achieve the correct workflow
22. Terrain DEM
• Utilized the Terrain to Raster tool in the 3D Analyst > Conversion Toolbox
• The input is the terrain feature class
• multipoint layer (created earlier)
• The output is specified to the default project geodatabase
• Default Inputs Accepted for:
• Data Type = Floating Point
• Method = Linear (calculates the cell value based on liner interpolation of TIN triangles)
• Sampling Distance = 250 (# of cells on the longest side is defined with a default distance)
• Chosen over Cell Size because this defaults to a raster cell size of 10, too large for accurate modeling
• Pyramid Level = 0 (preserves full resolution)
• Result is the Raster DEM
• Whole county processing time 2.5 hours
• AOI (9 tiles) processing time approximately 5 minutes
24. LAS Dataset DEM
• Before conversion, the LAS dataset Properties are opened and filtered to ground (2)
classification only
• The LAS Dataset to Raster tool found in the Conversion > To Raster toolbox is used
• The input is the filtered LAS Dataset
• Output to default project geodatabase
• Interpolation Type: Binning
• The cell values are derived by point values within each cell
• Defined by taking all the averages of all points within a cell
• Cells with no points (colorless) use a linear option where values are
triangulated across the colorless areas and interpolated for cell values
• Default floating type is maintained
• Cell size was used for sampling size, sampling value was defined at 3 for higher
resolution
• This value defaults at 10 but that was deemed to large
• 1 was too small and processing intensive
• Z factor was kept at 1 to maintain elevation values
• A change here would multiply by the input
25. LAS Dataset DEM
The image stretch on the right side is a result of the .LAS file footprint used for the AOI. This
encompasses two stands that contain ground data gathered by field foresters, this area is the baseline
for accuracy of results.
Whole Set AOI
28. Hydrology Workflow
• All tools in the workflow are found in the Spatial Analyst > Hydrology
Toolbox
• The workflow for both the Terrain and LAS DEM’s is the same
• The only variation is in the methodology for setting a conditional level
of detail to the output (will be discussed)
• Full processing was completed with the terrain dataset AOI initially
• Results were compared with the existing Internal Land Systems (ILS)
Hydrology Layer
• The Goal is to exceed this level of detail
• The result was not achieved with the Terrain DEM but was achieved with the
LAS DEM
29. Identify Sinks
• Sinks represent incorrect cell values in the
form of depressions
• Impact resulting flow direction model
• Sink depth evaluation was performed on
Terrain Dataset
• the evaluation provided a maximum z value or fill
value for input into the fill tool
• The sink tool was not time consuming on
the AOI as results were relatively small
• A total of 153 sinks were identified
• But how deep are they???
• The watershed tool
• Used to identify all the contributing areas to each
sink based on a flow direction raster (derived from
the un-filled DEM) and the output from the sink
tool (this serves as the pour point input)
• The Zonal Statistics tool
• a minimum elevation raster (output from the
watershed tool) “sink_area” is input with the DEM
and a selection of MINIMUM statistics is chosen
30. Identify Sinks
• The Zonal Statistics Tool then creates an
minimum elevation raster
• The output of the watershed tool is input
with the DEM and the selection of
minimal statistics
• The Zonal Fill tool is used to create
maximum sink rasters
• The Minus Tool subtracts the sink
minimum from sink maximum for overall
sink depth
• Result is a final sink depth of 10.62
meters
31. Fill & Flow Direction
• The Fill tool uses multiple tools behind the
scenes to calculate and fill cells
• Flow Direction
• Sink
• Watershed
• Zonal Fill
• Having relatively small variation in sink depth,
the fill tool was used without calculated z value
input for all further analysis after the terrain
dataset AOI
• All sinks were filled
• Flow Direction creates raster from the filled
DEM by creating direction from one cell to the
next based on steepest downslope neighbor
• Default range: 1-255
• Cells given lowest value of their neighbor
• Sink cells with multi direction are coded,
valued and assigned direction
• Non sink cells with multi direction are
assigned by reference and most likely
direction
32. Flow Accumulation
• After direction is established the weight
for cells that flow downslope is
determined by the Accumulation Tool
• Undefined values are classified as flow
with no change in direction until the next
weighted cell is encountered
• Output cells showing high accumulation
or concentration represent stream
channels
• Conversely little or no accumulation
represents ridges or higher elevation
• Flow direction is a required input for
accumulation
33. Conditional Threshold
• Flow Accumulation raster only shows highest concentrations of flow
• To see the hydrology the symbology must be reclassified
• Using Natural Breaks (Jenks) classification with 2 classes
• Decreasing lower break value increases level of detail
• Requires experimentation for adequate results
• 3 Methods to applying a conditional statement to the accumulation
raster:
1. Raster Calculator
2. Con Tool
3. Reclassify Tool
35. Conditional Threshold
Raster Calculator Reclassify Tool
Using the Raster calculator the flow accumulation raster
was selected and the conditional statement “setnull” is
selected, the statement is set to less than the lowest break
value followed by the highest.
Example equation: SetNull("FlowDir_DEM" < 8, 898)
The reclassify tool takes the raster with the reclassified
values as input, the first class is changed to “no data” and
the second is changed to one, excluding all values outside
the threshold
37. Stream Order
• Using the reclassified raster the
stream flow can be organized by 2
methods:
• Strahler: order increases when
streams of same order intersect
• Shreve: Based on magnitude, all
streams assigned a value of 1
and increases upon intersection
• Shreve became preferred
method due to the difference in
classification of 4th and 5th order
streams (more accurately
reflected forestry ideology)
38. Stream to Feature
• To create a linear (vector) stream layer
the Stream to Feature tool is employed
• Based on reclassified flow accumulation
and flow direction rasters
• Final step in hydrology creation
• True analysis takes place earlier in the
process to attain level of detail necessary
• In the process this phase was completed
prior to classification until desired results
were achieved
40. ILS Comparison
(Terrain Dataset)
• Terrain dataset hydrology does not
have a lot of detail
• Does not quite line up with existing
data
• Does not look like natural hydrology
lines
• Linear interpolation at a seemingly
lower resolution
• These results do not reflect the
quality of the LiDAR data
• Nor are they adequate for modeling
and classification
• The goal is to exceed current
hydrology data
41. ILS Comparison (LAS
Dataset)
• LAS Dataset hydrology yields much
better results
• Initially it can be seen after runner
the flow direction tool, the result is a
much finer raster
• Resulting stream features show
much more detail
• Resulting features follow existing
hydrology lines more accurately
42. Hydrology Results
• The final process was run on the county
wide dataset
• Results presented to the Northeast
Resources panel indicated a desire for
multiple layers
• Reclassification was performed on the
county wide dataset to produce 2 layers of a
hydrology
• Medium Detail
• High Detail
• The tract ownership for the company was
buffered by one mile, merged and dissolved
to provide for an adequate area for the
hydrology layers to be clipped to
• Results including both layers, further surface
modeling and the county DEM were
delivered via File Geodatabase
45. Surface Models
• The initial workflow for creating surface based models is similar to the
creation of the DEM
• All surface modeling is based on the full set of classes excluding noise
and first returns
• The digital surface model (DSM) will be the basis for a hill shade
raster and a canopy height model.
• The original surface return DEM will be the basis for another hill
shade (just to compare to the DSM hill shade), a slope raster, an
aspect raster and a contour dataset.
46. DSM Creation
• To create the Digital Surface Model
the filter properties on the LAS
Dataset were set to include all
classes
• Excluding Noise
• Including all first returns
• LAS to Raster tool
• The cell assignment is changed
to MAXIMUM to produce
greatest values
• Input = Re-Filtered LAS dataset
• Output = DSM
• Cell Assignment = MAXIMUM
to include the highest values
• Sampling cell size = 1 for high
resolution
47. Hillshades
• Derived from both the DEM and
DSM
• Provides effective means for terrain
visualization
• Ground features distinguishable
• Canopy visualized from DSM
• Process is the same except for input
raster
• Utilized the Hillshade tool in the 3D
Analyst Toolset
48. Canopy Height
• The DEM is Subtracted from the DSM
• Utilizing the 3D Analyst > Raster Math
Function Tool
• Establishes difference between ground
and all other first return points (height)
• Reclassified results can show height
and canopy density
Forestry Applications
• Tree Height
• Canopy Density
• Pattern Recognition
• Stand Delineation
• Age Class Determination
• Biomass Estimation
• Overall Forest Health
• Forested to Open Ratio
49. Slope
• Highlights the high risk areas
(steeper slopes)
• Utilized the 3D Analyst > Slope Tool
• Percent Slope = (Change in Elevation
/ Change in Distance) * 100
• Identify appropriate length of
riparian buffer zones based on slope
• Determine spacing between erosion
controlling water bars and drain dips
• Aids in following Best Management
Practices (BMP’s)
• Identifies areas for mitigation in
regulation
• Identifies areas where certain
machinery and harvest techniques
are / are not adequate
50. Aspect
• Utilized the 3D Analyst > Aspect Tool
• Compass direction a slope faces
• Aids management decisions in
reforestation methods
• Insight into species composition
• Aids in forest management by
identifying key aspects in planning
• Sunlight
• Moisture
• Temperature
• Shade with respect to streams
51. Contour Dataset
• Created survey grade contour lines at
specified at 1 and 3 meter intervals
• Based off of the DEM
• Utilized the 3D Analyst > Contour Function
• Most historically utilized method of
representing topography
• Still an important aid to forestry
• Widely used in forest engineering
• Helps to identify natural features such as
ridges and benches
• Immensely helpful when planning road
layouts and construction
• Aids in long term planning of boundary
layout and other feature locations (Log
decks, equipment staging areas)
• Can be cross referenced with other surface
layers
• Familiar look and feel to relatively new and
changing technologies
52. Utilization
• Developed with field foresters in mind
• Presented to a panel to discuss and define detail
• Medium and High Detail layers created here are now up and in action
on company servers
• Referenced and used by Foresters, Resource Managers and Analysts
for the company in the Northeast
• Contour layers ( 1 meter and 3 meter intervals) have been submitted
and are also up on company servers for use
• Canopy height and other surface models are under evaluation for
practical use at this time.
53. Conclusion
• Final outcome was a success!!!
• Northeast users were pleased with results, requested both levels of
hydrology detail for use
• QA/QC process proved to be extremely worthwhile in understanding that
data and its collection
• Further analysis and refining of datasets can be done
• Streamlining analysis results and organizing outputs was a huge constraint
in the end
• Results are actively being used
• Interested to see if surface (particularly Canopy_Height) models will be of
practical use in daily work
54. Deliverables
• 2 File Geodatabases
• Hydrology Layers
• Surface Models
• DEM and DSM
• 2 Hydrology layers
• Medium Detail
• High Detail
• 5 Surface Models
• Slope
• Aspect
• Canopy Height
• 1 meter and 3 meter Contour Interval Layers
• Model for Terrain Dataset
• Further work will be done to convert to LAS Datasets
• A Copy of this Report and associated PowerPoint
55. Cited
• Barton, Paul K. “Overview of Forest Hydrology
and Forest Management Effects”: Sustainable
Forest Management Network-Hydro-Ecological
Landscapes Project Workshop. University of
Western Ontario. November 10, 2006
• Behrend, Ron. Norris-Rodgers, Mark. “From
Points to Products”: Business Benefits from
LiDAR using ArcGIS 10.1 Functionality. May,
2013
• ESRI. “LiDAR Analysis in ArcGIS 10 for Forestry
Applications”: An ESRI White Paper. January,
2011
• Gritzner, Janet H. “Identifying Wetland
Depressions in Bare-Ground LIDAR for
Hydrologic Modeling”: Department of
Geography South Dakota State University.
Retrieved March, 2015
• National Oceanic and Atmospheric Association.
“Working with LIDAR in ArcGIS 10.1”: NOAA
Coastal Services Center. October, 2012
• Sanborn Geospatial Solutions. “LiDAR
Campaign Report”: St. Louis District Army Corps
of Engineers, USDA-NRCS Essex, VT. September,
2006
• Ssegane, H. Trettin, C. Panda, S. Amataya, D.
“Application of LiDAR Data for Hydrologic
Assessments of Low-Gradient Coastal
Watershed Drainage Characteristics”: Journal of
Geographic Information Systems. April, 2013
• Welty, Ethan. ”DEM – Hydro Manipulations”:
ESRI ArcGIS 9.3 University of Washington
August, 2009
56. Project Websites
A Method Using ArcMap to Create a Hydrologically – conditioned
Digital Elevation Model
http://www.iwinst.org/lidar/presentations/MN_DNR_Topo-to-
Grid_Tutorial.pdf
Convert USGS airborne LiDAR .las files (from USGS' CLICK) to DEMs
and TINs in ArcMap 10
http://www.uccs.edu/~bvogt/courses/ges4050/helpful_stuff/las_t
o_dem.html
ESRI 10.2: DEM’s & DSM’s from LiDAR
http://resources.arcgis.com/en/help/main/10.2/index.html#//015
w0000004q000000
ESRI Knowledge Base – Technical Articles: Create a threshold raster
to be used as an input for the Spatial Analyst Hydrology tools
http://support.esri.com/es/knowledgebase/techarticles/detail/42
068
LAS dataset in ArcMap 10.1
http://resources.arcgis.com/en/help/main/10.1/index.html#//015
w00000057000000
Leica Geosystems 2002 AL50 Airborne Laser Scanner
http://www.tayyareci.com/forsale/pilatus/camera/021205_ALS50
_Product_Description.pdf
Optech Inc. Orion C300-1 ALTM
http://www.optech.com/wp-content/uploads/ORION-C-
Specsheet-140624-WEB.pdf
LiDAR Training Materials UMN
http://wrc.umn.edu/randpe/agandwq/tsp/lidar/LiDARTrainingMat
erials/index.htm
Topo Mapping with LiDAR
https://www.e-education.psu.edu/geog481/l4_p3.html
University of Vermont: Pictures
http://www.uvm.edu/
Vermont Department of Forests, Parks and Recreation
http://fpr.vermont.gov/forest/vermonts_forests/amps
Washington State University: Pictures
https://wsu.edu/
