Read everything before doing anything with the software. 1
Accurate elevation and detailed image data are among the most important geospatial data layers needed to
support economic development and make better decisions about flood protection, emergency services, and
managing Pennsylvania’s resources. Pennsylvania needs to rebuild “PAMAP” (the Commonwealth’s official digital
map) for two reasons: (1) many natural changes and human modifications have occurred across the state’s terrain
since it was last mapped (back in 2008); and (2) LiDAR and aerial imaging technologies have improved since 2008
to produce much better resolution products at lower costs. Decisions made with obsolete data can have costly
consequences, especially when and where floods occur (Fig. 1).
Figure 1: FEMA’s 100-year floodplain near Johnstown, PA, is displayed with some 3D buildings. Building colors show dollar loss estimates, with higher values in red and orange. Source: Cambria County GIS Center.
So, what is lidar? LiDAR is a geospatial technology that makes precise distance measurements using laser light (Bolstad, 2019;
Abdullah, 2016; NEON Science, 2014; O-Neil-Dunne, 2013). The word “LiDAR” is an acronym for Light Detection
and Ranging. When used with high-precision GNSS (for exterior orientation) and an IMU (for interior
orientation)(see Figures 2 and 3), LiDAR data can be used to derive digital surface models (DSM); tree canopy
models; building models; and bare-earth digital elevation models (DEM). LiDAR systems come in different flavors
(e.g., bathymetric vs. terrestrial) and can produce different quality levels (QL) of data (Table 1). LiDAR data
support all kinds of geologic, ecologic, and archeologic work (e.g., WDNR, 2019; BEG, 2014; Asner, 2013).
Read everything before doing anything with the software. 2
Figure 2: Airborne LiDAR diagram: 1. During flight the aircraft uses GNSS hardware and software to constantly measure its location, altitude, time, and to simultaneously ingest and apply GNSS corrections in real-time. 2. Also simultaneously, the aircraft uses an Inertial Measurement Unit (IMU) to measure aircraft pitch, roll, and yaw. 3. With aircraft position and orientation known, laser light pulses can be emitted from a precisely known point, along a precisely known trajectory, and at a precisely known moment (see NEON Science, 2014)
Table 1: LiDAR Quality Level (QL) characteristics (from USGS, 2018: p23-24).
LiDAR
Quality Level
Nominal pulse
spacing (m)
Nominal pulse
density
(pulses/sq.m)
NVA @ 95% c.i.
(m)
VVA @ 95% c.i.
(m)
Minimum DEM
cell size (m)
QL0 ≤ 0.35 ≥ 8.0 ≤ 0.10 ≤ 0.15 0.5
QL1 ≤ 0.35 ≥ 8.0 ≤ 0.20 ≤ 0.30 0.5
QL2 ≤ 0.71 ≥ 2.0 ≤ 0.20 ≤ 0.30 1.0
QL3 ≤ 1.41 ≥ 0.5 ≤ 0.40 ≤ 0.60 2.0
DEM = digital elevation model; NVA = non-vegetated vertical accuracy; VVA = vegetated vertical accuracy.
Read everything before doing anything with the software. 3
Figure 3: Near-infrared (NIR) laser pulses are emitted downward in a scanning motion from a moving aircraft and
reflected back to the aircraft (at a forward position, of course) depending on the distance traveled, incident angle,
type of reflector, and the amount of atmospheric scattering. (Left) Relatively smooth and solid objects on the
ground can reflect most of the energy in each pulse as a single return; such returns are characterized as ‘first
returns’ and as ‘high intensity’ returns. Building rooftops, bridges, paved roads, bare ground, and exposed
bedrock surfaces typically generate high-intensity returns. (Right) Light pulses that are split, refracted, or partially
absorbed may return to the forward aircraft position as one or multiple lower-intensity returns (1st, 2nd, …, nth).
Trees, however, can scatter light among their leaves and branches, so trees usually return multiple low-intensity
signals from different elevations. (Both) In all cases, the time it takes each emitted laser pulse to travel from the
aircraft’s emitting position and arrive at the aircraft’s forward sensing position is used (insert some geometry,
trigonometry, and physics equations here) to find the 3-D position of the reflecting spot. Clear deep water (not
shown in this figure) absorbs NIR energy (i.e., no pulse energy is returned to the aircraft), so open water bodies
tend to create gaps or holes in LiDAR point cloud data whenever NIR LiDAR is used.
Pennsylvania’s PAMAP program Between 2006 and 2008, Pennsylvania became one of the first states in the country to capture wall-to-wall aerial
photography and QL3 LiDAR data, which were both considered excellent at the time. The QL3 data cost $15M
(17.4M in $2018) to collect and process, and has since informed billions of dollars of engineering, planning,
environmental, and investment decisions. By pure coincidence, these data were collected and released at just the
right time to support natural gas resource development, infrastructure and construction management, and
natural resource conservation in Marcellus Shale regions. PAMAP LiDAR and image products are still among the
most-downloaded datasets from PASDA despite the data no longer representing current conditions at many
locations. Demand for updated LiDAR and aerial image products has been growing statewide.
Read everything before doing anything with the software. 4
Current status of LiDAR updates in Pennsylvania Since 2014, the U.S. Geological Survey’s 3-D Elevation Program (3DEP; see USGS, 2019) has partially funded
several QL2 LiDAR acquisitions in Pennsylvania (Figure 4) that support national flood hazard mitigation programs,
transportation planning, and Chesapeake Bay Program compliance. The work is being accomplished, however, in
piecemeal fashion; each of the pieces is not always coordinated with the others. Nevertheless, all the new data
are eventually posted to PASDA and free to download.
When federal agencies are involved, most new PA LiDAR data are spatially referenced to one of the two
metric UTM grid zones (17N or 18N) and organized using square 1500-meter tiles that align with those grids
(Quantum Spatial, Inc., 2018). The data are stored in *.LAS 1.4 format. The filename 18TUK325446.las, for
example, indicates it is situated in zone 18N (N is assumed because … Pennsylvania) and anchored at the point
325000 m N, 4460000 m E. We’ll be working with four (4) LiDAR tiles collected in October 2018.
Figure 4: The current state of LiDAR data coverage for the Commonwealth. Cartography: Jeff Zimmerman @ Susquehanna River Basin Commission.
Read everything before doing anything with the software. 5
Current status of aerial imagery updates in Pennsylvania
High resolution aerial photography is usually collected in Pennsylvania on an ad hoc or a just-as-needed basis.
Wealthy counties or cities can often afford to fly annual repeat photography to help them maintain or improve
their spatial data. Meanwhile, depopulating rural counties may not have collected any new imagery since 2008.
Fortunately, the Pennsylvania Emergency Management Agency (PEMA, 2018) has begun collecting new high
resolution aerial imagery (6-inch cell size). Importantly, PEMA captured 4 spectral bands of information (R,G,B,
and NIR) and not just the usual three (R,G,B). The four spectral bands can be used in different combinations to
display a landscape in natural color (R,G,B), to display a landscape in false color infrared (NIR,B,G), or to derive
useful vegetation indices like NDVI (Eq. 1).
𝑁𝐷𝑉𝐼 = (𝑁𝐼𝑅−𝑅)
(𝑁𝐼𝑅+𝑅) Eq. 1
When only state agencies are involved, new image data in Pennsylvania are spatially referenced to one of
the imperial State Plane coordinate grids (PA-North or PA-South) and broken into square 10,000-foot tiles that
align with those grids. The image tiles are stored in *.jp2 format. The filename for tile 31002160PAS.jp2, for
example, indicates the tile is situated in the PA-South zone and anchored at 310000 USft E, 2160000 USft N.1
The purpose of this lab is to provide you with a hands-on opportunity to develop your knowledge and skills
associated with Pennsylvania’s new aerial imagery and new QL2 LiDAR data; which includes visualizing the data in
3-D and deriving some commonly used datasets from them. We are not going to couch this lab in terms of a
current event or some geoenvironmental problem; instead, we are just going to learn how to handle and
geoprocess these new data. They will be used across Pennsylvania for years to come. You are in the very first
cohort of GIS3 students to ever handle these new QL2 LiDAR data.
1. Prepare the new QL2 LiDAR and image data for 3-D visualization and terrain analysis.
2. Visualize your image and LiDAR data and derive some commonly used raster surfaces.
3. Build map figures for others to see what you discovered.
1 Yes, the convention used for naming aerial image tiles could not be any more annoyingly different than the convention used for naming LiDAR point cloud files.
Read everything before doing anything with the software. 6
We’ll focus our attention on a small 9 sq.km (3.5 sq.mi) area centered near Williams Grove Speedway and Park
(hereafter, just Williams Grove). Williams Grove hosts a working racetrack and an abandoned amusement park.
Williams Grove is located along the Yellow Breeches Creek, along the Cumberland County-York County border,
about 3 miles east of Boiling Springs, and about 3 miles south of Mechanicsburg Borough. Use Google Maps or a
similar map service to find Williams Grove and to get yourself acquainted with the speedway and the surrounding
area. Topographic relief is minimal, save the creek floodplain, but you’ll find multiple land uses and cover types.
You have been given a ZIP file that contains four (4) QL2 LiDAR tiles in *.LAS 1.4 format, one (1) shapefile that
represents the image tiling system, and one (1) shapefile that represents the LiDAR tiling system.
Advice The raw data alone consumes a lot of disk space (approx. 2 GB unzipped). I strongly recommend taking advantage
of your computer’s fast internal hard drive (the C:\Geotemp folder, best), an external hard drive with a USB3
connection (blue, 2nd best), or an external hard drive with a USB2 connection (white, 3rd best). Do not use thumb
drives – their glacial read-write speeds will torture you. Do not use your network drive – you’ll fill it.
Also, I strongly recommend watching O’Neil-Dunne (2019) before starting any work, for most of the methods you
need to handle LiDAR data in ArcGIS Pro are presented in his video, not in this handout.
Download the data for this lab and extract all ZIP files into your GIS3/Labs folder.
Start a new session of ArcGIS Pro
1. Use ArcGIS Pro’s > Catalog template > to create a new project in your
GIS3/Labs/GIS3_Lab_WilliamsGrove folder, but uncheck the box for creating yet another new
folder. Creating a new ArcGIS Pro project will create an ArcGIS Pro project file (*.aprx), a toolbox (*.tbx), a
file geodatabase (*.gdb), and it will automatically make your new geodatabase your default geodatabase.
2. To facilitate geoprocessing, we’ll use the spatial reference system (SRS) to which the LiDAR tiles are
already tied: (NAD83-2011) UTM Zone 18 N, metric. That means setting the coordinate system of our
map(s) and using the Toolbox’s Environment Settings to specify output coordinate systems accordingly.
Read everything before doing anything with the software. 7
3. Watch O’Neil-Dunne (2019) before continuing.
In ArcGIS Pro > Catalog view
4. Build a new LAS dataset (in your project folder, not in your geodatabase).
5. Right-click your LAS dataset. Add your *.las files to the LAS dataset and calculate statistics. No
surface constraints are needed for our study area.
6. Review the statistics for each file and for the entire LAS dataset.
7. Right-click your LAS dataset and Add it to a new map, where you can explore your LiDAR data. Next,
Add the PEMA tile system shapefile to see how and where it overlaps with your LAS dataset.
8. Leverage the spatial overlap to identify the PEMA image tiles that cover your LAS dataset. Download the
linked *.JP2 files into your GIS3_Lab_WilliamsGrove folder. Unzip them with your preferred utility.
Back in ArcGIS Pro > Catalog view
9. You might need to right-click > refresh your GIS3_Lab_WilliamsGrove folder to get ArcGIS to
realize you just unzipped some new stuff there. Wake up ArcGIS!!
10. Next, build a new mosaic dataset (in your geodatabase, not in your folder) to manage your image tiles.
Keep in mind that your JPG-2000 images (*.jp2) have 4 bands of spectral data (red, green, blue, and
NIR), and each band is stored with unsigned 8-bit pixel depth. The output SRS you assign to your new
mosaic dataset (see Step 2) will not match the SRS of your underlying *.jp2 images – that’s okay.
11. Right-click your mosaic dataset and add your PEMA raster datasets to it. Be sure to specify the input
SRS for your *.jp2 files (just import the SRS from one of the files).
12. Add your mosaic dataset to your existing map so you can explore it. You may need to change
your map scale (i.e., zoom in) to reveal the 6 inch (15 cm) pixels inside each footprint.
Back in ArcGIS Pro > Catalog view
13. Add your LAS dataset to a new local scene so you can visualize your point cloud in 3-D.
Read everything before doing anything with the software. 8
14. Right-click your new layer and change layer Symbology based on the LiDAR point attributes. Give your
computer a chance to redraw the point cloud before exploring.
a. Elevation (default)
b. Classification
c. Intensity
d. Elevation enhanced by intensity
15. Right-click your layer to Filter your LiDARdata. Revisit O’Neil-Dunne (2019) and step 14 as needed.
a. Bare ground surface: Ground category only, all returns
b. Covered terrain surface: all categories, first return only
i. Use your mosaic dataset to Colorize your LAS dataset
16. Use the knowledge you’re developing about filtering LiDAR point cloud data, one of your LAS dataset
layers, and ArcGIS Pro’s LAS Dataset tools to Export:
a. A raster Digital Surface Model (DSM) that represents the landscape as it is covered.
b. A raster Digital Elevation Model (DEM) that represents bare earth topography, a ground surface
devoid of trees, noise, and anthropogenic structures.
17. To help you visualize your exports, derive hillshade models from your DEM and your DSM.
18. From your DEM only, derive contours (both index and intermediate). By convention, index contours
are usually some multiple of 10 elevation units; the cartographer’s choice of contour index/label interval
depends on both: a) the amount of terrain relief in the study area that needs to be represented; and b)
the amount of space needed on the map for plotting contour labels, other labels, and other map symbols.
You should have completed at least this much geoprocessing in one week. Bring your results to class
and be prepared to discuss them. Use your second week to fix any problems, interpret your results,
and build your maps and report.
Read everything before doing anything with the software. 9
19. Layout and share at least three (3) map figures to show others what you found.
a. A clean annotated reference map so others can find the study area.
b. An annotated 2-D map that highlights your bare earth hillshade model and topographic contours.
c. A 3-D scene that highlights one or more anthropogenic ‘features’ lurking in the LiDAR point cloud.
In your report, be sure to include: 1. Three tables with an interpretation for each;
a. Table 1: The number of returns and average point spacing in each LiDAR tile. Add a row for totals
at the bottom and a column for percentages.
i. Using your table, compare and contrast the average point spacings among your four (4)
LiDAR tiles against the nominal point spacing threshold associated with QL2 data (Table 1)
ii. According to the USGS standards shows in Table 1 (this handout), what is the appropriate
cell size you should use when exporting raster surfaces from these QL2 LiDAR data? Did
you follow the USGS standard?
b. Table 2: The number of returns associated with each unique classification code in your LAS
dataset. Add a row for totals at the bottom and a column of percentages.
c. Table 3: The number of returns associated with each return value (1st, 2nd, …, nth ). Add a row for
totals at the bottom and add a column for percentages.
i. Using the data you presented in your Table 3, describe and interpret the relationship you
see between return value (1st, 2nd, …, nth ) and the number of returns.
2. For each raster surface you derive (DEM, DSM, other), report the exact LAS dataset filter and the exact
output raster cell size you chose.
3. For each map figure you build (Objection 3, Method 19), build a concise but complete paragraph that
describes what is illustrated your figure. Use your words to guide your reader’s eyes as they look at your
map. GIS Analysts don’t just make maps; they also help others read and interpret their maps.
4. Finally, compare and contrast what you learned can be interpreted easily from: a) the aerial photography,
b) the LiDAR point cloud, c) the DSM hillshade model, and d) the DEM hillshade model.
Read everything before doing anything with the software. 10
Build a well-written lab report that presents the data and the results of your investigation. Include your name,
date, and lab title on the first page. Insert page numbers. Your report should be printed on letter size paper. Set
all page margins to be 0.7” except for the left margin, which should be set to 1.2.” Use 1.5 line spacing, set the
normal font face to be Candara or Bookman Antiqua, and set the normal font size to be 11 points.
Your report should include five sections with bold and left-justified headings: Purpose, Objectives,
Methods and Data, Results and Answers, and Summary. Your Purpose and Objectives sections should be written
in your own words and must address the purpose and objectives this lab.
Your Methods and Data section should contain concise descriptions of your data sources and methods
used. Annotated cartographic models are always useful, but otherwise include screen captures (as figures) that
illustrate how you setup your geoprocessing tools (both parameters and environment settings). PC users can use
the Snipping tool to capture windows on your screen.
The Results and Answers section should include answers to the questions that arose during the process.
In your Summary, describe how well you accomplished your individual objectives and the overall purpose.
Note: All tables and figures must be numbered sequentially, have captions, and be referenced in your
text. Table and figure captions should not be orphaned. When appropriate, table and figure captions can be used
to declare units of measure. All tables and figures must be inserted inline with your text and not added as
attachments. Add a line space before and after each table/caption combo (or figure/caption combo) to buffer
them from your adjacent paragraphs. Table columns that contain text strings ought to be left-justified. Table
columns that contain numbers should be right-justified with all the decimal points aligned (i.e., all values should
be reported with the same decimal precision). Table captions should precede the tables they describe. Figure
captions should proceed the figures they describe. Use your normal font when building tables; avoid using
automatic styles that add useless colors or a lot of text decorations for no apparent reason.
Note: Maps and other figures should be legible when printed (not just on screen). Map layouts exported
into TIF image using a high pixel density (600 d.p.i.) usually don’t suffer any pixelated text or jagged linework
artifacts, but figures exported into JPG format with the default low pixel density of your screen (72 d.p.i.) will
almost always appear pixelated or even illegible when printed.
In GIS3, ‘innovation points’ are available to any and all students that document an attempt – whether successful or
unsuccessful – to push themselves and learn more than what was required by the lab assignment.
Read everything before doing anything with the software. 11
(required readings are shown in red)
Abdullah, Qassim. 2016. A Star is Born: The State of New Lidar Technologies. Photogrammetric Engineering and Remote
Sensing, May 2016, p307-312.
Asner, Greg. 2013. Ecology from the Air. YouTube. Last accessed on February 15, 2020 at
https://youtu.be/qCrVpRBBSvY
Bureau of Economic Geology. 2014. Unveiling the Earth's Surface: Airborne Lidar at UT's Bureau of Economic Geology.
YouTube. Last accessed on April 4, 2019 at https://youtu.be/JDR0ttLw6_A
Bolstad, Paul. 2019. GIS Fundamentals, 6th Edition. Eider Press.
Chapter 6: pages 245-251, 262-263, 288-292
Chapter 7: pages 307-308
Kimerling, Jon A. with Aileen R. Buckley, Phillip C. Muehrcke, and Juliana O. Muehrcke. 2012. Map Use: Reading,
Analysis, Interpretation, 7th Edition. Esri Press Academic. ISBN 978-1-58948-190-9
Chapter 6: page 124
National Geographic. 2018. Better Images of Cities Than From Satellites? It's Called LIDAR | National Geographic.
YouTube. Last accessed on April 4, 2019 at https://youtu.be/iSRK1NIT-vA
NEON Science. 2014. How Does LiDAR Remote Sensing Work? Light Detection and Ranging. YouTube. Last accessed
on February 15, 2020 at https://youtu.be/EYbhNSUnIdU
O’Neil-Dunne, Jarlath. 2013. LiDAR 101. YouTube. Last accessed on April 4, 2019 at https://youtu.be/1l0GwRLv2cM
O’Neil-Dunne, Jarlath. 2019. LiDAR Surface Models in ArcGIS Pro. YouTube. Last accessed on April 4, 2019 at
https://www.youtube.com/watch?v=L4tVXARSrUo
Pennsylvania Emergency Management Agency. 2018. PEMA 2018 0.5-foot Orthoimagery - Cycle 1 2018. Pennsylvania
Spatial Data Access (PASDA). Last accessed on January 20, 2020 at
https://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=5104
Quantum Spatial, Inc. 2018. South Central Pennsylvania 2017-2018 QL2 LiDAR, LAS 1.4 files. Pennsylvania Spatial
Data Access (PASDA). Last accessed on January 20, 2020 at
ftp://ftp.pasda.psu.edu/pub/pasda/usgs/LiDAR2017/LAS/
USGS. 2019. 3D Elevation Program: What is 3DEP? U.S. Department of the Interior, U.S. Geological Survey. Last
accessed on April 4, 2019 at https://www.usgs.gov/core-science-systems/ngp/3dep/what-is-3dep
USGS. 2018. Lidar Base Specification, Version 1.3, February 2018. Chapter 4, Techniques and Methods, of Section B,
U.S. Geological Survey Standards, Book 11, Collection and Delineation of Spatial Data. U.S. Department of the
Interior, U.S. Geological Survey. Last accessed on April 5, 2018 at https://pubs.usgs.gov/tm/11b4/pdf/tm11-
B4.pdf
Washington Department of Natural Resources. 2019. The Bare Earth Story Map: How lidar in Washington State
exposes geology and natural hazards. ArcGIS Online. Last accessed on January 20, 2020 at
https://arcg.is/1DGeqL