Using LiDAR to Manage the Urban Forest in DeKalb, Illinois

Thomas J. Pingel & Dustin BergmanDepartment of GeographyNorthern Illinois University

http://www.tpingel.orghttps://github.com/thomaspingel

2015 ILGISA Annual ConferenceSeptember 15, 2015Springfield, Illinois

Project History

• The NIU Department of Geography was approached by the City of DeKalb for student interest in conducting a tree survey.

• DeKalb• 43,862 pop. (2010)• 14.81 square miles• ~8,000 parkway trees• ~200 miles of road

• Goal to develop a current tree database that can serve as a reference for LiDAR-derived processing.

DeKalb is located approximately 65 miles west of Chicago.

DeKalb

Initial Data

• Hansen 1996 survey• 5417 trees• GPS was subject to

Selective Availability scrambling

• Heights estimated to nearest 5 feet; diameters measured.

• Ash 2010 survey• 1286 trees• Extent of Emerald Ash

Borer infestation• Overlaps with the Hansen

dataset, but different IDsPosition and deletion errors arecommon in the Hansen 1996dataset.

LiDAR provides a mechanism to acquire spatial data relatively quickly and cheaply.

Our initial proposal was focused on extraction (position, height, diameter, species) from aerial sources.

We are now looking toward terrestrial scanners for improved spatial and temporal resolution.

Initial Position Estimation• Although automated

techniques are the goal, since we started with an existing survey, we wanted to update the existing database first

• Use of orthoimagery (Google Earth, NAIP data), aerial lidar, and Google Street View in QGIS to correct positions and remove deleted trees.

• Entry-level GIS work and straightforward to apply crowdsourcing techniques.

Corrected merged Hansen/Ash points using 2011 NAIP (0.3m) leaf-off imagery.

Mean error was 7.5 meters.

5043 trees in merged database.

We used Perceptually Shading Slope Mapping (a slope shading

technique) applied to the 1m LiDAR-derived Digital Surface

Model (DSM) as a base surface.

LiDAR source: DeKalb County

The DeKalb City region10.0 x 10.5 km

304 million points2.9 returns per square meter

Value-added as canopy is distinct from surrounding vegetation.

750 meters sq.

A second pass used Google My Maps (with 2015 imagery) for addition with some correction and

deletion necessary, yielding 7984 trees.

In many cases, shadows are more helpful in identifying tree locations than the vegetation.

Using Street View to disambiguate vegetation

Simple height interpolation involves subtracting the nearby minimum (~DEM) from the nearby maximum (~DSM).

The urban environment poses challenges for automated methods.

Morphological operations can efficiently provide these estimates.

• Initially, the scan is gridded to two rasters (a maximum and a minimum) (~1m), and empty cells are interpolated.• lastools works well

• An erosion is applied to the minimum (~1m radius) and a dilation to the maximum (~6m radius)

• A kind of “global” buffer operation for rasters.

• These can be found in the “raster cleanup” toolbox in ArcGIS

A binary morphological erosion operation. The grayscale version

finds the local minimum.

min

imu

mm

axim

um dilation

erosion

Median absolute error is 1 meter.

Pearson r = 0.75

Why are the estimates of these heights too high?

Tree removal!

The largest error in our estimates came from this tree at 501 College Ave.In 1996, this was a 55 foot tall silver maple.

Since the aerial point cloud is not dense enough to provide good DBH estimates, these

are estimated from heights.

R² = 0.708

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(ft)

DBH (in)

All DeKalb Maples

Expected accuracy of diameter estimates from heights using 1996 data.

Terrestrial LiDAR provides one useful approach to solving the

problem of out-of-date coverages.

We use the Velodyne HDL-32E.

Compared to airborne LiDAR collection, it boasts higher

spatial and temporal resolution.

Most applicable for street capture, although Unmanned

Aerial Vehicle mounting is coming soon for our lab.

The instrument captures scanlines which must be assembled (by the end user!) into a full point cloud.

Cloud Compare showing section of Ellwood neighborhood.

Point Cloud registration courtesy of Real Earth.

Decimeter resolution rendering of a portion of the Elwood neighborhood in DeKalb, IL.

The side view mirrors of vehicles are visible at this resolution.

The structure of trees is visible.

Future Work

• Python module to assist with point cloud reconstruction using GPS, Inertial Sensors, and SLAM tools.

• Eccentric mounting of LiDAR and driving path guidelines to assist SLAM algorithms.

• Visualizations to support human correction of data.

• Employ on UAV.

• Work with city governments to continue unlocking the potential of LiDAR.

• Thanks Steve Charlton, Miguel Morales, Steve Wargaski, and the City of DeKalb.