Christopher Cole

BLM National Operations Center (NOC)

The Use of Unmanned Aircraft Systems

(UAS) for Resource Management and

Fine Scale Monitoring

• The BLM is a leader in the use of emerging Unmanned

Aircraft System (UAS) technologies for managing

natural and cultural resources on public lands

• The BLM is producing fine scale, high quality geospatial

products for multiple study sites using UAS

• We are actively pursuing the use of UAS to derive select

AIM core terrestrial indicators (i.e., cover type and

percentage cover)

• UAS-derived indicators supplement and enhance

existing sample collection efforts, and can help

facilitate effective resource management

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Introduction

• Provide an overview of UAS technologies, and their

relevance for resource management

• Describe some of the geospatial products which can be

derived using UAS

• Highlight selected resource management applications

and completed projects

• Discuss a photogrammetric point cloud classification

study which has relevance for fine scale monitoring

using UAS

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Presentation Objectives

• Defined as an aircraft system without a pilot onboard

• “Unmanned” systems are not unmanned - they require

human operation and control

• BLM focus = small UAS (<55 pounds)

What is an Unmanned Aircraft System (UAS)?

Manned aircraft flights

can be problematic due to

weather, safety concerns,

and operating costs

Satellite observations can

be hindered by spatial

resolution, weather

conditions, and long data

acquisition intervals

Field surveys can be

expensive, logistically

challenging and

geographically limited

UAS provide fine-

scale, cost-effective

scientific geospatial

products for resource monitoring

Why are we using UAS Technologies?

Landsat 7 ETM+ (30m)

NAIP 2010 (1M)

UAS at 400ft (5cm)

UAS at 200ft (2.5cm)

U.S. Dept. of the Interior UAS Platforms

AeroVironment – Raven RQ-11 A

Wing Span 55 inches

Air Vehicle Weight 4.2 lbs

Range 10+ km (LOS)

Airspeed 30 mph (cruise)

Endurance 90 min Lithium Battery

Payload

EO/IR Full Motion Video

GPS- Radio uplink & down link

GCS/RVT - Combined Weight – 14 lbs

Air Vehicle Weight 18 lbs

UAS System Weight 51 lbs

Range 10 km

Endurance 47 minutes - Gas Powered

Payload EO/IR Sensor

Max Speed 45 mph

Flight Characteristics Hover and Stare Capable

Honeywell – T-Hawk RQ-16

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MLB Super Bat (2015)

U.S. Dept. of the Interior UAS Platforms

Wing Span 103 inches (8.5 ft)

Air Vehicle Weight 35 lbs

Range 9 km

Airspeed 39 mph (cruise)

Endurance 10 hours (gas powered)

Payload

EO/IR , DSLR Camera, MS Imager

GPS- Radio uplink & down link

Cameras/Sensors

• Full Motion Video:

Natural Color/EO

Thermal IR

GoPro Hero 2 & 3 - 1080P,

4K HD camera

Sony ActionCam – GPS

enabled

• Camera/Imaging Systems:

Tetracam Multi Imager

Canon T3i EOS Rebel

Canon EOSm Micro

Canon SX260HS & S100 –

GPS enabled (RGB and IR)

Ricoh GR – no GPS

UHR ORTHOIMAGERY

3-D POINT CLOUD DATA ELEVATION MODELS FULL-MOTION VIDEO

SPECTRAL INDICES (NDVI)

Geospatial Data Products

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• Field data collection (AIM)

• Rangeland health

• Vegetation monitoring

• Habitat monitoring

• Fuels projects

• Fire rehab

• Noxious weeds

• Wildlife surveys

• Stream channel morphology

• Archaeological site inventories

• Recreation use and inventories

• Transportation planning/OHV management

• Hazardous Materials

• Trespass / ROW compliance

UAS Applications

• Used ultra high-resolution (UHR)

imagery collected from air and

ground-based manned systems to

measure cover at fine spatial scales

• Demonstrated use of UHR imagery

to:

Assess rangeland

condition (shrub cover,

grazing treatments)

Measure terrestrial

indicators (i.e., bare

ground)

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Previous Work – T. Booth and S. Cox (CO, WY)

• Produced a high-resolution DEM from

UHR imagery collected by UAS

• DEM accuracy comparable to field

elevation measurements (erosion

bridges)

• Can model rangeland topography and

quantify erosion at fine spatial scale

10 meters

Soil Erosion (Jornada Range, NM)

Jornada Range, New Mexico

Mammoth Trackway

• Used a UAS to provide

photogrammetric documentation

of extremely fragile fossilized

footprints from the late Ice Age

• Can be monitored over time

Pleistocene Trackway Mapping

(White Sands National Monument, NM)

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Benefits

- Aviation safety concerns were mitigated

- Refuge obtained improved bird count

- Birds were not disturbed by the flight

operations

Sandhill Crane Population (Monte Vista

National Wildlife Refuge, CO)

Method Costs

Fixed Wing Survey (Ocular Survey) $4,310

Fixed Wing Survey (Remote

Sensing - Contractor)

$35,000

UAV Survey (Remote Sensing) $2,645

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• Cooperative, multi-project effort (BLM,

Mesa County Sheriff’s Department)

• Included gravel pit volumetric change

estimation, landslide monitoring and

dinosaur quarry mapping

• Cost-effective effort which supported

gravel pit compliance inspection

Traditional Aerial Mapping:

$10,000

UAS Mission: $120

DEM Hillshade Orthophotography Volumetric Change (meters)

Volumetric Estimation (Mesa County, CO)

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• UAS imagery can be used to

produce cover estimates

similar to plot level data

collected in the field

• Stereo imagery facilitates

accurate cover

interpretation, and height

measurements

• Allows access to difficult to

reach, potentially unsafe

areas

• A complement to field data -

NOT a substitute

• Extends the field season

% Sagebrush Cover =

# Sagebrush “hits” (over all transects)

total # of transect points

% Sagebrush = 58/150 = 38.67%

Remote Sensing

as Field Data:

• Produced photogrammetrically-derived point clouds

over two study sites in the National Petroleum

Reserve – Alaska (NPRA) using a helicopter-mounted

digital camera

• Mapped cover types from point clouds for both sites Bare Soil

Graminoids

Shrubs

Forbs/Herbaceous

Rock

Litter

• Compared results to AIM plot data for accuracy

validation

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Semi-Automated Cover Classification

from Photogrammetry

Site 1 (left) & Site 2 (right)

Study Sites – NPRA, Alaska

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Parameters Site 1 Site 2

Number of images 129 142

Flying altitude 62.253 m 62.052 m

Ground resolution 0.00983 m/pixel 0.00985 m/pixel

Coverage area 0.0596 square km 0.0635 square km

Tie-points 1,629,957 189,799

Errors 0.148 pixel 0.153 pixel

• Imagery were acquired from a helicopter using a mounted

Nikon D800 (28 mm lens)

• Imagery were processed, and then classified using a Support

Vector Machine (SVM) learning algorithm

Data and Methods

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Classified cover for plot sites derived from image point clouds.

The black dots represent the computed location of 50 LPI points along each

transect for sites 1 and 2.

Site 1 Site 2

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Indicators

Site 1 Site 2

SVM AIM

SVM AIM

Points

Classified

Point Cloud Points

Classified

Point Cloud

Bare soil 299,859 0.128 0.280 145,783 0.052 0.231

Graminoid 165,489 0.065 0.040 91,721 0.033 0.034

Shrub 428,875 0.180 0.193 1,742,017 0.621 0.326

Forb/herb 142,152 0.065 0.087 113,316 0.040 0.041

Rock 656,242 0.286 0.253 156,067 0.056 0.081

Litter 651,645 0.286 0.087 557,209 0.199 0.286

Total 2,344,262 1.000 0.940 2,806,132 1.000 0.999

Comparison – Percent Cover (SVM

Classified Point Cloud vs Field Data)

Comparisons on the plot coverage for each life form indicator between

classified point clouds and AIM plots for both sites: Site 1 (left) and Site 2

(right).

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Comparisons on the plot coverage for each life form indicator between

classified point clouds and AIM plots for both sites: Site 1 (left) and Site 2

(right).

Comparison – Percent Cover (SVM

Classified Point Cloud vs Field Data)

AIM Site 1 AIM Site 2

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• UAS technologies are being used by the BLM for

many resource management applications

• UAS-based indicator products supplement, not

replace, existing field techniques

• Results and methods described in the

photogrammetric point cloud classification study

could be obtained using UAS technologies

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Conclusions

Questions?

BLM Contact Information:

Chris Cole Remote Sensing (303) 236-0913 cjcole@blm.gov

Brian Hadley Photogrammetry (303) 236-0167 bchadley@blm.gov

Lance Brady UAS (303) 236-4242 lrbrady@blm.gov

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