How Many Hippos (HOMHIP): Algorithm for automatic counts ... 6 Applications...This study aims at...

How Many Hippos (HOMHIP): Algorithm for automatic counts of animals with infrared thermal imagery from UAV

Communication presented at GeoUAV Worshop in the frame of the ISPRS Geospatial Week 2015

La Grande Motte, 2nd October 2015

Ir Simon Lhoest

SIMON LHOEST, JULIE LINCHANT, SAMUEL QUEVAUVILLERS, CÉDRIC VERMEULEN, PHILIPPE LEJEUNE Forest Resources Management Axis, Biosystems engineering (BIOSE) University of Liège – Gembloux Agro-Bio Tech

2

Introduction Methodology Results Conclusion Discussion & Perspectives

Hippopotamus amphibius L.

• Vulnerable species • Groups : 10 to 200 individuals • In water during the day • Drawbacks in classic monitoring methods

Objective

Construction of an algorithm for the automatic count of hippos groups from thermal infrared images acquired by UAV

Integration in the open source QGIS software

3

Introduction Methodology Results Conclusion Discussion & Perspectives

Why doing monitoring ?

1. Quantify the impact of poaching Natural growth = 6%/year Reinforcement/moving of monitoring troops

2. Complete demographic analyses More precise previsions of probable populations evolution

3. Evaluate the impact of hippos on their environment Erosion? Soils compaction? Food disponibility? Competition between herbivorous species?

4

Introduction Results Conclusion Discussion & Perspectives

• Detection + counting • Mainly for birds colonies

+ Save time & efforts + Easy to use + Generally reliable results - More difficult than human interpretation

Automatic procedures

© Chabot, 2009

Canada geese Snow geese

Criteria : Aggregation of individuals (but too close together) High contrast (animal – background) Sufficient image quality

Methodology

© Julie Linchant

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Methodology

Falcon Unmanned© UAV

Camera Tamarisk 640 (λ = 8-14 µm)

Garamba National Park (DRC)

Flight heights : 38 – 155 meters (Pixel GSD = 3.8 – 15.5 cm)

14 flights, 2 study sites

Manual extraction of images

Raster values = temperature

© Basile Luse Belanganayi

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Methodology

• Reference data = visual and manual digitization of hippos 3 categories of hippos : o Completely emerged animals (CEA)

o Pairs of Polygons of Single Animals (PPSA)

o Nearly Immerged Animals (NIA)

• 37 extracted images • 2126 digitized polygons = 1856 hippos

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Methodology

• Python script for all the geoprocessing • QGIS application • Graphical User Interface

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Methodology

Algorithm steps

Relative coordinate system (pixel unit)

Local maxima

detection

Radius

Threshold

Minimum distance

Local maxima

(Loc_max)

Clipped image

(Cl_im)

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Methodology

Algorithm steps Clipped image

(Cl_im)

Spatial

join

Local maxima

(Loc_max)

Local maxima

detection

Radius

Threshold

Minimum distance

Isolines

interval

Isolines

creation and

polygonization

Isoline polygons

(Iso_polyg)

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Methodology


(Cl_im)

Spatial

join

Local maxima

(Loc_max)

Local maxima

detection

Radius

Threshold

Minimum distance Isolines

interval Isolines creation

and polygonization

Isoline polygons

(Iso_polyg)

Polygons

selection

Minimum area

Maximum area

Minimum perimeter

Maximum perimeter

Emerged parts

(Em_parts)

y = 636631x-1.834

R² = 0.8423

0

100

200

300

400

500

600

700

800

0 50 100 150 200 Max

imal

surf

ace

(pix

el u

nit

²)

Flight height (m)

y = 195.87x-0.898

R² = 0.4713

0

2

4

6

8

10

12

14

16

0 50 100 150 200

Min

imal

surf

ace

(pix

el u

nit

²)

Flight height (m)

y = -0.9051x + 152.76

R² = 0.8805

0

20

40

60

80

100

120

140

0 50 100 150 200 Max

imal

per

imet

er (

pix

el u

nit

)

Flight height (m)

y = -0.0402x + 11.46

R² = 0.4551 0

2

4

6

8

10

12

14

16

0 50 100 150 200 Min

imal

per

imet

er (

pix

el u

nit

)

Flight height (m)

Each one contains at least 1 LM

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Methodology


(Cl_im)

Spatial

join

Local maxima

(Loc_max)

Local maxima

detection

Radius

Threshold

Minimum distance

Isolines

interval Isolines creation

and polygonization

Isoline polygons

(Iso_polyg)

Polygons

selection

Minimum area

Maximum area

Minimum perimeter

Maximum perimeter

Emerged parts

(Em_parts)

Polygons

classification

Completely

emerged

animals

(CEA)

Pairs of

Polygons of

Single

Animals

(PPSA)

Nearly

Immerged

Animals

(NIA)

Area limit between CEA and NIA

Maximum distance between PPSA polygons

Maximum |Δϑ| angle

Maximum |α-ϑ0| angle

y = 20536x-1.421

R² = 0.7828

0

20

40

60

80

100

120

140

160

0 50 100 150 200

Surf

ace

(pix

el u

nit

²)

Flight height (m)

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Methodology

Algorithm steps

Criteria for Pairs of Polygons corresponding to a Single Animal :

1. Polygons size Area < CEA polygons

2. Proximity Maximal distance between polygons = fct (flight height)

3. Alignment - Minimum Bounding Boxes orientation characteristics - 2 angles computed : |Δϑ| &|α-ϑ0| maxima values

y = -0.1957x + 32.068

R² = 0.7626

0

5

10

15

20

25

30

35

0 50 100 150 200

Max

imal

dis

tance

bet

wee

n

centr

oid

s (

pix

el u

nit

)

Flight height (m)

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Methodology

30% of images (rainy season)

Mean total error +3.9%

Mean NIA error +28.5%

Mean PPSA error -55.4%

Mean CEA error +20.2%

Minimal total error -4.9%

Maximal total error +12.9%

NIA (Nearly

Immerged Animals)

PPSA (Pairs of Polygons

corresponding to Single Animals)

CEA (Completely

Emerged Animals) Total

Automatic –

manual correlation 0.47 (p = 0.149) 0.14 (p = 0.674) 0.42 (p = 0.197) 0.98 (p < 0.001)

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Methodology

Image processing Influence of input parameters! Difficult to deal with hippos very close together Hippos categories tend to compensate in counts, but … necessary improvement! Automation of the masking process & selection of images?

Practical recommendations of flight Rainy season (April to November) Not in the end of the afternoon Avoid fog

Exploitation of results Some hippos (under water) are not detected Necessity of a correction factor: 1.25, according to Delvingt (1978) & Lhoest (2015)

Sensors and UAV improvement Combination of several sensors? Use of high resolution infrared photos? Multicopter platform?

Rainy season

Dry season

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Methodology

• Such algorithms are really important to deal with the huge amount of UAV data:

Great perspectives

Save time

Easy to use (open source software)

Standardized & reproducible procedure avoid operator effect in counts!

Adaptable parameters

• Improvement still necessary: Automation of pre-process

Determination of optimal input parameters values

+

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How Many Hippos (HOMHIP): Algorithm for automatic counts ... 6 Applications...This study aims at...

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