LiDAR as a tool to monitor biodiversity

Roland Brandl University of Marburg

Jörg Müller Bavarian Forest National Park

Thomas Nauß University of Marburg

Background

• Maps of biodiversity are important in basic

and applied ecology

• Sampling biodiversity is costly

• Mapping biodiversity using remote sensing

opens up perspectives

– large extent

– fine grain

Habtitat suitability

Capercaillie

LiDAR in the scientific literature

Web of Science: LiDAR Web of Science: LiDAR & …

Passive vs. active sensors

LiDAR

1.

2. 3. 1.

1.

● Scan angle

● Runtime

● Intensity

(individual images by openclipart.org)

Horizontal Resolution

cm (airborne) …

m (spaceborne)

LiDAR: Costs

• Compared to field

surveys LiDAR is

cheap if one

considers the extent

• LiDAR surveys are

partly available from

public resources

LiDAR Field campaign < 10 € ha-1

Processing 5 € ha-1

< 15 € ha-1

Ground survey 30 to 100 € ha-1

Vegetation density measured by space filling:

2 % 50 % 65 %

2 % 50 % 65 %

Müller et al. Oecologia 2012

20 m Radius

Courtesy D. Nill

LiDAR: Use as control variable

LiDAR: Use as predictor variable

f(LiDAR variables)

Variables derived

from LiDAR

Eco

log

ical

vari

ab

le

Statistical Model

Müller et al. BAE 2011

Use cases: Birds and arthropods

• Bavarian Forest National Park

• 171 / 223 plots of 1 ha along

4 transects

• Habitat characteristics

– field measurements

– aerial photographs

– airborne LiDAR

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Can

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y h

eig

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[m]

X [m

]

Y [m]

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X [m

]

Y [m]

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X [m

]

Y [m]

A B C

Open stands Mixed forest Mature beech forest

Müller et al. BAE 2011

Plot characteristics

Number of tree species 2.71 1 6 0.855 2.23

Number of cavity trees 1.66 0 12 1.93 3.49

Volume of snags [m³ ha-1] 37.6 0.00 411 2.86 9.03

Maximal diameter in breast height [cm] 57.6 8.00 130 0.57 1.08

Age of the oldest tree [years] 131 0 400 2.16 6.92

Gaps without regeneration [m²] 272 0.00 7376 5.48 36.1

Young broadleaf forest, height 0 to 6 m [m²] 801 0.00 8799 2.59 6.18

Young coniferous forest, height 0 to 6 m [m²] 446 0.00 9108 4.03 16.7

Middle aged broadleaf forest, height 6 to 12 m [m²] 1114 0.00 8641 1.97 3.39

Mature broadleaf forest. height above 12 m [m²] 2568 0.00 9620 0.751 -0.577

Mature coniferous forest. height above 12 m [m²] 3051 0.00 10,000 0.824 -0.428

Edge length of patches [m] 568 0.00 1257 0.048 -0.381

Mean Min. Max. Skewness Kurtosis

Mean canopy height [m] 14.6 1.75 28.6 -0.336 -0.242

Standard deviation of canopy height [m] 8.29 2.87 12.4 -0.286 -0.110

Maximum canopy height [m] 37.7 22.6 51.7 0.261 -0.0276

Penetration rate 5 to 1 m above ground [%] 71.3 35.1 93.5 -0.593 -0.508

Penetration rate 10 to 2 m above ground [%] 63.5 21.1 76.3 -0.367 -0.523

Variables derived from LiDAR

Variables extracted from aerial photographs

Variables from field measurements

Müller et al. BAE 2011

Uses: Birds – predictor variables

Canonical root 1Aerial photographs

-0.1 0.0 0.1 0.2

Ca

no

nic

al

roo

t 1

LiD

AR

-0.2

-0.1

0.0

0.1

0.2 a

Canonical root 1Field measurements

-0.1 0.0 0.1 0.2

1 2 3 4 5

0

0.5

1.0

1 2 3 4 5

0

0.5

1.0

b

Pairwise correlations between sets: < 0.5

But canonical correlations: > 0.8

Müller et al. BAE 2011

Uses: Birds – predictor variables

„predictive power“:

LiDAR > field measurements

Müller et al. BAE 2011

predictive power R²

0 0.1 0.2 0.3 0.4 0.5

Ph. trochilus

Ph. collybita

P. modularis

F. coelebs

T. troglodytes

S. atricapilla

S. europaea

L. curvirostra

C. familiaris

T. viscivorus

P. ater

R. ignicapillus

T. merula

E. rubecula

T. philomelus

C. spinus

Py. pyrrhula

P. caeruleus

P. palustris

P. cristatus

R. regulus

G. glandarius

P. major A B

unique contribution

0 0.1 0.2

Performance – R² (test data set)

Aerial photographs

LiDAR

Uses: Birds – abundance

223 plots

Predictive R²

0 0.2 0.4 0.6

Tenuiphantes alacris

Macrargus rufus

Drapetisca socialis

Walckenaeria cucullata

Amaurobius fenestralis

Cybaeus angustiarum

Pardosa ferruginea

Centromerus sellarius

Callobius claustrarius

Micrargus georgescuae

Diplocephalus picinus

Cryphoeca silvicola

Tenuiphantes tenebricola

Histopona torpida

Saloca diceros

Coelotes terrestris

Robertus scoticus

Pardosa lugubris

Harpactea lepida

Gnaphosa badia

Pardosa sordidata

Diplocephalus latifrons

Alopecosa taeniata

Centromerus pabulator

Eurocoelotes inermis

Ground

Lidar

Frequency

0 50 100

A B

„predictive power“:

LiDAR > field measurements

Vierling et al. Ecol Appl 2011 Performance – R² (test data set)

171 plots

pitfall traps

Eurocoelotes inermis

Centromerus pabulator

Uses: Spiders – abundance

Space

[3]

Assemblage of forest passerines

31 species – 20.6%

15.6 % 5.1 % 13.5 %

14.7% 5.2%

2.0%

21.0 %

0 20 40 0 20 40 0 20 40 0 20 400 20 40 0 20 400 20 40 0 20 40

0 20

Joint effects

Independent effects

Müller et al. BAE 2011

Uses: Birds – community composition

Müller & Brandl J Appl Ecol 2009

Uses: Arthropods – community

Pitfall traps

Flight-interception traps

Total

R2

Total

95% CI

LiDAR

%

Biotic

%

Abiotic

%

Total

R2

Total

95% CI

LiDAR

%

Biotic

%

Abiotic

%

Individuals 8.9 –0.3–19.9 17.7 –8.0 54.2 43.8 30.5–55.7 99.8 75.8 54.8

Richness 3.0 –8.7–10.7 99.5 99.0 5.9 26.4 13.6–39.4 89.1 30.1 48.4

Diversity 3.7 -6.5–19.8 99.0 54.3 -25.7 23.8 14.3–34.0 94.8 60.5 66.5

Body size 27.1 14.3–39.6 87.6 60.6 19.0 14.7 3.9–27.2 66.8 31.0 14.0

171 plots

50,910 individuals

782 species

Müller & Brandl J Appl Ecol 2009

Uses: Communities – species richness

Bässler et al. Biodivers Conserv 2010

Uses: Forest types

• LiDAR

• Climate and soil

• Vegetation (Cover-abundance of species)

a) b c)

LiDAR

Accuracy: 69%

Climate and soil

Accuracy: 68%

Vegetation

Accuracy: 67%

Bässler et al. Biodivers Conserv 2010

Asperulo-Fagetum

Picea-forests

Bog-forests

Luzulo-Fagetum

Uses: Forest types

40 50 60 70 80

0

10

20

30

40

50

Fre

qu

en

cy

LiDAR

40 50 60 70 80

0

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20

30

40

50

Overall accuracy [%]

Fre

qu

en

cy

Climate and soil

40 50 60 70 80

0

10

20

30

40

50

Overall accuracy [%]

Vegetation

Bässler et al. Biodivers Conserv 2010

Uses: Forest types

Reduced training data (50%)

Predictive R²

0 0.2 0.4 0.6

Tenuiphantes alacris

Macrargus rufus

Drapetisca socialis

Walckenaeria cucullata

Amaurobius fenestralis

Cybaeus angustiarum

Pardosa ferruginea

Centromerus sellarius

Callobius claustrarius

Micrargus georgescuae

Diplocephalus picinus

Cryphoeca silvicola

Tenuiphantes tenebricola

Histopona torpida

Saloca diceros

Coelotes terrestris

Robertus scoticus

Pardosa lugubris

Harpactea lepida

Gnaphosa badia

Pardosa sordidata

Diplocephalus latifrons

Alopecosa taeniata

Centromerus pabulator

Eurocoelotes inermis

Ground

Lidar

Frequency

0 50 100

A B

predictive power R²

0 0.1 0.2 0.3 0.4 0.5

Ph. trochilus

Ph. collybita

P. modularis

F. coelebs

T. troglodytes

S. atricapilla

S. europaea

L. curvirostra

C. familiaris

T. viscivorus

P. ater

R. ignicapillus

T. merula

E. rubecula

T. philomelus

C. spinus

Py. pyrrhula

P. caeruleus

P. palustris

P. cristatus

R. regulus

G. glandarius

P. major A B

unique contribution

0 0.1 0.2

Challenges

• Biodiversity modelling with LiDAR is

phenomenological

Why differ species in predictability?

• Hypothesis: Predictability as a species trait?

None Strong

Pagel´s

Body size

all values P < 0.003

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Challenges: Phylogenetic signal

100 possible trees from https://www.birds.org/

None Strong

0.0 0.2 0.4 0.6 0.8 1.0 1.2

100 possible trees from https://www.birds.org/

Pagel´s

Predictability

all values P < 0.003

predictive power R²

0 0.1 0.2 0.3 0.4 0.5

Ph. trochilus

Ph. collybita

P. modularis

F. coelebs

T. troglodytes

S. atricapilla

S. europaea

L. curvirostra

C. familiaris

T. viscivorus

P. ater

R. ignicapillus

T. merula

E. rubecula

T. philomelus

C. spinus

Py. pyrrhula

P. caeruleus

P. palustris

P. cristatus

R. regulus

G. glandarius

P. major A B

unique contribution

0 0.1 0.2

Challenges: Phylogenetic signal

Body size female [mm]

0 2 4 6 8 10 12

Pre

dic

tive R

²

0

0.2

0.4

0.6

Frequency

20 40 60 80 100

Niche position Shading

0.4 0.8 1.2 1.6

Pre

dic

tive R

²

0

0.2

0.4

0.6

Niche position Moisture

-0.4 0.0 0.4 0.8

R² = 0.08p = 0.17

R² = 0.12p = 0.08

R² = 0.27p = 0.01

R² = 0.01p = 0.56

Vierling et al. Ecol Appl 2011

open closed moist dry

Eurocoelotes inermis

Centromerus pabulator

Challenges

Summary

• LiDAR provides fine grained information of the vegetation structure

• LiDAR information captures similar characteristics than ground

measurements

• LiDAR information can be used to model biodiversity

• The performance of models differs considerably between…

– Species

– characteristics of assemblages

– composition of assemblages

• These variations in performance occurs also with other data sets

• Hypotheses and tests to predict predictability are badly needed