A case study applying the BCCVL (Biodiversity & Climate Change Virtual Laboratory)

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How can drone data be used in modelling?
A case study applying the BCCVL (Biodiversity &
Climate Change Virtual Laboratory)
Dr Fabiana Santana, University of Canberra
Fabiana.Santana@canberra.edu.au

2. Credit AGribuHon
• BCCVL slides and handout provided by
– Dr Chantal Huijbers, Training & ScienHfic Support Officer,
BCCVL (c.huijbers@griffith.edu.au)
• What’s Drones and Big Spa/al Data got to do
with it?
– Dr Fabiana Santana

3. Agenda
1. Intro to Species Distribution Modelling
2. What’s Drones and Big Spatial Data got to do
with it?
3. Practical Session
- Run an SDM in the BCCVL
- Overview of results
- Climate Change Projections
- Conclusions & Feedback

5. 08!
Prof Brendan Mackey
Climate Change
Griffith University
Prof Emeritus Henry Nix AO
Climate Change
A/Prof Shawn Laffan
Geospatial analysis/ Biodiversity
University of New South Wales
A/Prof Jeremy VanderWal
Ecology
James Cook University
Dr Linda Beaumont
Climate Ecology
Macquarie University
Scientific Advisors
A/Prof Sama Low Choy
Statistics
Griffith University
A/Prof Fabiana Santana
Engineering & Computer
Science
University of Canberra
Mr Lee Belbin
Ecology
Atlas of Living Australia
A/Prof Mark Kennard
Freshwater ecology
Griffith University

6. How?

7. Problem
Criteria
Data
Analysis
Results
EvaluaHon
The analytical process

8. The problem

12. Agriculture, research, industry…each of these scenarios
highlight the need for more information and scenario
planning to help identify land and habitats that are suitable
now and into the future and how they change with climate.
So where do we start?

13. What do we need to
answer those questions, and
how can the BCCVL help?

14. Lots of Data
Direct import of species data from
Atlas of Living Australia & GBIF
Photo: ©Shane Ruming
> 4000 Climate data layers
– Current and future climate
– Range of climate change scenarios
and global climate models
> 300 Environmental data layers
(soil, vegetaHon, run off, GPP, fPAR etc)
Australian Current Climate
30 arcsec (~1 km resoluHon)
Mean temperature of weGest quarter
NaHonal Soil Grids

15. Analysis: 6 different experiments (and counting…)
PotenHal distribuHon of species under current climaHc
and/or environmental condiHons
Species DistribuHon
Experiment
MulH-Species
DistribuHon Experiment
Effect of environment on
species traits
Species Trait
Experiment

16. Effect of climate change
on predicted species
distribuHons
Thorny devil, 2045
RCP 8.5 ‘business as usual’
Thorny devil, 2085
RCP 8.5 ‘business as usual’
Climate Change
ProjecHon
Analysis of biodiversity,
species richness, rarity,
endemism
Biodiverse
Experiment
Combine model outputs
to reduce uncertainty
Mean
Min
Max
Ensemble
Analysis
Analysis: 6 different experiments (and counting…)

17. Photo: ©Shane Ruming
Species
data

18. Photo: ©Shane Ruming

19. Max. temp of warmest month
Environmental
data
- Temperature
- PrecipitaHon
- Topography
- VegetaHon
- …

20. Species
data
Environmental
data
Algorithm
Species Distribution Model
Species Distribution
Model

21. ✗
✗ ✗
Occurrence data
✗ Absence data
Species data

22. ✗
✗ ✗
True absence
Temperature (°C)
27
-5
þ
ý
Species data

23. Inferred or Pseudo-absence
Pseudo-absence
✗
✗ ✗
Species data

24. Random
Contrasting environmental
conditions
Min & max radius
area where pseudo-
absence points can be
generated
no pseudo-absence
points
occurrence records Species data

25. e.g.
Rainfall
Evaporation
e.g.
Temperature
e.g.
Light (PAR)
e.g.
Soil type
Moisture Thermal Radiation
Mineral
nutrients
Environmental regimes
Environmental data

26. Source: Xu, T., & Hutchinson, M. F. (2013). New developments and applicaHons in the ANUCLIM spaHal climaHc and
bioclimaHc modelling package. Environmental Modelling & So7ware, 40, 267-279.
How is environmental data generated?
Recording staHons
Environmental data

27. Summary staHsHcs Recording staHons
Mean Max Min
Source: Xu, T., & Hutchinson, M. F. (2013). New developments and applicaHons in the ANUCLIM spaHal climaHc and
bioclimaHc modelling package. Environmental Modelling & So7ware, 40, 267-279.
How is environmental data generated?
Environmental data

28. Mean Max Min
SpaHal interpolaHon Summary staHsHcs Recording staHons
Temperature (°C) at stations
13 14 16 20 23
14 14 16 19 24
18 16 16 18 22
24 22 19 19 21
30 27 23 20 20
14
20
24
16
30 27 20
Temperature (°C) interpolated
Source: Adapted from hGp://planet.botany.uwc.ac.za/nisl/GIS/spaHal/chap_1_11.h
How is environmental data generated?
Environmental data

29. RESULT
Source: hGps://en.wikipedia.org/wiki/Contour_line
SpaHal interpolaHon Summary staHsHcs Recording staHons
How is environmental data generated?
Environmental data

30. A model is only
as good
as the data
you put in it Acknowledge
Data accuracy / quality
• AlternaHve species names
• Outliers
• Duplicate records
• Matching Hme frames

31. Species
data
Environmental
data
Algorithm
Species Distribution Model
Species Distribution
Model

32. Geographic
models
Statistical regression
models
Profile
models
Machine learning
models
Algorithm

33. Geographic
models
longitude
latitude
+
+
+
++
+
+
+
+
+
+
+
+ +
Geographic
models
• Presence only
• No environmental data

34. Profile
models
Profile
models
• Presence only
• Need environmental data
NOTES
> con/nuous variables only
> no interac/ons
Environmentalvariable2
Environmental variable 1
v2 max
v2 min
v1max
v1min
+
+
+
+
+
+
+
+
+
+ +
+
+
+
+
+ +
+
+
+
+ +

35. Geographic
models
Statistical regression
models
Linear predictor
Link
function
Logofoddsratio
Generalized Linear Model
• Presence and (pseudo)absence
• Use environmental data
NOTES
> continuous & categorical predictors
> include interactions
Linear predictor
Link
function
Logofoddsratio
Generalized AddiHve Model
MulHvariate AdapHve Regression Splines
Probabilityof
speciesoccurrence
Environmental variable
Knot

36. • Need environmental data
Machine learning
models
Tree-based models
> presence &
(pseudo) absence
0 1
1
0
0
0
ArHficial Neural
Networks
> presence &
(pseudo) absence
Maxent
> presence only
Probabilitydensity
Environmental variable
Merow et al. 2013

37. Geographic
models
Statistical regression
models
Profile
models
Machine learning
models
?

38. What’s Drones and
Big Spa/al Data
got to do with it?

39. Drones and Big SpaHal Data
• Input data for modelling purposes
… but it’s not that simple!

40. Let’s take
a look…
• Sample Data
– Sample Banana Plant
Available online at: hGp://doc.arcgis.com/en/drone2map/quick-start-exercises/sample-data.htm

41. Drone Data
• GeolocaHon

42. Drone Data
• Images:
BB001.jpg
• NOT species data
• NOT environmental
data

43. Can we obtain species data?
• Yes, but it requires some work...
• We have the geolocaHon, but what’s in the
image?
– Visual analysis, digital image processing, …

44. Digital Image Processing
Drones

45. Digital Image Processing

46. Environmental Data
• Extract informaHon from images:
– E.g., vegetaHon index

47. Big SpaHal Data
• Usually applied to define environmental layers

48. Running an SDM
in the BCCVL

49. Go to
www.bccvl.org.au

50. InterpreHng your results

51. Probability
of occurrence
Reality check:
☐ Are mapped predictions plausible?
☐ Consider dispersal barriers
Results: Species distribution map
Photo: ©Shane Ruming

52. Results: Compare distribution maps

53. Results: Overlay distribution maps

54. Generalized Linear Model
ClassificaHon Tree
Bioclim 01
(annual mean temperature)
Bioclim 12
(annual precipitaHon)
Results: Response curves

55. TSS
Accuracy
FAR
ROC
Kappa
MCRTPR
NPP
Biasscore
FNR
TNR
Oddsratio
FPR
ETS
Results: Evaluation statistics

56. Probability of presence (continuous)
0 (low)
1 (high)
threshold0.5
PresentAbsent
Results: Evaluation statistics
Categorical prediction: present/absent

57. Present
Absent
Present
(Suitable)
Absent
(Not suitable)
Prediction
Observation
Present Absent
True
positive
True
negative
False
positive
False
negative
Contingency tableResults: Evaluation statistics
True Positive Rate = proportion of observed presences correctly predicted =
TP
TP + FN
True Negative Rate = proportion of observed absences correctly predicted =
TN
TN + FP

58. perfect predicHon
False PosiHve Rate
0 1
True PosiHve Rate
0
1
beGer
worse
AUC = area under the curve
0.5-0.7 = poor model performance
0.7-0.9 = moderate
> 0.9 = excellent
Results: EvaluaHon stats – RelaHve OperaHng CharacterisHc (ROC)
= 1 – True NegaHve Rate
Evaluation statistics: ROC curveEvaluation statistics: ROC curve

60. Climate
Impact
Modelling
Experiment

61. SDM Algorithm
Climate Change
Projection
Predicted distribution
under current climate
Predicted distribution
under future climate
Representative
Concentration
Pathway
General
Circulation
Model

62. Representative Concentration Pathways (RCP)
Radiativeforcing(W/m2)
2000 2025 2050 2075 2100
10
8
6
4
2
0
RCP 2.6
RCP 4.5
RCP 8.5
RCP 6
Temperature
increase (°C)
4.9
3.0
2.4
1.5
Trajectory
Rising
Stabilising
Stabilising
Peak and decline
CO2 conc.
(ppm)
1370
850
650
490

63. Representative Concentration Pathways (RCP)
Radiativeforcing(W/m2)
2000 2025 2050 2075 2100
10
8
6
4
2
0
RCP 2.6
RCP 4.5
RCP 8.5
RCP 6
Business as usual: no policy changes
to reduce emissions
Intermediate:
Intermediate:
Peak and decline: ambitious scenario,
reducing emissions
use technology and
implement strategies to
stabilize emissions

64. General Circulation Models (GCM)

65. Running a Climate
Impact Model
in the BCCVL

66. Want more?
Online Open Course
Workshop program for:
• Undergraduate courses
• Researchers
• Industry professionals
Topics:
• Data cleaning
• Design your model: criteria,
consideraHons, limitaHons
• Species DistribuHon Models
• Biodiversity Analysis
• Climate Change projecHons

67. Thank you!
FEEDBACK