Data Discovery Day 03/06/2008 Lisa Wedding University of Hawaii Department of Geography

1. Using LIDAR Data to Examine Habitat Complexity &
Ecology of a Coral Reef
Lisa Wedding a,b, Alan Friedlander b,c
a University of Hawaii at Manoa, Department of Geography
b NOAA/NCCOS/CCMA/NOS Biogeography Branch
c The Oceanic Institute

2. Presentation outline
• Research objectives
• Background
– habitat complexity
• Data & methods
– Fish & habitat surveys
– LIDAR data & GIS rugosity analysis
• Results
– in-situ/LIDAR-derived rugosity
– Relationship between fish community structure
• Discussion & conclusions
– Implications for conservation & MPA design
– Future research directions

3. Research objectives
1. Evaluate the utility of LIDAR technology for deriving
rugosity (a measure of habitat structural complexity)
on a coral reef in Hawaii
2. Examine the relationship between coral reef fish
assemblage characteristics & LIDAR-derived
rugosity

4. Importance of habitat structural complexity
• Habitat complexity plays a major role
in the distribution & structure of fish
assemblages
• Provide niches, refuge from predation
–harbor high species diversity, richness &
biomass
• Significant management implications
- high complexity areas offer greater natural
protection
- ID these locations can help prioritize areas for
conservation
- inform MPA placement & design

5. Study site – No-take MPA, Est. 1967, 41 ha

6. Sampling design
Random stratified design
• Fish Censuses
• 25m x 5m transects
• Habitat metrics
• biotic cover
(coral, algae, inverts) Habitat Complexity
• abiotic in-situ (chain method)
(depth, habitat complexity)
Rugosity : R = dc/dl
dc = distance of chain across surface contour
dl = linear distance of the transect line
5m
25m

7. Macroalgae
Unconsolidated Sediment
Colonized hardbottom
Uncolonized hardbottom

8. Shoals LIDAR data at Hanauma Bay
USACE
Horizontal Accuracy + 1.5 m
Vertical Accuracy + 20 cm
Min. Depth Range 0-1 m
Max Depth Range 40 m
Sounding Density 4x4m
N (Hanauma Bay) 38,743
•USACE Shoals LIDAR surveys 1999-2000
•Irregularly spaced data, need to interpolate
into DEM

10. Work flow: LIDAR-derived rugosity
LIDAR data
acquisition
LIDAR collects x,y,z data
Data processing (QA/QC, project, clip to AOI)
DEMs created in GIS (4, 10, 15, 25 m)
LIDAR-derived
rugosity product
Rugosity grid created from DEM

11. Benthic terrain analysis
• ArcGIS Benthic terrain
modeler extension (Lundblad et al.
2004)
– www.csc.noaa.gov/products/
btm/
• Developed by NOAA Coastal
Services Center & OSU
– to classify habitats & derive slope
and rugosity measures from
multibeam data

12. Calculating rugosity from a bathymetric grid
• Obtains the surface area for the central cell
(165) based on the elevation values of the
eight surrounding cells
• Index of Rugosity = surface area
planimetric area
•Calculated by dividing the surface area of the cell
with the planimetric area of the cell to get a
measure of habitat complexity
In-situ Rugosity = distance of chain
linear distance of transect
Jenness (2004)

13. Research objectives
1. Evaluate the utility of LIDAR technology for deriving
rugosity on a coral reef
2. Examine the relationship between coral reef fish
assemblage characteristics & LIDAR-derived
rugosity

14. Correlation between in-situ chain rugosity &
LIDAR-derived rugosity
Spearman rank correlation coefficient (P-value)
Grid Size (m) 4 10 15 25
Chain rugosity 0.61 -0.01 -0.12 -0.09
(<0.01) (-0.98) (-0.60) (-0.70)
• LIDAR-derived rugosity was highly correlated w/ in-situ rugosity
(4 m grid)

15. Research objectives
1. Evaluate the utility of LIDAR technology for deriving
rugosity on a coral reef in Hawaii
2. Examine the relationship between coral reef fish
assemblage characteristics & LIDAR-derived
rugosity

16. Relationship between LIDAR-derived rugosity &
fish assemblage characteristics (hard bottom)
Fish assemblage metrics LIDAR-derived rugosity
25 m 15 m 10 m 4m
Numerical abundance 0.73 0.67 0.58 0.68
(<0.01) (<0.01) (<0.05) (<0.01)
Species richness 0.66 0.51 0.65 0.64
(<0.01) (0.06) (<0.01) (<0.05)
Biomass ( t ha-1) 0.65 0.61 0.50 0.52
(<0.05) (<0.05) (0.07) (0.06)
Species diversity (H’) 0.41 0.21 0.51 0.41
(0.14) (0.45) (0.06) (0.14)
Values are Spearman Rank Correlation (P-value) Wedding et al. (in press)

17. Relationship between LIDAR-derived rugosity &
fish assemblage characteristics (hard bottom)
Fish assemblage metrics LIDAR-derived rugosity
25 m 15 m 10 m 4m
Numerical abundance 0.73 0.67 0.58 0.68
(<0.01) (<0.01) (<0.05) (<0.01)
Species richness 0.66 0.51 0.65 0.64
(<0.01) (0.06) (<0.01) (<0.05)
Biomass ( t ha-1) 0.65 0.61 0.50 0.52
(<0.05) (<0.05) (0.07) (0.06)
Species diversity (H’) 0.41 0.21 0.51 0.41
(0.14) (0.45) (0.06) (0.14)
Values are Spearman Rank Correlation (P-value) Wedding et al. (in press)
•Hard bottom sites had sig. correlations w/ LIDAR rugosity & numerical
abundance, richness & biomass

18. Relationship between LIDAR-derived rugosity &
fish assemblage characteristics (hard bottom)
Fish assemblage metrics LIDAR-derived rugosity
25 m 15 m 10 m 4m
Numerical abundance 0.73 0.67 0.58 0.68
(<0.01) (<0.01) (<0.05) (<0.01)
Species richness 0.66 0.51 0.65 0.64
(<0.01) (0.06) (<0.01) (<0.05)
Biomass ( t ha-1) 0.65 0.61 0.50 0.52
(<0.05) (<0.05) (0.07) (0.06)
Species diversity (H’) 0.41 0.21 0.51 0.41
(0.14) (0.45) (0.06) (0.14)
Values are Spearman Rank Correlation (P-value) Wedding et al. (in press)
•Hard bottom sites had sig. correlations w/ LIDAR rugosity & numerical
abundance, richness & biomass

19. Relationship between LIDAR-derived rugosity &
fish assemblage characteristics (hard bottom)
Fish assemblage metrics LIDAR-derived rugosity
25 m 15 m 10 m 4m
Numerical abundance 0.73 0.67 0.58 0.68
(<0.01) (<0.01) (<0.05) (<0.01)
Species richness 0.66 0.51 0.65 0.64
(<0.01) (0.06) (<0.01) (<0.05)
Biomass ( t ha-1) 0.65 0.61 0.50 0.52
(<0.05) (<0.05) (0.07) (0.06)
Species diversity (H’) 0.41 0.21 0.51 0.41
(0.14) (0.45) (0.06) (0.14)
Values are Spearman Rank Correlation (P-value) Wedding et al. (in press)
•Hard bottom sites had sig. correlations w/ LIDAR rugosity & numerical
abundance, richness & biomass
•Sand sites were not correlated with fish assemblage characteristics

20. Relationship between fish biomass (t/ha) and
LIDAR-derived rugosity
Fish biomass (t/ha) observed on transects
Least-squares Simple Linear Regression
Grid Size (m) 4 10 15 25
R2 0.643 0.462 0.397 0.386
P-value <0.001 <0.001 <0.01 <0.01
• LIDAR-derived rugosity was a statistically significant predictor
of fish biomass in Hanauma Bay at all spatial scales

21. Summary
• Lidar-derived rugosity (4 m) was highly correlated w/ in-situ
rugosity & is a viable method for measuring habitat complexity
• Lidar-derived rugosity was a good predictor of fish biomass
and demonstrated a strong relationship with several fish
assemblage metrics in hard bottom habitat
• Relating LIDAR-derived rugosity to various fish assemblage
characteristics is an important step is applying remote
sensing for resource management applications

22. Implications for MPA design & function
• LIDAR data provides rugosity measures
in a min. amount of time at broad
geographic scales (~100km2/day)
relevant to regional-level management
actions
• LIDAR id specific areas that offer
greater natural protection to fish through
habitat complexity
– Predict fisheries potential of an area
– support optimal location & design of MPAs

23. Future work
• Continue to examine the associations between habitat
complexity & fish assemblages at a broader geographic
scale
– Expand pilot work to Hawaiian Archipelago
• Explore various measures of complexity (e.g. texture
measures, fractals)
• Predictive mapping of fish communities to inform MPA
design and management actions

24. Predictive mapping
GIS data layers Modeled Distribution Future MPA design
Geomorphic structure Species richness
Biological cover
Species diversity
Fish assemblage data
Depth Biomass
Slope
Current MPAs
Rugosity

25. Acknowledgements
• Eric Brown, Alan Hong, Brian Hawk, Ariel Rivera-
Vicente
• Hawaii Geographic Information Coordinating Council
• NOAA NOS NCCOS CCMA Biogeography Branch
• NOAA Coral Reef Conservation Program
• State of Hawaii, Division of Aquatic Resources
• UH, Department of Geography & Ecology, Evolution
& Conservation Biology