Using LIDAR Data to Examine Habitat Complexity and Ecology of a Coral Reef

Lisa Wedding a,b, Alan Friedlander b,ca University of Hawaii at Manoa, Department of Geography

b NOAA/NCCOS/CCMA/NOS Biogeography Branchc The Oceanic Institute

Using LIDAR Data to Examine Habitat Complexity & Using LIDAR Data to Examine Habitat Complexity & Ecology of a Coral ReefEcology of a Coral Reef

Presentation outline 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

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

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

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

Sampling design

Random stratified design

• Fish Censuses• 25m x 5m transects

• Habitat metrics• biotic cover

(coral, algae, inverts)• abiotic

(depth, habitat complexity)

5m

25m

Habitat Complexityin-situ (chain method)

Rugosity : R = dc/dl

dc = distance of chain across surface contour dl = linear distance of the transect line

Macroalgae

Unconsolidated SedimentColonized hardbottom

Uncolonized hardbottom

Shoals LIDAR data at Hanauma Bay

USACE

4 x 4 mSounding Density

38,743N (Hanauma Bay)

40 mMax Depth Range

0-1 mMin. Depth Range

+ 20 cmVertical Accuracy

+ 1.5 mHorizontal Accuracy

•USACE Shoals LIDAR surveys 1999-2000

•Irregularly spaced data, need to interpolate into DEM

Work flow: LIDAR-derived rugosityLIDAR 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)

Rugosity grid created from DEM

LIDAR-derived rugosity product

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

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 areaplanimetric 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 chainlinear distance of transect

Jenness (2004)

Research objectives

1. Evaluate the utility of LIDAR technology for deriving rugosity on a coral reef


(-0.70)(-0.60)(-0.98)(<0.01)

-0.09-0.12-0.010.61Chain rugosity2515104Grid Size (m)

Correlation between in-situ chain rugosity & LIDAR-derived rugosity

Spearman rank correlation coefficient (P-value)

• LIDAR-derived rugosity was highly correlated w/ in-situ rugosity(4 m grid)

Research objectives

1. Evaluate the utility of LIDAR technology for deriving rugosity on a coral reef in Hawaii


Relationship between LIDAR-derived rugosity & fish assemblage characteristics (hard bottom)

Values are Spearman Rank Correlation (P-value)

0.41(0.14)

0.51(0.06)

0.21(0.45)

0.41(0.14)

Species diversity (H’)

0.52(0.06)

0.50(0.07)

0.61(<0.05)

0.65(<0.05)

Biomass ( t ha-1)

0.64(<0.05)

0.65(<0.01)

0.51(0.06)

0.66(<0.01)

Species richness

0.68(<0.01)

0.58(<0.05)

0.67(<0.01)

0.73(<0.01)

Numerical abundance4 m 10 m 15 m 25 m

LIDAR-derived rugosityFish assemblage metrics

Wedding et al. (in press)



•Hard bottom sites had sig. correlations w/ LIDAR rugosity & numerical abundance, richness & biomass

0.41(0.14)

0.51(0.06)

0.21(0.45)

0.41(0.14)


0.52(0.06)

0.50(0.07)

0.61(<0.05)

0.65(<0.05)

Biomass ( t ha-1)

0.64(<0.05)

0.65(<0.01)

0.51(0.06)

0.66(<0.01)

Species richness

0.68(<0.01)

0.58(<0.05)

0.67(<0.01)

0.73(<0.01)






•Hard bottom sites had sig. correlations w/ LIDAR rugosity & numerical abundance, richness & biomass

•Sand sites were not correlated with fish assemblage characteristics

0.41(0.14)

0.51(0.06)

0.21(0.45)

0.41(0.14)


0.52(0.06)

0.50(0.07)

0.61(<0.05)

0.65(<0.05)

Biomass ( t ha-1)

0.64(<0.05)

0.65(<0.01)

0.51(0.06)

0.66(<0.01)

Species richness

0.68(<0.01)

0.58(<0.05)

0.67(<0.01)

0.73(<0.01)




Relationship between fish biomass (t/ha) and LIDAR-derived rugosity

Grid Size (m) 4 10 15 25R2 0.643 0.462 0.397 0.386P-value <0.001 <0.001 <0.01 <0.01

Least-squares Simple Linear Regression

• LIDAR-derived rugosity was a statistically significant predictor of fish biomass in Hanauma Bay at all spatial scales

Fish biomass (t/ha) observed on transects

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 biomassand 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

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

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

Predictive mapping

GIS data layers Future MPA designModeled Distribution

Species richness

Species diversity

Biomass

Current MPAs

Geomorphic structure

Biological cover

Fish assemblage data

Depth

Slope

Rugosity

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

Using LIDAR Data to Examine Habitat Complexity and Ecology of a Coral Reef

Technology

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