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

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Data Discovery Day …

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

Published in: Technology

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  • 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
  • 9. 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
  • 10. 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
  • 11. 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)
  • 12. 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
  • 13. 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)
  • 14. 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
  • 15. 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)
  • 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) •Hard bottom sites had sig. correlations w/ LIDAR rugosity & numerical abundance, richness & biomass
  • 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 •Sand sites were not correlated with fish assemblage characteristics
  • 19. 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
  • 20. 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
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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