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




             Lisa Wedding a,b, Al...
Presentation outline
• Research objectives
• Background
  – habitat complexity

• Data & methods
  – Fish & habitat survey...
Research objectives




1. Evaluate the utility of LIDAR technology for deriving
   rugosity (a measure of habitat structu...
Importance of habitat structural complexity

• Habitat complexity plays a major role
  in the distribution & structure of ...
Study site – No-take MPA, Est. 1967, 41 ha
Sampling design
Random stratified design

     • Fish Censuses
    • 25m x 5m transects

     • Habitat metrics
        • ...
Macroalgae

Unconsolidated Sediment
Colonized hardbottom
Uncolonized hardbottom
Shoals LIDAR data at Hanauma Bay



                                                        USACE


                      ...
Work flow: LIDAR-derived rugosity
                                             LIDAR data
                                ...
Benthic terrain analysis
• ArcGIS Benthic terrain
  modeler extension (Lundblad et al.
  2004)

   – www.csc.noaa.gov/prod...
Calculating rugosity from a bathymetric grid
 • Obtains the surface area for the central cell
 (165) based on the elevatio...
Research objectives




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

2. Examine t...
Correlation between in-situ chain rugosity &
          LIDAR-derived rugosity



                 Spearman rank correlatio...
Research objectives




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

2....
Relationship between LIDAR-derived rugosity &
 fish assemblage characteristics (hard bottom)
     Fish assemblage metrics ...
Relationship between LIDAR-derived rugosity &
 fish assemblage characteristics (hard bottom)
      Fish assemblage metrics...
Relationship between LIDAR-derived rugosity &
 fish assemblage characteristics (hard bottom)
      Fish assemblage metrics...
Relationship between LIDAR-derived rugosity &
 fish assemblage characteristics (hard bottom)
      Fish assemblage metrics...
Relationship between fish biomass (t/ha) and
          LIDAR-derived rugosity
                       Fish biomass (t/ha) o...
Summary


• Lidar-derived rugosity (4 m) was highly correlated w/ in-situ
  rugosity & is a viable method for measuring ha...
Implications for MPA design & function
• LIDAR data provides rugosity measures
  in a min. amount of time at broad
  geogr...
Future work
• Continue to examine the associations between habitat
  complexity & fish assemblages at a broader geographic...
Predictive mapping
GIS data layers      Modeled Distribution       Future MPA design
                  Geomorphic structur...
Acknowledgements

•   Eric Brown, Alan Hong, Brian Hawk, Ariel Rivera-
    Vicente


•   Hawaii Geographic Information Coo...
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
03/06/2008
Lisa Wedding
University of Hawaii
Department of Geography

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

  1. 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. 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. 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. 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. 5. Study site – No-take MPA, Est. 1967, 41 ha
  6. 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. 7. Macroalgae Unconsolidated Sediment Colonized hardbottom Uncolonized hardbottom
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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