Using LIDAR Data to Examine Habitat Complexity and Ecology of a Coral Reef
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Transcript of 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
2. Examine the relationship between coral reef fish assemblage characteristics & LIDAR-derived rugosity
(-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
2. Examine the relationship between coral reef fish assemblage characteristics & LIDAR-derived rugosity
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)
Relationship between LIDAR-derived rugosity & fish assemblage characteristics (hard bottom)
Values are Spearman Rank Correlation (P-value)
•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)
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)
Relationship between LIDAR-derived rugosity & fish assemblage characteristics (hard bottom)
Values are Spearman Rank Correlation (P-value)
•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)
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)
Relationship between LIDAR-derived rugosity & fish assemblage characteristics (hard bottom)
Values are Spearman Rank Correlation (P-value)
•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)
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)
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