Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

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Giant Kelp Canopy Cover and Biomass from High Resolution Multispectral Imagery for the Santa Barbara Channel Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

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Giant Kelp Canopy Cover and Biomass from High Resolution Multispectral Imagery for the Santa Barbara Channel. Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed. Macrocystis pyrifera. High economic and ecologic importance “ecosystem engineer” Kelp abundance highly dynamic - PowerPoint PPT Presentation

Transcript of Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Page 1: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Giant Kelp Canopy Cover and Biomass from High Resolution Multispectral Imagery for the

Santa Barbara Channel

Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Page 2: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Macrocystis pyrifera

• High economic and ecologic importance– “ecosystem engineer”

• Kelp abundance highly dynamic– Growth rates up to 0.5 m/day– Avg. frond life: 3-5 months– Ave. plant life: 2-3 years

Page 3: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Macrocystis growth and mortality

• Growth and mortality regulated by water temp, nutrients, depth, bottom type, predation, wave action

• Nice model system for studying the role of disturbance in regulating ecosystems

Kelp biomass data from Kelco visual estimates; Fish observations from Brooks et al 2002

Page 4: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Previous SB Channel surveys

• Aerial visual canopy biomass estimates by ISP Alginates (monthly from 1958-2005; entire coast)

• CDFG 2m resolution aerial surveys using NIR imagery (annual from 2002-present; entire coast)

• LTER SCUBA transects (monthly for 3 SBC kelp beds from 2002-present)

• Scale issues…

Page 5: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Research goals

1. Expand spatial and temporal resolution of kelp canopy cover and biomass datasets using high resolution satellite imagery

2. Use this data to model kelp population dynamics in relation to patch size, connectivity, and biophysical forcing

Page 6: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Research Area

Page 7: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Remote Sensing of Macrocystis

• Surface canopy of giant kelp exhibits typical vegetation spectral signature (red-edge)– Low red reflectance– high near infrared (NIR) reflectance

• Canopy biomass well correlated to entire forest biomass (r2 = 0.92)

Page 8: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

SPOT Imagery• Well suited to differentiate kelp

– Spectral bands in the green, red, NIR, SWIR– 10 m resolution

Page 9: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

SPOT Imagery Datasets

1. Canopy Cover

2. Biomass

Page 10: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Methods: Canopy Cover

• Principal components analysis calculated for kelp habitat (0-60 m depths)

PC band 1

PC band 2

False color SPOT image(8/15/2006)

• Positive contribution from all 3 bands• Glint, sediment loads, atmosphere variations, etc.

• High NIR, low green and red reflectance• Kelp

Page 11: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Methods: Canopy Cover Classification

• Minimum kelp threshold value selected from 99.9th%-tile value of offshore (35-60 m) pixels

Page 12: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Validation: Canopy Cover

• Cover measurements compared with high resolution 2004 CDFG aerial kelp survey

SPOT: Oct 29, 2004

CDFG: Sept-Nov 2004

r2 = 0.98p < 1*10-7

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Biomass Data

• More useful for understanding and modeling ecosystem interactions– Turnover rates, export, NPP, etc.

• Difficult to measure directly– Time and effort intensive

Page 14: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

SBC-LTER SCUBA Measurements of Frond Density and Biomass

• Monthly SCUBA measurements of frond density and biomass made at Arroyo Quemado (AQUE), Arroyo Burro (ABUR), and Mohawk (MOHK) kelp beds.

• Limited spatial scale

Page 15: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Seasonal kelp biomass changes along 3 LTER transects

• Maximums in late 2002• Wave driven seasonality apparent

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Methods: Biomass• Normalized Difference Vegetation Index (NDVI)

(NIR-RED)(NIR+RED)

• Calculated for areas of kelp cover

NDVITransform

Page 17: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Validation: Biomass

r2 = 0.71p < 1*10-7

y = 14.33x - 0.13

r2 = 0.54p < 1*10-7

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Seasonal kelp biomass changes at Mohawk

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Comparison of SPOT vs. Kelco Biomass Data

r2 = 0.73p < 1*10-7

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Population Dynamics Modeling

• Persistence, extinction, and biomass changes of kelp patches as a function of size, connectivity, and biophysical factors– High spatial resolution kelp maps will allow us

to include effects of sea temperature, nutrients, wave energy, substrate, light attenuation, spore production and dispersal

Page 21: Kyle C Cavanaugh, David A Siegel, Brian P Kinlan, Dan C Reed

Data RequirementsKelp Ecological Process Forcing Factor Method of Assessment Spatial Scale Temporal Scale

Kelp cover and biomass change

- multispectral imagery (SPOT from 2006-2008; Landsat/IKONOS/QB before and after)

<30 m seasonally

Extinction swell wave stress buoy data/ CDIP models 100 m hourly-daily

Spore dispersal currents HF radar. Bottom mounted ADCP observations

~ 100 m hourly-daily

Colonization substrate sidescan mapped substrate ~ 100 m annual-decadal

Colonization bathymetry hi-res gridded bathymetry ~ 100 m decadal

Productivity background light limitation

MODIS incident PAR & Kd490 imagery

500-1000 m daily

Productivity nutrient limitation MODIS/AVHRR SST/nutrient relationships

500-1000 m daily