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Riparian Zone Health ProjectRiparian Zone Health Project
Agriculture and Agri-Food CanadaAgriculture and Agri-Food Canada
Grant S. Wiseman, BS.c, MSc.Grant S. Wiseman, BS.c, MSc.
World Congress of Agroforestry World Congress of Agroforestry
Nairobi, KenyaNairobi, Kenya
August 23-28, 2009August 23-28, 2009
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Riparian Health Identification ProjectRiparian Health Identification Project
Riparian zones are natural vegetative buffers along water corridors separating agricultural land from water
Benefits of Riparian Zones Absorption of excess nutrient excess runoff Carbon Sequestration Provides habitat for biodiversity
Currently no riparian zones data layers exist
Province wide ground surveys too costly, time consuming
OBJECTIVE
Develop a remote sensing methodology to:
1) Identify riparian zones by vegetative classes 2) Infer riparian zone health indicators
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Riparian Project: Data SetsRiparian Project: Data SetsRiparian Project: Data SetsRiparian Project: Data Sets
High resolution true colour orthophotos 1:40,000 scale (62.5 cm pixel resolution)
Synthetic Apeture Radar (SAR) Radarsat-2 quad polarization imagery All four polarization sending\receiving (1 m resolution)
Surface validation data from 2008-09 is being collected to identify riparian zone health by riparian project experts Cows and Fish Survey
METHODOLOGY
Analyze the spectral, spatial and relational characteristics derived from Radarsat-2 satellite imagery and high resolution colour orthophotos using Object Image Analysis (OBIA)
Correlate to surface validation data using multivariate analysis
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Project Study AreaProject Study Area Found in Manitoba, Canada within the Upper Assiniboine River
Conservation District (UARCD)
Approximately 35 km in length. 300 meter buffers on both sides of creek.
Selected for diversity in riparian zone vegetation, health and management practices
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Level 1
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Level 2
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Level 3
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• Visit segmented polygons on ground using GPS and hand held GIS equipment
• Collect detailed habitat, vegetation, agricultural usage, vegetation and health information riparian areas
• Over 150 ecological survey questions at 100+ sites
Surface ValidationSurface Validation Surface ValidationSurface Validation
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96.5% Accuracy
(based on 3,600+ objects)
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Synthetic Aperture Radar (SAR)Synthetic Aperture Radar (SAR)
Frequency
X-BandTerraSAR-X: 9.7 GHz (3.1 cm)
C-BandRADARSAT-1 and ASAR: 5.3GHz (5.6 cm)
RADARSAT-2: 5.405 GHz (5.6 cm)
L-BandALOS PALSAR: 1.27 GHz (23.6 cm)
The Electromagnetic Spectrum
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Polarimetric SARsPolarimetric SARs
Active sensor, sends energy to earth’s surface, receives reflected energy
Transmit and receive all 4 mutually orthogonal polarizations
H = Horizontal, V = Vertical (HH,HV, VV and VH)
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Radarsat 2: Health IndicatorsRadarsat 2: Health Indicators
Acquire Fine Beam Mode R-2 imagery Summer 2009 Quad-polarization – HH, HV, VH, VV
Use Band Ratios to generate Health Indicators
CS (average Cross-Polarized magnitude) = HV + VH / 2
CSI (Canopy Structure Index) = VV / VV + HH
BMI (Biomass Index) = VV + HH / 2
VSI (Volume Scattering Index) = CS / CS + BMI
Summarize band ratio layers with existing image objects to generate spatial, spectral and relational SAR attributes
Pope, et al., 1993
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Multivariate AnalysesMultivariate Analyses
Multiple Discriminate Analysis (MDA) and Canonical Correlations Analysis (CanCor) to identify relationships between: Intensive ecological data collection survey
High resolution imagery objects
Radarsat-2 objects
Determine what remotely sensed information correlates with riparian zone health surface validation information
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Once the riparian zone health information gap can be addressed effective
agroforestry management practices can be implemented on a watershed scale
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