Overview of Biomass Mapping The Woods Hole Research Center
Alessandro Baccini, Wayne Walker and Ned Horning November 8 12,
Samarinda, Indonesia
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Forest Inventories Stratify & Multiply (SM) Approach Assign
an average biomass value to land cover /vegetation type map Combine
& Assign (CA) Approach Extension of SM, with GIS and
multi-layer information / weightings (Gibbs et al. 2007) Direct
Remote Sensing (DR) Approach Empirical Models where RS data is
calibrated to field estimates ( Baccini et al. 2004, Blackard et
al. 2008 Baccini et al. 2008 ) Goetz et al. 2009 Large Area Biomass
Estimation
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Apply Model to Regional Data Inputs and Model Flow Surface
Reflectance (NBAR) View-angle corrected surface reflectance Pixel
mosaic of best quality NBAR 7 land bands + derived metrics Biomass
training set Extract Training for Biomass Sites Biomass Map
Estimate Tree Model
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Available Forest Inventory Data Only few countries/regions have
updated forest inventory data Measurements are not consistent
(D.B.H, species sampled, design) Spatial distribution non optimal
for remote sensing integration and scaling up MODIS 500 m grid over
Landsat data and FAO field transects (white lines)
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Remote Sensing Biomass Calibration 500 m MODIS pixel Average
Value Biomass Value Field data measurements LiDAR observations
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The Geoscience Laser Altimeter System (GLAS)
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Vegetation structure from Lidar (GLAS) 70 m Lidar metrics have
been extensively used to characterize vegetation structure (Sun et
al. 2008, Lefsky et al. 2005, Lefsky et al. 1999) Drake et al.
(2003), Lefsky et al. 2005, Drake et al. 2002 found a strong
relationship between AGB and Lidar metrics (HOME)
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Distribution of forest inventory data in Central Africa
Cameroon, Rep of Congo, Uganda Field biomass measurements MODIS 1km
NBAR (RGB 2,6,1)
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The Geoscience Laser Altimeter System (GLAS) The figure shows
30 % of the GLAS L2A (year 2003) shots after screening procedure.
We used 1.3 million observation
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Forest Inventory Design for Remote Sensing Calibration and
Validation Objective: A network of new field measurements using a
standardized methodology at the sub-national, national and
international level Optimized for remote sensing integration
Predefined locations Plot size smaller then remote sensing foot
print
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500 m MODIS pixel Scaling to RS Resolution Average Value
Biomass Value
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Sample Plot Predefined location Square shape 40 m by 40 m Only
basic variables recorded Nord West South East 40 meters
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Field Measurements and Data Collection Tree DBH measurements
All trees with D.B.H > 5 cm Tree Height Tallest 3 trees within
25 m Land cover/Land use Description
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Equipment for Forest Measurement
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GPSGPS
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CompassCompass
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Nylon Measuring Tape
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Diameter Tape
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ClinometerClinometer
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CameraCamera
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Goals To bring together practitioners in forest biometrics to
share information on tools, techniques, and protocols to improve
forest inventory designs To standardize protocols that ensure
consistency in measurement acquisition and statistical soundness in
sampling design To collect field data to help validate and improve
above- ground biomass estimates throughout the area