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Overview of Biomass Mapping. Alessandro Baccini, Wayne Walker a nd Ned Horning. The Woods Hole Research Center. November 8 – 12, Samarinda , Indonesia. Large Area Biomass Estimation. Forest Inventories Stratify & Multiply (SM) Approach

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overview of biomass mapping

Overview of Biomass Mapping

Alessandro Baccini, Wayne Walker

and Ned Horning

The Woods Hole Research Center

November 8 – 12, Samarinda, Indonesia

slide2

Large Area Biomass Estimation

  • 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

inputs and model flow
Inputs and Model Flow

Extract Training for

Biomass Sites

  • Surface Reflectance (NBAR)
    • View-angle corrected surface reflectance
    • Pixel mosaic of best quality NBAR
    • 7 land bands + derived metrics
  • Biomass training set

Estimate Tree Model

Apply Model

to Regional Data

Biomass Map

available forest inventory data
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)

remote sensing biomass calibration
Remote Sensing – Biomass Calibration

MODIS pixel

Average Value Biomass Value

500 m

  • Field data measurements
  • LiDAR observations

500 m

vegetation structure from lidar glas
Vegetation structure from Lidar (GLAS)

Lidar metrics have been extensively used to characterize vegetation

structure (Sun et al. 2008, Lefsky et al. 2005, Lefsky et al. 1999)

70 m

Drake et al. (2003), Lefsky et al. 2005, Drake et al. 2002 found a strong relationship between AGB and Lidar metrics (HOME)

distribution of forest inventory data in central africa cameroon rep of congo uganda
Distribution of forest inventory data in Central AfricaCameroon, Rep of Congo, Uganda

Field biomass

measurements

MODIS 1km NBAR (RGB 2,6,1)

the geoscience laser altimeter system glas
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

forest inventory design for remote sensing calibration and validation
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
slide12

Scaling to RS Resolution

MODIS pixel

Average Value Biomass Value

500 m

500 m

sample plot
Sample Plot

Nord

  • Predefined location
  • Square shape 40 m by 40 m
  • Only basic variables recorded

East

West

40 meters

South

field measurements and data collection
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
goals
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