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Decision Making Capability with a World Forest Virtual Observatory

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Title: Decision Making Capability with a World Forest Virtual Observatory


1
Decision Making Capability with a World Forest
Virtual Observatory
  • Molly K. Macauley
  • Senior Fellow, Resources for the Future
  • ESIP Workshop
  • 8 July 2009
  • macauley_at_rff.org

2
Project Team
Molly Macauley Resources for the Future Roger
SedjoResources for the Future Jim BoydResources
for the Future Mark CohenResources for the
Future Danny MorrisResources for the
Future Pekka Kauppi University of
Helsinki Jingyun FangPeking University Alan
Grainger University of Leeds Paul Waggoner
Connecticut Agricultural Experiment Station
Brent SohngenOhio State University and RFF Ruth
DeFriesColumbia University and University of
Maryland Matthew FaganColumbia University Josef
KellndorferWoods Hole Research Institute Michael
ObersteinerGEO Secretariat and IIASA Michael
TomanWorld Bank Craig DobsonNASA Mark Brender
GeoEye, Inc. and GeoEye Foundation
3
The Problem
  • Discrepancies and uncertainties in fundamental
    measures (area, volume, biomass, carbon) of the
    worlds forests
  • Inadequacies in self-reported national forest
    inventories
  • Failure to incorporate new remote sensing
    information in forest inventories
  • Inadequate information to support increasing
    policy emphasis on forest attributes (e.g.,
    payments for ecosystem services, role of forests
    in the global carbon cycle, possibility of forest
    carbon offsets in domestic and international
    climate policy)

4
Differences in Estimated Carbon Density (source
Waggoner, 2009)
Table 2. Comparisons of six IPCC Tier 1 and IPCC
Good Practice measurements of carbon density
Units of t tons C/ha equal Mg carbon/ha. Tier 1
estimated by forest type and continent. Source
Brown et al. (2007) box 4.2.
5
Differences Among Reported Area (source
Waggoner, 2009)
Table W. A comparison of directions of changing
forest area assayed by three alternative methods
FRA assessment reports published by FAO, reports
submitted to FAO for preparation of assessments,
and actual surveys by nations recognized as valid
by them in their reports for assessments.
Source Grainger (2009).
6
Forest Area and Density 1990 - 2005 (source
Kauppi et al. 2006, fig. 5)
Unchanged density for 15 years
7
Example of Estimating the Value of Improved
Information
  • The case of forest carbon value
  • Tropical forests may sequester some 300 billion
    tons, and old growth forests some 0.3 to 0.5
    tons, of above-ground carbon (Sedjo, 2008
    Luyssaert and coauthors, 2008).
  • Back-of-the-envelope estimate of value using
    carbon prices (convert from CO2 with the ECX
    average trading price during 2008) 33.06
    trillion)
  • Emergence of a forest carbon market would change
    the supply of carbon credits and in turn
    influence carbon prices

8
Forestry Provisions in the American Clean Energy
and Security Act (H.R. 2454)
  • Offsets
  • 1 domestic offset credit 1 allowance
  • After 2018, 1.25 international offset 1
    allowance
  • Total offsets cannot exceed 2 billion tons, split
    between domestic and international
  • Even split between domestic and international,
    though international increases up to 1.5 billion
    if domestic ceiling is not reached
  • USDA has authority over determination of domestic
    forestry and agriculture offsets, EPA all other
    types
  • Supplemental reductions from forest conservation
  • 5 percent of allowances go to international
    forest conservation through 2025, 3 percent
    through 2030, 2 percent through 2050
  • 6 billion tons in reductions required between
    2012 and 2025

9
Forestry Provisions in the American Clean Energy
and Security Act (H.R. 2454), contd
  • International offset credits
  • Factors considered include the capability of
    measuring, monitoring, reporting and verifying
    the performance of sources across a sector
  • Offsets from reduced deforestation
  • Factors considered include whether country has
    technical capability to monitor and measure
    forest carbon fluxes with an acceptable level of
    uncertainty

10
Design Elements
  • Data ingest, combination, and cross-reference and
    cross-check
  • National sovereignty and Wiki-based design
  • Virtual observatory (metaphor)
  • Use of tools, including Google
  • Output with independence yet community commentary

11
Approaches to Estimating the Forest Identity
Ground Inventory
Remote Sensing
Forest Identity
Measured locally, at one time, Measured
regionally and repeatedly, in one to a few forest
types. distinguishing forest types
and ages (Optical, SAR). Estimated using
diameter at Estimated from measures
of forest breast height, tree height.
height/structure (SAR, LIDAR). Estimated
from volume and Estimated from area
and forest height. wood density measurements.
Estimates are improved by measures
Extrapolated regionally. of
forest flammability, productivity, leaf
area, phenology, and gas flux. Estimated from
biomass and Same as biomass.
Estimates are site- carbon density measurements.
specific, across entire regions. Extrapolat
ed regionally.
Area
Volume
Biomass
Carbon
12
Forest Identity Measurements
Remote Sensing Forest Measurements
Phenology
Volume Biomass Carbon
Carbon gas flux
Flammability
Productivity
Area (Optical, SAR)
Height (SAR, LIDAR)
Age/Disturbance
Composition
In the Forest Identity (FI), volume is estimated
using allometric equations. Biomass and carbon
are then calculated from volume. In remote
sensing, area and height are measured and can be
used to estimate the other FI quantities.
Measurements of area are improved by measures of
forest age and composition, and estimates of
biomass and carbon are improved by measures of
productivity and fire frequency.
13
The Global Forest Observatory - Information and
analysis
Observation
Assimilation
Output
Validation
Forest Identity
Q A D B C
Fluxnet data
Visualization Platform
Global Forest Observatory Model
Continually updating -Global Forest Area
(A) -Density(D) -Ratio (B) -Carbon Concentration
(C) to continually calculate-Carbon stock (Q)
Remote Sensing
Visualize Discrepancy
Land use, Timber, Biomass, Carbon Inventories
Interpolation
Validate Image
Land cover
Regional and Country level Statistics of A, D, B,
C and hence Q
Compare Model to Data
Validate Locally
14
GEO Link, FAO Link, and Related Activities
  • Coordination with forest-related tasks of the
    Group on Earth Observations (GEO)
  • Coordination among observing systems by way of
    GEO
  • Coordination with periodic self-reported
    inventories of the UN Food and Agricultural
    Organization (FAO)
  • Coordination with proposals such as Brazils
    Global Forest Information System (INPE)

15
Catalog of Philanthropic Initiatives
Area
Carbon
Biomass
Volume
Observing the Forests (Resources for the
Future/Alfred P. Sloan Foundation)
Design to Win/Tropical Forest Carbon (Packard
Foundation)
Tropical Forest Mapping for Disease Surveillance
(Woods Hole Research Center/Google)
Terrestrial Carbon Group (Heinz)
Global Forest Watch (WRI on-going initiative)
Carbon and Poverty Reduction (Clinton and
Rockefeller Foundations)
Monitoring Spatial and Temporal Change
Carbon
Area
Volume
Biomass
International Forest Carbon Initiative
(Australian government) partnering with
consortium led by Clinton Foundation
See My Forest (WRI/Toyota Motor Corp.
D R A F T Not yet complete
Rainforest Skin/Planetary Skin (NASA Ames/Cisco)
16
Catalog of Government Initiatives
Measurement
Monitoring Spatial and Temporal Change
Carbon
Area
Volume
Biomass
Area
Biomass
Carbon
Volume
GEO Forest Monitoring Task CL-09-03b (projects
for overall coordination of systems, Forest Work
Plan includes CEOS communiqué of 4th March
2009)
Global Observation of Forest and Land Cover
Dynamics (GOFC-GOLD)
Global Forest Resources Assessment 2010 (Food and
Agriculture Organization of the UN/Forestry
Department)
Global Carbon Project (International
Geosphere-Biosphere Program)
CBERS for Africa (INPE/CRESDA Forest Initiative)
Forest Inventory and Analysis Program (census of
nations forest with projections 10-50 yrs
out) USDA Forest Service
D R A F T not yet complete
Carbon Benefits Project Modeling, Measurement,
and Monitoring (GEF/UNEP) measuring carbon
benefits from sustainable land management
17
Catalog of Technological Initiatives
Monitoring Spatial and Temporal Change
Synoptic View additivity, leakage, permanence of
the worlds forests for overall carbon budget
or other long-term benefits of having an
observatory
Global coverage afforded by space-based view
Landsat Follow-On (Opt, US)
Ultra high spatial resolution low-cost mapper
(Surrey Satellite Technology, EADS Astrium)
Area
GeoEye, Digital Globe (Opt, US commercial)
Advanced Land Observing Satellite/L-band PALSAR
(ALOS JAXA) 09/04/09
Volume
Forest BIOMASS (SAR, ESA) (03/04/09)
Biomass
Greenhouse Gases Observing Satellite Ibuki
(GOSAT JAXA) 09/04/09
Carbon
DESDynI (LIDAR, InSAR) (NASA) http//desdyni.jpl.n
asa.gov/ (03/04/09)
CanX-2 (Canada/ York Univ) (Spectrometer, GHG,
CSA and industry) http//www.utias-sfl.net/nanosat
ellites/CanX2/ (03/04/09)
ALU GHG Inventory Software (CSU/NREL/USFS) (GIS,
mgmt data, emissions factors) funding from EPA,
USAID (07/04/09)
D R A F T not yet complete
18
Ten Questions as Organizing Principles for
Realizing the Value of Information
  • 1. Why use these data and information?
  • What is the technical improvement relative to
    existing discrepancies and uncertainties
  • What do the data enable
  • Wholly new opportunities
  • Otherwise unattainable or costly validation of
    priors
  • 2. What actions does (1) allow (can be actions
    that are taken or deliberately not taken)?
  • a. Resource management
  • b. Policy design, evaluation, reform
  • c. Regulatory enforcement, compliance,
    monitoring
  • d. Shift in scientific understanding of a
    phenomena

19
Organizing principles, contd
  • 3. If (2) includes nondomestic courses of action,
    what coordination is required?
  • 4. Who else is carrying out similar activities?
  • a. Do the data duplicate?
  • b. Do the data augment/extend?
  • c. Do the data otherwise complement?
  • d. Do the data build bridges?
  • 5. What additional information --and at what cost
    is required for decisions (take/not take
    action) in (2)?
  • 6. What other complements are required to render
    information actionable?

20
Organizing principles, contd
  • 7. What institutions are required for taking
    action?
  • 8. What are economies of scale in coordinated
    observing capability?
  • 9. What are economies of scope in coordinated
    observing capability (among platforms, across
    countries/owners, across phenomena and themes)?
  • 10. What is the estimated value to communicate to
    decision makers investing in observing capability
    (and who is the spokesperson)?

21
Project Funding Alfred Sloan Foundation
  • Previous initiatives to support basic information
    infrastructure
  • Census of Marine Life
  • Barcode of Life
  • Sloan Digital Sky Survey
  • Example of external philanthropic funder asking
    our community whether our information can help to
    solve a problem and how our community might
    organize and coordinate the institutions to
    provide the solutions
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