Case Study: Rezatec Amazonas Project
Fraser Harper
Product Director, Rezatec Ltd.
20 May 2013
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Uncertainty - why is it important?
...inventories consistent with good practice are those which
contain neither over- nor underestimates so far as can be
judged, and in which uncertainties are reduced as far as
practicable
IPCC Good Practice Guidelines for LULUCF, 2006
...all methodologies must impose confidence deductions
when the uncertainty exceeds +/-15 percent of the mean at
the 95 percent confidence level or +/-10 percent of the
mean at the 90 percent confidence level
VCS Standard version 3.1 (Section 4.1)
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Accuracy and Precision
Important concepts in estimating REDD uncertainties
 Accurate
 Precise
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 Accurate
 Precise
 Accurate
 Precise
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Cut your coat according to the cloth
Two basic inputs for calculating GHG inventories
Activities
Approaches for estimating change in area of land categories
Total area per category
Net changes only
Total area per category
Estimate conversion rates
Spatially explicit tracking of
conversions over time
Emission factors
GHG inventory estimates
for each land category
Tier 1: IPCC default factors
• Increasing accuracy and precision
• Increasing complexity
• Increasing monitoring costs
Tier 2: Country-specific data
for key factors
Tier 3: Modelling, plus
repeated
measurement of key
stocks through time
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Cut your coat according to the cloth
Different approaches and tiers can apply at different times
Activities
Approaches for estimating change in area of land categories
Suitable for REDD+
Total area per category
Net changes only
Total area per category
Estimate conversion rates
Spatially explicit tracking of
conversions over time
Emission factors
GHG inventory estimates
for each land category
Tier 1: IPCC default factors
Tier 2: Country-specific data
for key factors
Tier 3: Modelling, plus
repeated
measurement of key
stocks through time
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Project
pre-screening
• Increasing
accuracy
and precision
• Increasing complexity
• Increasing monitoring costs
Project Idea Note (PIN)
Project Design Document (PDD)
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Cut your coat according to the cloth
Uses for different resolution sensors in REDD monitoring
Resolution
Cost
Use
Coarse (250-1000m)
Generally free
• Consistent monitoring of wide areas
• Large clearings
• Identify hotspots for further analysis
Medium (10-60 m)
Free to ~ $0.5/km2
• Primary mapping tools for area change and deforestation
mapping
Fine (<5m)
$2-30 /km2
• Results validation
• Algorithm training
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Cut your coat according to the cloth
Brazilian Legal Amazonia forest monitoring systems
• PRODES
– Annual wall-to-wall assessment of the gross deforestation rate
– Moderate resolution imagery (30 meters)
• DEGRAD/DETEX
– Annual wall-to-wall assessment of the forest degradation
– Moderate resolution imagery (30 meters)
• DETER
– Near real time detection of deforestation and forest degradation
– Coarse resolution imagery (250 meters)
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Uncertainty in biomass distribution
Largest source of uncertainty (20% variation to baseline between sources)
Dry season
Dry season
Dry season
Jul
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Jan
Jul
Jan
Jul
Jan
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Best practices for reducing uncertainty
GOFC-GOLD Sourcebook recommendations
•
For Activity assessment:
–
Use consistent data
•
•
–
–
Choose data with quality data provided
Apply consistent and transparent processes
•
•
•
•
•
•
Same season images
Time-phased data using the same sensor
Cartographic and thematic standards
Accepted interpretation methods
Provide initial data and intermediate data products
Document processing steps
Apply proper treatment of no data areas
For Emission Factors
–
Identify the significant carbon pools (‘key categories’)
•
•
•
Varies by country/region
If significant, use higher tiers
If not, Tier 1 may be enough
–
Use ‘conservativeness’
–
Design and deploy sound statistical sampling methods
•
•
•
Consideration during estimation phase helps with cost-effective resource allocation
Local values can vary greatly from country or ecosystem averages
Target ground truthing to areas of highest uncertainty
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Rezatec’s process
Earth Information for business value
Platform-as-a-Service (PaaS)
Source Data
Assimilate
Target Data
Primary Source
Data
Ancillary Data
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Transform
Earth Information
Collate
Model
Analyse
Disseminate
Extracted Data
Analytical
Models
Regressions
Data Products
Formatted Data
Simulation
Models
Classifications
Data extracts
Metadata
Discover
Visualisations
Spatial
Analysis
Temporal (Time)
Analysis
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Case Study: Rezatec Amazonas Project
Fraser Harper
Product Director, Rezatec Ltd.
16 May 2013
© Copyright Rezatec Ltd. 2013
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