Transcript LELUP_2.3_Data_Capacity_Gap_2015_05x

Introduction
10 minutes
Objectives
30 minutes
Example, Case Study
10 minutes
Group Discussion
30 minutes
Exercise
10 minutes
Conclusions
10 minutes
Name
Affiliation
David Saah; Co-Lead
University of San Francisco, SIG
Name
Affiliation
Phan Xuan Thieu
Vinh University, Vietnam
Mohd Zaki Hamzah; Co-Lead University Putra Malaysia
Chalita Sriladda
USAID-LEAD
Khamla Phanvilay, Co-Lead
National University of Laos
Hoang Thi Thu Duyen
Vietnam Forestry University, Vietnam
Cao Thuy Anh
Dalat University, Vietnam
Ladawan Puangchit
Kasetsart University, Thailand
Chalermpol Samranpong
Chiang Mai University, Thailand
Do Anh Tuan
Vietnam Forestry University, Vietnam
Pham Thanh Nam
USAID LEAF Vietnam
Lyna Khan
Royal University of Phnom Penh, Cambodia
Peter Stephen
USAID LEAF Bangkok
Le Ba Thuong
Vietnam Forestry University, Vietnam
Hoang Vinh Phu
Vinh University, Vietnam
Napat Jakwattana
University of Phayao, Thailand
Vipak Jintana
Kasetsart University, Thailand
Nur Anishah Binti Aziz
University Kebangsaan Malaysia
Kulala Mulung
PNG University of Technology
Ratcha Chaichana
Kasetsart University, Thailand
Sureerat Lakanavichian
Chiang Mai University, Thailand
Somvilay Chanthalounnavong National University of Laos
Thavrak Huon
Royal University of Agriculture, Cambodia Vongphet Sihapanya
National University of Laos
Athsaphangthong Munelith
USAID LEAF Laos
David Ganz
USAID LEAF Bangkok
Attachai Jintrawet
Chiang Mai University, Thailand
Chi Pham, Project Coordinator USAID LEAF Bangkok
Chanin Chiumkanokchai
USAID LEAF Bangkok
Kent Elliott
US Forest Service
Lam Ngoc Tuan
Dalat University, Vietnam
Beth Lebow
US Forest Service
Mark Fenn
USAID Vietnam Forests & Deltas
Geoffrey Blate
US Forest Service
Low Emission Land Use Planning (LELUP)
Section 2. Assessment of Current and Historical Condition
2.3. Data and Capacity Gap Assessment
Regional Climate Change Curriculum Development
1.1. Regulatory Assessments
1.2. Stakeholder Engagement
1.3. Planning & Development
Goals & Objectives
MONITORING &
EVALUATION
NEGOTIATING
&
PRIORITIZING
IMPLEMENTATION PLAN
ENABLING
ENVIRONMENT
Low
Emission
Land Use
Planning
ANALYSIS OF
FUTURE
OPTIONS
ASSESSMENT
OF CURRENT
CONDITION
2.1. Environment, Social, &
Economic Data Needs
2.2. Understanding Historic Land
Use Change
2.3. Data & Capacity Gap
Assessment

Quality Assurance and Quality Control

Accuracy and Precision

Ethics of Uncertainty

UNFCCC Principles

Gap Audit
At the end of this session, learners will be able to:

Identify gaps in data and information needed.

Determine what skill would be required to establish in a
multidisciplinary team.
Plans need to be made to monitor for:

Quality Assurance (QA): is a way of preventing
mistakes

Quality Control (QC): is a process by which entities
review the quality of all factors involved in an analysis.
The QA/QC plan should become part of project
documentation and cover the following procedures:

Field measurements

Laboratory measurements

Data entry

Data analysis

Data maintenance and archiving
Accuracy:
Precision:
Agreement between the true
value and repeated measured
observations or estimations
The level of agreement among
repeated measurements of the
same quantity
Accurate
but not precise
Precise
but not accurate
Accurate and Precise
 Standard Operating Procedures
should be created
Ensure Accuracy of measurements
(consistency of methods)
 Thorough training of all field crews
in procedures
 Followed by:
Hot Checks
Cold Checks
Blind Checks
Blind Checks

Used to access the amount of error

Remeasure 10 - 20% of plots (guide)
Measurement Error (%) =

(Biomass before corrections - Biomass after corrections)
Biomass after corrections
This error level should be reported
x 100

Standard Operating Procedures
(SOP) should be developed and
implemented.

Data should be examined for
extreme numbers - may be caused
by data entry mistakes.

If problems exist, the plot (s)
should be removed.
SOP for laboratory analysis.
Blind Checks:

Used to access the amount of error

Re-measure 10 – 20% of samples

This error level should be reported
Measurement Error (%) =
(Estimate 1 - Estimate 2)
Estimate 2
x 100

SOP to update and backup all data
is needed.

Copies of all data should be stored
in a secured location.

Important to Update all electronic
data to new types of data storage.
http://www.leafasia.org/tools/manual-carbonstock-calculation-tool

Uncertainty means the lack of
knowledge of the true value of
a variable, including both bias
and random error.

Error: Something that is not
correct.
Uncertainty:

Imperfect and inexact knowledge

Data uncertainty

Rule uncertainty
Higher Certainty
Lower Certainty

90% of data points will fall within 1.645 standard
deviations of the mean.

Calculate the 90% confidence interval using
Standard deviation (σ)
Sample size (n)
  
90%CI  1.645

 n

Report C stock as mean ± 90%CI

Uncertainty can also be estimated: (90% CI / mean) x 100
-> should be <10%

List the importance of understanding Error and
Uncertainty?

In small groups list down ALL the common sources of
error?
From an ethics point of view:

Poor quality data should not be used for sensitive
applications where it poses a risk of harm

Need appropriate safeguards to avoid the harm, and to
provide effective warnings

Not enough just to anticipate intended uses and data
quality requirements. Must anticipate the possible
misuses of the system as well
Specifications,
Quality control
Quality analysis Context-sensitive
system
warnings
Metadata
management
Spatial
Integrity constraints
Methods to select
best sources
Spatial
Database
Error-aware GIS,
Fuzzy operators
Users
...
Users
Internet
Paper map
Web services
Data
collection
Data
production
Data
Diffusion
Data
Data
Selection Usage
web
services
 -Training
 -Manuals
 -Access control
COP 15, Copenhagen (2009). Decision 4/CP.15, paragraph 1(d)
“Requests” Parties to:
“…establish, according to national circumstances and capabilities, robust
and transparent national forest monitoring systems and, if appropriate,
sub-national systems as part of national forest monitoring systems that:
i) Use a combination of remote sensing and ground-based forest
carbon inventory approaches for estimating, as appropriate,
anthropogenic forest-related greenhouse gas emissions by sources
and removals by sinks, forest carbon stocks and forest area changes;
ii) Provide estimates that are transparent, consistent, as far as
possible accurate, and that reduce uncertainties, taking into account
national capabilities and capacities;
iii) Are transparent and their results are available and suitable for
review as agreed by the Conference of the Parties;

Transparency

Consistency

Comparability

Completeness

Accuracy

Conservative
From: GOFC-GOLD 2009
Unknown
Question
Known
Data
Known
Unknown

We knew what happened in the
past

We knew the current condition

Why do we have different pictures
of the future?
Things To Include

Presence of Data

Presence of Results

Presence of Thresholds

Measurement of
Uncertainty

Spatial Extent

Temporal Extent
Things Not to Include

Interpretation of
Results

Information that will
bias the monitoring
effort
Data/Knowledge
Known
Unknown
LE LUP Issues
Known
Unknown
Most of LELUP
work will fit into
this box
1.
2.
3.
Studies selected
based on geography
and context
Study Quality (peer,
white, gray, ..)
GAP analysis
Table 9. ESV Gap Analysis for Non-Forested Land Cover Types
AGR
Gas and Climate Regulation
Disturbance prevention
Water regulation
Water supply
Soil retention & formation
Nutrient regulation
Waste treatment
Pollination
Biological Control
Habitat Refugium
Aesthetic & Recreational
Cultural & Spiritual
EST
FWET
SWET
4
1
2
1
3
2
1
7
4
5
WAT
RIPF
2
1
URBG
3

Gaps in the data
mean results should
be treated as
conservative
baselines, not upper
bound estimates.

Technical reports
and grey literature
are not included in
this analysis.

These estimates are
likely to
underestimate ESVs
2
1
7
1
5
1
1
1
2
1
2
2
7
10
1
2
8
17
Total Non-Forest Estimates:
4
111
Table 10. ESV Gap Analysis for Forested Land Cover Types
Gas Regulation (CO2)
Disturbance prevention
Water regulation
Water supply
Soil retention & formation
Nutrient regulation
Waste treatment
Pollination
Biological Control
Habitat Refugium (S. Owl) *
Habitat Refugium *
Aesthetic & Recreational *
Cultural & Spiritual
MIX
1
4
12
HDW
1
4
1
RWOG
1
4
12
RW2
1
4
12
CON
1
4
12
Total Forest Estimates:
* Sources are the same across Forest types except Oak Woodland and S. Owl Habitat
OWLF
1
3
4
12
94
Unknown
Known
Climate Change Drivers
Our knowledge
Known
Unknown

Non-spatial gap

Spatial gap

Temporal gap

Knowledge gap (how well do we understand the
process?)
Category
Objective
Indicator
Environmental
Maintain at least 61% forest
cover by 2015
Percent forest cover
Maintaining or improving
ecological integrity
1) Ratio of natural forest to
plantations
2) Species type diversity
3) Richness
Increase annual GDP growth
rate from 12-15%
GDP growth rate
GDP per capita will reach
2300 USD by 2015
GDP per capita
Population growth reduced
to 1.3% (2015) and 1.2%
(2020)
Population growth rate by
urban and rural sectors
No poor households by 2020
General poverty rate by
urban and rural sectors
Economic
Social
RICHNESS in FIPI data
1.
Include Stakeholders
2.
Select Experts
3.
Integrate Team
4.
Team Cohesiveness
5.
Resource Availability
Expert A
Indicator 1
Indicator 2
Indicator 3
Indicator 4
Expert B
Expert C
Expert D
1.
Identify the limitations of your data
2.
Determine that selecting data has an ethical element
that is dependent on the QA/QC results
3.
Leverage your teams to build up capacity
Reference for QA/QC details:
EPA 1996, Environmental Protection Agency Volunteer Monitor’s
Guide to: Quality Assurance Project Plans. 1996. EPA 841-B-96-003, Sep 1996,
U.S. EPA, Office of Wetlands, Washington, D.C. 20460, USA
http://www.epa.gov/owowwtr1/monitoring/volunteer/qappexec.htm
Reference for Uncertainty in REDD+:
Reference: GOFC-GOLC report “A sourcebook of methods and procedures
for monitoring measuring and reporting” Chapter 2.7