Introduction 10 minutes Objectives 30 minutes Example, Case Study 10 minutes Group Discussion 30 minutes Exercise 10 minutes Conclusions 10 minutes

NameAffiliationNameAffiliation David Saah; Co-LeadUniversity of San Francisco, SIGPhan Xuan ThieuVinh University, Vietnam Mohd Zaki Hamzah; Co-LeadUniversity Putra MalaysiaChalita SriladdaUSAID-LEAD Khamla Phanvilay, Co-LeadNational University of LaosHoang Thi Thu DuyenVietnam Forestry University, Vietnam Cao Thuy AnhDalat University, VietnamLadawan PuangchitKasetsart University, Thailand Chalermpol SamranpongChiang Mai University, ThailandDo Anh TuanVietnam Forestry University, Vietnam Pham Thanh NamUSAID LEAF VietnamLyna KhanRoyal University of Phnom Penh, Cambodia Peter StephenUSAID LEAF BangkokLe Ba ThuongVietnam Forestry University, Vietnam Hoang Vinh PhuVinh University, VietnamNapat JakwattanaUniversity of Phayao, Thailand Vipak JintanaKasetsart University, ThailandNur Anishah Binti AzizUniversity Kebangsaan Malaysia Kulala MulungPNG University of TechnologyRatcha ChaichanaKasetsart University, Thailand Somvilay ChanthalounnavongNational University of LaosSureerat LakanavichianChiang Mai University, Thailand Thavrak HuonRoyal University of Agriculture, CambodiaVongphet SihapanyaNational University of Laos Athsaphangthong MunelithUSAID LEAF LaosDavid GanzUSAID LEAF Bangkok Attachai JintrawetChiang Mai University, ThailandChi Pham, Project CoordinatorUSAID LEAF Bangkok Chanin ChiumkanokchaiUSAID LEAF BangkokKent ElliottUS Forest Service Lam Ngoc TuanDalat University, VietnamBeth LebowUS Forest Service Mark FennUSAID Vietnam Forests & DeltasGeoffrey BlateUS 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

ENABLING ENVIRONMENT ASSESSMENT OF CURRENT CONDITION ANALYSIS OF FUTURE OPTIONS NEGOTIATING & PRIORITIZING IMPLEMENTA- TION PLAN MONITORING & EVALUATION Low Emission Land Use Planning 1.1. Regulatory Assessments 1.2. Stakeholder Engagement 1.3. Planning & Development Goals & Objectives 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: Agreement between the true value and repeated measured observations or estimations Precision: 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

 Used to access the amount of error  Remeasure % of plots (guide)  This error level should be reported

 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

 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.

Add link to the LEAF field tool stock-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 standard deviations of the mean.  Calculate the 90% confidence interval using Standard deviation (σ) Sample size (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

Spatial Database Internet Data Diffusion Data production Data collection Data Selection Paper map Web services Data Usage Users Error-aware GIS, Fuzzy operators Quality analysis system Metadata management Context-sensitive warnings Methods to select best sources Spatial Integrity constraints Specifications, Quality control web services Users...  -Training  -Manuals  -Access control from Bedard et al., U. of Laval

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

Data Question KnownUnknown 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

Most of LELUP work will fit into this box Data/Knowledge LE LUP Issues KnownUnknown Known

1.Studies selected based on geography and context 2.Study Quality (peer, white, gray,..) 3.GAP analysis

 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

Our knowledge Climate Change Drivers KnownUnknown Known

 Non-spatial gap  Spatial gap  Temporal gap  Knowledge gap (how well do we understand the process?)

CategoryObjectiveIndicator 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 Economic Increase annual GDP growth rate from 12-15% GDP growth rate GDP per capita will reach 2300 USD by 2015 GDP per capita Social Population growth reduced to 1.3% (2015) and 1.2% (2020) Population growth rate by urban and rural sectors No poor households by 2020General poverty rate by urban and rural sectors

RICHNESS in FIPI data

1. Include Stakeholders 2. Select Experts 3. Integrate Team 4. Team Cohesiveness 5. Resource Availability

Expert AExpert BExpert CExpert D Indicator 1 Indicator 2 Indicator 3 Indicator 4

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 EPA 841-B , Sep 1996, U.S. EPA, Office of Wetlands, Washington, D.C , USA Reference for Uncertainty in REDD+: Reference: GOFC-GOLC report “A sourcebook of methods and procedures for monitoring measuring and reporting” Chapter 2.7A sourcebook of methods and procedures for monitoring measuring and reporting