1. USAID LEAF Regional Climate Change Curriculum Development Module: Carbon Measurement and Monitoring (CMM) Section 4. Carbon Stock Measurement Methods 4.2. Design of field sampling framework for carbon stock inventory

2. NameAffiliationNameAffiliation Deborah Lawrence, Co-leadUniversity of VirginiaMegan McGroddy, Co-leadUniversity of Virginia Bui The Doi, Co-leadVietnam Forestry UniversityAhmad Ainuddin NuruddinUniversiti Putra Malaysia Prasit Wang, Co-leadChiang Mai University, Thailand Mohd Nizam SaidUniversiti Kebangsaan Malaysia Sapit DiloksumpunKasetsart University, ThailandPimonrat TiansawatChiang Mai University, Thailand Pasuta SunthornhaoKasetsart University, ThailandPanitnard TunjaiChiang Mai University, Thailand Wathinee SuanpagaKasetsart University, ThailandLawong BalunUniversity of Papua New Guinea Jessada PhattralerphongKasetsart University, ThailandMex Memisang PekiPNG University of Technology Pham Minh ToaiVietnam Forestry UniversityKim SobenRoyal University of Agriculture, Cambodia Nguyen The DzungVietnam Forestry UniversityPheng SoklineRoyal University of Phnom Penh, Cambodia Nguyen Hai HoaVietnam Forestry UniversitySeak SophatRoyal University of Phnom Penh, Cambodia Le Xuan TruongVietnam Forestry UniversityChoeun KimsengRoyal University of Phnom Penh, Cambodia Phan Thi Quynh NgaVinh University, VietnamRajendra ShresthaAsian Institute of Technology, Thailand Erin SwailsWinrock InternationalIsmail ParlanFRIM Malaysia Sarah WalkerWinrock InternationalNur Hajar Zamah ShariFRIM Malaysia Sandra BrownWinrock InternationalSamsudin MusaFRIM Malaysia Karen VandecarUS Forest ServiceLy Thi Minh HaiUSAID LEAF Vietnam Geoffrey BlateUS Forest ServiceDavid GanzUSAID LEAF Bangkok Chi PhamUSAID LEAF Bangkok

3. IOVERVIEW: CLIMATE CHANGE AND FOREST CARBON 1.1Overview: Tropical Forests and Climate Change 1.2Tropical forests, the global carbon cycle and climate change 1.3Role of forest carbon and forests in global climate negotiations 1.4Theoretical and practical challenges for forest-based climate mitigation IIFOREST CARBON STOCKS AND CHANGE 2.1Overview of forest carbon pools (stocks) 2.2Land use, land use change, and forestry (LULUCF) and CO 2 emissions and sequestration 2.3Overview of Forest Carbon Measurement and Monitoring 2.4IPCC approach for carbon measurement and monitoring 2.5 Reference levels – Monitoring against a baseline (forest area, forest emissions) 2.6 Establishing Lam Dong’s Reference Level for Provincial REDD+ Action Plan : A Case Study IIICARBON MEASUREMENT AND MONITORING DESIGN 3.1Considerations in developing a monitoring system IVCARBON STOCK MEASUREMENT METHODS 4.1Forest Carbon Measurement and Monitoring 4.2Design of field sampling framework for carbon stock inventory 4.3Plot Design for Carbon Stock Inventory 4.4Forest Carbon Field Measurement Methods 4.5Carbon Stock Calculations and Available Tools 4.6Creating Activity Data and Emission Factors 4.7Carbon Emission from Selective Logging 4.8Monitoring non-CO 2 GHGs VNATIONAL SCALE MONITORING SYSTEMS

4. Lecture (50 minutes)  Why sampling is important  Major sampling approach  Stratification  Examples of stratification approaches used in forests  Class activity (15 minutes)  Homework

5. At the end of this session, learners will be able to:  Explain why sampling is necessary  Distinguish among random, stratified, and systematic sampling, and know where each is appropriate  Determine the advantages and drawbacks of different sampling schemes:

8.  Often it is impractical to examine an entire population  Instead, we select a sample from our population of interest and, on the basis of this sample, information about the entire population will be inferred

9. It is extremely unlikely that we would have the time and resources needed to measure the entire carbon stock in a forest or landscape

10.  Instead we select a sample from an area of interest, on the basis of this sample, we can infer information about the entire area  Conclusions about an entire population will be drawn based on the sample information through statistical inference

11. 1. Measure carbon stocks in sampled areas 2. Assume sampled carbon stocks represent a reasonable estimate of population carbon stocks, 3. Multiply measured carbon per unit area by entire area of interest to calculate the carbon stocks 4. Use the variation among your plot values to estimate uncertainty

12.  The sample must provide an accurate picture of the population from which it is drawn  The sample should be random; each individual in the population should have an equal chance of being selected

13. Different sampling schemes can be used: i. Simple random sampling ii. Systematic sampling iii. Stratified sampling iv. Cluster sampling i

14. Different sampling schemes can be used: i. Simple random sampling ii. Systematic sampling iii. Stratified sampling iv. Cluster sampling i ii

15. Different sampling schemes can be used: i. Simple random sampling ii. Systematic sampling iii. Stratified sampling iv. Cluster sampling i ii iii

16. Different sampling schemes can be used: i. Simple random sampling ii. Systematic sampling iii. Stratified sampling iv. Cluster sampling i ii iii iv

17.  Sampling units are independently selected one at a time until the desired sample size is achieved  Each study unit in the finite population has an equal chance of being included in sample without any bias http://www.youtube.com/watch?v=yx5KZi5QArQ

18. A random sample Advantages Advantages:  Representativeness and freedom from bias  Ease of sampling and analysis Disadvantages:  Errors in sampling  Time and labor requirements

19.  Distributes the sample evenly over the entire population  Bias may arise if there is some type of periodic variation in carbon stocks, but such patterns are rare http://www.youtube.com/watch?v=QFoisfSZs8I

20. Advantages Advantages:  Spatially well distributed  Small standard errors  Long history of use Disadvantages:  Bias in overestimating the actual standard error  Less flexible to increase or decrease the sampling size  Not applicable for fragmented strata

21.  Involves grouping the population of interest into strata to estimate characteristics of each stratum and to improve the precision of an estimate for entire population http://www.youtube.com/watch?v=sYRUYJYOpG0

22. Advantages Advantages:  Allows specifying the sample size within each stratum  Allows for different sampling design for each stratum Disadvantages:  Yields large standard error if the sample size selected is not appropriate  Not effective if all variables are equally important

23.  Involves a grouping of the spatial units or objects sampled  All observations in the selected clusters are included in the sample http://www.youtube.com/watch?v=QOxXy-I6ogs

24. Primary Sampling Unit (PSU) Secondary Sampling Unit (SSU) - cluster Advantages  Can reduce the time and expense of sampling by reducing travel distance Disadvantages  Can yield higher sampling error  Can be difficult to select representative clusters

25. i. Divide class in 4 groups (pick students randomly or systematically) ii. Randomly assign each group one of the sampling techniques and a map of land cover either national or regional iii. Each group should meet outside of class and decide on how to locate sampling plots to estimate per cent of each major land cover class based on the technique they were assigned. Next class they should be prepared to present their maps with sampling plots marked on them

27.  Allows for measuring and monitoring areas where changes are likely to occur  Reduces sampling effort while maintaining accuracy and precision in carbon stocks estimates  Allows for wise spending of the resources

28. By threat of deforestation  Use historical evidence to identify critical factors of deforestation  Create potential for deforestation map  Identify areas with high probability of deforestation By forest type  Use existing maps of vegetation types  Use existing forest inventory By accessibility  Define accessibility criteria (e.g. 5 km accessibility to main roads)  Use spatial analysis to model accessibility

29.  Stratifying by carbon stock reduces the sampling effort required to achieve targeted precision level

30.  Develop initial stratification plan  Land use  Vegetation  Slope  Drainage  Proximity to settlement  Collect preliminary data (~10 plots per stratum)

32. 1. Use spatially explicit land use change model 2. Identify key factors impacting historical deforestation patterns 3. Identify areas with high suitability for deforestation 4. Create deforestation threat map

34.  Sampling is very important in forest inventory in order to estimate information about an entire population  There are a number of sampling techniques but stratified sampling is most commonly used in forest carbon inventory  Forest types (or Carbon stocks) and threat of deforestation/ degradation are two main factors that are used to stratify the study area.

35. Asner, G.P. 2009. Tropical forest carbon assessment: integrating satellite and airborne mapping Approaches. Environ. Res. Lett. 4 034009 Czaplewski, R., R. McRoberts and E. Tomppo. 2004. Sample designs. FAO-IUFRO National Forest Assessments Knowledge reference http://www.fao.org/forestry/7367/en/ Maniatis, D. and D. Mollicone. 2010. Options for sampling and stratification for national forest inventories to implement REDD+ under the UNFCCC Carbon Balance and Management, 5:9 doi:10.1186/1750-0680-5-9