usaid leaf regional climate change curriculum development module: carbon measurement and monitoring...
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USAID LEAF Regional Climate Change Curriculum DevelopmentModule: Carbon Measurement and Monitoring (CMM)
Section 4. Carbon Stock Measurement Methods4.2. Design of field sampling framework for carbon stock inventory
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mName Affiliation Name Affiliation
Deborah Lawrence, Co-lead University of Virginia Megan McGroddy, Co-lead University of Virginia
Bui The Doi, Co-lead Vietnam Forestry University Ahmad Ainuddin Nuruddin
Universiti Putra Malaysia
Prasit Wang, Co-lead Chiang Mai University, Thailand
Mohd Nizam Said Universiti Kebangsaan Malaysia
Sapit Diloksumpun Kasetsart University, Thailand Pimonrat Tiansawat Chiang Mai University, Thailand
Pasuta Sunthornhao Kasetsart University, Thailand Panitnard Tunjai Chiang Mai University, Thailand
Wathinee Suanpaga Kasetsart University, Thailand Lawong Balun University of Papua New Guinea
Jessada Phattralerphong Kasetsart University, Thailand Mex Memisang Peki PNG University of Technology
Pham Minh Toai Vietnam Forestry University Kim Soben Royal University of Agriculture, Cambodia
Nguyen The Dzung Vietnam Forestry University Pheng Sokline Royal University of Phnom Penh, Cambodia
Nguyen Hai Hoa Vietnam Forestry University Seak Sophat Royal University of Phnom Penh, Cambodia
Le Xuan Truong Vietnam Forestry University Choeun Kimseng Royal University of Phnom Penh, Cambodia
Phan Thi Quynh Nga Vinh University, Vietnam Rajendra Shrestha Asian Institute of Technology, Thailand
Erin Swails Winrock International Ismail Parlan FRIM Malaysia
Sarah Walker Winrock International Nur Hajar Zamah Shari FRIM Malaysia
Sandra Brown Winrock International Samsudin Musa FRIM Malaysia
Karen Vandecar US Forest Service Ly Thi Minh Hai USAID LEAF Vietnam
Geoffrey Blate US Forest Service David Ganz USAID LEAF Bangkok
Chi Pham USAID LEAF Bangkok
Acknowledgements
I OVERVIEW: CLIMATE CHANGE AND FOREST CARBON
1.1 Overview: Tropical Forests and Climate Change
1.2 Tropical forests, the global carbon cycle and climate change
1.3 Role of forest carbon and forests in global climate negotiations
1.4 Theoretical and practical challenges for forest-based climate mitigation
II FOREST CARBON STOCKS AND CHANGE
2.1 Overview of forest carbon pools (stocks)
2.2 Land use, land use change, and forestry (LULUCF) and CO2 emissions and sequestration
2.3 Overview of Forest Carbon Measurement and Monitoring
2.4 IPCC 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
III CARBON MEASUREMENT AND MONITORING DESIGN
3.1 Considerations in developing a monitoring system
IV CARBON STOCK MEASUREMENT METHODS
4.1 Forest Carbon Measurement and Monitoring
4.2 Design of field sampling framework for carbon stock inventory
4.3 Plot Design for Carbon Stock Inventory
4.4 Forest Carbon Field Measurement Methods
4.5 Carbon Stock Calculations and Available Tools
4.6 Creating Activity Data and Emission Factors
4.7 Carbon Emission from Selective Logging
4.8 Monitoring non-CO2 GHGs
V NATIONAL SCALE MONITORING SYSTEMS
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Session Outline
Lecture (50 minutes) Why sampling is important Major sampling approach Stratification Examples of stratification approaches used in forests Class activity (15 minutes) Homework
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:
Learning Objectives
Class Exercise
Class Exercise
What is sampling?
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
Reasons for sampling
It is extremely unlikely that we would have the time and resources needed to measure the entire carbon stock in a forest or landscape
Value of sampling
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
Carbon sampling example
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
Sampling theory
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
Sampling theory
Different sampling schemes can be used:
i. Simple random sampling
ii. Systematic sampling
iii. Stratified sampling
iv. Cluster sampling
i
Sampling theory
Different sampling schemes can be used:
i. Simple random sampling
ii. Systematic sampling
iii. Stratified sampling
iv. Cluster sampling
i
ii
Sampling theory
Different sampling schemes can be used:
i. Simple random sampling
ii. Systematic sampling
iii. Stratified sampling
iv. Cluster sampling
i
ii
iii
Sampling theory
Different sampling schemes can be used:
i. Simple random sampling
ii. Systematic sampling
iii. Stratified sampling
iv. Cluster sampling
i
ii
iii
iv
Simple random sampling
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
Simple random sampling
A random sample
Advantages: Representativeness and
freedom from bias Ease of sampling and analysis
Disadvantages: Errors in sampling Time and labor requirements
Systematic sampling
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
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
Systematic sampling
Stratified sampling
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
Stratified sampling
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
Cluster sampling
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
Cluster sampling
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
Class Homework
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
Forest Carbon Stratification Techniques
Why stratify for carbon inventory?
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
Types of stratification
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
Stratification by carbon stocks
Stratifying by carbon stock reduces the sampling effort required to achieve targeted precision level
Stratification by carbon stocks & forest type
Develop initial stratification plan Land use Vegetation Slope Drainage Proximity to settlement
Collect preliminary data (~10 plots per stratum)
Not all forests are equally threatened
Stratification by threat
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
Sampling design
TAKE HOME MESSAGE
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.
References
Asner, G.P. 2009. Tropical forest carbon assessment:integrating satellite and airborne mappingApproaches. 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