Section 4. Carbon Stock Measurement
Methods
4.2. Design of field sampling framework for
carbon stock inventory
USAID LEAF
Regional Climate Change Curriculum Development
Module: Carbon Measurement and Monitoring (CMM)
Name
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
I
II
III
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
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
CARBON MEASUREMENT AND MONITORING DESIGN
3.1
IV
V
Considerations in developing a monitoring system
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
NATIONAL SCALE MONITORING SYSTEMS
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:

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
It is extremely unlikely that we would
have the time and resources needed
to measure the entire carbon stock in
a forest or landscape

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
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

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
Different sampling schemes can be used:
i.
Simple random sampling
ii.
Systematic sampling
iii.
Stratified sampling
iv.
Cluster sampling
i
Different sampling schemes can be used:
i.
Simple random sampling
ii.
Systematic sampling
iii.
Stratified sampling
iv.
Cluster sampling
i
ii
Different sampling schemes can be used:
i.
Simple random sampling
ii.
Systematic sampling
iii.
Stratified sampling
iv.
Cluster sampling
i
ii
iii
Different sampling schemes can be used:
i.
Simple random sampling
ii.
Systematic sampling
iii.
Stratified sampling
iv.
Cluster sampling
i
ii
iv
iii

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
A random sample
Disadvantages:

Errors in sampling

Time and labor requirements
Advantages:

Representativeness and
freedom from bias

Ease of sampling and analysis

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
Disadvantages:

Bias in overestimating the
actual standard error

Less flexible to increase or
decrease the sampling size

Not applicable for fragmented
strata
Advantages:

Spatially well distributed

Small standard errors

Long history of use

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
Disadvantages:

Yields large standard error if
the sample size selected is not
appropriate

Not effective if all variables
are equally important
Advantages:

Allows specifying the sample
size within each stratum

Allows for different sampling
design for each stratum

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
Primary Sampling
Unit (PSU)
Advantages

Can reduce the time and
expense of sampling by reducing
travel distance
Disadvantages
Secondary Sampling
Unit (SSU) - cluster

Can yield higher sampling error

Can be difficult to select
representative clusters
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

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
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

Stratifying by carbon stock
reduces the sampling
effort required to achieve
targeted precision level


Develop initial stratification plan

Land use

Vegetation

Slope

Drainage

Proximity to settlement
Collect preliminary data (~10 plots
per stratum)
4. Create deforestation
threat map
3. Identify areas with high
suitability for deforestation
2. Identify key factors impacting
historical deforestation patterns
1. Use spatially explicit
land use change model

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.
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