Section 4. Carbon Stock Measurement
Methods
4.1. Forest Carbon Measurement and Monitoring
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 foret 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
At the end of the section, learners will be able to:

Describe fundamental concepts and technical
options for the measurement and monitoring of
forest carbon,

Explain the components of carbon stock inventories

Apply remote sensing principles for forest carbon
monitoring.
 Overview
 Fundamental concepts for
carbon measurement
 Major carbon pools to
consider
 How do we estimate biomass
 How do we monitor forest
carbon
 Remote Sensing
 Challenges of Remote
Sensing

Carbon is one of the most
common elements on earth,
and is found in all living
organisms

Carbon is the basis of most
molecules found in
vegetation:

Carbohydrates, Sugars,
Fats, Proteins, Alcohols,
DNA, Chlorophyll…

In living and dead organisms, carbon (C) is always combined
with other elements to create molecules

Trees (or other vegetation) are 47% C by mass
Litter is 37% C by mass
Soils vary significantly so soil C% must be determined in a
laboratory
Example:

Mass of carbon in a tree with a dry mass of 2.30 t

2.30 t dry biomass * 47% = 1.08 t Carbon
Carbon (C) vs. Carbon Dioxide (CO2)

CO2 emissions are sometimes expressed in units of t CO2
instead of t C

Molecular Weights:


C = 12g/mol
O = 16 g/mol CO2 = 44g/mol
To convert C  CO₂

multiply by ratio of molecular weights (44 g CO2/mol) to
12(12 g C per mol):

1.08 t C * 44/12 = 3.96 t CO2
Task: Review on Forest Carbon Pools
Objective: To differentiate different types of forest carbon pools
Activity: Each group choose one carbon pool and describe the
carbon pool include all major fluxes in and out.
Time: 15 min
Forest carbon is stored in five pools within and around
vegetation
1.
Above-ground biomass:
stems, bark, leaves, etc.
2.
Below-ground biomass:
roots of all sizes
3.
Dead wood or dead
organic matter in dead wood
4.
Litter or dead organic matter in
litter
5.
Soil organic carbon (SOC)

Measurement of forest carbon involves
accurately assessing the carbon held in
each of the forest pools

Monitoring forest carbon requires
repeated measurements over time to
assess the rate and direction of change in
both pool sizes and forest area
Pool
Methods
Suitability for carbon measurement
Above-ground biomass
Plot
Very suitable and cost-effective,
commonly adopted and familiar. Plot
selection is key to the method
Harvest
Expensive, time consuming, not
appropriate all the time. Used to
develop allometric equations
Plot-less,
transect
Good but not suitable in dense
vegetation and for periodic monitoring
Below-ground
biomass
Pool
Methods
Root extraction and
mass measurement
Suitability for carbon
measurement
Expensive and not suitable at
large scales
Root to shoot ratio
Most commonly used
Requires AGB measurement
Biomass equations
Requires input data e.g. height,
diameter, girth
Pool
Methods
Litter trap
Litter
Stock measurement
Suitability for carbon
measurement
Not always suitable and
requires intense effort
Feasible and commonly
adopted
Pool
Methods
Suitability for carbon
measurement
Diffuse reflectance
spectroscopy
Requires a lab and skilled
manpower, may have
future potential
Soil
Modeling
carbon
Laboratory
estimation
Suitable for projection,
requires input data from other
methods
Most suitable and commonly
adopted
Above-ground biomass
Pool
Methods
Suitability for carbon measurement
Modeling
Suitable for projections, requires basic
input parameters from field
measurements
Carbon flux
Expensive and needs skilled human
measurements resources
Remote
sensing
Needs field measurements for
calibration. Data are usually at large
spatial scales, needs expertise to be
used and can be expensive
Primary techniques for forest carbon
monitoring: ground measurements



Measure different
carbon ‘pools’ within
sampling areas of a
specific size
Repeat
measurements in
many sample plots
across the landscape
Measurements must
be repeated over
time to monitor
changes

Field measurements are
collected from multiple
sampling plots for a carbon
stock inventory

A sampling plan must be
created

Measurements are recorded in the field

Models and conversion factors are used to estimate carbon
stocks in each major pool based on field measurements

Statistical analysis is used to calculate average forest carbon
stocks based on plot data
Repeated field measurements allow us to monitor changes
in carbon stocks in an area of interest over time
Time
T1
T2
T3
Field measuring
techniques for
estimating forest
carbon stocks
+
Remote sensing (RS)
techniques for
monitoring forests
from space
What is Remote Sensing?

Have the students discuss in small groups and then share
with the class:

A definition of Remote Sensing

Examples of Remote Sensing ( either sensor names or
approaches)
“Remote sensing is the science of obtaining information about
objects or areas from a distance, typically from aircraft or
satellites.” – NOAA
Remote sensors detect some segments of the electromagnetic
spectrum (EMS)
Source: http://amazing-space.stsci.edu

Sensors measure and
digitally record data in
certain segments of the
electromagnetic spectrum
(visible or not) and this
data can be converted into
images

Satellite images are not
photographs!
A
B
Passive sensors detect
amounts of reflected
energy transmitted to them
Active sensor systems
produce the energy they
are recording

Satellites have provided continuous global data coverage since
the 1970s

Numerous satellites orbit the Earth capturing information
every day

Remote sensing can capture information from large areas on a
relatively frequent basis
Landsat 7
SPOT 5
Quick Bird

RS data by itself does not provide direct information
about carbon stocks

RS data must be combined with ground-based data to
measure and monitor terrestrial carbon

Associate carbon stocks with remotely sensed land covers

Calibrate remote sensing systems

Classification of vegetation cover and generation of a
vegetation type map.

This partitions the spatial variability of vegetation into
relatively uniform zones or vegetation classes.

These can be very useful in the identification of groups of
species and in the spatial interpolation and extrapolation
of biomass estimates.

Indirect estimation of biomass through some form of
quantitative relationship (e.g. regression equations)
between band ratio indices (NDVI, GVI, etc.) or,

Other measures such as direct radiance values per pixel
or digital numbers per pixel, with direct measures of
biomass or with parameters related directly to biomass,
e.g. leaf area index (LAI).

Different types of RS images are used to monitor forests:

Multispectral images - capture data at specific frequencies
across the electromagnetic spectrum

Radar images - capture elevation data, biomass
applications are being developed

Lidar images - capture three dimensional information
about surface features
Source: http://csc.noaa.gov/digitalcoast/tools/fusion
Spatial
resolution
Coarse
>250 m
Medium
20-250m
Fine
<20m
Sensor
MODIS
LANDSAT MSS
Landsat ETM 7
ASTER
RADARSAT
IKONOS
SPOT-5
Lidar
Source: Rogan and Chen (2004)
Acquisition cost
(US$/km2)
0.00
0.0088
0.0162
00152
2.53
29.00
0.73
74.00
Pre-processing
costs (US$/km2)
0.00005
0.0044
0.0081
0.0076
1.20
14.50
0.27
37.00

Multispectral sensors measure the amount of light an
object (pond, forest, etc.) reflects

The amount of light an object reflects is its “spectral
signature”
Tree
Water
Grass

By knowing the
“signature” of particular
land covers, land cover
maps can be generated
for large geographic
areas

Data from the carbon stock
inventory is used to estimate
average carbon stock for each
land cover and forest type
included in the land cover map

Remote sensing data help us
understand the spatial
distribution of forest carbon
stocks and changes over time
By comparing land
cover maps from
different points in
time, we can generate
information on land
cover and land use
change or “Activity
Data” for large areas
Ground based data on carbon stocks for different forest
types and other land covers is used to create emission
factors for different land cover transitions

Forest  Cropland

Forest  Pasture

Etc.

Activity data is combined with emission factors to estimate
changes in carbon stocks and associated emissions
Total change
= 550 t C

RS needs to be calibrated with field measurements

Some satellite imagery is very expensive

RS data requires technical expertise to be interpreted

Clear and practical methodologies are needed not only in field
measurements, but also in the application of remote sensing

New technology and methodologies (e.g. Lidar technique data
acquisition, radar data) could contribute further to improve
precision and accuracy of assessment, if their costs could be
brought down.
Learners should feel comfortable with …

Defining the major carbon pools in forests and options for
measuring them

How measurements and monitoring are related

The basic concepts underlying Remote Sensing technology

How Remote Sensing is used in forest carbon monitoring

Challenges of Remote Sensing

Assign individuals or groups to read publications on
remote sensing systems for forest carbon monitoring
(CLASlite, LiDAR, etc)

Students prepare five minute presentations on remote
sensing systems

Data collected by sensor

Ground measurement requirements

Applications

Winrock International version: 2012, Standard operating
procedures for carbon measurement.

Goetz S. et al. 2009. Mapping and Monitoring Carbon Stocks with
Satellite Observations: An update. Carbon Balance and
Management 4:2 doi:10.1186/1750-0680-4-2

FAO; Raul Ponce-Hernandez, 2004: Assessing carbon stocks and
modeling win–win scenarios of carbon sequestration through landuse changes

Rogan, J and Chen, D. 2004. Remote sensing technology for
mapping and land-cover and land-use change. Progress in Planning.
61:301-325