This document provides an overview of forest carbon measurement and monitoring methods from Bangladesh Climate-Resilient Ecosystem Curriculum (BACUM) Module 3. It discusses the importance of sampling in forest carbon inventories, as it is not practical to examine entire populations. Different sampling techniques are described, including simple random, systematic, stratified, and cluster sampling. Stratifying sampling areas by factors like forest type and threat of deforestation can help reduce sampling effort while maintaining accuracy. The document provides examples of previous forest plot designs used in Bangladesh and emphasizes that stratified sampling is most commonly used for forest carbon inventories.
Hierarchical Evaluation of Geologic Carbon Storage Resource Estimates: Cambri...Cristian Medina
The Midwest Regional Carbon Sequestration Partnership (MRCSP) aims to study the regional distribution and geologic storage suitability of units within the Cambrian-Ordovician sequences, including the Knox Supergroup, St. Peter Sandstone, Trenton and Lexington Limestones, and equivalent units across the MRCSP region.
To date, we have compiled a comprehensive data set of wireline logs and petrophysical information that include core analysis for porosity and permeability and mercury injection capillary pressure (MICP) analyses. Using these data, carbon storage resource estimates (SRE) are evaluated using a hierarchical approach that addresses uncertainty in the estimates by incorporating different models of formation porosity based on a series of increasingly complex portrayals of the pore system. The simplest analysis follows the USDOE methodology whereby a SRE is calculated using a single value for porosity in the assessed formation. Additional estimates follow the same general methodology but employ increasingly precise spatially variable porosity models based on formation diagenesis (depth-dependent function), reservoir suitability (effective porosity), distinct petrofacies (advanced reservoir characterization), and multiple realizations of porosity using data-driven geostatistical methods.
Results from this hierarchical approach help illuminate the magnitude of uncertainty that should be expected in SREs as a function of data availability and the level of reservoir characterization that is achievable for a given formation. A semi-probabilistic SRE calculation methodology using Monte Carlo simulations to create models for porosity generally tends to underestimate the range of uncertainty in storage resource. Conceivably, the higher the order model, the lower the uncertainty in the SRE. Ongoing research is investigating whether improved precision implicit in higher orders of the hierarchy are generating more accurate estimates of storage volumes.
The performance of portable mid-infrared spectroscopy for the prediction of s...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Martin Soriano-Disla, CSIRO Land and Water - Australia, in FAO Hq, Rome
Hierarchical Evaluation of Geologic Carbon Storage Resource Estimates: Cambri...Cristian Medina
The Midwest Regional Carbon Sequestration Partnership (MRCSP) aims to study the regional distribution and geologic storage suitability of units within the Cambrian-Ordovician sequences, including the Knox Supergroup, St. Peter Sandstone, Trenton and Lexington Limestones, and equivalent units across the MRCSP region.
To date, we have compiled a comprehensive data set of wireline logs and petrophysical information that include core analysis for porosity and permeability and mercury injection capillary pressure (MICP) analyses. Using these data, carbon storage resource estimates (SRE) are evaluated using a hierarchical approach that addresses uncertainty in the estimates by incorporating different models of formation porosity based on a series of increasingly complex portrayals of the pore system. The simplest analysis follows the USDOE methodology whereby a SRE is calculated using a single value for porosity in the assessed formation. Additional estimates follow the same general methodology but employ increasingly precise spatially variable porosity models based on formation diagenesis (depth-dependent function), reservoir suitability (effective porosity), distinct petrofacies (advanced reservoir characterization), and multiple realizations of porosity using data-driven geostatistical methods.
Results from this hierarchical approach help illuminate the magnitude of uncertainty that should be expected in SREs as a function of data availability and the level of reservoir characterization that is achievable for a given formation. A semi-probabilistic SRE calculation methodology using Monte Carlo simulations to create models for porosity generally tends to underestimate the range of uncertainty in storage resource. Conceivably, the higher the order model, the lower the uncertainty in the SRE. Ongoing research is investigating whether improved precision implicit in higher orders of the hierarchy are generating more accurate estimates of storage volumes.
The performance of portable mid-infrared spectroscopy for the prediction of s...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Martin Soriano-Disla, CSIRO Land and Water - Australia, in FAO Hq, Rome
LEAP Partnership – 1st meeting of the Technical Advisory Group on Soil Carbon...ExternalEvents
This presentation was presented during the 1 Parallel session on Theme 3.2, Managing SOC in: Grasslands and livestock production systems, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Aaron Simmons, from Orange Agricultural Institute – Australia, in FAO Hq, Rome
Week 2 Environmental Monitoring_Sampling & Data Quality Objectives.pptxSansenHandagJr
The basic principles of monitoring and characterization are described
for different environments, considering their most relevant processes. Initially, sampling protocols are described,
followed by documentation of quality control issues and
statistical methods for data analysis. Methods for making
field measurements in soil, vadose zone, water, and atmospheric environments are described. This includes
real-time monitoring, temporal and spatial issues, and
the issues of scale of measurement.
Use of Probabilistic Statistical Techniques in AERMOD Modeling EvaluationsSergio A. Guerra
The advent of the short term National Ambient Air Quality Standards (NAAQS) prompted modelers to reassess the common practices in dispersion modeling analyses. The probabilistic nature of the new short term standards also opens the door to alternative modeling techniques that are based on probability. One of these is the Monte Carlo technique that can be used to account for emission variability in permit modeling.
Currently, it is assumed that a given emission unit is in operation at its maximum capacity every hour of the year. This assumption may be appropriate for facilities that operate at full capacity most of the time. However, in most cases, emission units operate at variable loads that produce variable emissions. Thus, assuming constant maximum emissions is overly conservative for facilities such as power plants that are not in operation all the time and which exhibit high concentrations during very short periods of time.
Another element of conservatism in NAAQS demonstrations relates to combining predicted concentrations from the AMS/EPA Regulatory Model (AERMOD) with observed (monitored) background concentrations. Normally, some of the highest monitored observations are added to the AERMOD results yielding a very conservative combined concentration.
A case study is presented to evaluate the use of alternative probabilistic methods to complement the shortcomings of current dispersion modeling practices. This case study includes the use of the Monte Carlo technique and the use of a reasonable background concentration to combine with the AERMOD predicted concentrations. The use of these methods is in harmony with the probabilistic nature of the NAAQS and can help demonstrate compliance through dispersion modeling analyses, while still being protective of the NAAQS.
Applications of quartering method in soils and foodsIJERA Editor
Sampling is a technique and a science. If the appropriate technique is followed it reduces the bulk mass and helps to respect the batch composition as best as possible. Non-representative sampling results in incorrect analysis. Soils and foods are materials constantly assessed. Subsampling methods, as conning and quartering are applied in solid samples. Then they could be functional in soils, and granular foods, like grains, cereals or nuts. The method is very dependent on the skill of the operator then great care must be taken when obtaining a sample by coning and quartering. It has some advantages, like the easiness, cleanness and inexpensiveness. But it is usually inaccurate and can provide non-representative samples.
Recommendations to better align FREL with the TACCC principlesCIFOR-ICRAF
Presented by Zuelclady M.F Araujo Gutierrez, IDOM, at Online Workshop Capacity Building on the IPCC 2013 Wetlands Supplement, FREL Diagnostic and Uncertainty Analysis, April 16th, 2020
Quality Measurements Using NIR/MIR Spectroscopy: A Rotten Apple Could Turn Yo...TechRentals
Light interacts with a product's organic molecules causing variations in light absorption. The transmitted or reflected light can be measured with a spectrometer and the resultant spectral signature used to qualify or quantify properties of the product. The discussion will include - how light interacts with molecules, characteristics of the different electromagnetic spectral bands, in-line hardware required to collect light, and fundamentals of chemometrics.
Presenter -- Gary Brown
Gary Brown is one of the principle engineers with Australian Innovative Engineering and has spent the last 12+ years developing in-line instrumentation using NIR spectroscopy to measure properties of fresh fruit. He is now concentrating his efforts in applying the technology for in-line product authentication for the food and pharmaceutical industries.
This presentation was delivered at the third Asia-Pacific Forestry Week 2016, in Clark Freeport Zone, Philippines.
The five sub-thematic streams at APFW 2016 included:
Pathways to prosperity: Future trade and markets
Tackling climate change: challenges and opportunities
Serving society: forestry and people
New institutions, new governance
Our green future: green investment and growing our natural assets
LEAP Partnership – 1st meeting of the Technical Advisory Group on Soil Carbon...ExternalEvents
This presentation was presented during the 1 Parallel session on Theme 3.2, Managing SOC in: Grasslands and livestock production systems, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Aaron Simmons, from Orange Agricultural Institute – Australia, in FAO Hq, Rome
Week 2 Environmental Monitoring_Sampling & Data Quality Objectives.pptxSansenHandagJr
The basic principles of monitoring and characterization are described
for different environments, considering their most relevant processes. Initially, sampling protocols are described,
followed by documentation of quality control issues and
statistical methods for data analysis. Methods for making
field measurements in soil, vadose zone, water, and atmospheric environments are described. This includes
real-time monitoring, temporal and spatial issues, and
the issues of scale of measurement.
Use of Probabilistic Statistical Techniques in AERMOD Modeling EvaluationsSergio A. Guerra
The advent of the short term National Ambient Air Quality Standards (NAAQS) prompted modelers to reassess the common practices in dispersion modeling analyses. The probabilistic nature of the new short term standards also opens the door to alternative modeling techniques that are based on probability. One of these is the Monte Carlo technique that can be used to account for emission variability in permit modeling.
Currently, it is assumed that a given emission unit is in operation at its maximum capacity every hour of the year. This assumption may be appropriate for facilities that operate at full capacity most of the time. However, in most cases, emission units operate at variable loads that produce variable emissions. Thus, assuming constant maximum emissions is overly conservative for facilities such as power plants that are not in operation all the time and which exhibit high concentrations during very short periods of time.
Another element of conservatism in NAAQS demonstrations relates to combining predicted concentrations from the AMS/EPA Regulatory Model (AERMOD) with observed (monitored) background concentrations. Normally, some of the highest monitored observations are added to the AERMOD results yielding a very conservative combined concentration.
A case study is presented to evaluate the use of alternative probabilistic methods to complement the shortcomings of current dispersion modeling practices. This case study includes the use of the Monte Carlo technique and the use of a reasonable background concentration to combine with the AERMOD predicted concentrations. The use of these methods is in harmony with the probabilistic nature of the NAAQS and can help demonstrate compliance through dispersion modeling analyses, while still being protective of the NAAQS.
Applications of quartering method in soils and foodsIJERA Editor
Sampling is a technique and a science. If the appropriate technique is followed it reduces the bulk mass and helps to respect the batch composition as best as possible. Non-representative sampling results in incorrect analysis. Soils and foods are materials constantly assessed. Subsampling methods, as conning and quartering are applied in solid samples. Then they could be functional in soils, and granular foods, like grains, cereals or nuts. The method is very dependent on the skill of the operator then great care must be taken when obtaining a sample by coning and quartering. It has some advantages, like the easiness, cleanness and inexpensiveness. But it is usually inaccurate and can provide non-representative samples.
Recommendations to better align FREL with the TACCC principlesCIFOR-ICRAF
Presented by Zuelclady M.F Araujo Gutierrez, IDOM, at Online Workshop Capacity Building on the IPCC 2013 Wetlands Supplement, FREL Diagnostic and Uncertainty Analysis, April 16th, 2020
Quality Measurements Using NIR/MIR Spectroscopy: A Rotten Apple Could Turn Yo...TechRentals
Light interacts with a product's organic molecules causing variations in light absorption. The transmitted or reflected light can be measured with a spectrometer and the resultant spectral signature used to qualify or quantify properties of the product. The discussion will include - how light interacts with molecules, characteristics of the different electromagnetic spectral bands, in-line hardware required to collect light, and fundamentals of chemometrics.
Presenter -- Gary Brown
Gary Brown is one of the principle engineers with Australian Innovative Engineering and has spent the last 12+ years developing in-line instrumentation using NIR spectroscopy to measure properties of fresh fruit. He is now concentrating his efforts in applying the technology for in-line product authentication for the food and pharmaceutical industries.
This presentation was delivered at the third Asia-Pacific Forestry Week 2016, in Clark Freeport Zone, Philippines.
The five sub-thematic streams at APFW 2016 included:
Pathways to prosperity: Future trade and markets
Tackling climate change: challenges and opportunities
Serving society: forestry and people
New institutions, new governance
Our green future: green investment and growing our natural assets
The Team Member and Guest Experience - Lead and Take Care of your restaurant team. They are the people closest to and delivering Hospitality to your paying Guests!
Make the call, and we can assist you.
408-784-7371
Foodservice Consulting + Design
Senior Project and Engineering Leader Jim Smith.pdfJim Smith
I am a Project and Engineering Leader with extensive experience as a Business Operations Leader, Technical Project Manager, Engineering Manager and Operations Experience for Domestic and International companies such as Electrolux, Carrier, and Deutz. I have developed new products using Stage Gate development/MS Project/JIRA, for the pro-duction of Medical Equipment, Large Commercial Refrigeration Systems, Appliances, HVAC, and Diesel engines.
My experience includes:
Managed customized engineered refrigeration system projects with high voltage power panels from quote to ship, coordinating actions between electrical engineering, mechanical design and application engineering, purchasing, production, test, quality assurance and field installation. Managed projects $25k to $1M per project; 4-8 per month. (Hussmann refrigeration)
Successfully developed the $15-20M yearly corporate capital strategy for manufacturing, with the Executive Team and key stakeholders. Created project scope and specifications, business case, ROI, managed project plans with key personnel for nine consumer product manufacturing and distribution sites; to support the company’s strategic sales plan.
Over 15 years of experience managing and developing cost improvement projects with key Stakeholders, site Manufacturing Engineers, Mechanical Engineers, Maintenance, and facility support personnel to optimize pro-duction operations, safety, EHS, and new product development. (BioLab, Deutz, Caire)
Experience working as a Technical Manager developing new products with chemical engineers and packaging engineers to enhance and reduce the cost of retail products. I have led the activities of multiple engineering groups with diverse backgrounds.
Great experience managing the product development of products which utilize complex electrical controls, high voltage power panels, product testing, and commissioning.
Created project scope, business case, ROI for multiple capital projects to support electrotechnical assembly and CPG goods. Identified project cost, risk, success criteria, and performed equipment qualifications. (Carrier, Electrolux, Biolab, Price, Hussmann)
Created detailed projects plans using MS Project, Gant charts in excel, and updated new product development in Jira for stakeholders and project team members including critical path.
Great knowledge of ISO9001, NFPA, OSHA regulations.
User level knowledge of MRP/SAP, MS Project, Powerpoint, Visio, Mastercontrol, JIRA, Power BI and Tableau.
I appreciate your consideration, and look forward to discussing this role with you, and how I can lead your company’s growth and profitability. I can be contacted via LinkedIn via phone or E Mail.
Jim Smith
678-993-7195
jimsmith30024@gmail.com
Artificial intelligence (AI) offers new opportunities to radically reinvent the way we do business. This study explores how CEOs and top decision makers around the world are responding to the transformative potential of AI.
The case study discusses the potential of drone delivery and the challenges that need to be addressed before it becomes widespread.
Key takeaways:
Drone delivery is in its early stages: Amazon's trial in the UK demonstrates the potential for faster deliveries, but it's still limited by regulations and technology.
Regulations are a major hurdle: Safety concerns around drone collisions with airplanes and people have led to restrictions on flight height and location.
Other challenges exist: Who will use drone delivery the most? Is it cost-effective compared to traditional delivery trucks?
Discussion questions:
Managerial challenges: Integrating drones requires planning for new infrastructure, training staff, and navigating regulations. There are also marketing and recruitment considerations specific to this technology.
External forces vary by country: Regulations, consumer acceptance, and infrastructure all differ between countries.
Demographics matter: Younger generations might be more receptive to drone delivery, while older populations might have concerns.
Stakeholders for Amazon: Customers, regulators, aviation authorities, and competitors are all stakeholders. Regulators likely hold the greatest influence as they determine the feasibility of drone delivery.
Specific ServPoints should be tailored for restaurants in all food service segments. Your ServPoints should be the centerpiece of brand delivery training (guest service) and align with your brand position and marketing initiatives, especially in high-labor-cost conditions.
408-784-7371
Foodservice Consulting + Design
2. Module 3: Forest Carbon Measurement and Monitoring
SECTION III: FOREST CARBON MEASUREMENT AND MONITORING METHODS
3.3. Field Sampling Design Methods
3. Forest
Carbon
Measurement
and
Monitoring
(FCMM)
I. FUNDAMENTALS OF FOREST CARBON IN REDD+ CONTEXT
1.1. Forests, the Global Carbon Cycle and Climate Change
1.2. The Roles of Forests and Forest Carbon in Global Climate Negotiations
1.3. Challenges for Forest-based Climate Change Mitigation
II. FOREST CARBON STOCKS AND IPCC GUIDELINES
2.1. Overview of Forest Carbon Pools (Stocks)
2.2. Land Use, Land Use Change, and Forestry (LULUCF)
2.3. IPCC Guidelines for Forest Carbon Measurement and Monitoring
2.4. Reference Levels – Monitoring against a Baseline
III. FOREST CARBON MEASUREMENT AND MONITORING METHODS
3.1. Overview of Forest Carbon Measurement and Monitoring
3.2. Quality Assurance and Quality Control (QA/QC)
3.3. Field Sampling Design Methods
3.4. Forest Carbon Field Measurement Methods
3.5. Carbon Stock Calculation and Available Tools
3.6. Creating Activity Data and Emissions Factors
3.7. Considerations in Developing a Monitoring System
4. Acknowledgements
UNIVERSITIES
Bangladesh Agricultural University
University of Chittagong
Dhaka University
Independent University, Bangladesh
Khulna University
Noakhali University of Science and Technology
Shahjalal University of Science and Technology
Sher-e-Bangla Agriculture University
North South University
EXPERT CONTRIBUTORS SPECIFIC INPUTS
Prof. (Dr.) Manzoor Rashid Curriculum Development for all
topics
Prof. (Dr.) Md. Danesh Miah REDD+, Forest Carbon
Prof. (Dr.) Md. Jakariya Community NR Management,
Climate Change, Natural Resources
Management
DESIGN, LAYOUT AND CONTENT DEVELOPMENT: Ms. Chi Pham, Curriculum Development Expert, Bangkok, Thailand
CREL STAFF CREL STAFF
John A Dorr Utpal Dutta
Abu Mostafa Kamal Uddin Ruhul Mohaiman Chowdhury
Kevin T. Kamp Rahima Khatun
Paul Thompson Sultana Razia Zummi
Abdul Wahab Shams Uddin
Shahzia Mohsin Khan
5. At the end of this session, students will be able to:
• Explain why sampling is necessary
• Distinguish among random, stratified, and systematic
sampling, and know where each is appropriate
• Identify the advantages and drawbacks of different sampling
schemes
Learning Objectives
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
What is Sampling?
9. 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
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
Value of Sampling
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
Carbon Sampling Example
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
Sampling Theory
13. Different sampling schemes can be used:
1. Simple random sampling
2. Systematic sampling
3. Stratified sampling
4. Cluster sampling
Sampling Theory
18. • 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
Simple Random Sampling
http://www.youtube.com/watch?v=yx5KZi5QArQ
19. 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
20. • 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
Systematic Sampling
http://www.youtube.com/watch?v=QFoisfSZs8I
21. 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
22. • 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
Stratified Sampling
http://www.youtube.com/watch?v=sYRUYJYOpG0
23. 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
24. • Involves a grouping of the
spatial units or objects
sampled
• All observations in the
selected clusters are included
in the sample
Cluster Sampling
http://www.youtube.com/watch?v=QOxXy-I6ogs
25. 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
26. 1. Divide class in 4 groups (pick students randomly or systematically)
2. Randomly assign each group one of the sampling techniques and a map of
land cover either national or regional
3. 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
Homework
28. • 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
Why Stratify for Carbon Inventory?
29. 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
Types of Stratification
30. • Stratifying by carbon stock reduces the sampling effort
required to achieve targeted precision level
Stratification by Carbon Stocks
31. Develop initial stratification plan
• Land use
• Vegetation
• Slope
• Drainage
• Proximity to settlement
Collect preliminary data (~10
plots per stratum)
Stratification by Carbon Stocks & Forest Type
33. 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
35. Background: Existing forest inventories in Bangladesh
E Carbon Inventory (PA) 2014 (CREL)
D Carbon Inventroy 2010 (CREL)
! FD-97-coastal
! FD-97-hill
! FD-97-sund
) FD-NFA
! FD-SAL
! MKNP
! Sund RF
In total 17,371 plots
have already been
measured in
Bangladesh since 1958.
9 930 sample plots are
still available, out of
them
368 510 trees have
been inventoried
36. Example of previous plot designs in Bangladesh
NFA
- Rectangle
- 4 sub plots
- Large area
- Less intensity
- 298 plots
- Circular
- Nested
- 17.84m: > 50cm
- 10m: > 20
- 4m: > 5
- 2m: saplings
Protected Area Carbon Inventory
Sal
- Circular
- Cluster
- 3x volume plots
(17.84m)
- 4x area plots
(5.64 – 7.98)
Plot size different
depending on
location
Sundarban Carbon Assessment
- Circular
- Cluster
- 10m: > 10cm
- 4m: Non-tree veg
- 3m: >10cm
- 2m: Herbs
37. • 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.
Take Home Message
38. • USAID. 2015. USAID LEAF‘s Climate Change Curriculum. USAID Lowering Emissions in Asia Forests
Program (USAID LEAF). Winrock International and US Forest Service. Bangkok, Thailand.
• 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
References
39. The curriculum of USAID’s Climate-Resilient Ecosystems and Livelihoods (CREL) in Bangladesh is a free
resource of teaching materials for university professors, teachers and climate change training experts.
Reproduction of CREL’s curriculum materials for educational or other non-commercial purposes is
authorized without prior written permission from the copyright holder, provided the source is fully
acknowledged.
Suggested citation: Winrock International. 2016. USAID‘s Climate-Resilient Ecosystems and Livelihoods
(CREL). Winrock International. Dhaka, Bangladesh.
Disclaimer: The CREL’s curriculum is made possible by the support of the American People through the
United States Agency for International Development (USAID). The contents of the curriculum do not
necessarily reflect the views of USAID or the US Government.
References and Resources
40. USAID's Climate-Resilient Ecosystems and Livelihoods (CREL) Project
Winrock International Headquarters
2101 Riverfront Drive, Little Rock
Arkansas 72202-1748 USA
Tel: 1-501-280-3000
Web: www.winrock.org