PCS TA 007 Sample Plot Calculation Tool_v1.0
Document Control
Document identification
Document code: PCS-TA-007
Title: Sample Plot Calculation Tool
Scope: Quantitative tool for determining required number of sample plots per stratum to meet PCS sampling precision requirements, including statistical parameters, confidence levels, acceptable sampling error thresholds, and reassessment during monitoring periods.
Application: Used during baseline design and updated during each monitoring period where plot-based sampling is required by applicable PCS methodologies and tools.
Version history and change log
Table DC-1. Revision history
v1.0
TBD
Draft
Release for public consultation
PCS
TBD
Superseded versions
No superseded versions for v1.0.
Governance note on versioning and archiving
Only the latest approved version of this tool shall be used for new project registrations and for quantification/verification of monitoring periods unless PCS specifies otherwise. Superseded versions shall be archived and retained for traceability and audit purposes, including for projects assessed under earlier versions where applicable, consistent with PCS governance rules.
Chapter 1 - Introduction and Purpose
The Sample Plot Number Determination Tool establishes the procedures for calculating the minimum number of field plots required to achieve acceptable precision in biomass and carbon stock estimates under the Planetary Carbon Standard (PCS). Accurate estimation of sampling requirements is essential for ensuring that project results are statistically robust, representative of vegetation conditions, and suitable for independent verification.
The purpose of this tool is to provide a standardized approach for determining sampling intensity across project strata. Tree and shrub biomass, dead wood, litter, and other carbon pools may vary significantly within natural landscapes. Without adequate sampling, these variations can lead to large uncertainties that compromise the reliability of carbon stock estimates. This tool enables project developers to determine the minimum number of plots needed to meet the statistical precision requirements described in the applicable PCS methodology.
The tool applies to all Nature-Based Solutions (NBS) projects in which field sampling is required. It is used during project design to determine initial sampling intensity and during monitoring to reassess whether existing plots or additional plots are needed. The tool is applicable across diverse ecosystem types, including forests, shrublands, mangroves, drylands, and restoration landscapes.
This tool relies on statistical principles that relate sampling variance, confidence intervals, and allowable uncertainty. It incorporates preliminary or historical data from reconnaissance plots, prior measurements, or published literature to estimate the variability of biomass within each stratum. Based on this variability and the methodology’s required precision level, the tool calculates the minimum number of sample plots required.
By providing a clear, replicable, and rigorous framework for designing sampling programs, this tool enhances the transparency and environmental integrity of carbon estimates under PCS. It ensures that sampling programs produce results sufficiently precise for verification and issuance of Planetary Carbon Units (PCUs).
Chapter 2 - Scope and Applicability
This tool applies to all PCS Nature-Based Solutions projects that require field-based sampling of biomass or carbon pools. Sampling requirements vary across ecosystem types, but the statistical foundation for determining the number of sample plots remains consistent. This tool ensures that sampling intensity meets precision thresholds defined in applicable methodologies and that sampling programs reflect the spatial variability of each stratum.
The tool is applicable to all carbon pools that rely on field measurements, including:
Above-ground and below-ground tree biomass
Shrub biomass
Dead wood carbon stocks
Litter biomass
Additional pools requiring plot-based sampling
The tool must be used separately for each stratum identified in the project boundary. Strata typically differ in vegetation type, disturbance history, biomass density, stand structure, or management conditions. Because variability within a stratum directly influences sampling requirements, the sample size must be determined independently for each stratum.
This tool applies during both project design and monitoring. At the design stage, preliminary values of variability may be obtained from reconnaissance sampling, prior inventories, scientific literature, or analogous ecosystems. During monitoring, updated plot data must be used to reassess whether the existing sample size continues to satisfy precision requirements. If sampling uncertainty exceeds acceptable thresholds, additional plots must be established.
The tool does not apply to pools measured through remote sensing, modeling, or full enumeration, nor does it apply to soil carbon sampling unless required by a specific methodology. Where soil sampling is needed, specialized soil sampling tools or guidelines may be used.
Sampling procedures, once determined using this tool, must be implemented consistently throughout the crediting period. Adjustments to sampling intensity must be documented and justified if biomass variability increases or if stratification changes.
Chapter 3 - Key Concepts and Definitions
3.1 Sampling Plot
A sampling plot is a defined area where field measurements of biomass or carbon-related attributes are collected. Plot size and shape vary depending on vegetation type and sampling objectives. Plot measurements serve as the basis for estimating mean biomass and variance within a stratum.
3.2 Stratum
A stratum is a subdivision of the project area grouped by ecological similarity or biomass characteristics. Each stratum requires an independent sampling design and an independent determination of the number of sampling plots needed. Variance within a stratum drives sampling intensity.
3.3 Variance (S²)
Variance represents the degree of variability in biomass among plots within a stratum. Higher variance indicates greater heterogeneity and therefore requires more sample plots to achieve a given precision level.
3.4 Standard Deviation (S)
Standard deviation is the square root of variance and reflects the spread of biomass values across plots within the stratum. It is used in estimating sampling error and calculating required sample size.
3.5 Standard Error of the Mean (SE)
The standard error quantifies uncertainty in the estimate of mean biomass per hectare. It decreases as sample size increases. It is calculated as:
Where:
= standard deviation
= number of sample plots
3.6 Confidence Interval (CI)
The confidence interval represents the range within which the true population mean is expected to lie. It is calculated using:
Where is the t-value corresponding to the desired confidence level (typically 90% or 95%).
3.7 Relative Precision (RP)
Relative precision expresses sampling uncertainty as a percentage of the estimated mean. It is used to determine whether the level of sampling precision meets PCS or methodology-specific thresholds.
Where is the mean biomass of sample plots.
3.8 Required Precision Level
PCS methodologies often specify a maximum allowable sampling error (e.g., 10% at 90% confidence). Sampling intensity must be sufficient to ensure relative precision does not exceed the threshold.
3.9 Preliminary (Reconnaissance) Sampling
Preliminary sampling refers to a limited number of initial plots established to estimate biomass variability and calculate the required number of final sampling plots. Preliminary sampling results must be representative of the stratum.
3.10 Sample Size (n)
Sample size refers to the number of sampling plots required to achieve the desired precision level. It is derived using statistical formulas that incorporate variance, desired confidence level, and acceptable sampling error.
3.11 t-Value
The t-value is a statistical coefficient based on the Student’s t-distribution used to calculate confidence intervals. It depends on the desired confidence level and sample size.
3.12 Acceptable Sampling Error (E)
The acceptable sampling error is the maximum permissible deviation between the estimated mean biomass and the true mean, expressed as a proportion of the mean. The error threshold is defined by the methodology (e.g., E = 0.10 for 10%).
3.13 Conservative Plot Allocation
When preliminary sampling indicates extremely high variance, conservative plot allocation ensures that additional plots are assigned to reduce sampling error and meet required precision levels.
Chapter 4 - Parameters and Symbols
This chapter defines the parameters and symbols used in calculating the minimum number of sample plots required to achieve acceptable precision levels. All parameters must be applied consistently and documented in the Monitoring Report.
Table 1. Parameters and Symbols Used in Sample Size Calculation
Required number of sample plots
—
Calculated using variance and desired precision
Preliminary estimate of sample size
—
Based on reconnaissance sampling
Final number of plots used
—
Must not be less than calculated requirement
Mean biomass of sample plots
t/ha
Used for relative precision calculations
Standard deviation of biomass
t/ha
Derived from preliminary or historical data
Variance of biomass
(t/ha)²
Influences required sampling intensity
Standard error of the mean
t/ha
Confidence interval
t/ha
; used for precision
Relative precision
%
t-value
—
Depends on confidence level (90%, 95%)
Acceptable sampling error
—
Typically 0.10 (10%) or as required by methodology
Total area of the stratum
ha
Used only for scaling, not for n
Coefficient of variation
%
4.1 Confidence Levels and t-Values
PCS methodologies typically use:
90% confidence level for sampling precision
t-values dependent on sample size
If sample size is large (n > 30), the z-value approximation may be used (z ≈ 1.645 for 90%).
For smaller sample sizes, exact t-values must be applied to avoid underestimating uncertainties.
4.2 Acceptable Sampling Error (E)
The acceptable sampling error represents the maximum allowable deviation between the estimated mean and the true population mean. Common values are:
10% relative precision (E = 0.10)
15% for highly heterogeneous vegetation (methodology-dependent)
The applicable methodology sets the permissible threshold.
4.3 Preliminary Parameter Estimation
Preliminary sampling (or historical data) provides estimates of:
Standard deviation
Mean biomass
Coefficient of variation
These values inform the initial calculation of required sample size . Once full sampling begins, the calculation may be updated if significant differences emerge.
4.4 Interpretation of Calculated Parameters
If calculated sample size exceeds the number of available or feasible plots, the project developer must:
Increase sampling effort, or
Apply conservativeness as required by methodology
All decisions must be documented transparently.
Chapter 5 - Determining Required Sample Size
The determination of sample size ensures that biomass estimates meet the precision requirements established by PCS methodologies. This chapter presents the statistical formula for calculating required sample size, describes how preliminary data are used, and outlines the steps for applying the procedure independently within each stratum.
5.1 Statistical Basis for Sample Size Determination
Sampling uncertainty arises from natural variability in biomass within a stratum. To estimate the number of plots needed to achieve a desired precision level, the following statistical relationship is applied:
Where:
= required number of sample plots
= t-value for chosen confidence level
= standard deviation of preliminary plot data
= acceptable sampling error (as a proportion of the mean)
= mean biomass per hectare from preliminary data
This formula ensures that sampling uncertainty does not exceed the threshold defined in the applicable methodology.
5.2 Obtaining Preliminary Estimates
Preliminary data are required to estimate variance and mean biomass. These preliminary values may be obtained from:
Reconnaissance plots established specifically for initial variance estimation
Previous inventory data from the same area
Peer-reviewed literature describing similar ecosystems
Historical forest inventory data
Preliminary sampling must sufficiently represent variability within each stratum. Using fewer than 5 plots for preliminary estimation is discouraged unless no alternatives exist.
5.3 Calculating Initial Sample Size (n₀)
Using preliminary data, an initial estimate of required sample size is calculated:
This value reflects the sampling intensity needed under the assumption of an infinite population.
If the calculated value is below the minimum number of plots required by the methodology, the minimum requirement must be applied.
5.4 Applying the Finite Population Correction (FPC)
If a stratum is small or contains a limited number of potential sampling locations, the initial sample size may be adjusted using the finite population correction:
Where:
is the total number of possible plot locations in the stratum.
This adjustment ensures that sampling intensity remains proportionate to the size of the stratum.
5.5 Rounding the Required Sample Size
The final required sample size must always be rounded up to the next whole number. Fractional values are not permitted. If rounding reduces the ability to meet precision requirements, additional plots must be added.
When methodologies require minimum sampling thresholds (e.g., at least 10 plots per stratum), these thresholds override calculated sample sizes.
5.6 Reassessing Sample Size During Monitoring
Variance within a stratum may change across monitoring periods due to:
Growth and structural changes
Disturbances
Restoration activities
Stratification updates
Natural shifts in vegetation composition
For each monitoring period, sample size must be reassessed using updated plot data. If variance increases and relative precision exceeds the allowable threshold, additional plots must be established.
Sample reduction is not allowed even if variance decreases; sample size must remain at least equal to the highest number required during prior periods unless the methodology explicitly allows reduction.
5.7 Documentation Requirements
The Monitoring Report must include:
Preliminary biomass data used for sample size calculation
Summary statistics (mean, standard deviation, variance)
t-value and acceptable error threshold used
Calculated and any FPC adjustments
Final required number of sample plots per stratum
Justification for using default or literature values when preliminary data are limited
All calculations must be transparent and reproducible.
Chapter 6 - Sampling Workflow and Implementation Requirements
The sampling workflow ensures that the process of determining and implementing sample sizes is systematic, statistically sound, and consistent across all monitoring periods. This chapter outlines the required steps—from initial reconnaissance to long-term maintenance of the sampling network—and defines the documentation standards necessary for verification.
6.1 Overview of the Sampling Workflow
This workflow must be applied uniformly to ensure methodological integrity and replicability.
6.2 Stratification as the Foundation of Sampling
Accurate stratification is the first essential step in sampling design. The sampling workflow requires that:
Strata reflect meaningful ecological or structural differences
Each stratum is internally homogeneous enough for statistical sampling
Sample size calculations are performed per stratum, not project-wide
Stratification must be documented with maps, area estimates, and justification.
6.3 Conducting Preliminary Sampling
Preliminary sampling provides the variance estimates needed to calculate sample size. Requirements include:
A representative spread of plots across the stratum
At least 5 reconnaissance plots whenever feasible
Use of the same plot size and measurement protocol as in full sampling
Accurate measurement of biomass-relevant attributes
When preliminary sampling is not feasible, analogous data must be thoroughly justified and appropriately conservative.
6.4 Establishing Permanent Sample Plots
Permanent sample plots must be established after determining required sample size. These plots must:
Follow consistent size and shape rules applied in the biomass tool (PCS-TA-008)
Be located according to the sampling design, not convenience
Be permanently marked and GPS-located
Remain fixed across monitoring periods except in cases of force majeure
If a plot becomes inaccessible, a replacement plot must be established using the same placement rules.
6.5 Maintaining Sampling Consistency Across Monitoring Periods
Consistency in sampling ensures comparability across monitoring cycles. Requirements include:
Using the same plot locations unless justified otherwise
Applying identical measurement protocols
Reassessing sample size only when necessary due to increased variance
Maintaining or increasing sample size; reductions are not permitted unless explicitly allowed
Consistency is essential for assessing real changes in carbon stocks rather than changes in sampling design.
6.6 Integration With Field Measurement Activities
Sample size determination must align with field implementation:
Field teams must be informed of the required number of plots per stratum
Plot identifiers must match those used in the biomass and dead wood tools
Sampling timelines must align with seasonal or climate-related measurement windows
Additional plots required due to increased variance must be integrated before verification
Sampling plans must be operationally feasible and properly resourced.
6.7 Documentation Requirements
Documentation must be complete, accurate, and sufficient for replication. The Monitoring Report must include:
The sampling workflow used
Preliminary sampling procedures and data
Calculation of sample sizes
Maps showing plot locations
Field protocols and deviations
Changes to the sampling network over time
All results must be archived for validation and verification.
Chapter 7 - Uncertainty and Conservativeness
Sampling uncertainty is an inherent component of biomass estimation because vegetation varies across space. The sample size calculation process must ensure that sampling error remains within the acceptable threshold defined by the applicable PCS methodology. This chapter outlines the principles for quantifying sampling uncertainty, interpreting results, and applying conservativeness when precision requirements are not met.
7.1 Relationship Between Uncertainty and Sample Size
Sampling uncertainty decreases as sample size increases. The standard error of the mean reflects how well sample plots represent the true biomass of the stratum. When variance is high or when plot numbers are insufficient, sampling uncertainty increases and may exceed acceptable levels.
For a stratum with mean biomass and standard deviation :
The confidence interval is:
Relative precision (RP) is:
Relative precision must not exceed the allowable threshold (e.g., ±10%).
7.2 Uncertainty Thresholds Defined by Methodology
Each PCS methodology specifies the maximum allowable relative precision for sampling-based estimates. Common values include:
10% relative precision at 90% confidence, or
15% relative precision at 95% confidence
These thresholds dictate the minimum number of sampling plots required for each stratum.
If relative precision is greater than the allowable threshold, sampling is inadequate and corrective action is required.
7.3 When Uncertainty Exceeds Thresholds
If sampling results exceed uncertainty limits, the project must take one or more of the following actions:
Increase the number of sample plots in the affected stratum.
Revise stratification to reduce internal variability.
Apply conservativeness by reducing estimated biomass or carbon stock values.
Conservativeness may only be applied when methodological provisions permit it; otherwise, additional sampling is required.
7.4 Sampling Variance and High-Heterogeneity Strata
Strata with high ecological heterogeneity—such as disturbed or recovering forests, mangroves with uneven structure, drylands with patchy vegetation, or landscapes with recent fires or storms—typically exhibit high variance and thus require larger sample sizes.
If variance is extremely high, a methodological review of stratification is recommended before determining new sample sizes. Poorly designed strata often lead to excessive uncertainty.
7.5 Applying Conservativeness in Sample Size Determination
Conservativeness may be applied in limited and clearly defined situations:
When preliminary data indicate unusually low variance that is not representative of the stratum
When species composition is uncertain
When plot-level measurements exhibit unusual clustering
When field conditions limit plot accessibility and sampling cannot be expanded fully
In such cases, the project developer must:
use higher variance estimates than measured,
choose more conservative biomass or carbon values, or
apply a deduction at the stratum level.
All conservative adjustments must be justified in the Monitoring Report.
7.6 Documentation of Uncertainty Calculations
The Monitoring Report must present:
Standard deviation (S)
Standard error (SE)
Confidence interval (CI)
Relative precision (RP)
Acceptable error threshold (E)
The t-value used
Final sample size required
Plot numbers actually sampled
The report must also explain whether uncertainty decreased or increased relative to prior monitoring periods, and why.
7.7 Treatment of Uncertainty Over Multiple Monitoring Cycles
Uncertainty must be evaluated independently for each monitoring period. Plot numbers must remain at least equal to the historical maximum unless:
the methodology explicitly allows reduction, and
updated variance is demonstrably lower and stable across multiple cycles.
Under no circumstances may sample size be reduced solely to minimize fieldwork effort.
7.8 Transparency Requirements
All assumptions, reasoning, and calculations influencing uncertainty must be fully traceable. A VVB must be able to replicate every step:
sampling design,
variance calculations,
sample size determination,
uncertainty results,
and any conservativeness applied.
Uncertainty analysis must never obscure unfavorable results; full transparency is required.
Chapter 8 - Reporting Requirements
Reporting requirements ensure that all sampling decisions, calculations, and justifications are transparent, replicable, and readily verifiable. Because sample size directly affects uncertainty and the credibility of biomass estimates, the Monitoring Report must present complete documentation of how sampling intensity was determined and implemented for each stratum.
8.1 Description of Strata and Sampling Framework
The report must begin by describing each stratum used in the project. This includes:
Ecological characteristics
Vegetation type and structural features
Disturbance history
Rationale for stratification
Each stratum must be clearly mapped, and the total area must be stated. The sampling framework applied to each stratum must be described in detail, including whether sampling was random, systematic, or stratified-random.
8.2 Preliminary Sampling and Variance Estimation
If preliminary sampling was used to estimate variance, the report must include:
Number of preliminary plots measured
Plot measurements and biomass results
Calculated mean () and standard deviation (S)
Justification for preliminary data quality and representativeness
If analogous data (literature, historical inventories) were used instead of preliminary plots, the report must justify why such data are appropriate.
8.3 Calculation of Required Sample Size
The report must show:
Acceptable error threshold (E) required by the methodology
Confidence level used and corresponding t-value
Calculation of initial sample size ()
Application of the finite population correction factor, if used
Final required number of plots for each stratum
Each calculation must be presented clearly, including formulas and intermediate values.
8.4 Final Sample Allocation by Stratum
The report must specify:
Number of plots actually established in each stratum
Any differences between required and actual plot numbers
Reasons for additional plots (e.g., high variance)
Reasons for deviations from calculated sample size (e.g., inaccessible areas)
If more plots were established than required, the report must explain whether these were retained for future monitoring cycles.
8.5 Documentation of Plot Locations
Plot locations must be documented with:
GPS coordinates
Maps or geospatial files
Stratum identifiers
Field access notes where relevant
Plots must be uniquely identified and mapped in a way that allows complete relocation in future monitoring periods.
8.6 Integration With Biomass Measurement Tools
The report must demonstrate how the sampling design integrates with:
PCS-TA-008 (Tree & Shrub Biomass Tool)
PCS-TA-007 (Dead Wood and Litter Tool)
PCS-TA-004 (Peat Tool, if applicable)
Any other pool requiring field measurement
The Monitoring Report must confirm that sample sizes meet precision requirements for each pool, not only for tree biomass.
8.7 Reporting of Sampling Consistency Across Monitoring Periods
Sampling consistency must be documented by:
Comparing previous monitoring period sample sizes to current sizes
Explaining any increases in sample plots (due to variance increases)
Confirming that sample sizes have not been reduced unless explicitly permitted
Describing any lost or inaccessible plots and their replacements
All sampling changes must be justified in terms of methodological requirements, not convenience.
8.8 Reporting of Uncertainty and Compliance With Precision Thresholds
The report must present:
Standard deviation, standard error, and confidence interval for each stratum
Relative precision (RP) values compared to required thresholds
Explanations of any exceedances
Description of conservativeness applied, if required
Any additional plots added to meet precision requirements
Uncertainty results must be easy for a VVB to replicate.
8.9 Archiving and Supporting Documentation
The following must be archived and made available for verification:
Preliminary sampling data
Sampling design and plot layout documents
Calculation sheets for sample size
Maps and GPS files
Variance and uncertainty analyses
Field logs from sampling activities
These documents must be retained for the entire crediting period.
Chapter 9 - Quality Assurance and Quality Control (QA/QC)
Quality assurance and quality control procedures ensure that sampling design, sample size determination, and implementation are scientifically defensible, accurate, and consistent across monitoring periods. Because sample size directly affects uncertainty and, ultimately, credit issuance, QA/QC must be rigorous, transparent, and thoroughly documented.
9.1 QA/QC for Stratification
Stratification defines the framework for sampling and must undergo internal quality review before field implementation. QA/QC steps include:
Verifying that stratification reflects ecological differences relevant to biomass variability
Checking stratum boundary accuracy in geospatial layers
Confirming that stratification is applied consistently between baseline and monitoring periods unless justified changes occur
If stratification changes over time, the project must show how consistency in sampling is maintained and how the change improves accuracy.
9.2 QA/QC for Preliminary Sampling
Preliminary sampling produces the variance estimates used in sample size calculations. QA/QC procedures must ensure that:
Preliminary plots are representative of the full range of biomass conditions
Measurement protocols match those used for permanent plots
Outliers or errors in preliminary data are investigated and corrected
Sample size calculations are based on reliable variance figures
If preliminary variance is unrealistically low or high, the project must evaluate whether preliminary plots were appropriately distributed.
9.3 QA/QC for Sample Size Calculations
Sample size calculations must be independently reviewed to ensure that:
Correct formulas were applied
Acceptable error threshold (E) matches methodology requirements
The appropriate t-value was used for the chosen confidence level
Standard deviation and mean biomass values were calculated correctly
Finite population correction was applied where appropriate
Rounding rules were followed consistently
The entire calculation process must be transparent and replicable.
9.4 QA/QC for Plot Establishment
Once the required number of plots is calculated, QA/QC must verify:
Plots are placed according to the sampling design (random, systematic, or stratified-random)
GPS coordinates are accurate and recorded in standardized format
Plot markers are durable and secure
Plot boundaries, center points, and layout are consistent across teams
Inaccessible plots are documented and replaced following the same placement rules
Plots must not be moved or replaced for convenience.
9.5 QA/QC for Consistency Across Monitoring Periods
Sampling consistency must be ensured across all monitoring cycles. QA/QC includes:
Verifying that existing plots are remeasured correctly
Confirming sample size is not reduced unless methodology explicitly allows it
Ensuring that any added plots meet the same standards as original plots
Checking that changes reflect real ecological or statistical needs, not operational convenience
Any adjustments must be justified in the Monitoring Report.
9.6 QA/QC for Integration With Biomass Tools
This tool must align seamlessly with other PCS tools. QA/QC must confirm that:
Sample size is sufficient for PCS-TA-008 (tree biomass), PCS-TA-007 (dead wood and litter), and any other pool relying on sampling
Shared plots meet the strictest requirements across tools
Variance assumptions used in sample size determination match those observed during biomass calculation
Cross-tool consistency enhances verification reliability.
9.7 QA/QC for Uncertainty Analysis
Because sample size directly affects uncertainty, QA/QC must confirm that:
Uncertainty calculations are correct and traceable
Confidence intervals are computed using appropriate formulas
Relative precision was checked against required thresholds
Conservativeness was applied when limits were exceeded
Results match those presented in biomass estimation sections of the Monitoring Report
Uncertainty analysis must be reproducible by a VVB.
9.8 Data Management and Documentation
All data generated during sampling design and implementation must be archived securely. QA/QC must ensure:
Calculation sheets are error-free and version-controlled
Preliminary and final datasets are complete
Metadata accompany all spatial files
Documentation of field decisions is clear and comprehensive
All changes across monitoring periods are recorded
A VVB should be able to reconstruct the entire sampling design from the record alone.
9.9 Continuous Improvement
Projects are encouraged to refine sampling procedures over time. QA/QC must support:
Improved stratification based on updated ecological information
Adoption of better sampling technologies or measurement tools
Enhanced training for field personnel
Adjustments based on verification findings or lessons learned
Any improvements must maintain consistency with baseline sampling or be justified transparently.
Annex A - Sample Size Calculation Templates
This annex provides standardized templates for calculating required sample size for each stratum. These templates ensure that all statistical steps and assumptions are transparent and replicable during validation and verification.
A.1 Template for Preliminary Data Summary
The Monitoring Report must include real field data in this format.
A.2 Sample Size Calculation Template
Acceptable Error (E)
As per methodology (e.g., 0.10)
Confidence Level
Typically 90% or 95%
t-value
Based on chosen confidence level
Mean Biomass ()
From preliminary sampling
Standard Deviation (S)
From preliminary sampling
Initial Sample Size ()
Calculated using
Total Possible Plots (N)
If finite population correction applies
Final Required Sample Size (n)
Rounded up
A.3 Finite Population Correction (FPC) Template
This table is required only when stratum area is small or plot density is high.
A.4 Sample Size Reassessment Template (Monitoring Periods)
This shows whether additional sampling is required during monitoring.
Annex B - Statistical Reference Tables
This annex provides essential reference values used in sample size calculations.
B.1 t-values for Common Confidence Levels
90%
~1.645
Recommended for PCS field sampling
95%
~1.960
Occasionally required by methodologies
When sample size is < 30, the appropriate t-value must be taken from a full t-distribution table.
B.2 Example Relative Precision Calculation
The following example illustrates how relative precision (RP) is computed:
Mean Biomass ()
120 t/ha
Standard Deviation (S)
36 t/ha
Sample plots (n)
12
t-value (90%)
1.645
Step calculations:
Standard error:
Confidence interval:
Relative precision:
If methodology requires ≤10% precision, additional plots are required.
B.3 Default Values for Preliminary Sampling (If No Field Data Available)
These values may be used only with strong justification and must be conservative:
Tropical forest
40–60 t/ha
Highly variable biomass
Dryland woodland
15–30 t/ha
Patchy vegetation
Mangrove
30–50 t/ha
Strong spatial gradients
Shrubland
10–20 t/ha
Smaller biomass range
Using overly optimistic values is prohibited.
Annex C - Sampling Design and Plot Allocation Forms
These forms support documentation and must be included in the Monitoring Report.
C.1 Sampling Design Summary Form
C.2 Plot Allocation and Location Form
This table ensures traceability and allows VVBs to relocate all permanent plots.
C.3 Plot Replacement and Adjustment Record
Plot replacement must never be arbitrary; documentation is mandatory.