Designing Forest Inventory Plots for Carbon Projects

Updated 2026-11-02 · 4 min read · By the akar.earth specialist team

Forest inventory plot design for carbon: plot size and shape, stratification, sample size for target precision, and permanent plots that pass verification.

A good carbon inventory uses stratified random or systematic plots, sized for the forest type, numbered to reach the methodology’s precision target, and installed as permanent plots that can be relocated at every verification. The design is set before fieldwork, documented in the project description and followed exactly; ad-hoc changes are what verifiers find first.

Step 1: Define strata

Stratification groups land that has similar carbon stocks and change dynamics. It cuts variance, so you need fewer plots for the same precision.

Typical strata in Indonesian projects:

• Natural forest by condition (intact, logged, degraded), often split by peat vs mineral soil.
• ARR by planting year, species mix and site class.
• Agroforestry by crop system (coffee, cocoa, rubber) and shade density.
• Mangrove by zone and species dominance.

Use satellite land cover, canopy height models and management records to draw strata. Keep the number manageable: each stratum needs enough plots of its own.

Step 2: Choose plot size and shape

Nested plots measure small trees in a small inner plot and large trees in the full plot. They save time without losing the big-tree carbon. Circular plots are faster to lay out; rectangular plots handle slopes and edges better. Apply slope correction to horizontal area on steep terrain.

Step 3: Calculate sample size

Most methodologies set a precision target, often expressed as a confidence interval half-width as a percentage of the mean at 90% or 95% confidence. The CDM tool AR-TOOL14 is a common reference for sample-size calculation in ARR.

The workflow:

1. Run a pilot inventory or use data from similar sites to estimate the standard deviation of carbon per hectare in each stratum.
2. Use the stratified sample-size formula (with stratum areas, variances and cost if you want an optimal allocation).
3. Add a margin for lost or inaccessible plots.

Under-sampling does not stop crediting, but it leads to uncertainty deductions that can cost more than the extra plots.

Step 4: Place plots without bias

• Use systematic grids with a random start, or random points within strata.
• Generate locations in GIS before fieldwork and do not let crews move plots to easier spots.
• Have a written rule for inaccessible plots (unsafe slope, river, conflict) and record each case.
• Do not cluster plots along roads or rivers unless the design explicitly accounts for it.

Step 5: Install permanent plots

• Fix the centre with RTK GNSS and a durable marker.
• Tag and number every tree above the minimum diameter, and mark the point of measurement at 1.3 m with paint.
• Record species, DBH, height on a subsample, status (live, dead, broken) and photos.
• Measure dead wood and litter if those pools are included. See carbon pools.

Step 6: Plan quality control

Remeasure a random share of plots with an independent crew. Compare tree counts and DBH, and set acceptable tolerances in the standard operating procedure. Keep the results; verifiers ask for them.

Remeasurement schedule

Decide how often each plot is remeasured before the first campaign. Most projects align remeasurement with verification events, but fast-growing ARR sites may benefit from more frequent measurement early on, and slow natural forest less. Measure all plots in a short window, in the same season, to avoid mixing growth periods. Record any plot that cannot be remeasured and why, and never replace it with a new, easier location without following the documented rule.

Common mistakes

• Plot size too small for natural forest, giving very high variance.
• Moving plots in the field “because the forest was not representative”.
• No stratification, or strata defined after the data is in.
• Forgetting buffer zones at plot edges and boundary trees rules.
• Measuring DBH at different heights each round, which creates false growth or loss.
• Losing tree tags, so permanent plots become temporary in practice.

What good evidence looks like

A VVB will check the sampling plan in the project document against what happened in the field. Provide: strata maps and areas; the sample-size calculation with inputs; the list of planned vs measured plots and reasons for any changes; plot coordinates and photos; raw tree data; the QA/QC remeasurement report; and the scripts that turn trees into stratum means and confidence intervals.

Indonesia specifics

Access is the main constraint. In Kalimantan and Papua, a single remote plot can take a day of travel, so cost-weighted allocation is worth doing. Peat swamp forest needs boardwalk-safe procedures. Smallholder agroforestry in Sumatra, Java and Sulawesi often suits whole-farm tree censuses with mobile measurement instead of classic plots. Always agree plot access with landholders and village authorities first.

Our field data campaigns service designs and runs inventories to methodology standard. See the dMRV hub for how plots feed the wider system.