¶ Data collection and carbon accounting
Data collection and carbon accounting in forestry encompasses the measurement, monitoring, reporting, and verification (MMRV) processes used to quantify forest carbon stocks, stock changes, and greenhouse gas fluxes for compliance, voluntary carbon market, and national inventory purposes. Accurate carbon accounting requires robust data on forest area, species composition, stand age, biomass density, soil carbon, and disturbance history — variables that are collected through a combination of field-based inventory plots, remote sensing, and modelling approaches. Field data collection has traditionally involved manual measurement of tree dimensions at permanent sample plots, feeding into allometric equations that convert DBH and height measurements into above-ground biomass and, with additional parameters, into below-ground and soil carbon estimates. Modern data collection increasingly integrates terrestrial LiDAR and handheld mobile laser scanning (HMLS) for improved plot-level biomass estimation, drone-based photogrammetry for stand-level canopy characterisation, and satellite time-series analysis for landscape-scale change detection and area estimation. Carbon accounting frameworks that govern how this data is used vary by purpose: national greenhouse gas inventories in Australia follow the IPCC 2006 Guidelines and Tier 3 modelling approaches using the FullCAM (Full Carbon Accounting Model) developed by ABARES, which simulates carbon dynamics in forests, wood products, and soils based on forest age, climate, and management inputs. Project-level carbon accounting for Australian Carbon Credit Units (ACCUs) under the Emissions Reduction Fund uses approved methodologies developed by the Clean Energy Regulator, including the Plantation Forestry and Human-Induced Regeneration methods, each specifying acceptable data collection protocols and audit requirements. The increasing automation of data collection — through continuous canopy monitoring with satellite constellations, IoT soil sensors, and AI-powered change detection — is improving the frequency, consistency, and cost-efficiency of carbon stock monitoring, supporting both regulatory compliance and the credibility of forest carbon credits in voluntary markets.
| Field |
Value |
| Company |
OCELL |
| Country |
Germany |
| Type |
Company |
| FWPA RD&E |
7.3 |