A comparison of approaches to quantify carbon for ecosystem service assessments through time

Abstract

Monitoring of global climate regulation ecosystem services is needed to inform national accounts, meet emission targets, and evaluate nature-based climate solutions. As carbon monitoring is context-dependent, the most useful methodological approach will depend on the spatial extent and resolution, temporal frequency, baseline, available data, funding, and dominant drivers of change, all of which will impact results and interpretation. Here, focusing on above and belowground carbon storage and sequestration, we review four groups of methods for estimating trends in carbon over time: (1) field-based measurements, (2) land cover maps with reference carbon values by land cover type, (3) statistical and machine learning models linking field measurements to remotely sensed data, and (4) mass balance models representing key carbon pools and flows between them. We discuss strengths, limitations, and best practices for each method to assist researchers in implementing an approach or critically evaluating whether an existing carbon dataset can be used for a different project. The best methods often account for spatial variability of carbon, ecosystem interconnections, and temporal stability of carbon stocks against future environmental changes. Effective carbon monitoring can help determine optimal conservation, restoration, and/or land management interventions with win-win outcomes for both conservation and nature-based climate solutions.