Hedgerows provide a variety of essential ecosystem services, acting as a major tool for carbon sequestration to counteract the impacts of climate change. However, determining the amount of carbon stored in hedgerows at a regional level has been challenging. Field surveys can be precise but are time-consuming and labor-intensive, primarily deriving biomass from stem measurements under the canopy. Whereas remote sensing technologies can cover large geographic scales, they often lack the ability to estimate the detailed sub-canopy structure of linear woody features at the local scale. To bridge this gap and calibrate remote sensing models using local field data, a method was developed in this study to estimate hedgerow carbon storage by integrating field survey data with canopy height model (CHM) volume estimation for the Salzlandkreis region in central Germany. Field sampling was conducted across three study sites, where diameter at breast height (DBH) and height were measured for 4,382 woody plants (diameter > 3.5 cm). Based on this data, forestry form-factor equations were used to calculate individual tree volumes, and above-ground biomass was subsequently estimated based on species-specific wood density. To generalize these localized measurements to the regional scale, volume-to-biomass ratios (VBR) were established. These VBR values were derived from correlations between field-measured biomass and volumes obtained from multi-resolution remote sensing datasets. By multiplying the converted woody biomass by the canopy volume, the regional estimates of hedgerow above-ground carbon storage ranged from 96,538 to 104,341 Mg C (354,294 –382,931 Mg CO₂e). This corresponds to carbon densities ranging from 57 to 70 Mg C ha⁻¹ when normalized to the total hedgerow area in Salzlandkreis. This research demonstrates that moderate-resolution state datasets, when properly calibrated and validated against field measurements and high-resolution UAV data, can produce regional-scale carbon estimates, which would be impossible through field-only approaches. This study provides a framework for integrating remote sensing data with varying spatial resolutions and field validations, supporting evidence-based regional carbon estimation.
Kim et al. (Thu,) studied this question.