Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors

Abstract

Primary productivity is a robust indicator of ecosystem functioning because of its close relationships with the stability of the ecological systems. In ecological research, the above ground biomass (AGB) is the most commonly used proxy of primary productivity. However, despite their ecological relevance, the estimates of primary productivity are not addressed by current protocols for monitoring the conservation status of the habitats of Community interest. In this paper, we analyse the accuracy of AGB measurements obtained by image-derived 3D reconstructions of two contrasting mountain grasslands listed as habitats of Community interest in the Annex I of the Habitats Directive. More specifically, we compared the accuracy of the AGB estimates provided by four models, based on four different predictors (height, volume, volume adjusted, and cover volume), in order to evaluate their robustness against within- and between-community heterogeneity. Our study revealed that AGB measures computed from 3D vegetation reconstructions can be an effective way to evaluate primary productivity in herbaceous communities with complex structure and composition patterns. In particular, the vegetation height showed to have the highest correlation with direct AGB measurements. However, the vegetation volume, once adjusted by the coefficient of density, resulted to be the most effective proxy due to the lowest error level. Therefore, such a parameter could be routinely used as a non-destructive indicator for monitoring habitats of particular conservation concern. As a major limitation for this approach, we detected some loss of predictivity power at very low productivity rates.

Keywords

• Biomass
• Coefficient of density
• Non-destructive biomass measurements
• Pasture community
• Photogrammetry
• Structure from Motion