Technical note: Rapid image-based field methods improve the quantification of termite mound structures and greenhouse-gas fluxes

Termite mounds (TMs) mediate biogeochemical processes with global relevance, such as turnover of the important greenhouse gas methane (CH₄). However, the complex internal and external morphology of TMs impede an accurate quantitative description. Here we present two novel field methods, photogrammetry (PG) and cross-sectional image analysis, to quantify TM external and internal mound structure of 29TMs of three termite species. Photogrammetry was used to measure epigeal volume (V_E), surface area (A_E) and mound basal area (AB) by reconstructing 3-D models from digital photographs, and compared against a waterdisplacement method and the conventional approach of approximating TMs by simple geometric shapes. To describe TM internal structure, we introduce TM macro- and microporosity (θ_M and θ_μ), the volume fractions of macroscopic chambers, and microscopic pores in the wall material, respectively. Macro-porosity was estimated using image analysis of single TM cross sections, and compared against full Xray computer tomography (CT) scans of 17 TMs. For these TMs we present complete pore fractions to assess speciesspecific differences in internal structure. The PG method yielded V_E nearly identical to a water-displacement method, while approximation of TMs by simple geometric shapes led to errors of 4-200 %. Likewise, using PG substantially improved the accuracy of CH₄ emission estimates by 10-50 %. Comprehensive CT scanning revealed that investigated TMs have species-specific ranges of θ_M and θ_μ, but similar total porosity. Image analysis of single TM cross sections produced good estimates of θ_M for species with thick walls and evenly distributed chambers. The new image-based methods allow rapid and accurate quantitative characterisation of TMs to answer ecological, physiological and biogeochemical questions. The PG method should be applied when measuring greenhouse-gas emissions from TMs to avoid large errors from inadequate shape approximations.