Advanced Photogrammetry to Assess Lichen Colonization in the Hyper-Arid Namib Desert

Abstract

The hyper-arid central region of the Namib Desert is characterized by quartz desert pavement terrain that is devoid of vascular plant covers. In this extreme habitat the only discernible surface covers are epilithic lichens that colonize exposed surfaces of quartz rocks. These lichens are highly susceptible to disturbance and so field surveys have been limited due to concerns about disturbing this unusual desert feature. Here we present findings that illustrate how non-destructive surveys based upon advanced photogrammetry techniques can yield meaningful and novel scientific data on these lichens. We combined ‘structure from motion analysis,’ computer vision and GIS to create 3-dimensional point clouds from two-dimensional imagery. The data were robust in its application to estimating absolute lichen cover. An orange Stellarangia spp. assemblage had coverage of 22.8% of available substrate, whilst for a black Xanthoparmelia spp. assemblage coverage was markedly lower at 0.6% of available substrate. Hyperspectral signatures for both lichens were distinct in the near-infra red range indicating that Xanthoparmelia spp. was likely under relatively more moisture stress than Stellarangia spp. at the time of sampling, and we postulate that albedo effects may have contributed to this in the black lichen. Further transformation of the data revealed a colonization preference for west-facing quartz surfaces and this coincides with prevailing winds for marine fog that is the major source of moisture in this system. Furthermore, a three-dimensional ‘fly through’ of the lichen habitat was created to illustrate how the application of computer vision in microbiology has further potential as a research and education tool. We discuss how advanced photogrammetry could be applied in astrobiology using autonomous rovers to add quantitative ecological data for visible surface colonization on the surface of Mars.

Water inputs across the Namib Desert: implications for dryland edaphic microbiology

“…Fog events are a known, documented and important water source for many species in the fog zone of the Namib Desert (Lange et al 1994, Mary Seely et al 2005, Lange et al 2006, Lange et al 2007, Ebner et al 2011 and the presence of extensive epiphytic lichen growth in the area of the near-coastal sites (Lange et al 1994, Lange et al 2006, Lange et al 2007, Hinchliffe et al 2017) is a strong indication that this area receives regular fog water input. However, previous research has found that, at a depth of 2 cm, no change in soil moisture can be associated with visible fog events (Warren-Rhodes et al 2006, Ebner et al 2011, Li et al 2018.…”

‘Follow the Water’: Microbial Water Acquisition in Desert Soils

“…This conclusion was recently supported by an extensive remote sensing drone survey of coastal Namib Desert lichen fields using advanced photogrammetry, which showed that Xanthoparmelia and Stellanrangia spp. preferentially colonized ocean-facing rock surfaces, i.e., the direction from which fog originates [ 62 ].…”

Rapid imaging in the field followed by photogrammetry digitally captures the otherwise lost dimensions of plant specimens

“…These 2D images can be taken with widely available commercial cameras (or those in mobile devices), rather than the dedicated equipment required for other 3D modeling approaches reviewed for natural history collections by Nieva de la Hidalga et al (2019) and Brecko and Mathys (2020). Because many cameras and their accessories have been designed to be mobile and for outdoor use, photogrammetry has been implemented widely in field conditions, providing insights into areas such as lichen colonization (Hinchliffe et al, 2017), the size of Weddell seals (Beltran et al, 2018), coral growth rates (Lange and Perry, 2020), and many others. Measurements of models produced via photogrammetry can be comparable to those from the physical subject, as demonstrated for plants in agricultural research studies (e.g., Gao et al, 2021), and to those from other modeling approaches, as demonstrated for skeletons by Giacomini et al (2019;compared with laser scanning and micro-computed tomography).…”