In order to understand the spatial distribution patterns of lichens on large scales at the Earth's three poles, we developed a dataset of key lichen attributes (i.e., the color type and growth form), which includes the scientific name of the lichen, information on the corresponding color type and growth form attributes, latitude and longitude, information on the ecoregion to which the site belongs and whether it is a bare or vegetated area, and the corresponding five environmental factors (i.e., average temperature(℃), precipitation(mm), solar radiation(kJ /m²/day), wind speed(m/s), relative humidity). Here, we have attempted to use this dataset to uncover important relationships between the physiological and biochemical characteristics of lichens and their tolerance to environmental extremes.
This dataset serves as a foundational resource for extensive investigations into the intricate interplay between lichen physiology and the environment, addressing a significant knowledge gap in the field. Furthermore, our dataset holds the potential to address challenges associated with remote sensing monitoring of lichens, a longstanding issue in vegetation remote sensing. Precise in situ observation records, as provided by our dataset, can facilitate the development of remote sensing techniques tailored for lichen monitoring.
The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology. It is composed by 6249 randomly distributed buildings in the urban area of Chía (Colombia). The survey has been carried out between May and July 2020 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (and footprints from OpenStreetMap (OSM), both with vintages of May 2020. The buildings were inspected by dozens of local students of civil engineering students from the Universidad de La Sabana (Chía, Colombia). Their attribute values in terms of the GEM v.2.0 taxonomy.