Automated image processing for quantitative characterization of
grassland vegetation structure: microhabitat selection in threatened
meadow and steppe vipers
Abstract
1. Understanding animals’ selection of microhabitats is important in
both ecology and biodiversity conservation. However, there is no
generally accepted methodology for the characterisation of
microhabitats, especially for vegetation structure. 2. Here we present a
method that objectively characterises vegetation structure by using
automated processing of images taken of the vegetation against a
whiteboard under standardised conditions. We developed an R script for
automatic calculation of four vegetation structure variables derived
from raster data stored in the images: leaf area (LA), height of closed
vegetation (HCV), maximum height of vegetation (MHC), and foliage height
diversity (FHD). 3. We demonstrate the applicability of this method by
testing the influence of vegetation structure on the occurrence of three
viperid snakes in three grassland ecosystems: Vipera graeca in mountain
meadows in Albania, V. renardi in loess steppes in Ukraine and V.
ursinii in sand grasslands in Hungary. 4. We found that the variables
followed normal distribution and there was minimal correlation between
those. Generalized linear mixed models revealed that snake occurrence
was positively related to HCV in V. graeca, to LA in V. renardi and to
LA and MHC in V. ursinii, and negatively to FHD in V. renardi, and to
HCV in V. ursinii. 5. Our results demonstrate that biologically
meaningful vegetation structure variables can be derived from automated
image processing. Our method minimises the risk of subjectivity in
measuring vegetation structure, allows upscaling if neighbouring pixels
are combined, and is suitable for comparison of or extrapolation across
different grasslands, vegetation types or ecosystems.