Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain
Creators: name: R. Niederheiser
name: M. Winkler
name: V. Di Cecco
name: B. Erschbamer
name: R. Fernández
name: C. Geitner
name: Hofbauer, Hannah
name: C. Kalaitzidis
name: Klingraber, Barbara
name: A. Lamprecht
name: J. Lorite
name: L. Nicklas
name: P. Nyktas
name: H. Pauli
name: A. Stanisci
name: K. Steinbauer
name: J.-P. Theurillat
name: P. Vittoz
name: M. Rutzinger
Publisher: name: Taylor & Francis
Year: 2021
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: Ecology
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Inorganic Chemistry
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: Plant Biology

description: In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and <i>in-situ</i> vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and <i>in-situ</i> vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance.