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dc.contributor.authorNiederheiser, R.
dc.contributor.authorFernández, R.
dc.contributor.authorLorite Moreno, Juan 
dc.date.accessioned2021-03-04T09:13:30Z
dc.date.available2021-03-04T09:13:30Z
dc.date.issued2021-01-13
dc.identifier.citationR. Niederheiser , M. Winkler , V. Di Cecco , B. Erschbamer , R. Fernández , C. Geitner , Hannah Hofbauer , C. Kalaitzidis , Barbara Klingraber , A. Lamprecht , J. Lorite , L. Nicklas , P. Nyktas , H. Pauli , A. Stanisci , K. Steinbauer , J.-P. Theurillat , P. Vittoz & M. Rutzinger (2021): Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain, GIScience & Remote Sensing, [10.1080/15481603.2020.1859264]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/66864
dc.descriptionThis work has been conducted within the project MEDIALPS (Disentangling anthropogenic drivers of climate change impacts on alpine plant species), which was funded by the Earth System Sciences Program of the Austrian Academy of Sciences. We thank all field workers who helped gather in-situ vegetation data and photogrammetric data.es_ES
dc.description.abstractIn 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 in-situ 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 in-situ 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.es_ES
dc.description.sponsorshipEarth System Sciences Program of the Austrian Academy of Scienceses_ES
dc.language.isoenges_ES
dc.publisherTaylor & Francises_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectHigh-mountain vegetationes_ES
dc.subjectVegetation stand propertieses_ES
dc.subjectTopographic parameterses_ES
dc.subject3D point cloud processinges_ES
dc.subjectRandom Forestes_ES
dc.subjectLinear mixed-effects modellinges_ES
dc.titleUsing automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terraines_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1080/15481603.2020.1859264
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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