Centre de documentation
scientifique

Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain / R. Niederheiser in GIScience and remote sensing, vol 58 n° 1 (February 2021)  

Public [article]  inGIScience and remote sensing > vol 58 n° 1 (February 2021) . - pp 120 - 137 Titre : Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain Type de document : Article/Communication Auteurs : R. Niederheiser, Auteur ; M. Winkler, Auteur ; V. Di Cecco, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 120 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre [Termes IGN] Alpes [Termes IGN] caméra numérique [Termes IGN] carte de la végétation [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification semi-dirigée [Termes IGN] couvert végétal [Termes IGN] distribution de Poisson [Termes IGN] données topographiques [Termes IGN] indice de végétation [Termes IGN] module linéaire [Termes IGN] montagne [Termes IGN] occupation du sol [Termes IGN] photogrammétrie métrologique [Termes IGN] semis de points Résumé : (auteur) 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 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. Numéro de notice : A2021-258 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2020.1859264 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1080/15481603.2020.1859264 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97295 [article]