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Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning / Stefano Puliti in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)  

Public [article]  inForestry, an international journal of forest research > vol 96 n° 1 (January 2023) . - pp 37 - 48 Titre : Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning Type de document : Article/Communication Auteurs : Stefano Puliti, Auteur ; J. Paul McLean, Auteur ; Nicolas Cattaneo, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 37 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] Betula pendula [Termes IGN] croissance des arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Fraxinus excelsior [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] Norvège [Termes IGN] semis de points Résumé : (auteur) Information on tree height-growth dynamics is essential for optimizing forest management and wood procurement. Although methods to derive information on height-growth information from multi-temporal laser scanning data already exist, there is no method to derive such information from data acquired at a single point in time. Drone laser scanning data (unmanned aerial vehicles, UAV-LS) allows for the efficient collection of very dense point clouds, creating new opportunities to measure tree and branch architecture. In this study, we examine if it is possible to measure the vertical positions of branch whorls, which correspond to nodes, and thus can in turn be used to trace the height growth of individual trees. We propose a method to measure the vertical positions of whorls based on a single-acquisition of UAV-LS data coupled with deep-learning techniques. First, single-tree point clouds were converted into 2D image projections, and a YOLOv5 (you-only-look-once) convolutional neural network was trained to detect whorls based on a sample of manually annotated images. Second, the trained whorl detector was applied to a set of 39 trees that were destructively sampled after the UAV-LS data acquisition. The detected whorls were then used to estimate tree-, plot- and stand-level height-growth trajectories. The results indicated that 70 per cent (i.e. precision) of the measured whorls were correctly detected and that 63 per cent (i.e. recall) of the detected whorls were true whorls. These results translated into an overall root-mean-squared error and Bias of 8 and −5 cm for the estimated mean annual height increment. The method’s performance was consistent throughout the height of the trees and independent of tree size. As a use case, we demonstrate the possibility of developing a height-age curve, such as those that could be used for forecasting site productivity. Overall, this study provides proof of concept for new methods to analyse dense aerial point clouds based on image-based deep-learning techniques and demonstrates the potential for deriving useful analytics for forest management purposes at operationally-relevant spatial-scales. Numéro de notice : A2023-100 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1093/forestry/cpac026 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.1093/forestry/cpac026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102418 [article]

A stand-level growth and yield model for thinned and unthinned even-aged Scots pine forests in Norway / Christian Kuehne in Silva fennica, vol 56 n° 1 (January 2022)  

Public [article]  inSilva fennica > vol 56 n° 1 (January 2022) . - n° 1 Titre : A stand-level growth and yield model for thinned and unthinned even-aged Scots pine forests in Norway Type de document : Article/Communication Auteurs : Christian Kuehne, Auteur ; J. Paul McLean, Auteur ; Kobra Maleki, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] branche (arbre) [Termes IGN] éclaircie (sylviculture) [Termes IGN] forêt équienne [Termes IGN] gestion forestière [Termes IGN] hauteur des arbres [Termes IGN] modèle de croissance végétale [Termes IGN] Norvège [Termes IGN] Pinus sylvestris [Termes IGN] rendement [Termes IGN] surface terrière [Termes IGN] volume en bois [Vedettes matières IGN] Sylviculture Résumé : (auteur) Management of Scots pine (Pinus sylvestris L.) in Norway requires a forest growth and yield model suitable for describing stand dynamics of even-aged forests under contemporary climatic conditions with and without the effects of silvicultural thinning. A system of equations forming such a stand-level growth and yield model fitted to long-term experimental data is presented here. The growth and yield model consists of component equations for (i) dominant height, (ii) stem density (number of stems per hectare), (iii) total basal area, (iv) and total stem volume fitted simultaneously using seemingly unrelated regression. The component equations for stem density, basal area, and volume include a thinning modifier to forecast stand dynamics in thinned stands. It was shown that thinning significantly increased basal area and volume growth while reducing competition related mortality. No significant effect of thinning was found on dominant height. Model examination by means of various fit statistics indicated no obvious bias and improvement in prediction accuracy in comparison to existing models in general. An application of the developed stand-level model comparing different management scenarios exhibited plausible long-term behavior and we propose this is therefore suitable for national deployment. Numéro de notice : A2022-171 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14214/sf.10627 Date de publication en ligne : 26/01/2022 En ligne : https://doi.org/10.14214/sf.10627 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99806 [article]