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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]

An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds / Chaoquan Zhang in Photogrammetric record, vol 37 n° 179 (September 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 179 (September 2022) . - pp 260 - 284 Titre : An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds Type de document : Article/Communication Auteurs : Chaoquan Zhang, Auteur ; Hongchao Fan, Auteur Année de publication : 2022 Article en page(s) : pp 260 - 284 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie [Termes IGN] analyse de groupement [Termes IGN] apprentissage profond [Termes IGN] classification barycentrique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] fusion de données [Termes IGN] Norvège [Termes IGN] Ransac (algorithme) [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] toit Résumé : (auteur) Roof plane segmentation is an essential step in the process of 3D building reconstruction from airborne laser scanning (ALS) point clouds. The existing approaches either rely on human intervention to select the appropriate input parameters for different data-sets or they are not automatic and efficient. To tackle these issues, an improved multi-task pointwise network is proposed to simultaneously segment instances (that is, individual roof planes) and semantics (that is, groups of roof planes with similar geometric shapes) in point clouds. PointNet++ is used as a backbone network to extract robust features in the first step. The features from semantics branch are then added to the instance branch to facilitate the learning of instance embeddings. After that, a feature fusion module is added to the semantics branch to acquire more discriminative features from the backbone network. To increase the accuracy of semantic predictions, fused semantic features of the points belonging to the same instance are aggregated together. Finally, a mean-shift clustering algorithm is employed on instance embeddings to produce the instance predictions. Furthermore, a new roof data-set (called RoofNTNU) is established by taking ALS point clouds as training data for automatic and more general segmentation. Experiments on the new roof data-set show that the method achieves promising segmentation results: the mean precision (mPrec) of 96.2% for the instance segmentation task and mean accuracy (mAcc) of 94.4% for the semantic segmentation task. Numéro de notice : A2022-936 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12420 Date de publication en ligne : 13/07/2022 En ligne : https://doi.org/10.1111/phor.12420 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102682 [article]

The RTM harmonic correction revisited / R. Klees in Journal of geodesy, vol 96 n° 6 (June 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 6 (June 2022) . - n° 39 Titre : The RTM harmonic correction revisited Type de document : Article/Communication Auteurs : R. Klees, Auteur ; Kurt Seitz, Auteur ; D.C. Slobbe, Auteur Année de publication : 2022 Article en page(s) : n° 39 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse harmonique [Termes IGN] anomalie de pesanteur [Termes IGN] Auvergne [Termes IGN] correction des altitudes [Termes IGN] géoïde local [Termes IGN] harmonique sphérique [Termes IGN] hauteur ellipsoïdale [Termes IGN] méthode des moindres carrés [Termes IGN] modèle de géopotentiel local [Termes IGN] modèle numérique de terrain [Termes IGN] Norvège [Termes IGN] quasi-géoïde [Termes IGN] résidu Résumé : (auteur) In this paper, we derive improved expressions for the harmonic correction to gravity and, for the first time, expressions for the harmonic correction to potential and height anomaly. They need to be applied at stations buried inside the masses to transform internal values into harmonically downward continued values, which are then input to local quasi-geoid modelling using least-squares collocation or least-squares techniques in combination with the remove-compute-restore approach. Harmonic corrections to potential and height anomaly were assumed to be negligible so far resulting in yet unknown quasi-geoid model errors. The improved expressions for the harmonic correction to gravity, and the new expressions for the harmonic correction to potential and height anomaly are used to quantify the approximation errors of the commonly used harmonic correction to gravity and to quantify the magnitude of the harmonic correction to potential and height anomaly. This is done for two test areas with different topographic regimes. One comprises parts of Norway and the North Atlantic where the presence of deep, long, and narrow fjords suggest extreme values for the harmonic correction to potential and height anomaly and corresponding large errors of the commonly used approximation of the harmonic correction to gravity. The other one is located in the Auvergne test area with a moderate topography comprising both flat and hilly areas and therefore may be representative for many areas around the world. For both test areas, two RTM surfaces with different smoothness are computed simulating the use of a medium-resolution and an ultra-high-resolution reference gravity field, respectively. We show that the errors of the commonly used harmonic correction to gravity may be as large as the harmonic correction itself and attain peak values in areas of strong topographic variations of about 100 mGal. Moreover, we show that this correction may introduce long-wavelength biases in the computed quasi-geoid model. Furthermore, we show that the harmonic correction to height anomaly can attain values on the order of a decimetre at some points. Overall, however, the harmonic correction to height anomaly needs to be applied only in areas of strong topographic variations. In flat or hilly areas, it is mostly smaller than one centimetre. Finally, we show that the harmonic corrections increase with increasing smoothness of the RTM surface, which suggests to use a RTM surface with a spatial resolution comparable to the finest scales which can be resolved by the data rather than depending on the resolution of the global geopotential model used to reduce the data. Numéro de notice : A2022-414 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01625-w Date de publication en ligne : 30/05/2022 En ligne : https://doi.org/10.1007/s00190-022-01625-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100769 [article]

Lines of power: The eighteenth-century struggle over the Norwegian–Swedish border in Central Scandinavia / Anne Christine Lien in Cartographic journal (the), vol 59 n° 2 (May 2022)  

Public [article]  inCartographic journal (the) > vol 59 n° 2 (May 2022) . - pp 102 - 119 Titre : Lines of power: The eighteenth-century struggle over the Norwegian–Swedish border in Central Scandinavia Type de document : Article/Communication Auteurs : Anne Christine Lien, Auteur ; Anders Lundberg, Auteur Année de publication : 2022 Article en page(s) : pp 102 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie ancienne [Termes IGN] carte transfrontalière [Termes IGN] délimitation de frontière [Termes IGN] dix-huitième siècle [Termes IGN] géographie historique [Termes IGN] géographie politique [Termes IGN] géopolitique [Termes IGN] Norvège [Termes IGN] Suède Résumé : (auteur) The final position of the Norwegian–Swedish border was determined in 1751, after challenging negotiations. This paper focuses on central parts of Scandinavia and investigates the role of cartography in the border positioning process. The examination of a wide variety of historical maps before and after the border treaty provides insight into the differing opinions on the border region's shifting affiliation. Other factors that helped to shape the borderline were a turbulent political situation with shifting sovereignty over the area in question, as well as conflicts over valuable resources. The findings indicate that cartographic evidence had an important role in the position of the Norwegian–Swedish border in central Scandinavia. The paper adds to our understanding of maps as a political tool as well as of the role of resources in border processes, and provides new knowledge on how cartography influenced a national border between two countries fighting for land, resources and hegemony. Numéro de notice : A2022-858 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/00087041.2021.1995124 Date de publication en ligne : 11/04/2022 En ligne : https://doi.org/10.1080/00087041.2021.1995124 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102279 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
030-2022021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Wood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data / Michele Dalponte in Remote sensing, vol 14 n° 8 (April-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 8 (April-2 2022) . - n° 1892 Titre : Wood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data Type de document : Article/Communication Auteurs : Michele Dalponte, Auteur ; Alvar J. I. Kallio, Auteur ; Hans Ole Ørka, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] bois sur pied [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dépérissement [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] Norvège [Termes IGN] Perceptron multicouche [Termes IGN] Picea abies [Termes IGN] régression linéaire [Termes IGN] régression logistique [Termes IGN] santé des forêts [Termes IGN] semis de points Résumé : (auteur) Wood decay caused by pathogenic fungi in Norway spruce forests causes severe economic losses in the forestry sector, and currently no efficient methods exist to detect infected trees. The detection of wood decay could potentially lead to improvements in forest management and could help in reducing economic losses. In this study, airborne hyperspectral data were used to detect the presence of wood decay in the trees in two forest areas located in Etnedal (dataset I) and Gran (dataset II) municipalities, in southern Norway. The hyperspectral data used consisted of images acquired by two sensors operating in the VNIR and SWIR parts of the spectrum. Corresponding ground reference data were collected in Etnedal using a cut-to-length harvester while in Gran, field measurements were collected manually. Airborne laser scanning (ALS) data were used to detect the individual tree crowns (ITCs) in both sites. Different approaches to deal with pixels inside each ITC were considered: in particular, pixels were either aggregated to a unique value per ITC (i.e., mean, weighted mean, median, centermost pixel) or analyzed in an unaggregated way. Multiple classification methods were explored to predict rot presence: logistic regression, feed forward neural networks, and convolutional neural networks. The results showed that wood decay could be detected, even if with accuracy varying among the two datasets. The best results on the Etnedal dataset were obtained using a convolution neural network with the first five components of a principal component analysis as input (OA = 65.5%), while on the Gran dataset, the best result was obtained using LASSO with logistic regression and data aggregated using the weighted mean (OA = 61.4%). In general, the differences among aggregated and unaggregated data were small. Numéro de notice : A2022-352 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.3390/rs14081892 Date de publication en ligne : 14/04/2022 En ligne : https://doi.org/10.3390/rs14081892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100541 [article]

Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models / Ana de Lera Garrido in Silva fennica, vol 56 n° 2 (April 2022)  

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A user-centric optimization of emergency map symbols to facilitate common operational picture / Tomasz Opach in Cartography and Geographic Information Science, vol 49 n° 2 (March 2022)  

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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)  

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Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat / Stefano Puliti in Remote sensing of environment, vol 265 (November 2021)  

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Quantifying historical landscape change with repeat photography: an accuracy assessment of geospatial data obtained through monoplotting / Ulrike Bayr in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)  

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Large-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)  

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A compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)  

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Models for integrating and identifying the effect of senescence on individual tree survival probability for Norway spruce / Jouni Siipilehto in Silva fennica, vol 55 n° 2 (April 2021)  

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Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)  

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Stand-level mortality models for Nordic boreal forests / Jouni Siipilehto in Silva fennica, vol 54 n° 5 (December 2020)  

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