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Topology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds / Xin Xu in International journal of applied Earth observation and geoinformation, vol 116 (February 2023)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 116 (February 2023) . - n° 103145 Titre : Topology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds Type de document : Article/Communication Auteurs : Xin Xu, Auteur ; Federico Iuricich, Auteur ; Kim Calders, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 103145 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction d'arbres [Termes IGN] houppier [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] topologie Résumé : (auteur) Terrestrial laser scanning (TLS) is a ground-based approach to rapidly acquire 3D point clouds via Light Detection and Ranging (LiDAR) technologies. Quantifying tree-scale structure from TLS point clouds requires segmentation, yet there is a lack of automated methods available to the forest ecology community. In this work, we consider the problem of segmenting a forest TLS point cloud into individual tree point clouds. Different approaches have been investigated to identify and segment individual trees in a forest point cloud. Typically these methods require intensive parameter tuning and time-consuming user interactions, which has inhibited the application of TLS to large area research. Our goal is to define a new automated segmentation method that lifts these limitations. Our Topology-based Tree Segmentation (TTS) algorithm uses a new topological technique rooted in discrete Morse theory to segment input point clouds into single trees. TTS algorithm identifies distinctive tree structures (i.e., tree bottoms and tops) without user interactions. Tree tops and bottoms are then used to reconstruct single trees using the notion of relevant topological features. This mathematically well-established notion helps distinguish between noise and relevant tree features. To demonstrate the generality of our approach, we present an evaluation using multiple datasets, including different forest types and point densities. We also compare our TTS approach with open-source tree segmentation methods. The experiments show that we achieve a higher segmentation accuracy when performing point-by-point validation. Without expensive user interactions, TTS algorithm is promising for greater usage of TLS point clouds in the forest ecology community, such as fire risk and behavior modeling, estimating tree-level biodiversity structural traits, and above-ground biomass monitoring. Numéro de notice : A2023-129 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103145 Date de publication en ligne : 12/12/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102517 [article]

Mapping tropical forest trees across large areas with lightweight cost-effective terrestrial laser scanning / Shengli Tao in Annals of Forest Science, vol 78 n° 4 (December 2021)  

Public [article]  inAnnals of Forest Science > vol 78 n° 4 (December 2021) . - n° 103 Titre : Mapping tropical forest trees across large areas with lightweight cost-effective terrestrial laser scanning Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Nicolas Labrière, Auteur ; Kim Calders, Auteur ; Fabian Jörg Fischer, Auteur ; E-Ping Rau, Auteur ; Laetitia Plaisance, Auteur ; Jérôme Chave, Auteur Année de publication : 2021 Article en page(s) : n° 103 Note générale : bibliographie This work has benefitted from an “Investissement d'Avenir” grant managed by Agence Nationale de la Recherche (AnaEE France ANR-11-INBS-0001; CEBA, ref. ANR-10-LABX-25–01), the CNRS Nouragues station, and a CNES postdoctoral fellowship granted to S.T. Langues : Anglais (eng) Descripteur : [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt tropicale [Termes IGN] Guyane (département français) [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] placette d'échantillonnage [Termes IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message : We used lightweight terrestrial laser scanning (TLS) to detect over 3000 stems per hectare across a 12-ha permanent forest plot in French Guiana, 81% of them  Context : Accurate position mapping of tropical rainforest trees is crucial for baseline studies of tropical forest ecology but is labor-intensive. Terrestrial lidar scanning (TLS) is broadly used in temperate forest inventories, but its use in rainforests is restricted to the determination of individual tree volumes within small survey areas. Aims : Mapping tree stems across one large (12-ha) rainforest plot, including trees less than 10 cm DBH, and evaluating the precision of traditional mapping approaches. Methods : We used lightweight TLS, co-registered the acquisitions, and developed a new efficient algorithm to process the TLS data. Results : We detected 36,422 stems of which 29,665 (81%) were  Conclusion : Lightweight TLS technology is a promising tool for the estimation of stem tapering and volume. Here, we show that it also facilitates the establishment of large tropical forest inventories, by improving the positioning of trees, thus increasing the accuracy of forest inventories and their cost-effectiveness. Numéro de notice : A2021-954 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s13595-021-01113-9 Date de publication en ligne : 28/12/2021 En ligne : https://doi.org/10.1007/s13595-021-01113-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99998 [article]

Tree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 170 - 181 Titre : Tree species classification using structural features derived from terrestrial laser scanning Type de document : Article/Communication Auteurs : Louise Terryn, Auteur ; Kim Calders, Auteur ; Mathias I. Disney, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 170 - 181 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] arbre (flore) [Termes IGN] classification barycentrique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] composition d'un peuplement forestier [Termes IGN] couvert forestier [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] espèce végétale [Termes IGN] extraction de traits caractéristiques [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] ombre [Termes IGN] régression logistique [Termes IGN] semis de points Résumé : (auteur) Fast and automated collection of forest data, such as species composition information, is required to support climate mitigation actions. Recently, there have been significant advances in the use of terrestrial laser scanning (TLS) instruments, which facilitate the capture of detailed forest structure. However, for tree species recognition the structural information from TLS has mainly been used to complement spectral information. TLS-only classification studies have been limited in size and diversity of plot forest types. In this paper, we investigate the potential of TLS for tree species classification. We used quantitative structure models to determine 17 structural tree features. These features were computed for 758 trees of five tree species, including two understory species, of a 1.4 hectare mixed deciduous forest plot. Three classification methods were compared: k-nearest neighbours, multinomial logistic regression and support vector machine. We assessed the potential underlying causes for structural differences with principal component analysis. We obtained classification success rates of approximately 80%, however, with producer accuracies for three of the five species ranging from 0 to 60%. Low producer accuracies were the result of a high intra- and low inter-species variability. These effects were, respectively, caused by a high size-dependency of the structural features and a convergence of structural traits across species as a result of the individual tree position in the forest canopy and shade tolerance. Nevertheless, the producer accuracies could be improved through sensitivity vs. specificity trade-offs, with over 50% for all species being obtainable. The high intra -and low inter-species variability complicate the classification. Furthermore, the classification performance and best classification method greatly depend on its targeted application. In conclusion, this study proves the added value of TLS for tree species classification but also shows that TLS opens up potential for testing and further development of ecological theory. Numéro de notice : A2020-636 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.009 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96059 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020103	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070 Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] atmosphère terrestre [Termes IGN] canopée [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] faisceau laser [Termes IGN] feuille (végétation) [Termes IGN] foresterie [Termes IGN] forêt de feuillus [Termes IGN] forêt tropicale [Termes IGN] plus proche voisin, algorithme du [Termes IGN] précision de la classification [Termes IGN] Python (langage de programmation) [Termes IGN] semis de points [Termes IGN] transfert radiatif Résumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 Date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970 [article]

Remote sensing technology applications in forestry and REDD+ / Kim Calders (2020)  

Public Titre : Remote sensing technology applications in forestry and REDD+ Type de document : Monographie Auteurs : Kim Calders, Éditeur scientifique ; Inge Jonckheere, Éditeur scientifique ; Mikko Vastaranta, Éditeur scientifique ; Joanne Nightingale, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 244 p. ISBN/ISSN/EAN : 978-3-03928-471-9 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] canopée [Termes IGN] cartographie des risques [Termes IGN] déboisement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image Landsat [Termes IGN] image multibande [Termes IGN] image Sentinel [Termes IGN] Pinus massoniana [Termes IGN] polarimétrie radar [Termes IGN] Réduction des émissions dues à la déforestation et la dégradation des forêts, REDD [Termes IGN] télémétrie laser aéroporté [Termes IGN] télémétrie laser terrestre Résumé : (Editeur) Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion. Numéro de notice : 26296 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03928-471-9 Date de publication en ligne : 07/04/2020 En ligne : https://doi.org/10.3390/books978-3-03928-471-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95009

Innovations in ground and airborne technologies as reference and for training and validation: Terrestrial Laser Scanning (TLS) / Mathias I. Disney in Surveys in Geophysics, vol 40 n° 4 (July 2019)  

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Assessing the structural differences between tropical forest types using Terrestrial Laser Scanning / Mathieu Decuyper in Forest ecology and management, vol 429 (1 December 2018)  

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Evaluation of the range accuracy and the radiometric calibration of multiple terrestrial laser scanning instruments for data interoperability / Kim Calders in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

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