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

Public [article]  inAnnals of Forest Science [en ligne] > 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. 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]

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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)  

Public [article]  inSurveys in Geophysics > vol 40 n° 4 (July 2019) . - pp 937 - 958 Titre : Innovations in ground and airborne technologies as reference and for training and validation: Terrestrial Laser Scanning (TLS) Type de document : Article/Communication Auteurs : Mathias I. Disney, Auteur ; A. Burt, Auteur ; Kim Calders, Auteur ; Crystal Schaaf, Auteur ; A. Stovall, Auteur Année de publication : 2019 Article en page(s) : pp 937 - 958 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] biomasse aérienne [Termes IGN] données allométriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt [Termes IGN] modèle numérique de surface de la canopée Résumé : (auteur) The use of terrestrial laser scanning (TLS) to provide accurate estimates of 3D forest canopy structure and above-ground biomass (AGB) has developed rapidly. Here, we provide an overview of the state of the art in using TLS for estimating forest structure for AGB. We provide a general overview of TLS methods and then outline the advantages and limitations of TLS for estimating AGB. We discuss the specific type of measurements that TLS can provide, tools and methods that have been developed for turning TLS point clouds into quantifiable metrics of tree size and volume, as well as some of the challenges to improving these measurements. We discuss the role of TLS for enabling accurate calibration and validation (cal/val) of Earth observation (EO)-derived estimates of AGB from spaceborne lidar and RADAR missions. We give examples of the types of TLS equipment that are in use and how these might develop in future, and we show examples of where TLS has already been applied to measuring AGB in the tropics in particular. Comparing TLS with harvested AGB shows r2 > 0.95 for all studies thus far, with absolute agreement to within 10% at the individual tree level for all trees and to within 2% in the majority of cases. Current limitations to the uptake of TLS include the capital cost of some TLS equipment, processing complexity and the relatively small coverage that is possible. We argue that combining TLS measurements with the existing ground-based survey approaches will allow improved allometric models and better cal/val, resulting in improved regional and global estimates of AGB from space, with better-characterised, lower uncertainties. The development of new, improved equipment and methods will accelerate this process and make TLS more accessible. Numéro de notice : A2019-670 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s10712-019-09527-x En ligne : https://doi.org/10.1007/s10712-019-09527-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100212 [article]

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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