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A density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January-2 2021)  

Public [article]  inRemote sensing > Vol 13 n° 2 (January-2 2021) . - n° 322 Titre : A density-based algorithm for the detection of individual trees from LiDAR data Type de document : Article/Communication Auteurs : Melissa Latella, Auteur ; Fabio Sola, Auteur ; Carlo Camporeal, Auteur Année de publication : 2021 Article en page(s) : n° 322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre (flore) [Termes IGN] comptage [Termes IGN] densité de la végétation [Termes IGN] détection d'arbres [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt de feuillus [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] semis de points [Termes IGN] sous-étage Résumé : (auteur) Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management. Numéro de notice : A2021-196 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13020322 date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/rs13020322 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97146 [article]

Unprecedented pluri-decennial increase in the growing stock of French forests is persistent and dominated by private broadleaved forests / Jean-Daniel Bontemps in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 77 n° 4 (December 2020) . - n° 98 Titre : Unprecedented pluri-decennial increase in the growing stock of French forests is persistent and dominated by private broadleaved forests Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps , Auteur ; Anaïs Denardou-Tisserand , Auteur ; Jean-Christophe Hervé (1961-2017) , Auteur ; Jean Bir , Auteur ; Jean-Luc Dupouey, Auteur Année de publication : 2020 Projets : ARBRE / AgroParisTech Article en page(s) : n° 98 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bois sur pied [Termes IGN] changement d'utilisation du sol [Termes IGN] forêt de feuillus [Termes IGN] forêt privée [Termes IGN] inventaire forestier national (données France) [Termes IGN] modèle de régression [Termes IGN] politique forestière [Termes IGN] puits de carbone [Termes IGN] série temporelle [Termes IGN] surface forestière [Vedettes matières IGN] Economie forestière Résumé : (auteur) Key message: French forests exhibit the fastest relative changes across Europe. Growing stock increases faster than area, and is greatest in low-stocked private broadleaved forests. Past areal increases and current GS levels show positive effects on GS expansion, with GS increases hence expected to persist. Context: Strong increases in growing stocks (GS) of European forests for decades remain poorly understood and of unknown duration. French forests showing the greatest relative changes across Europe form the investigated case study. Aims: The magnitudes of net area, GS, and GS density (GSD) changes were evaluated across forest categories reflecting forest policy and land-use drivers. The roles of forest areal changes, GS and GSD levels on GS changes were investigated. Methods: National Forest Inventory data were used to produce time series of area, GS and GSD across forest categories over 1976–2014, and exploratory causal models of GS changes. Results: GS (+ 57%) increased three times faster than area, highlighting an advanced stage in the forest transition. Low-stocked private forests exhibited strong changes in GS/GSD, greatest in private broadleaved forests, stressing the contribution of returning forests on abandoned lands. Regression models demonstrated positive effects of both past areal increases and current GS, on GS expansion. Conclusion: Aerial C-sink in French forests is expected to persist in future decades. Numéro de notice : A2020-647 Affiliation des auteurs : LIF+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01003-6 date de publication en ligne : 12/10/2020 En ligne : https://doi.org/10.1007/s13595-020-01003-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96075 [article]

Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest / Luka Jurjević in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 227 - 241 Titre : Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest Type de document : Article/Communication Auteurs : Luka Jurjević, Auteur ; Xinlian Liang, Auteur ; Mateo Gašparović, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 227 - 241 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse comparative [Termes IGN] balayage laser [Termes IGN] corrélation [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] échantillonnage [Termes IGN] forêt de feuillus [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] modèle numérique de terrain [Termes IGN] parcelle forestière [Termes IGN] photogrammétrie métrologique [Termes IGN] Quercus pedunculata [Termes IGN] semis de points Résumé : (auteur) Tree height is one of the most important tree attributes in forest inventory. However, using conventional field methods to measure tree height is a laborious and time-consuming process. Despite the great interest in the past to facilitate tree height measurements, new, upcoming solutions are not yet thoroughly investigated. In this study, we investigated the applicability of different close-range remote sensing options for tree height measurement in a complex lowland deciduous forest. Six sample plots in a pedunculate oak forest were measured in detail using conventional methods. Close-range remote sensing datasets used in this study represent solutions from low-cost sensors used for hand-held personal laser scanning (PLShh), unmanned–borne laser scanning (ULS) and unmanned aerial vehicle photogrammetry (UAVimage). Each tree in the sample plots was interactively measured directly from the point cloud, and correspondence of the field- and remote sensing measured trees was verified using tree positions collected during fieldwork. Cross-comparisons of different datasets were performed to evaluate the performances of different data sources in the tree height estimation with respect to crown class, tree height and species. All remote sensing data sources correlated well, e.g. biases between remote sensing sources were around ± 1%. The field-measured tree height in general correlated well with remote sensing data sources. The uncertainties and bias of the field measurements were dependent on the tree height and crown class. Field measurements tended to underestimate codominant and intermediate trees at the approximately 1 m magnitude, whilst remote sensing data sources were robust to crown classes. Low-cost ULS used in this study, and very likely in general, may not have enough penetration capability when measuring low and mostly occluded trees, causing missed treetops. PLShh gave tree height estimates closer to the real tree height than those derived from conventional field measurements for trees above 21 m height. Numéro de notice : A2020-641 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.014 date de publication en ligne : 03/10/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96064 [article]

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CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia / Louis de Wergifosse in Annals of Forest Science [en ligne], vol 77 n° 3 (September 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 77 n° 3 (September 2020) . - 23 p. Titre : CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia Type de document : Article/Communication Auteurs : Louis de Wergifosse, Auteur ; Frédéric André, Auteur ; Hugues Goosse, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] dioxyde de carbone [Termes IGN] écosystème forestier [Termes IGN] émission de gaz [Termes IGN] évapotranspiration [Termes IGN] forêt de feuillus [Termes IGN] gaz à effet de serre [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] production primaire brute [Termes IGN] stress hydrique [Termes IGN] Wallonie (Belgique) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message: The change in forest productivity was simulated in six stands in Wallonia (Belgium) following different climate scenarios using a process-based and spatially explicit tree growth model. Simulations revealed a strong and positive impact of the CO 2 fertilization while the negative effect of the transpiration deficit was compensated by longer vegetation periods. The site modulated significantly the forest productivity, mainly through the stand and soil characteristics. Context: Forest net primary production (NPP) reflects forest vitality and is likely to be affected by climate change. Aims: Simulating the impact of changing environmental conditions on NPP and two of its main drivers (transpiration deficit and vegetation period) in six Belgian stands and decomposing the site effect. Methods: Based on the tree growth model HETEROFOR, simulations were performed for each stand between 2011 and 2100 using three climate scenarios and two CO2 modalities (constant vs time dependent). Then, the climate conditions, soils and stands were interchanged to decompose the site effect in these three components. Results: In a changing climate with constant atmospheric CO2, NPP values remained constant due to a compensation of the negative effect of increased transpiration deficit by a positive impact of longer vegetation periods. With time-dependent atmospheric CO2, NPP substantially increased, especially for the scenarios with higher greenhouse gas (GHG) emissions. For both atmospheric CO2 modalities, the site characteristics modulated the temporal trends and accounted in total for 56 to 73% of the variability. Conclusion: Long-term changes in NPP were primarily driven by CO2 fertilization, reinforced transpiration deficit, longer vegetation periods and the site characteristics. Numéro de notice : A2020-594 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00966-w date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1007/s13595-020-00966-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95932 [article]

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 aléatoires [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) [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]

Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests / Sadeepa Jayathunga in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

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Expansion tendancielle du stock de bois dans les forêts françaises (1976–2015) [diaporama] / Jean-Daniel Bontemps (2018)  

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The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation / Chaoyang Wu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)

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