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Predicting vegetation stratum occupancy from airborne LiDAR data with deep learning / Ekaterina Kalinicheva in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)    

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102863 Titre : Predicting vegetation stratum occupancy from airborne LiDAR data with deep learning Type de document : Article/Communication Auteurs : Ekaterina Kalinicheva , Auteur ; Loïc Landrieu , Auteur ; Clément Mallet , Auteur ; Nesrine Chehata , Auteur Année de publication : 2022 Projets : TOSCA-FRISBEE / Article en page(s) : n° 102863 Note générale : bibliographie This study has been co-funded by CNES (TOSCA FRISBEE Project, convention no200769/00) and CONFETTI Project (Nouvelle Aquitaine Region project, France). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] apprentissage semi-dirigé [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] parcelle agricole [Termes IGN] régression [Termes IGN] semis de points [Termes IGN] strate végétale Résumé : (auteur) We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cover. Our weakly-supervised training scheme allows our network to only be supervised with vegetation occupancy values aggregated over cylindrical plots containing thousands of points. Such ground truth is easier to produce than pixel-wise or point-wise annotations. Our method outperforms handcrafted and deep learning baselines in terms of precision by up to 30%, while simultaneously providing visual and interpretable predictions. We provide an open-source implementation along with a dataset of 199 agricultural plots to train and evaluate weakly supervised occupancy regression algorithms. Numéro de notice : A2022-578 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.102863 Date de publication en ligne : 19/07/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99425 [article]

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Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD) / Langning Huo in Remote sensing of environment, vol 270 (March 2022)  

Public [article]  inRemote sensing of environment > vol 270 (March 2022) . - n° 112857 Titre : Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD) Type de document : Article/Communication Auteurs : Langning Huo, Auteur ; Eva Lindberg, Auteur ; Johan Holmgren, Auteur Année de publication : 2022 Article en page(s) : n° 112857 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt boréale [Termes IGN] hauteur à la base du houppier [Termes IGN] houppier [Termes IGN] inventaire forestier étranger (données) [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] sous-bois [Termes IGN] sous-étage [Termes IGN] strate végétale [Termes IGN] structure d'un peuplement forestier [Termes IGN] Suède Résumé : (auteur) Obtaining low vegetation data is important in order to quantify the structural characteristics of a forest. Dense three-dimensional (3D) laser scanning data can provide information on the vertical profile of a forest. However, most studies have focused on the dominant and subdominant layers of the forest, while few studies have tried to delineate the low vegetation. To address this issue, we propose a framework for individual tree crown (ITC) segmentation from laser data that focuses on both overstory and understory trees. The framework includes 1) a new algorithm (SSD) for 3D ITC segmentation of dominant trees, by detecting the symmetrical structure of the trees, and 2) removing points of dominant trees and mean shift clustering of the low vegetation. The framework was tested on a boreal forest in Sweden and the performance was compared 1) between plots with different stem density levels, vertical complexities, and tree species composition, and 2) using airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data, and merged ALS and TLS data (ALS + TLS data). The proposed framework achieved detection rates of 0.87 (ALS + TLS), 0.86 (TLS), and 0.76 (ALS) when validated with field-inventory data (of trees with a diameter at breast height ≥ 4 cm). When validating the estimated number of understory trees by visual interpretation, the framework achieved 19%, 21%, and 39% root-mean-square error values with ALS + TLS, TLS, and ALS data, respectively. These results show that the SSD algorithm can successfully separate laser points of overstory and understory trees, ensuring the detection and segmentation of low vegetation in forest. The proposed framework can be used with both ALS and TLS data, and achieve ITC segmentation for forests with various structural attributes. The results also illustrate the potential of using ALS data to delineate low vegetation. Numéro de notice : A2022-127 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112857 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112857 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99707 [article]

Relationships between species richness and ecosystem services in Amazonian forests strongly influenced by biogeographical strata and forest types / Gijs Steur in Scientific reports, vol 12 (2022)  

Public [article]  inScientific reports > vol 12 (2022) . - n° 5960 Titre : Relationships between species richness and ecosystem services in Amazonian forests strongly influenced by biogeographical strata and forest types Type de document : Article/Communication Auteurs : Gijs Steur, Auteur ; Hans Ter Steege, Auteur ; René W. Verburg, Auteur ; Daniel Sabatier, Auteur ; Jean-François Molino, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5960 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Amazonie [Termes IGN] forêt tropicale [Termes IGN] produit forestier non ligneux [Termes IGN] puits de carbone [Termes IGN] richesse floristique [Termes IGN] service écosystémique [Termes IGN] strate végétale [Termes IGN] volume en bois [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Despite increasing attention for relationships between species richness and ecosystem services, for tropical forests such relationships are still under discussion. Contradicting relationships have been reported concerning carbon stock, while little is known about relationships concerning timber stock and the abundance of non-timber forest product producing plant species (NTFP abundance). Using 151 1-ha plots, we related tree and arborescent palm species richness to carbon stock, timber stock and NTFP abundance across the Guiana Shield, and using 283 1-ha plots, to carbon stock across all of Amazonia. We analysed how environmental heterogeneity influenced these relationships, assessing differences across and within multiple forest types, biogeographic regions and subregions. Species richness showed significant relationships with all three ecosystem services, but relationships differed between forest types and among biogeographical strata. We found that species richness was positively associated to carbon stock in all biogeographical strata. This association became obscured by variation across biogeographical regions at the scale of Amazonia, resembling a Simpson’s paradox. By contrast, species richness was weakly or not significantly related to timber stock and NTFP abundance, suggesting that species richness is not a good predictor for these ecosystem services. Our findings illustrate the importance of environmental stratification in analysing biodiversity-ecosystem services relationships. Numéro de notice : A2022-308 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1038/s41598-022-09786-6 Date de publication en ligne : 08/04/2022 En ligne : http://dx.doi.org/10.1038/s41598-022-09786-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100403 [article]

Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany / Katja Felsmann in Annals of Forest Science, vol 75 n° 1 (March 2018)  

Public [article]  inAnnals of Forest Science > vol 75 n° 1 (March 2018) Titre : Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany Type de document : Article/Communication Auteurs : Katja Felsmann, Auteur ; Mathias Baudis, Auteur ; Zachary E. Kayler, Auteur ; Heike Puhlmann, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne [Termes IGN] biodiversité végétale [Termes IGN] chaleur [Termes IGN] écosystème forestier [Termes IGN] sécheresse [Termes IGN] sous-bois [Termes IGN] strate végétale [Termes IGN] surveillance de la végétation [Vedettes matières IGN] Végétation et changement climatique Résumé : (Auteur) Key message: Understory plant communities are essential for the recruitment of trees making up future forests. Independent of plant diversity, the understory across different forest ecosystems shows considerable physiological acclimation and structural stability towards drought events, which are expected to occur more frequently in future. Context: Understory plant communities are essential for the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory in beech and conifer forests managed at different intensity levels. Aims: We hypothesized that drought would affect transpiration and carbon isotope discrimination but not species richness and diversity. Moreover, we assumed that forest management intensity will modify the responses to drought of the understory community. Methods: We set up roofs in forests with a gradient of management intensities (unmanaged beech—managed beech—intensively managed conifer forests) in three regions across Germany. A drought event close to the 2003 drought was imposed in two consecutive years. Results: After 2 years, the realized precipitation reduction was between 27% and 34%. The averaged water content in the top 20 cm of the soil under the roof was reduced by 2% to 8% compared with the control. In the 1st year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group leaf area. Acclimation effects in most functional groups were observed in the 2nd year. Conclusion: Forest understory shows high plasticity at the leaf and community level, and high structural stability to changing climate conditions with drought events. Numéro de notice : A2018-319 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-017-0681-7 Date de publication en ligne : 22/12/2017 En ligne : https://doi.org/10.1007/s13595-017-0681-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90452 [article]

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 106 - 119 Titre : Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar Type de document : Article/Communication Auteurs : Melissa Fedrigo, Auteur ; Glenn J. Newnham, Auteur ; Nicholas C. Coops, Auteur ; Darius S. Culvenor, Auteur ; Douglas K. Bolton, Auteur ; Craig R. Nitschke, Auteur Année de publication : 2018 Article en page(s) : pp 106 - 119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse en composantes principales [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] Australie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Eucalyptus (genre) [Termes IGN] forêt tempérée [Termes IGN] peuplement forestier [Termes IGN] prédiction [Termes IGN] strate végétale Résumé : (Auteur) Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen’s kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the ‘ecotone’) between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation. Numéro de notice : A2018-074 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.018 Date de publication en ligne : 29/12/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89438 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018023	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018022	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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Approche d’estimation du volume-tige de peuplements forestiers par combinaison de données Landsat et données terrain : Application à la pineraie de Tlemcen-Algérie / Kada Bencherif in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)  

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Effective number of layers: A new measure for quantifying three-dimensional stand structure based on sampling with terrestrial LiDAR / Martin Ehbrecht in Forest ecology and management, vol 380 (15 november 2016)  

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Assessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area / Phil Wilkes (2016)  

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Large-scale dynamics of a heterogeneous forest resource are driven jointly by geographically varying growth conditions, tree species composition and stand structure / Holger Wernsdörfer in Annals of Forest Science, Vol 69 n° 7 (October 2012)  

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3-D mapping of a multi-layered Mediterranean forest using ALS data / António Ferraz in Remote sensing of environment, vol 121 (June 2012)  

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3D segmentation of forest structure using an adaptive mean shift based procedure / António Ferraz (2010) 

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3D segmentation of forest structure using a mean-shift based algorithm / António Ferraz (2010)  

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Analyse par télédétection et à différentes échelles de formations forestières hétérogènes : rôle de la structure de la végétation. Application aux boisements lâches méditerranéens / Jean Guy Boureau in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 140 (Octobre 1995)

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