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scientifique

An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data / Van-Tho Nguyen in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 2 (June 2021) . - Article 32 Titre : An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data Type de document : Article/Communication Auteurs : Van-Tho Nguyen, Auteur ; Thiéry Constant, Auteur ; Francis Colin, Auteur Année de publication : 2021 Article en page(s) : Article 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] détection d'anomalie [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] écorce [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] qualité du bois [Termes descripteurs IGN] quercus sessiliflora [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] télémétrie laser terrestre [Termes descripteurs IGN] tronc Résumé : (Auteur) We designed a novel method allowing to automatically detect and measure defects on the surface of trunks including branches, branch scars, and epicormics from terrestrial LiDAR data by using only high-density 3D information. We could automatically detect and measure the defects with a diameter as small as 0.5 cm on either oak (Quercus petraea (Matt.) Liebl.) or beech (Fagus sylvatica L.) trees that display either rough or smooth bark. Numéro de notice : A2021-326 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01022-3 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1007/s13595-020-01022-3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97484 [article]

DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques / Ali H. Ahmed Suliman in Geocarto international, vol 36 n° 7 ([01/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 7 [01/04/2021] . - pp 803 - 819 Titre : DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques Type de document : Article/Communication Auteurs : Ali H. Ahmed Suliman, Auteur ; W. Gumindoga, Auteur ; Taymoor A. Awchi, Auteur ; Ayob Katimon, Auteur Année de publication : 2021 Article en page(s) : pp 803 - 819 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Advanced Spaceborne Thermal Emission and Reflection Radiometer [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] Iran [Termes descripteurs IGN] limite de résolution géométrique [Termes descripteurs IGN] MNS ASTER [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] ruissellement Résumé : (Auteur) The accurate estimation of terrain characteristics is central in rainfall runoff modelling. In this study, influences of Digital Elevation Models (DEMs) obtained from different sources, resolutions and rescaling techniques are compared for Peak flow prediction in a large-scale watershed by the Topographic driven model (TOPMODEL). The comparison includes graphical representation and statistical assessments using daily time series data. As a result, DEM extracted from contour map (DEM-Con) showed better performance when DEM resolutions increased, but the Advanced Space-borne Thermal Emission and Reflection Radiometer (DEM-Aster) continued to achieve less Relative Error (RE) at low resolution. Moreover, better RE values were found at cubic convolution technique to predict the peaks followed by nearest neighbor and bilinear. In addition, this study indicated that DEM resolution is more sensitive factor for TOPMODEL simulation compared to DEM sources and rescaling techniques for streamflow and peaks prediction. Numéro de notice : A2021-295 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1622599 date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1080/10106049.2019.1622599 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97355 [article]

Are pine-oak mixed stands in Mediterranean mountains more resilient to drought than their monospecific counterparts? / Francisco J. Muñoz-Gálvez in Forest ecology and management, vol 484 ([15/03/2021])  

Public [article]  inForest ecology and management > vol 484 [15/03/2021] . - n° 118955 Titre : Are pine-oak mixed stands in Mediterranean mountains more resilient to drought than their monospecific counterparts? Type de document : Article/Communication Auteurs : Francisco J. Muñoz-Gálvez, Auteur ; Asier Herrero, Auteur ; Maria Esther Pérez-Corona, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 118955 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] Espagne [Termes descripteurs IGN] forêt méditerranéenne [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] module linéaire [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] Quercus pyrenaica [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] vulnérabilité [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Climate change projections point to an increase in the intensity and frequency of extreme drought events with important negative impacts on forest functioning. Predicting these impacts constitutes a crucial challenge for forest managers and for the maintenance of ecosystem services supply. Promoting mixed stands seems a promising strategy for adapting forest ecosystems to ongoing climate change. However, some uncertainty exists regarding whether admixture can improve growth resilience to extreme drought events. Here, we aim to assess tree growth response to drought in mixed and monospecific stands of Pinus sylvestris L. and Quercus pyrenaica Willd. in central Spain. We built tree-ring chronologies and evaluated tree growth sensitivity to water availability and growth resilience components to extreme droughts using linear mixed models. We found contrasting species- and climate-specific responses to admixture. Q. pyrenaica growth was significantly higher in mixed than in monospecific stands, especially in years without water limitations, while P. sylvestris showed higher growth in mixed stands under dry conditions. However, our results showed a species-specific trade-off between resistance and recovery. While P. sylvestris showed higher resistance but lower recovery to drought events in mixed than monospecific stands, Q. pyrenaica showed higher recovery but lower resistance. This trade-off might explain the absence of admixture effects on species resilience. Our results highlight the importance of considering species-specific responses to water availability and associated trade-offs when evaluating admixture effects on drought vulnerability. Overall, we show a positive effect of admixture on the long-term growth stability in response to average climate conditions, but no effects in short-term resilience capacity to increasingly common extreme dry conditions. Consequently, admixture can promote forest productivity stability but should be carefully considered as a management solution for promoting the resilience of Mediterranean mountain forests to increasing aridity. Numéro de notice : A2021-264 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.118955 date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.118955 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97316 [article]

Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 1 (March 2021) . - n° 4 Titre : Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest Type de document : Article/Communication Auteurs : Seyedeh Kosar Hamidi, Auteur ; Eric K. Zenner, Auteur ; Mahmoud Bayat, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Acer velutinum [Termes descripteurs IGN] alnus cordata [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] carpinus betulus [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] dynamique de la végétation [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] Fagus orientalis [Termes descripteurs IGN] forêt inéquienne [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Iran [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Sylviculture Résumé : (auteur) Key message: We modeled 10-year net stand volume growth with four machine learning (ML) methods, i.e., artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and nearest neighbor analysis (NN), and with linear regression analysis. Incorporating interactions of multiple variables, the ML methods ANN and SVM predicted nonlinear system behavior and unraveled complex relations with greater accuracy than regression analysis. Context: Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies. Aims: The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models. Methods: In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis. Results: ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%). Conclusion: These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management. Numéro de notice : A2021-071 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01011-6 date de publication en ligne : 12/01/2021 En ligne : https://doi.org/10.1007/s13595-020-01011-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96794 [article]

Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model / Francisco Mauro in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 1 (March 2021) . - n° 12 Titre : Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model Type de document : Article/Communication Auteurs : Francisco Mauro, Auteur ; Antonio Garcia-Abril, Auteur ; Esperanza Ayuga-Téllez, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] densité de la végétation [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] diamètre des arbres [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] Espagne [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] structure d'un peuplement forestier [Vedettes matières IGN] Sylviculture Résumé : (auteur) Key message: We successfully transformed Pinus sylvestris yield tables into diameter distribution models. The best results were obtained with the parameter recovery method based on both mean and quadratic mean diameter, which explained 70% of the variability of frequencies by diameter classes and provided better results in the analysis of errors. On the other hand, the method based on stand density, dominant diameter and quadratic mean diameter explained less variability of frequencies by diameter classes (64.4%). Context: Old datasets used to develop yield table models can be recovered to transform those yield tables into diameter distribution models that provide a more detailed description of size variability and forest structure. Methods: We compared two different parameter recovery methods, one based on both mean and quadratic mean diameter and another one based on dominant diameter, stand density and quadratic mean diameter and used a set of 104 even aged plots to analyze the performance of the said methods for the transformation of Pinus sylvestris L yield tables in central Spain into a diameter distribution model. Results: The parameter recovery method based on both mean and quadratic mean diameter explained 70% of the variability of frequencies by diameter classes and provided better results than the method based on stand density, dominant diameter and quadratic mean diameter that explained 64.4% of the variability of frequencies by diameter classes. However, more important than the method itself were the errors that propagated from the models predicting the different variables used in the parameter recovery. Conclusion: Based on the results from the analysis of errors by diameter classes, the method using both mean and quadratic mean diameter outperformed the method using dominant diameter, stand density and quadratic mean diameter and is the best option to transform P. sylvestris yield tables into diameter distribution models. Numéro de notice : A2021-164 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01028-5 date de publication en ligne : 28/01/2021 En ligne : https://doi.org/10.1007/s13595-021-01028-5 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97094 [article]

Famous charts and forgotten fragments: exploring correlations in early Portuguese nautical cartography / Bruno Almeida in International journal of cartography, vol 7 n° 1 (March 2021)  

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Uncertainties and errors in algorithms for elevation gradients / Dong Shi in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC4A / Pierre Bosser in Earth System Science Data, vol 13 n° inconnu ([01/01/2021])  

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The strong and the stronger: The effects of increasing ozone and nitrogen dioxide concentrations in pollen of different forest species / Sónia Pereira in Forests, vol 12 n° 1 (January 2021)  

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Variations of precipitable water vapor using GNSS CORS in Thailand / Chokchai Trakolkul in Survey review, vol 53 n°376 (January 2021)  

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Intercomparisons of precipitable water vapour derived from radiosonde, GPS and sunphotometer observations / Shaoqi Gong in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)  

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A novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December 2020)  

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Les stations virtuelles au service de la cartographie mobile / Mathieu Regul in XYZ, n° 165 (décembre 2020)

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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)  

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Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])  

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Comparing features of single and multi-photon lidar in boreal forests / Xiaowei Yu in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

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A LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching / Chuang Qian in Journal of geodesy, vol 94 n° 10 (October 2020)  

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OpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning / Zhouxin Xi in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

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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)  

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Use of visible and near-infrared reflectance spectroscopy models to determine soil erodibility factor (K) in an ecologically restored watershed / Qinghu Jiang in Remote sensing, vol 12 n° 18 (September 2020)  

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Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

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Comparison of tree-based classification algorithms in mapping burned forest areas / Dilek Kucuk Matci in Geodetski vestnik, vol 64 n° 3 (September - November 2020)  

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Comparison of two methods for multiresolution terrain modelling in GIS / Turkay Gokgoz in Geocarto international, vol 35 n° 12 ([01/09/2020])  

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Heliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

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