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CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data / Cyrille B.K. Rathgeber in Tree Physiology, vol 38 n° 8 (August 2018)
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Titre : CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data Type de document : Article/Communication Auteurs : Cyrille B.K. Rathgeber, Auteur ; Philippe Santenoise, Auteur ; Henri E. Cuny , Auteur
Année de publication : 2018 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : pp 1246 - 1260 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cerne
[Termes IGN] données allométriques
[Termes IGN] dynamique de la végétation
[Termes IGN] forêt boréale
[Termes IGN] forêt tempérée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Loi de Gompertz
[Termes IGN] phénologie
[Termes IGN] Pinophyta
[Termes IGN] R (langage)
[Termes IGN] régression logistique
[Termes IGN] visualisation de données
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) In the last decade, the pervasive question of climate change impacts on forests has revived investigations on intra-annual dynamics of wood formation, involving disciplines such as plant ecology, tree physiology and dendrochronology. This resulted in the creation of many research groups working on this topic worldwide and a rapid increase in the number of studies and publications. Wood-formation-monitoring studies are generally based on a common conceptual model describing xylem cell formation as the succession of four differentiation phases (cell division, cell enlargement, cell wall thickening and mature cells). They generally use the same sampling techniques, sample preparation methods and anatomical criteria to separate between differentiation zones and discriminate and count forming xylem cells, resulting in very similar raw data. However, the way these raw data are then processed, producing the elaborated data on which statistical analyses are performed, still remains quite specific to each individual study. Thereby, despite very similar raw data, wood-formation-monitoring studies yield results that are still quite difficult to compare. CAVIAR—an R package specifically dedicated to the verification, visualization and manipulation of wood-formation-monitoring data—can help to improve this situation. Initially, CAVIAR was built to provide efficient algorithms to compute critical dates of wood formation phenology for conifers growing in temperate and cold environments. Recently, we developed it further to check, display and process wood-formation-monitoring data. Thanks to new and upgraded functions, raw data can now be consistently verified, standardized and modelled (using logistic regressions and Gompertz functions), in order to describe wood phenology and intra-annual dynamics of tree-ring formation. We believe that CAVIAR will help strengthening the science of wood formation dynamics by effectively contributing to the standardization of its concepts and methods, making thereby possible the comparison between data and results from different studies. Numéro de notice : A2018-657 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/treephys/tpy054 Date de publication en ligne : 19/05/2018 En ligne : https://doi.org/10.1093/treephys/tpy054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93813
in Tree Physiology > vol 38 n° 8 (August 2018) . - pp 1246 - 1260[article]Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
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Titre : Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques Type de document : Article/Communication Auteurs : Chinsu Lin, Auteur ; Shih-Yu Chen, Auteur ; Chia-Chun Chen, Auteur ; Chia-Huei Tai, Auteur Année de publication : 2018 Article en page(s) : pp 174 - 189 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse d'image orientée objet
[Termes IGN] changement climatique
[Termes IGN] croissance des arbres
[Termes IGN] drone
[Termes IGN] feuille (végétation)
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image RVB
[Termes IGN] indice de végétation
[Termes IGN] Kappa de Cohen
[Termes IGN] Taïwan
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Phenological events of tree leaves from initiation to senescence is generally influenced by temperature and water availability. Detection of newly grown leaves (NGL) is useful in the diagnosis of growth of trees, tree stress and even climatic change. Utilizing very high resolution UAV images, this paper examines the feasibility of NGL detection using hyperspectral detection algorithms and anomaly detectors. The issues of pixel resolution and hard decision thresholding in deriving accurate NGL maps are also explored. Results showed that the blind-detection algorithms RXDs are not suitable for NGL detection due to the spectra similarity between NGL and both mature leaves and grass, while brighter pixels, such as those produced by soil and concrete materials, are more easily recognized as anomaly in contrast to forest. Matching filter (MF) based detectors are, however, able to accurately detect NGL over forest stands and are even more effective in the sense of achieving satisfactory true positives and true negatives while providing minimal false alarms. Of the tested partial knowledge MF algorithms, the covariance matched filter based distance (KMFD) detector performed very well with overall accuracy (OA) 0.97 and kappa coefficient () 0.60 on a natural resolution of 6.75 cm image. When a variety of mature-leaf nonobjective targets are included in the detection, the orthogonal subspace projector (OSP) tends to suppress NGL pixels as an unwanted signature and this leads to poor detection. Conversely, the target constrained interference minimized filter (TCIMF) detector is still able to effectively detect NGL with a satisfactory OA and through effective matching filter of the target signature as the hard-decision threshold is subject to a level of 5% or 1% probability of false alarms. From decimeter resolution satellite images, the KMFD and TCIMF detectors are capable of achieving an accuracy of OA = 0.94 and = 0.56 or OA = 0.87 and = 0.48 for images with a resolution of 33.75 cm or 67.50 cm respectively. This indicates that hyperspectral target detection techniques have great potential in NGL detection via high spatial resolution satellite multispectral images. Numéro de notice : A2018-294 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.022 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90412
in ISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 174 - 189[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt A generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning / Samuel Alleaume in Methods in ecology and evolution, vol 9 n° 8 (August 2018)
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Titre : A generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning Type de document : Article/Communication Auteurs : Samuel Alleaume, Auteur ; Pauline Dusseux, Auteur ; Vincent Thieron, Auteur ; Loïc Commagnac , Auteur ; Sylvio Laventure, Auteur ; Marc Lang, Auteur ; Jean-Baptiste Féret, Auteur ; Laurence Hubert-Moy, Auteur ; Sandra Luque, Auteur
Année de publication : 2018 Projets : 3-projet - voir note / AgroParisTech (2007 -) Article en page(s) : pp 1822 - 1836 Note générale : bibliographie
The authors thank the French Ministry of Ecology, Sustainable Development and Energy (MEDDE) for partial financial supportLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Environnement
[Termes IGN] biodiversité
[Termes IGN] carte de la végétation
[Termes IGN] écosystème
[Termes IGN] habitat (nature)
[Termes IGN] image à très haute résolution
[Termes IGN] indicateur de biodiversité
[Termes IGN] phénologie
[Termes IGN] politique de conservation (biodiversité)
[Termes IGN] protection de la biodiversité
[Termes IGN] variableRésumé : (auteur) The open access availability of satellite images from new sensors characterized by various spatial and temporal resolutions provides new challenges and possibilities for biodiversity conservation. Methodologies aiming at characterizing vegetation type, phenology, and function can now benefit from metric spatial resolution imagery combined with an improved revisit capability. Here, we test hybrid methods and data fusion, using very high spatial resolution (VHSR) sensors in different complex landscapes encompassing three French biogeographical regions.
The methodological approach presented herein has a generic value in response to national conservation targets based on the concept of essential biodiversity variables accessed by remote sensing (RS‐enabled EBVs). We focused on deriving five RS‐enabled EBVs from natural and seminatural open ecosystems: (1) ecosystem distribution, (2) land cover, (3) heterogeneity, (4) primary productivity and (5) vegetation phenology. The challenge was to develop a method that would be technically feasible, economically viable, and sustainable in time.
We demonstrated that it is possible to derive key parameters required to develop a set of EBVs from remote sensing (RS) data. The combined use of remote sensing data sources with various spatial, temporal, and spectral resolutions is essential to obtain different indicators of natural habitats.
One major current challenge for an improved contribution of RS to conservation is to strengthen multiple collaborative frameworks among remote sensing scientists, conservation biologists, and ecologists in order to increase the efficiency of methodological exchange and draw benefits for successful conservation planning strategies.Numéro de notice : A2018-659 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : BIODIVERSITE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1111/2041-210X.13033 Date de publication en ligne : 06/08/2018 En ligne : https://doi.org/10.1111/2041-210X.13033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93817
in Methods in ecology and evolution > vol 9 n° 8 (August 2018) . - pp 1822 - 1836[article]Intra-annual phenology for detecting understory plant invasion in urban forests / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
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Titre : Intra-annual phenology for detecting understory plant invasion in urban forests Type de document : Article/Communication Auteurs : Kunwar K. Singh, Auteur ; Yin-Hsuen Chen, Auteur ; Lindsey Smart, Auteur ; Josh Gray, Auteur ; Ross K. Meentemeyer, Auteur Année de publication : 2018 Article en page(s) : pp 151 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité de la végétation
[Termes IGN] détection d'anomalie
[Termes IGN] espèce exotique envahissante
[Termes IGN] flore urbaine
[Termes IGN] forêt tempérée
[Termes IGN] image Landsat-TM
[Termes IGN] indice de végétation
[Termes IGN] Ligustrum sinense
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] phénologie
[Termes IGN] protection de la biodiversité
[Termes IGN] surveillance forestièreRésumé : (Auteur) Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Numéro de notice : A2018-293 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.023 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90411
in ISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 151 - 161[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Evolutionary approach for detection of buried remains using hyperspectral images / Leon Dozal in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 7 (juillet 2018)
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Titre : Evolutionary approach for detection of buried remains using hyperspectral images Type de document : Article/Communication Auteurs : Leon Dozal, Auteur ; José L. Silvan-Cardenas, Auteur ; Daniela Moctezuma, Auteur ; Oscar S. Siordia, Auteur ; Enrique Naredo, Auteur Année de publication : 2018 Article en page(s) : pp 435 - 450 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme génétique
[Termes IGN] image hyperspectrale
[Termes IGN] Mexique
[Termes IGN] précision de la classification
[Termes IGN] teneur en eau de la végétation
[Termes IGN] tombeRésumé : (Auteur) Hyperspectral imaging has been successfully utilized to locate clandestine graves. This study applied a Genetic Programming technique called Brain Programming (BP) for automating the design of Hyperspectral Visual Attention Models (H-VAM.), which is proposed as a new method for the detection of buried remains. Four graves were simulated and monitored during six months by taking in situ spectral measurements of the ground. Two experiments were implemented using Kappa and weighted Kappa coefficients as classification accuracy measures for guiding the BP search of the best H-VAM. Experimental results demonstrate that the proposed BP method improves classification accuracy compared to a previous approach. A better detection performance was observed for the image acquired after three months from burial. Moreover, results suggest that the use of spectral bands that respond to vegetation and water content of the plants and provide evidence that the number of buried bodies plays a crucial role on a successful detection. Numéro de notice : A2018-359 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.7.435 Date de publication en ligne : 01/07/2018 En ligne : https://doi.org/10.14358/PERS.84.7.435 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90599
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 7 (juillet 2018) . - pp 435 - 450[article]Réservation
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