Descripteur
Termes IGN > sciences naturelles > sciences de la vie > biologie > botanique > phytogéographie
phytogéographie
Commentaire :
écologie végétale. >> inventaire de la végétation, distribution géographique, acclimatation (botanique), phytogéographie, introduction (botanique), migration (botanique), plante endémique, réintroduction (botanique), plante allochtone. >>Terme(s) spécifique(s) : limite de la végétation. Equiv. LCSH : Phytogeography. Domaine(s) : 570; 580. |
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Offering the appetite for the monitoring of European forests a diversified diet / Jean-Daniel Bontemps in Annals of Forest Science, vol 79 n° 1 (2022)
[article]
Titre : Offering the appetite for the monitoring of European forests a diversified diet Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps , Auteur ; Olivier Bouriaud , Auteur ; Cédric Vega , Auteur ; Laura Bouriaud , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 19 Note générale : bibliographie
NB article d'opinionLangues : Anglais (eng) Descripteur : [Termes IGN] Europe (géographie politique)
[Termes IGN] intégration
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] politique publique
[Termes IGN] ressources forestières
[Termes IGN] santé des forêts
[Termes IGN] surveillance forestière
[Termes IGN] Union Européenne
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Forest monitoring in Europe is turning matter of renewed political concern, and a possible role for ICP Forests health monitoring has been suggested to meet this goal (Ann For Sci 78:94, 2021). Multipurpose national forest inventory (NFI) surveys yet offer a sampling effort by two orders of magnitude greater than ICP level 1, have accomplished substantial methodological and harmonization progresses in the recent years, and therefore form a decisive contributor to future European forest monitoring incentives. Possible paths for the future development of a pan-European, comprehensive and more accurate monitoring are designed that stress a crucial need to build on the assets of the existing forest monitoring programs and favor their cooperation, in order to limit the co-existence of distinct forest monitoring processes. Numéro de notice : A2022-320 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01139-7 Date de publication en ligne : 11/04/2022 En ligne : http://dx.doi.org/10.1186/s13595-022-01139-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100432
in Annals of Forest Science > vol 79 n° 1 (2022) . - n° 19[article]Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al. / Guido Ceccherini in Annals of Forest Science, vol 79 n° 1 (2022)
[article]
Titre : Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al. Type de document : Article/Communication Auteurs : Guido Ceccherini, Auteur ; Grégory Duveiller, Auteur ; Giacomo Grassi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 31 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] exploitation forestière
[Termes IGN] Finlande
[Termes IGN] foresterie
[Termes IGN] image Landsat
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] placette d'échantillonnage
[Termes IGN] récolte de bois
[Termes IGN] ressources forestières
[Termes IGN] Suède
[Termes IGN] surface forestière
[Termes IGN] Union EuropéenneRésumé : (auteur) The timely and accurate monitoring of forest resources is becoming of increasing importance in light of the multi-functionality of these ecosystems and their increasing vulnerability to climate change. Remote sensing observations of tree cover and systematic ground observations from National Forest Inventories (NFIs) represent the two major sources of information to assess forest area and use. The specificity of two methods is calling for an in-depth analysis of their strengths and weaknesses and for the design of novel methods emerging from the integration of satellite and surface data. On this specific debate, a recent paper by Breidenbach et al. published in this journal suggests that the detection of a recent increase in EU forest harvest rate—as reported in Nature by Ceccherini et al.—is largely due to technical limitations of satellite-based mapping. The article centers on the difficulty of the approaches to estimate wood harvest based on remote sensing. However, it does not discuss issues with the robustness of validation approaches solely based on NFIs. Here we discuss the use of plot data as a validation set for remote sensing products, discussing potentials and limitations of both NFIs and remote sensing, and how they can be used synergistically. Finally, we highlight the need to collect in situ data that is both relevant and compatible with remote sensing products within the European Union. Numéro de notice : A2022-630 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01150-y Date de publication en ligne : 13/07/2022 En ligne : https://doi.org/10.1186/s13595-022-01150-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101393
in Annals of Forest Science > vol 79 n° 1 (2022) . - n° 31[article]Wall-to-wall mapping of forest biomass and wood volume increment in Italy / Francesca Giannetti in Forests, vol 13 n° 12 (December 2022)
[article]
Titre : Wall-to-wall mapping of forest biomass and wood volume increment in Italy Type de document : Article/Communication Auteurs : Francesca Giannetti, Auteur ; Gherardo Chirici, Auteur ; Elia Vangi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1989 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] carte thématique
[Termes IGN] écosystème forestier
[Termes IGN] forêt méditerranéenne
[Termes IGN] gestion forestière
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Italie
[Termes IGN] puits de carbone
[Termes IGN] volume en bois
[Vedettes matières IGN] ForesterieRésumé : (auteur) Several political initiatives aim to achieve net-zero emissions by the middle of the twenty-first century. In this context, forests are crucial as a carbon sink to store unavoidable emissions. Assessing the carbon sequestration potential of forest ecosystems is pivotal to the availability of accurate forest variable estimates for supporting international reporting and appropriate forest management strategies. Spatially explicit estimates are even more important for Mediterranean countries such as Italy, where the capacity of forests to act as sinks is decreasing due to climate change. This study aimed to develop a spatial approach to obtain high-resolution maps of Italian forest above-ground biomass (ITA-BIO) and current annual volume increment (ITA-CAI), based on remotely sensed and meteorological data. The ITA-BIO estimates were compared with those obtained with two available biomass maps developed in the framework of two international projects (i.e., the Joint Research Center and the European Space Agency biomass maps, namely, JRC-BIO and ESA-BIO). The estimates from ITA-BIO, JRC-BIO, ESA-BIO, and ITA-CAI were compared with the 2nd Italian NFI (INFC) official estimates at regional level (NUT2). The estimates from ITA-BIO are in good agreement with the INFC estimates (R2 = 0.95, mean difference = 3.8 t ha−1), while for JRC-BIO and ESA-BIO, the estimates show R2 of 0.90 and 0.70, respectively, and mean differences of 13.5 and of 21.8 t ha−1 with respect to the INFC estimates. ITA-CAI estimates are also in good agreement with the INFC estimates (R2 = 0.93), even if they tend to be slightly biased. The produced maps are hosted on a web-based forest resources management Decision Support System developed under the project AGRIDIGIT (ForestView) and represent a key element in supporting the new Green Deal in Italy, the European Forest Strategy 2030 and the Italian Forest Strategy. Numéro de notice : A2022-864 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.3390/f13121989 Date de publication en ligne : 24/11/2022 En ligne : https://doi.org/10.3390/f13121989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102156
in Forests > vol 13 n° 12 (December 2022) . - n° 1989[article]Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)
[article]
Titre : Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning Type de document : Article/Communication Auteurs : Thiên-Anh Nguyen, Auteur ; Benjamin Kellenberger, Auteur ; Devis Tuia, Auteur Année de publication : 2022 Article en page(s) : n° 113217 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Alpes
[Termes IGN] apprentissage profond
[Termes IGN] canopée
[Termes IGN] carte forestière
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] écotone
[Termes IGN] hauteur des arbres
[Termes IGN] image à très haute résolution
[Termes IGN] image aérienne
[Termes IGN] image RVB
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] SuisseRésumé : (auteur) Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Common methods usually predict a map from remote sensing images without deliberately considering intermediate semantic concepts that are relevant to the final map. This makes the mapping process difficult to interpret, especially when using opaque deep learning models. Moreover, such procedure is entirely agnostic to the definitions of the mapping targets (e.g., forest types depending on variables such as tree height and tree density). Common models can at best learn these rules implicitly from data, which greatly hinders trust in the produced maps. In this work, we aim at building an explainable deep learning model for forest mapping that leverages prior knowledge about forest definitions to provide explanations to its decisions. We propose a model that explicitly quantifies intermediate variables like tree height and tree canopy density involved in the forest definitions, corresponding to those used to create the forest maps for training the model in the first place, and combines them accordingly. We apply our model to mapping forest types using very high resolution aerial imagery and lay particular focus on the treeline ecotone at high altitudes, where forest boundaries are complex and highly dependent on the chosen forest definition. Results show that our rule-informed model is able to quantify intermediate key variables and predict forest maps that reflect forest definitions. Through its interpretable design, it is further able to reveal implicit patterns in the manually-annotated forest labels, which facilitates the analysis of the produced maps and their comparison with other datasets. Numéro de notice : A2022-794 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2022.113217 Date de publication en ligne : 01/09/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113217 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101928
in Remote sensing of environment > vol 281 (November 2022) . - n° 113217[article]Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information / Shaohui Zhang in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)
[article]
Titre : Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information Type de document : Article/Communication Auteurs : Shaohui Zhang, Auteur ; Cédric Vega , Auteur ; Christine Deleuze, Auteur ; Sylvie Durrieu, Auteur ; Pierre Barbillon, Auteur ; Olivier Bouriaud , Auteur ; Jean-Pierre Renaud , Auteur Année de publication : 2022 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : n° 103072 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] empreinte
[Termes IGN] gestion forestière
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier local
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation de la forêt
[Termes IGN] placette d'échantillonnage
[Termes IGN] Sologne (France)
[Termes IGN] variogramme
[Termes IGN] volume en boisRésumé : (auteur) The French National Forest Inventory provides detailed forest information up to large national and regional scales. Forest inventory for small areas of interest within a large population is equally important for decision making, such as for local forest planning and management purposes. However, sampling these small areas with sufficient ground plots is often not cost efficient. In response, small area estimation has gained increasing popularity in forest inventory. It consists of a set of techniques that enables predictions of forest attributes of subpopulation with the help of auxiliary information that compensates for the small field samples. Common sources of auxiliary information usually come from remote sensing technology, such as airborne laser scanning and satellite imagery. The newly launched NASA’s Global Ecosystem Dynamics Investigation (GEDI), a full waveform Lidar instrument, provides an unprecedented opportunity of collecting large-scale and dense forest sample plots given its sampling frequency and spatial coverage. However, the geolocation uncertainty associated with GEDI footprints create important challenges for their use for small area estimations. In this study, we designed a process that provides NFI measurements at plot level with GEDI auxiliary information from nearby footprints. We demonstrated that GEDI RH98 is equivalent to NFI dominant height at plot level. We stressed the importance of pairing NFI plots with nearby GEDI footprints, based on not only the distance in between but also their similarities, i.e., forest heights and forest types. Subsequently, these NFI-GEDI pairs were used for small area estimations following a two-phase sampling scheme. We showcased that, with an adequate sample size, small area estimation with GEDI auxiliary data can improve the accuracy of forest volume estimates. Numéro de notice : A2022-786 Affiliation des auteurs : LIF+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.103072 Date de publication en ligne : 22/10/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103072 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101890
in International journal of applied Earth observation and geoinformation > vol 114 (November 2022) . - n° 103072[article]An estimation method to reduce complete and partial nonresponse bias in forest inventory / James A. Westfall in European Journal of Forest Research, vol 141 n° 5 (October 2022)PermalinkMultisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products / Ankit Sagar in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)PermalinkForest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (September-15 2022)PermalinkUsing multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil / Adrián Pascual in Ecological Informatics, vol 70 (September 2022)PermalinkAn automatic approach for tree species detection and profile estimation of urban street trees using deep learning and Google street view images / Kwanghun Choi in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)PermalinkClimatic sensitivities derived from tree rings improve predictions of the forest vegetation simulator growth and yield model / Courtney L. Giebink in Forest ecology and management, vol 517 (August-1 2022)PermalinkModeling and propagating inventory-based sampling uncertainty in the large-scale forest demographic model “MARGOT” / Timothée Audinot in Natural Resource Modelling, vol 35 n° 3 (August 2022)PermalinkAnalysis of structure from motion and airborne laser scanning features for the evaluation of forest structure / Alejandro Rodríguez-Vivancos in European Journal of Forest Research, vol 141 n° 3 (June 2022)PermalinkDirect and automatic measurements of stem curve and volume using a high-resolution airborne laser scanning system / Eric Hyyppä in Science of remote sensing, vol 5 (June 2022)PermalinkUncertainty of biomass stocks in Spanish forests: a comprehensive comparison of allometric equations / Aitor Ameztegui in European Journal of Forest Research, vol 141 n° 3 (June 2022)Permalink