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Development of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan / Eunbeen Park in GIScience and remote sensing, vol 59 n° 1 (2022)
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Titre : Development of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan Type de document : Article/Communication Auteurs : Eunbeen Park, Auteur ; Hyun-Woo Jo, Auteur ; Sujong Lee, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 36 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] changement temporel
[Termes IGN] image Terra-MODIS
[Termes IGN] Indice de précipitations antérieures
[Termes IGN] indice de végétation
[Termes IGN] Kirghizistan
[Termes IGN] message d'alerte
[Termes IGN] modèle de simulation
[Termes IGN] plan de prévention des risques
[Termes IGN] prévision météorologique
[Termes IGN] sécheresseRésumé : (auteur) Drought is a natural disaster that occurs globally and is a main trigger of secondary environmental and socio-economic damages, such as food insecurity, land degradation, and sand-dust storms. As climate change is being accelerated by human activities and environmental changes, both the severity and uncertainties of drought are increasing. In this study, a diagnostic drought prediction model (DDPM) was developed to reduce the uncertainties caused by environmental diversity at the regional level in Kyrgyzstan, by predicting drought with meteorological forecasts and satellite image diagnosis. The DDPM starts with applying a prognostic drought prediction model (PDPM) to 1) estimate future agricultural drought by explaining its relationship with the standardized precipitation index (SPI), an accumulated precipitation anomaly, and 2) compensate for regional variances, which were not reflected sufficiently in the PDPM, by taking advantage of preciseness in the time-series vegetation condition index (VCI), a satellite-based index representing land surface conditions. Comparing the prediction results with the monitored VCI from June to August, it was found that the DDPM outperformed the PDPM, which exploits only meteorological data, in both spatiotemporal and spatial accuracy. In particular, for June to August, respectively, the results of the DDPM (coefficient of determination [R2] = 0.27, 0.36, and 0.4; root mean squared error [RMSE] = 0.16, 0.13, and 0.13) were more effective in explaining the spatial details of drought severity on a regional scale than those of the PDPM (R2 = 0.09, 0.10, and 0.11; RMSE = 0.17, 0.15, and 0.16). The DDPM revealed the possibility of advanced drought assessment by integrating the earth observation big data comprising meteorological and satellite data. In particular, the advantage of data fusion is expected to be maximized in areas with high land surface heterogeneity or sparse weather stations by providing observational feedback to the PDPM. This research is anticipated to support policymakers and technical officials in establishing effective policies, action plans, and disaster early warning systems to reduce disaster risk and prevent environmental and socio-economic damage. Numéro de notice : A2022-132 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2021.2012370 Date de publication en ligne : 20/12/2021 En ligne : https://doi.org/10.1080/15481603.2021.2012370 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99720
in GIScience and remote sensing > vol 59 n° 1 (2022) . - pp 36 - 53[article]GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules / Xuke Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)
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Titre : GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules Type de document : Article/Communication Auteurs : Xuke Hu, Auteur ; Hussein S. Al-Olimat, Auteur ; Jens Kersten, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 310 - 337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] classification hybride
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données topographiques
[Termes IGN] extraction de données
[Termes IGN] géobalise
[Termes IGN] microblogue
[Termes IGN] OpenStreetMap
[Termes IGN] répertoire toponymique
[Termes IGN] toponyme
[Termes IGN] TwitterRésumé : (auteur) Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84. Numéro de notice : A2022-164 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1947507 Date de publication en ligne : 07/07/2021 En ligne : https://doi.org/10.1080/13658816.2021.1947507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99787
in International journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 310 - 337[article]Genome-wide evolutionary response of European oaks during the Anthropocene / Dounia Saleh in Evolution letters, vol 6 n° 1 (February 2022)
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Titre : Genome-wide evolutionary response of European oaks during the Anthropocene Type de document : Article/Communication Auteurs : Dounia Saleh, Auteur ; Jun Chen, Auteur ; Jean-Charles Leple, Auteur ; Thibault Leroy, Auteur ; Laura Truffaut, Auteur ; Benjamin Dencausse, Auteur ; Céline Lalanne, Auteur ; Karine Labadie, Auteur ; Isabelle Lesur, Auteur ; Didier Bert, Auteur ; Frédéric Lagane, Auteur ; François Morneau , Auteur ; Jean-Marc Aury, Auteur ; Christophe Plomion, Auteur ; Martin Lascoux, Auteur ; Antoine Kremer, Auteur
Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : pp 4 - 20 Note générale : bibliographie
This research was supported by the European Research Council through an Advanced Grant (project TREEPEACE # FP7-339728), by France Génomique (project EVOL-OAK, ANR-10-INBS-09-08), and by the French Forest Service (ONF) (INRAE-ONF TREEPEACE contract).Langues : Anglais (eng) Descripteur : [Termes IGN] dix-huitième siècle
[Termes IGN] dix-neuvième siècle
[Termes IGN] France (géographie physique)
[Termes IGN] génétique forestière
[Termes IGN] Quercus sessiliflora
[Termes IGN] vingt-et-unième siècle
[Termes IGN] vingtième siècle
[Vedettes matières IGN] BotaniqueRésumé : (auteur) The pace of tree microevolution during Anthropocene warming is largely unknown. We used a retrospective approach to monitor genomic changes in oak trees since the Little Ice Age (LIA). Allelic frequency changes were assessed from whole-genome pooled sequences for four age-structured cohorts of sessile oak (Quercus petraea) dating back to 1680, in each of three different oak forests in France. The genetic covariances of allelic frequency changes increased between successive time periods, highlighting genome-wide effects of linked selection. We found imprints of parallel linked selection in the three forests during the late LIA, and a shift of selection during more recent time periods of the Anthropocene. The changes in allelic covariances within and between forests mirrored the documented changes in the occurrence of extreme events (droughts and frosts) over the last 300 years. The genomic regions with the highest covariances were enriched in genes involved in plant responses to pathogens and abiotic stresses (temperature and drought). These responses are consistent with the reported sequence of frost (or drought) and disease damage ultimately leading to the oak dieback after extreme events. They provide support for adaptive evolution of long-lived species during recent climatic changes. Although we acknowledge that other sources (e.g., gene flow, generation overlap) may have contributed to temporal covariances of allelic frequency changes, the consistent and correlated response across the three forests lends support to the existence of a systematic driving force such as natural selection. Numéro de notice : A2022-076 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1002/evl3.269 Date de publication en ligne : 05/01/2022 En ligne : https://doi.org/10.1002/evl3.269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99572
in Evolution letters > vol 6 n° 1 (February 2022) . - pp 4 - 20[article]A geographically weighted artificial neural network / Julian Haguenauer in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)
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Titre : A geographically weighted artificial neural network Type de document : Article/Communication Auteurs : Julian Haguenauer, Auteur ; Marco Helbich, Auteur Année de publication : 2022 Article en page(s) : pp 215 - 235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] analyse de sensibilité
[Termes IGN] Autriche
[Termes IGN] coût
[Termes IGN] évaluation foncière
[Termes IGN] hétérogénéité spatiale
[Termes IGN] logement
[Termes IGN] régression géographiquement pondérée
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal artificielRésumé : (auteur) While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between the dependent and the independent variables are linear. In practice, however, it is often the case that variables are nonlinearly associated. To address this issue, we propose a geographically weighted artificial neural network (GWANN). GWANN combines geographical weighting with artificial neural networks, which are able to learn complex nonlinear relationships in a data-driven manner without assumptions. Using synthetic data with known spatial characteristics and a real-world case study, we compared GWANN with GWR. While the results for the synthetic data show that GWANN performs better than GWR when the relationships within the data are nonlinear and their spatial variance is high, the results based on the real-world data demonstrate that the performance of GWANN can also be superior in a practical setting. Numéro de notice : A2022-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1871618 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1871618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99785
in International journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 215 - 235[article]Growing stock monitoring by European National Forest Inventories: Historical origins, current methods and harmonisation / Thomas Gschwantner in Forest ecology and management, vol 505 (February-1 2022)
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Titre : Growing stock monitoring by European National Forest Inventories: Historical origins, current methods and harmonisation Type de document : Article/Communication Auteurs : Thomas Gschwantner, Auteur ; Iciar A. Alberdi, Auteur ; Sébastien Bauwens, Auteur ; Susann Bender, Auteur ; Dragan Borota, Auteur ; Michal Bosela, Auteur ; Olivier Bouriaud , Auteur ; Johannes Breidenbach, Auteur ; Janis Donis, Auteur ; Christoph Fischer, Auteur ; Patrizia Gasparini, Auteur ; Luke Heffernan, Auteur ; Jean-Christophe Hervé (1961-2017)
, Auteur ; László Kolozs, Auteur ; Kari T. Korhonen, Auteur ; Nikos Koutsias, Auteur ; Pál Kovácsevics, Auteur ; Miloš Kučera, Auteur ; Gintaras Kulbokas, Auteur ; Andrius Kuliesis, Auteur ; Adrian Lanz, Auteur ; Philippe Lejeune, Auteur ; Torgny Lind, Auteur ; Gheorghe Marin, Auteur ; François Morneau
, Auteur ; Thomas Nord-Larsen, Auteur ; Leonia Nunes, Auteur ; Damjan Pantić, Auteur ; John Redmond, Auteur ; Francisco C. Rego, Auteur ; Thomas Riedel, Auteur ; Vladimir Šebeň, Auteur ; Allan Sims, Auteur ; Mitja Skudnik, Auteur ; Stein Michael Tomter, Auteur
Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 119868 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bois sur pied
[Termes IGN] changement climatique
[Termes IGN] Europe (géographie politique)
[Termes IGN] gestion forestière durable
[Termes IGN] harmonisation des données
[Termes IGN] histoire
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] peuplement forestier
[Termes IGN] ressources forestières
[Termes IGN] volume en bois
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Wood resources have been essential for human welfare throughout history. Also nowadays, the volume of growing stock (GS) is considered one of the most important forest attributes monitored by National Forest Inventories (NFIs) to inform policy decisions and forest management planning. The origins of forest inventories closely relate to times of early wood shortage in Europe causing the need to explore and plan the utilisation of GS in the catchment areas of mines, saltworks and settlements. Over time, forest surveys became more detailed and their scope turned to larger areas, although they were still conceived as stand-wise inventories. In the 1920s, the first sample-based NFIs were introduced in the northern European countries. Since the earliest beginnings, GS monitoring approaches have considerably evolved. Current NFI methods differ due to country-specific conditions, inventory traditions, and information needs. Consequently, GS estimates were lacking international comparability and were therefore subject to recent harmonisation efforts to meet the increasing demand for consistent forest resource information at European level. As primary large-area monitoring programmes in most European countries, NFIs assess a multitude of variables, describing various aspects of sustainable forest management, including for example wood supply, carbon sequestration, and biodiversity. Many of these contemporary subject matters involve considerations about GS and its changes, at different geographic levels and time frames from past to future developments according to scenario simulations. Due to its historical, continued and currently increasing importance, we provide an up-to-date review focussing on large-area GS monitoring where we i) describe the origins and historical development of European NFIs, ii) address the terminology and present GS definitions of NFIs, iii) summarise the current methods of 23 European NFIs including sampling methods, tree measurements, volume models, estimators, uncertainty components, and the use of air- and space-borne data sources, iv) present the recent progress in NFI harmonisation in Europe, and v) provide an outlook under changing climate and forest-based bioeconomy objectives. Numéro de notice : A2022-040 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2021.119868 Date de publication en ligne : 12/12/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119868 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99386
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