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Unprecedented pluri-decennial increase in the growing stock of French forests is persistent and dominated by private broadleaved forests / Jean-Daniel Bontemps in Annals of Forest Science, vol 77 n° 4 (December 2020)
[article]
Titre : Unprecedented pluri-decennial increase in the growing stock of French forests is persistent and dominated by private broadleaved forests Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps , Auteur ; Anaïs Denardou-Tisserand , Auteur ; Jean-Christophe Hervé (1961-2017) , Auteur ; Jean Bir , Auteur ; Jean-Luc Dupouey, Auteur Année de publication : 2020 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : n° 98 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bois sur pied
[Termes IGN] changement d'utilisation du sol
[Termes IGN] forêt de feuillus
[Termes IGN] forêt privée
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] modèle de régression
[Termes IGN] politique forestière
[Termes IGN] puits de carbone
[Termes IGN] série temporelle
[Termes IGN] surface forestière
[Vedettes matières IGN] Economie forestièreRésumé : (auteur) Key message: French forests exhibit the fastest relative changes across Europe. Growing stock increases faster than area, and is greatest in low-stocked private broadleaved forests. Past areal increases and current GS levels show positive effects on GS expansion, with GS increases hence expected to persist.
Context: Strong increases in growing stocks (GS) of European forests for decades remain poorly understood and of unknown duration. French forests showing the greatest relative changes across Europe form the investigated case study.
Aims: The magnitudes of net area, GS, and GS density (GSD) changes were evaluated across forest categories reflecting forest policy and land-use drivers. The roles of forest areal changes, GS and GSD levels on GS changes were investigated.
Methods: National Forest Inventory data were used to produce time series of area, GS and GSD across forest categories over 1976–2014, and exploratory causal models of GS changes.
Results: GS (+ 57%) increased three times faster than area, highlighting an advanced stage in the forest transition. Low-stocked private forests exhibited strong changes in GS/GSD, greatest in private broadleaved forests, stressing the contribution of returning forests on abandoned lands. Regression models demonstrated positive effects of both past areal increases and current GS, on GS expansion.
Conclusion: Aerial C-sink in French forests is expected to persist in future decades.Numéro de notice : A2020-647 Affiliation des auteurs : LIF+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01003-6 Date de publication en ligne : 12/10/2020 En ligne : https://doi.org/10.1007/s13595-020-01003-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96075
in Annals of Forest Science > vol 77 n° 4 (December 2020) . - n° 98[article]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])
[article]
Titre : 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 Type de document : Article/Communication Auteurs : Bappa Das, Auteur ; Rabi N. Sahoo, Auteur ; Sourabh Pargal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1415 - 1432 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] blé (céréale)
[Termes IGN] canopée
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] réflectance spectrale
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] séparateur à vaste marge
[Termes IGN] spectroradiomètreRésumé : (auteur) Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors. Numéro de notice : A2020-607 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581271 Date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95967
in Geocarto international > vol 35 n° 13 [01/10/2020] . - pp 1415 - 1432[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020101 RAB Revue Centre de documentation En réserve L003 Disponible Using OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)
[article]
Titre : Using OpenStreetMap data and machine learning to generate socio-economic indicators Type de document : Article/Communication Auteurs : Daniel Feldmeyer, Auteur ; Claude Meisch, Auteur ; Holger Sauter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] arbre aléatoire
[Termes IGN] base de données spatiotemporelles
[Termes IGN] changement climatique
[Termes IGN] chômage
[Termes IGN] classification par réseau neuronal
[Termes IGN] collectivité territoriale
[Termes IGN] données localisées des bénévoles
[Termes IGN] données socio-économiques
[Termes IGN] inégalité
[Termes IGN] limite administrative
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] OpenStreetMapRésumé : (auteur) Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities. Numéro de notice : A2020-663 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090498 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.3390/ijgi9090498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96139
in ISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - 16 p.[article]A regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2020 (August 2020)
[article]
Titre : A regression model of spatial accuracy prediction for Openstreetmap buildings Type de document : Article/Communication Auteurs : Ibrahim Maidaneh Abdi , Auteur ; Arnaud Le Guilcher , Auteur ; Ana-Maria Olteanu-Raimond , Auteur Année de publication : 2020 Projets : 1-Pas de projet / AgroParisTech (2007 -) Conférence : ISPRS 2020, Commission 4, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 4 Article en page(s) : pp 39 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] modèle de régression
[Termes IGN] OpenStreetMap
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] qualité des donnéesRésumé : (auteur) Data quality assessment of OpenStreetMap (OSM) data can be carried out by comparing them with a reference spatial data (e.g authoritative data). However, in case of a lack of reference data, the spatial accuracy is unknown. The aim of this work is therefore to propose a framework to infer relative spatial accuracy of OSM data by using machine learning methods. Our approach is based on the hypothesis that there is a relationship between extrinsic and intrinsic quality measures. Thus, starting from a multi-criteria data matching, the process seeks to establish a statistical relationship between measures of extrinsic quality of OSM (i.e. obtained by comparison with reference spatial data) and the measures of intrinsic quality of OSM (i.e. OSM features themselves) in order to estimate extrinsic quality on an unevaluated OSM dataset. The approach was applied on OSM buildings. On our dataset, the resulting regression model predicts the values on the extrinsic quality indicators with 30% less variance than an uninformed predictor. Numéro de notice : A2020-506 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2020-39-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2020-39-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95647
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2020 (August 2020) . - pp 39 - 47[article]Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)
[article]
Titre : Los Angeles as a digital place: The geographies of user‐generated content Type de document : Article/Communication Auteurs : Andrea Ballatore, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse spatiale
[Termes IGN] centre urbain
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] distribution spatiale
[Termes IGN] données multisources
[Termes IGN] données socio-économiques
[Termes IGN] exploration de données géographiques
[Termes IGN] Foursquare
[Termes IGN] Los Angeles
[Termes IGN] modèle de régression
[Termes IGN] OpenStreetMap
[Termes IGN] participation du public
[Termes IGN] représentation géographique
[Termes IGN] réseau social
[Termes IGN] réseau social géodépendant
[Termes IGN] TwitterRésumé : (auteur) Online representations of places are becoming pivotal in informing our understanding of urban life. Content production on online platforms is grounded in the geography of their users and their digital infrastructure. These constraints shape place representation, that is, the amount, quality, and type of digital information available in a geographic area. In this article we study the place representation of user‐generated content (UGC) in Los Angeles County, relating the spatial distribution of the data to its geo‐demographic context. Adopting a comparative and multi‐platform approach, this quantitative analysis investigates the spatial relationship between four diverse UGC datasets and their context at the census tract level (about 685,000 geo‐located tweets, 9,700 Wikipedia pages, 4 million OpenStreetMap objects, and 180,000 Foursquare venues). The context includes the ethnicity, age, income, education, and deprivation of residents, as well as public infrastructure. An exploratory spatial analysis and regression‐based models indicate that the four UGC platforms possess distinct geographies of place representation. To a moderate extent, the presence of Twitter, OpenStreetMap, and Foursquare data is influenced by population density, ethnicity, education, and income. However, each platform responds to different socio‐economic factors and clusters emerge in disparate hotspots. Unexpectedly, Twitter data tend to be located in denser, more deprived areas, and the geography of Wikipedia appears peculiar and harder to explain. These trends are compared with previous findings for the area of Greater London. Numéro de notice : A2020-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12600 Date de publication en ligne : 02/01/2020 En ligne : https://doi.org/10.1111/tgis.12600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96156
in Transactions in GIS > Vol 24 n° 4 (August 2020) . - 23 p.[article]Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)PermalinkFine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method / Zhenzhong Peng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkSoil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkSpectral Interference of Heavy Metal Contamination on Spectral Signals of Moisture Content for Heavy Metal Contaminated Soils / Haein Shin in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkA comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data / Haiyan Tao in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)PermalinkRegression modeling of reduction in spatial accuracy and detail for multiple geometric line simplification procedures / Timofey Samsonov in International journal of cartography, Vol 6 n° 1 (March 2020)PermalinkEstimation et suivi de la ressource en bois en France métropolitaine par valorisation des séries multi-temporelles à haute résolution spatiale d'images optiques (Sentinel-2) et radar (Sentinel-1, ALOS-PALSAR) / David Morin (2020)PermalinkIndividual tree detection and classification for mapping pine wilt disease using multispectral and visible color imagery acquired from unmanned aerial vehicle / Takeshi Hoshikawa in Journal of The Remote Sensing Society of Japan, vol 40 n° 1 (2020)PermalinkComprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])PermalinkClimate variability and climate change impacts on land surface, hydrological processes and water management / Yongqiang Zhang (2019)PermalinkAbove-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China / Ran Jing in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)PermalinkThorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping / Wojciech Drzewiecki in Geodesy and cartography, vol 66 n° 2 (December 2017)PermalinkPermalinkPermalinkA new climatology of maximum and minimum temperature (1951–2010) in the Spanish mainland: a comparison between three different interpolation methods / D. Peña-Angulo in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)PermalinkA functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds / Magnussen, Steen in Remote sensing of environment, vol 184 (October 2016)PermalinkDisaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models / Subit Chakrabarti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkA bootstrap test for constant coefficients in geographically weighted regression models / Chang-Lin Mei in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)PermalinkApproximating prediction uncertainty for random forest regression models / John W. Coulston in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)PermalinkComparison of three Landsat TM compositing methods: A case study using modeled tree canopy cover / Bonnie Ruefenacht in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)Permalink