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Perspectives: Critical zone perspectives for managing changing forests / Marissa Kopp in Forest ecology and management, vol 528 (January-15 2023)
[article]
Titre : Perspectives: Critical zone perspectives for managing changing forests Type de document : Article/Communication Auteurs : Marissa Kopp, Auteur ; Denise Alving, Auteur ; Taylor Blackman, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120627 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique
[Termes IGN] écosystème forestier
[Termes IGN] Etats-Unis
[Termes IGN] géologie locale
[Termes IGN] gestion de l'eau
[Termes IGN] gestion forestière
[Termes IGN] incendie de forêt
[Termes IGN] Insecta
[Termes IGN] parasite (biologie)
[Termes IGN] planification
[Termes IGN] productivité
[Termes IGN] stress hydrique
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Forest management is under intensifying ecological and societal pressures amid the current geological epoch, which some see becoming the Anthropocene. These pressures extend to temporal and physical scales typical of geology; however, integrating geological processes into forest management has lagged behind the inclusion of shorter-term and surficial ecosystem processes. As such, we examine the field of critical zone science for connections that translate geologic knowledge to forest management and planning. Earth’s critical zone is the thin near-surface zone spanning from the bottom of circulating groundwater to the top of the atmospheric boundary layer of forest canopies. We explore four case studies from regions of the U.S.A. to highlight how recent critical zone discoveries inform contemporary forest management challenges. Some examples of management-relevant research include mediation of the impacts of climate change on forest productivity across gradients in geology, aspect, and topography; the role of bedrock water storage on drought resistance; hydrology-vegetation interactions following pest outbreaks; and quantification of water partitioning and erosion following fire. The accelerated pace of critical zone discovery has been synchronous with increased availability of open-source data resources for forest managers to expand this framework in management and planning. Numéro de notice : A2023-034 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120627 Date de publication en ligne : 16/11/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120627 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102297
in Forest ecology and management > vol 528 (January-15 2023) . - n° 120627[article]The ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science / Médéric Gravelle in Earth System Science Data, vol 15 n° 1 (2023)
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Titre : The ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science Type de document : Article/Communication Auteurs : Médéric Gravelle, Auteur ; Guy Wöppelmann , Auteur ; Kevin Gobron, Auteur ; Zuheir Altamimi , Auteur ; Mikaël Guichard, Auteur ; Thomas Herring, Auteur ; Paul Rebischung , Auteur Année de publication : 2023 Article en page(s) : pp 497 - 509 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] déformation verticale de la croute terrestre
[Termes IGN] données marégraphiques
[Termes IGN] littoral
[Termes IGN] série temporelle
[Termes IGN] système d'observation du niveau des eaux littorales SONEL
[Termes IGN] vitesse de déplacementRésumé : (auteur) A new reanalysis of GNSS data at or near tide gauges worldwide was produced by the university of La Rochelle (ULR) group within the 3rd International GNSS Service (IGS) reprocessing campaign (repro3). The new solution, called ULR-repro3, complies with the IGS standards adopted for repro3, implementing advances in data modelling and corrections since the previous reanalysis campaign, and extending the average record length by about 7 years. The results presented here focus on the main products of interest for sea level science, that is, the station position time series and associated velocities on the vertical component at tide gauges. These products are useful to estimate accurate vertical land motion at the coast and supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas. To provide realistic velocity uncertainty estimates, the noise content in the position time series was investigated considering the impact of non-tidal atmospheric loading. Overall, the ULR-repro3 position time series show reduced white noise and power-law amplitudes and station velocity uncertainties compared to the previous reanalysis. The products are available via SONEL (https://doi.org/10.26166/sonel_ulr7a; Gravelle et al., 2022). Numéro de notice : A2023-079 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-15-497-2023 Date de publication en ligne : 01/02/2023 En ligne : https://doi.org/10.5194/essd-15-497-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102521
in Earth System Science Data > vol 15 n° 1 (2023) . - pp 497 - 509[article]
Titre : Artificial intelligence oceanography Type de document : Monographie Auteurs : Xiaofeng Li, Éditeur scientifique ; Fan Wang, Éditeur scientifique Editeur : Springer Nature Année de publication : 2023 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-19637-5-9 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algue
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cyclone
[Termes IGN] détection d'objet
[Termes IGN] iceberg
[Termes IGN] intelligence artificielle
[Termes IGN] océanographie
[Termes IGN] température de surface de la merRésumé : (éditeur) This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing. Note de contenu : 1- Artificial Intelligence Foundation of smart ocean
2- Forecasting tropical instability waves based on artificial intelligence
3- Sea surface height anomaly prediction based on artificial intelligence
4- Satellite data-driven internal solitary wave forecast based on machine learning techniques
5- AI-based subsurface thermohaline structure retrieval from remote sensing observations
6- Ocean heat content retrieval from remote sensing data based on machine learning
7- Detecting tropical cyclogenesis using broad learning system from satellite passive microwave observations
8- Tropical cyclone monitoring based on geostationary satellite imagery
9- Reconstruction of pCO2 data in the Southern ocean based on feedforward neural network
10- Detection and analysis of mesoscale eddies based on deep learning
11- Deep convolutional neural networks-based coastal inundation mapping from SAR imagery: with one application case for Bangladesh, a UN-defined least developed country
12- Sea ice detection from SAR images based on deep fully convolutional networks
13- Detection and analysis of marine green algae based on artificial intelligence
14- Automatic waterline extraction of large-scale tidal flats from SAR images based on deep convolutional neural networks
15- Extracting ship’s size from SAR images by deep learning
16- Benthic organism detection, quantification and seamount biology detection based on deep learningNuméro de notice : 24105 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.1007/978-981-19-6375-9 En ligne : https://link.springer.com/book/10.1007/978-981-19-6375-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103058 Comparative analysis of estimation of slope-length gradient (LS) factor for entire Afghanistan / Ahmad Ansari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)
[article]
Titre : Comparative analysis of estimation of slope-length gradient (LS) factor for entire Afghanistan Type de document : Article/Communication Auteurs : Ahmad Ansari, Auteur ; Gökmen Tayfur, Auteur Année de publication : 2023 Article en page(s) : n° 2200890 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Afghanistan
[Termes IGN] bassin hydrographique
[Termes IGN] érosion
[Termes IGN] gradient de pente
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle RUSLE
[Termes IGN] système d'information géographiqueRésumé : (auteur) Slope length gradient (LS) is one of the crucial factors in the Universal Soil Loss Equations (USLE, RUSLE). This study aimed at estimating the slope-length and slope-steepness (LS) factor for the entire watersheds of Afghanistan by using three different methods, namely; (1) LS-TOOLMFD (Method 1); (2) The Method of Equations (Method 2); and (3) The approach of Moore and Burch (Method 3). The first method uses the digital elevation model (DEM) in the ASCII format, and the other two methods use the DEM in the spatial domain. The results show that the LS-factor of the study area ranges from 0.01 to 44.31, with a mean of 5.24 and standard deviation of 6.95, according to Method 1; 0.03 to 163.49, with a mean of 9.6 and standard deviation of 13.58, according to Method 2; and 0 to 3985, with a mean of 7.16 and standard deviation of 29.7, according to Method 3. The study reveals that Methods 1 and 2 are more appropriate than Method 3 because Method 3 yields high LS-factor values close to or at streamlines located near mountainous regions. The highest LS values are found to be in the northeast, north, and central regions of Afghanistan, which is consistent with the high mountains and deep valley geomorphology, indicating that these regions are particularly vulnerable to soil erosion by rainfall-runoff processes. The sediment delivery ratio (SDR) for the Upper-Helmand River Basin (Upper-HRB) is also estimated by the RUSLE, employing the LS factors produced by the three methods. The results revealed that the average annual soil loss is found to be, respectively, 9.3, 18.2, and 11.1 (ton/ha/year) by using the three methods, corresponding to SDR of 23.5%, 12.1%, and 19.9%. Numéro de notice : A2023-193 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2023.2200890 Date de publication en ligne : 18/04/2023 En ligne : https://doi.org/10.1080/19475705.2023.2200890 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103074
in Geomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - n° 2200890[article]Cross-supervised learning for cloud detection / Kang Wu in GIScience and remote sensing, vol 60 n° 1 (2023)
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Titre : Cross-supervised learning for cloud detection Type de document : Article/Communication Auteurs : Kang Wu, Auteur ; Zunxiao Xu, Auteur ; Xinrong Lyu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2147298 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] détection d'objet
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] nuageRésumé : (auteur) We present a new learning paradigm, that is, cross-supervised learning, and explore its use for cloud detection. The cross-supervised learning paradigm is characterized by both supervised training and mutually supervised training, and is performed by two base networks. In addition to the individual supervised training for labeled data, the two base networks perform the mutually supervised training using prediction results provided by each other for unlabeled data. Specifically, we develop In-extensive Nets for implementing the base networks. The In-extensive Nets consist of two Intensive Nets and are trained using the cross-supervised learning paradigm. The Intensive Net leverages information from the labeled cloudy images using a focal attention guidance module (FAGM) and a regression block. The cross-supervised learning paradigm empowers the In-extensive Nets to learn from both labeled and unlabeled cloudy images, substantially reducing the number of labeled cloudy images (that tend to cost expensive manual effort) required for training. Experimental results verify that In-extensive Nets perform well and have an obvious advantage in the situations where there are only a few labeled cloudy images available for training. The implementation code for the proposed paradigm is available at https://gitee.com/kang_wu/in-extensive-nets. Numéro de notice : A2023-190 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2022.2147298 Date de publication en ligne : 03/01/2023 En ligne : https://doi.org/10.1080/15481603.2022.2147298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102969
in GIScience and remote sensing > vol 60 n° 1 (2023) . - n° 2147298[article]Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data / Thuong V. Tran in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkDiscrete element analysis of deformation features of slope controlled by karst fissures under the mining effect: a case study of Pusa landslide, China / Qian Zhao in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)PermalinkEstablishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP / Pengfei Xia in GPS solutions, vol 27 n° 1 (January 2023)PermalinkLa forêt progresse mais la mortalité des arbres s’accroît / Anonyme in Géomètre, n° 2209 (janvier 2023)PermalinkGeographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model / Zhenyang Du in Journal of Marine Systems, vol 237 (January 2023)PermalinkA GIS-based study on the layout of the ecological monitoring system of the Grain for Green project in China / Ke Guo in Forests, vol 14 n° 1 (January 2023)PermalinkHow to optimize the 2D/3D urban thermal environment: Insights derived from UAV LiDAR/multispectral data and multi-source remote sensing data / Rongfang Lyu in Sustainable Cities and Society, vol 88 (January 2023)PermalinkA machine learning method for Arctic lakes detection in the permafrost areas of Siberia / Piotr Janiec in European journal of remote sensing, vol 56 n° 1 (2023)PermalinkMachine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)PermalinkManagement of birch spruce mixed stands with consideration of carbon stock in biomass and harvested wood products / Jānis Vuguls in Forests, vol 14 n° 1 (January 2023)Permalink