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Climatic 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)
[article]
Titre : Climatic sensitivities derived from tree rings improve predictions of the forest vegetation simulator growth and yield model Type de document : Article/Communication Auteurs : Courtney L. Giebink, Auteur ; R. Justin DeRose, Auteur ; Mark Castle, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120256 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cerne
[Termes IGN] croissance des arbres
[Termes IGN] gestion forestière
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] Picea (genre)
[Termes IGN] Pinus ponderosa
[Termes IGN] Pseudotsuga menziesii
[Termes IGN] puits de carbone
[Termes IGN] rendement
[Termes IGN] Utah (Etas-Unis)
[Termes IGN] variation saisonnière
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Forest management has the potential to contribute to the removal of greenhouse gasses from the atmosphere via carbon sequestration and storage. To identify management actions that will maximize carbon removal and storage over the long term, models are needed that accurately and realistically represent forest responses to changing climate. The most widely used growth and yield model in the United States (U.S.), the Forest Vegetation Simulator (FVS), which also forms the basis for several forest carbon calculators, does not currently include the direct effect of climate variation on tree growth. We incorporated the effects of climate on tree diameter growth by combining tree-ring data with forest inventory data to parameterize a suite of alternative models characterizing the growth of three dominant tree species in the arid and moisture-limited state of Utah. These species, Pinus ponderosa Dougl. ex Laws, Pseudotsuga menziesii var. glauca Mayr (Franco), and Picea engelmannii Parry ex Engelm., encompass the full elevational range of montane forest types. The alternative models we considered differed progressively from the current FVS large-tree diameter growth model, first by changing to an annual time step, then by adding interannual climate effects, followed by model simplification (removal of predictors), and finally, complexification, including effects of spatial variation in climate and two-way interactions between predictors. We validated diameter growth predictions from these models with independent observations, and evaluated model performance in terms of accuracy, precision, and bias. We then compared predictions of future growth made by the existing large-tree diameter growth model used in FVS, i.e., without climate effects, to those of our updated models, including those with climate effects. We found that simpler models of tree growth outperform the current FVS model, and that the incorporation of climate effects improves model performance for two out of three species, in which growth is currently overpredicted by FVS. Diameter growth projected with improved, climate-sensitive models is less than the future tree growth projected by the current climate-insensitive FVS model. Tree rings can be used to identify and incorporate drivers of growth variation into a stand-level growth and yield model, giving more accurate predictions of the carbon uptake potential of forests under climate change. Numéro de notice : A2022-390 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120256 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120256 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100681
in Forest ecology and management > vol 517 (August-1 2022) . - n° 120256[article]Ground surface elevation changes over permafrost areas revealed by multiple GNSS interferometric reflectometry / Yufeng Hu in Journal of geodesy, vol 96 n° 8 (August 2022)
[article]
Titre : Ground surface elevation changes over permafrost areas revealed by multiple GNSS interferometric reflectometry Type de document : Article/Communication Auteurs : Yufeng Hu, Auteur ; Ji Wang, Auteur ; Zhenhong Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 56 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] Alaska (Etats-Unis)
[Termes IGN] analyse diachronique
[Termes IGN] dégel
[Termes IGN] données Galileo
[Termes IGN] données GLONASS
[Termes IGN] pergélisol
[Termes IGN] rapport signal sur bruit
[Termes IGN] réflecteur
[Termes IGN] réflectométrie par GNSS
[Termes IGN] signal GNSS
[Termes IGN] surface du sol
[Termes IGN] variation saisonnièreRésumé : (auteur) Ground subsidence and uplift caused by the annual thawing and freezing of the active layer are important variables in permafrost studies. Global positioning system interferometric reflectometry (GPS-IR) has been successfully applied to retrieve the continuous ground surface movements in permafrost areas. However, only GPS signals were used in previous studies. In this study, using multiple global navigation satellite system (GNSS) signal-to-noise ratio (SNR) observations recorded by a GNSS station SG27 in Utqiaġvik, Alaska during the period from 2018 to 2021, we applied multiple GNSS-IR (multi-GNSS-IR) technique to the SNR data and obtained the complete and continuous ground surface elevation changes over the permafrost area at a daily interval in snow-free seasons in 2018 and 2019. The GLONASS-IR and Galileo-IR measurements agreed with the GPS-IR measurements at L1 frequency, which are the most consistent measurements among all multi-GNSS measurements, in terms of the overall subsidence trend but clearly showed periodic noises. We proposed a method to reconstruct the GLONASS- and Galileo-IR elevation changes by specifically grouping and fitting them with a composite model. Compared with GPS L1 results, the unbiased root mean square error (RMSE) of the reconstructed Galileo measurements reduced by 50.0% and 42.2% in 2018 and 2019, respectively, while the unbiased RMSE of the reconstructed GLONASS measurements decreased by 41.8% and 25.8% in 2018 and 2019, respectively. Fitting the composite model to the combined multi-GNSS-IR, we obtained seasonal displacements of − 3.27 ± 0.13 cm (R2 = 0.763) and − 10.56 ± 0.10 cm (R2 = 0.912) in 2018 and 2019, respectively. Moreover, we found that the abnormal summer heave was strongly correlated with rain events, implying hydrological effects on the ground surface elevation changes. Our study shows the feasibility of multi-GNSS-IR in permafrost areas for the first time. Multi-GNSS-IR opens up a great opportunity for us to investigate ground surface movements over permafrost areas with multi-source observations, which are important for our robust analysis and quantitative understanding of frozen ground dynamics under climate change. Numéro de notice : A2022-606 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01646-5 Date de publication en ligne : 13/08/2022 En ligne : https://doi.org/10.1007/s00190-022-01646-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101385
in Journal of geodesy > vol 96 n° 8 (August 2022) . - n° 56[article]Losses of tree cover in California driven by increasing fire disturbance and climate stress / Jonathan A. Wang in AGU Advances, vol 3 n° 4 (August 2022)
[article]
Titre : Losses of tree cover in California driven by increasing fire disturbance and climate stress Type de document : Article/Communication Auteurs : Jonathan A. Wang, Auteur ; James T. Randerson, Auteur ; Michael L. Goulden, Auteur ; Clarke A. Knight, Auteur ; John J. Battles, Auteur Année de publication : 2022 Article en page(s) : n° e2021AV000654 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] carte de la végétation
[Termes IGN] changement climatique
[Termes IGN] stress hydrique
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Forests provide natural climate solutions for sequestering carbon and mitigating climate change, yet are increasingly threatened by increasing temperature and disturbance. Understanding these threats requires accurate information on vegetation dynamics and their drivers, which is currently lacking in many regions experiencing rapid climate change such as California. To address this, we combined remote sensing observations with geospatial databases to develop annual maps of vegetation cover (tree, shrub, and herbaceous) and disturbance type (fire, harvest, and forest die-off) in California at 30 m resolution from 1985 to 2021. Considering both changes in cover fraction and areal extent, California lost 4,566 km2 of its tree cover area (6.7% relative to initial cover) since 1985. Substantial gains in tree cover area during the 1990s were more than offset by fire-driven declines since 2000, resulting in greater shrub and herbaceous cover area. Tree cover loss occurred in all ecoregions but was most severe in the southern mountains, where losses from wildfire were not compensated by regrowth in undisturbed areas. Fires and tree cover area loss generally occurred where summer temperatures were greater than 17.5°C, whereas net tree cover gain often occurred in cooler areas, suggesting that ongoing climate warming is threatening forests in many areas. California's vegetation is undergoing rapid transformation, with disturbance rates and climate change posing substantial potential risks to the integrity of California's terrestrial carbon sink. Numéro de notice : A2022-143 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1029/2021AV000654 Date de publication en ligne : 06/07/2022 En ligne : https://doi.org/10.1029/2021AV000654 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102447
in AGU Advances > vol 3 n° 4 (August 2022) . - n° e2021AV000654[article]Using attributes explicitly reflecting user preference in a self-attention network for next POI recommendation / Ruijing Li in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)
[article]
Titre : Using attributes explicitly reflecting user preference in a self-attention network for next POI recommendation Type de document : Article/Communication Auteurs : Ruijing Li, Auteur ; Jianzhong Guo, Auteur ; Chun Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 440 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] distance
[Termes IGN] filtrage d'information
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] point d'intérêt
[Termes IGN] réseau social géodépendant
[Termes IGN] Tokyo (Japon)Résumé : (auteur) With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average. Numéro de notice : A2022-647 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080440 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.3390/ijgi11080440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101463
in ISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 440[article]Fusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco bay area and Southern California / Huineng Yan in Journal of geodesy, vol 96 n° 7 (July 2022)
[article]
Titre : Fusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco bay area and Southern California Type de document : Article/Communication Auteurs : Huineng Yan, Auteur ; Wujiao Dai, Auteur ; Lei Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données GNSS
[Termes IGN] faille géologique
[Termes IGN] filtrage spatiotemporel
[Termes IGN] fusion de données
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modélisation spatiale
[Termes IGN] rééchantillonnage
[Termes IGN] série temporelleRésumé : (auteur) The spatio-temporal random effects (STRE) model is a classic dynamic filtering model, which can be used to fuse GNSS and InSAR deformation data. The STRE model uses a certain time span of high spatial resolution Interferometric Synthetic Aperture Radar (InSAR) time series data to establish a spatial model and then obtain a deformation result with high spatio-temporal resolution through the state transition equation recursively in time domain. Combined with the Kalman filter, the STRE model is continuously updated and modified in time domain to obtain higher accuracy result. However, it relies heavily on the prior information and personal experience to establish an accurate spatial model. To the authors' knowledge, there are no publications which use the STRE model with multiple sets of different deformation monitoring data to verify its applicability and reliability. Here, we propose an improved STRE model to automatically establish accurate spatial model to improve the STRE model, then apply it to the fusion of GNSS and InSAR deformation data in the San Francisco Bay Area covering approximately 6000 km2 and in Southern California covering approximately 100,000 km2. Our experimental results show that the improved STRE model can achieve good fusion effects in both study areas. For internal inspection, the average error RMS values in the two regions are 0.13 mm and 0.06 mm for InSAR and 2.4 and 2.8 mm for GNSS, respectively; for Jackknife cross-validation, the average error RMS values are 6.0 and 1.3 mm for InSAR and 4.3 and 4.8 mm for GNSS in the two regions, respectively. We find that the deformation rate calculated from the fusion results is highly consistent with the existing studies, the significant difference in the deformation rate on both sides of the major faults in the two regions can be clearly seen, and the area with abnormal deformation rate corresponds well to the actual situation. These results indicate that the improved STRE model can reduce the reliance on prior information and personal experience, realize the effective fusion of GNSS and InSAR deformation data in different regions, and also has the advantages of high accuracy and strong applicability. Numéro de notice : A2022-553 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/s00190-022-01636-7 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.1007/s00190-022-01636-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101165
in Journal of geodesy > vol 96 n° 7 (July 2022) . - n° 47[article]Towards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkAutomated inventory of broadleaf tree plantations with UAS imagery / Aishwarya Chandrasekaran in Remote sensing, vol 14 n° 8 (April-2 2022)PermalinkAssessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data / Cheng-Chun Lee in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkDiscovering co-location patterns in multivariate spatial flow data / Jiannan Cai in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)PermalinkHuman movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? / Meiliu Wu in Annals of GIS, vol 28 n° 2 (April 2022)PermalinkUrban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)PermalinkAssessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework / Evgeny Noi in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)PermalinkMeasuring and mapping long-term changes in migration flows using population-scale family tree data / Caglar Koylu in Cartography and Geographic Information Science, vol 49 n° 2 (March 2022)PermalinkSimultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)PermalinkUnderstanding the geodetic signature of large aquifer systems: Example of the Ozark plateaus in central United States / Stacy Larochelle in Journal of geophysical research : Solid Earth, vol 127 n° 3 (March 2022)PermalinkComprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event / Victoria Graffigna in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkSuspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms / Marzieh Fadaee in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkAn integrated framework of global sensitivity analysis and calibration for spatially explicit agent-based models / Jeon-Young Kang in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkDeriving a tree growth model from any existing stand growth model / Quang V. Cao in Canadian Journal of Forest Research, Vol 52 n° 2 (February 2022)PermalinkDiscovering transition patterns among OpenStreetMap feature classes based on the Louvain method / Yijiang Zhao in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkMonthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning / Feng Zhao in Remote sensing of environment, vol 269 (February 2022)PermalinkCIME: Context-aware geolocation of emergency-related posts / Gabriele Scalia in Geoinformatica, vol 26 n° 1 (January 2022)PermalinkMonitoring and modeling of the Sacramento Valley aquifer (California) using geodetic and piezometric measurements / Stacy Larochelle (2022)PermalinkUnderstory plant community responses to widespread spruce mortality in a subalpine forest / Trevor A. Carter in Journal of vegetation science, vol 33 n° 1 (January 2022)PermalinkPermalink