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Self-thinning tree mortality models that account for vertical stand structure, species mixing and climate / David I. Forrester in Forest ecology and management, Vol 487 ([01/05/2021])
[article]
Titre : Self-thinning tree mortality models that account for vertical stand structure, species mixing and climate Type de document : Article/Communication Auteurs : David I. Forrester, Auteur ; Thomas G. Backer, Auteur ; Stephen R. Elms, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 118936 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba
[Termes IGN] Australie
[Termes IGN] auto-éclaircie
[Termes IGN] changement climatique
[Termes IGN] Chine
[Termes IGN] dépérissement
[Termes IGN] Eucalyptus nitens
[Termes IGN] Fagus sylvatica
[Termes IGN] modèle de croissance végétale
[Termes IGN] mortalité
[Termes IGN] peuplement forestier
[Termes IGN] peuplement mélangé
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] Quercus pedunculata
[Termes IGN] Quercus sessiliflora
[Termes IGN] structure de la végétation
[Termes IGN] Suisse
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Self-thinning dynamics are often considered when managing stand density in forests and are used to constrain forest growth models. However, self-thinning relationships are often quantified using only data at a conceptualised self-thinning line, even though self-thinning can begin before the stand actually reaches a self-thinning line. Also, few self-thinning relationships account for the effects of species composition in mixed-species forests, and stand structure such as relative height of species (in mixtures), and/or size or age cohorts in uneven-aged forests. Such considerations may be important given the effects of global climate change and interest in mixed-species and uneven-aged forests. The objective of this study was to develop self-thinning relationships based on changes in the tree density relative to mean tree diameter, instead of focusing only on data for state variables (e.g. tree density) at the self-thinning line. This was done while also considering how the change in tree density is influenced by site quality and stand structure (species composition and relative height). The relationships were modelled using data from temperate Australian Eucalyptus plantations (436 plots), subtropical forests in China (88 plots), and temperate forests in Switzerland (1055 plots). Zero-inflated and hurdle generalized linear models with Poisson and negative binomial distributions were fit for several species, as well as for all-species equations. The intercepts and slopes of the self-thinning lines were higher than many published studies which may have resulted from both the less restrictive equation form and data selection. The rates of self-thinning often decreased as the proportion of the object species increased, as relative height increased (species or size cohort became more dominant), and as site (quality) index increased. The effects of aridity varied between species, with self-thinning increasing with aridity index for Abies alba, Pinus sylvestris, Quercus petraea and Quercus robur, but decreasing with aridity index for Eucalyptus nitens, Fagus sylvatica and Picea abies as sites became wetter and cooler. Self-thinning model parameters were not correlated with species traits, including specific leaf area, wood basic density or crown diameter – stem diameter allometry. All-species self-thinning relationships based on all data could be adjusted using a correction factor for rarer species where there were insufficient data to develop species-specific equations. The approach and equations developed could be used in forest growth models to calculate how the tree density declines as mean tree size increases, as height changes relative to other cohorts or species, as species proportions change, and as climatic and edaphic conditions change. Numéro de notice : A2021-355 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.118936 Date de publication en ligne : 18/02/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.118936 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97612
in Forest ecology and management > Vol 487 [01/05/2021] . - n° 118936[article]Deep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)
Titre : Deep learning for wildfire progression monitoring using SAR and optical satellite image time series Type de document : Thèse/HDR Auteurs : Puzhao Zhang, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 2021 Importance : 100 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-91-7873-935-6 Note générale : bibliographie
Doctoral Thesis in GeoinformaticsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alberta (Canada)
[Termes IGN] apprentissage profond
[Termes IGN] bande C
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] détection de changement
[Termes IGN] gestion des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie de forêt
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] Sydney (Nouvelle-Galles du Sud)
[Termes IGN] zone sinistréeRésumé : (auteur) Wildfires have coexisted with human societies for more than 350 million years, always playing an important role in affecting the Earth's surface and climate. Across the globe, wildfires are becoming larger, more frequent, and longer-duration, and tend to be more destructive both in lives lost and economic costs, because of climate change and human activities. To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even real-time. Satellite remote sensing enables cost-effective, accurate, and timely monitoring on the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8 and Sentinel-1/2 data opens the new era for global land surface monitoring, providing an opportunity to analyze wildfire impacts around the globe. The advances in both cloud computing and deep learning empower the automatic interpretation of spatio-temporal remote sensing big data on a large scale. The overall objective of this thesis is to investigate the potential of modern medium resolution earth observation data, especially Sentinel-1 C-Band synthetic aperture radar (SAR) data, in wildfire monitoring and develop operational and effective approaches for real-world applications. This thesis systematically analyzes the physical basis of earth observation data for wildfire applications, and critically reviews the available wildfire burned area mapping methods in terms of satellite data, such as SAR, optical, and SAR-Optical fusion. Taking into account its great power in learning useful representations, deep learning is adopted as the main tool to extract wildfire-induced changes from SAR and optical image time series. On a regional scale, this thesis has conducted the following four fundamental studies that may have the potential to further pave the way for achieving larger scale or even global wildfire monitoring applications. To avoid manual selection of temporal indices and to highlight wildfire-induced changes in burned areas, we proposed an implicit radar convolutional burn index (RCBI), with which we assessed the roles of Sentinel-1 C-Band SAR intensity and phase in SAR-based burned area mapping. The experimental results show that RCBI is more effective than the conventional log-ratio differencing approach in detecting burned areas. Though VV intensity itself may perform poorly, the accuracy can be significantly improved when phase information is integrated using Interferometric SAR (InSAR). On the other hand, VV intensity also shows the potential to improve VH intensity-based detection results with RCBI. By exploiting VH and VV intensity together, the proposed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the 2017 Thomas Fire and the 2018 Carr Fire. For the scenario of near real-time application, we investigated and demonstrated the potential Sentinel-1 SAR time series for wildfire progression monitoring with Convolutional Neural Networks (CNN). In this study, the available pre-fire SAR time series were exploited to compute temporal average and standard deviation for characterizing SAR backscatter behaviors over time and highlighting the changes with kMap. Trained with binarized kMap time series in a progression-wise manner, CNN showed good capability in detecting wildfire burned areas and capturing temporal progressions as demonstrated on three large and impactful wildfires with various topographic conditions. Compared to the pseudo masks (binarized kMap), CNN-based framework brought an 0.18 improvement in F1 score on the 2018 Camp Fire, and 0.23 on the 2019 Chuckegg Creek Fire. The experimental results demonstrated that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals. For continuous wildfire progression mapping, we proposed a novel framework of learning U-Net without forgetting in a near real-time manner. By imposing a temporal consistency restriction on the network response, Learning without Forgetting (LwF) allows the U-Net to learn new capabilities for better handling with newly incoming data, and simultaneously keep its existing capabilities learned before. Unlike the continuous joint training (CJT) with all available historical data, LwF makes U-Net learning not dependent on the historical training data any more. To improve the quality of SAR-based pseudo progression masks, we accumulated the burned areas detected by optical data acquired prior to SAR observations. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019-2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also found that the SAR cross-polarization ratio (VH/VV) can be very useful in highlighting burned areas when VH and VV have diverse temporal change behaviors. SAR-based change detection often suffers from the variability of the surrounding background noise, we proposed a Total Variation (TV)-regularized U-Net model to relieve the influence of SAR-based noisy masks. Considering the small size of labeled wildfire data, transfer learning was adopted to fine-tune U-Net from pre-trained weights based on the past wildfire data. We quantified the effects of TV regularization on increasing the connectivity of SAR-based areas, and found that TV-regularized U-Net can significantly increase the burned area mapping accuracy, bringing an improvement of 0.0338 in F1 score and 0.0386 in IoU score on the validation set. With TV regularization, U-Net trained with noisy SAR masks achieved the highest F1 (0.6904) and IoU (0.5295), while U-Net trained with optical reference mask achieved the highest F1 (0.7529) and IoU (0.6054) score without TV regularization. When applied on wildfire progression mapping, TV-regularized U-Net also worked significantly better than vanilla U-Net with the supervision of noisy SAR-based masks, visually comparable to optical mask-based results. On the regional scale, we demonstrated the effectiveness of deep learning on SAR-based and SAR-optical fusion based wildfire progression mapping. To scale up deep learning models and make them globally applicable, large-scale globally distributed data is needed. Considering the scarcity of labelled data in the field of remote sensing, weakly/self-supervised learning will be our main research directions to go in the near future. Note de contenu : 1- Introduction
2- Literature review
3- Study areas and data
4- Metodology
5- Results and discussionNuméro de notice : 28309 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics : RTK Stockholm : 2021 DOI : sans En ligne : http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1557429 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98130 Positional uncertainty of network RTK observations in a modern datum / T. Bernstein in Journal of geodetic science, vol 11 n° 1 (January 2021)
[article]
Titre : Positional uncertainty of network RTK observations in a modern datum Type de document : Article/Communication Auteurs : T. Bernstein, Auteur ; Volker Janssen, Auteur Année de publication : 2021 Article en page(s) : pp 38 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] compensation de coordonnées
[Termes IGN] Geocentric Datum of Australia 2020
[Termes IGN] incertitude de position
[Termes IGN] Nouvelle-Galles du Sud
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] station virtuelle de référenceRésumé : (auteur) The Geocentric Datum of Australia 2020 (GDA2020) is Australia’s new and much improved national datum. It is based on a single, nationwide leastsquares network adjustment that rigorously propagates uncertainty. This paper explores three options to include Network Real-Time Kinematic (NRTK) observations and their Positional Uncertainty (PU) in the survey control network of New South Wales (NSW) via the GDA2020 state adjustment. In the first option, PU is empirically estimated based on a dataset of more than 1,500 observations to obtain values that can be uniformly applied to all NRTK observations. In the second option, PU is calculated for each NRTK observation, based on the coordinate quality indicators provided by the Global Navigation Satellite System (GNSS) equipment. Both options continue to treatNRTK observations as point-based position solutions, resulting in poor correlation with surrounding surveycontrol marks. The third option overcomes this issue by utilising the automatically computed GNSS baselines between NRTK observations and their Virtual Reference Station (VRS) to create a connected network that can be adjusted like a static GNSS network. Using a typical urban NRTK survey in Sydney as an example, it is shown that this method offers a rigorous computation of PU, while maintaining the quick and easy nature of NRTK positioning. Numéro de notice : A2021-980 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jogs-2020-0116 Date de publication en ligne : 10/08/2021 En ligne : https://doi.org/10.1515/jogs-2020-0116 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100977
in Journal of geodetic science > vol 11 n° 1 (January 2021) . - pp 38 - 47[article]Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? / Oliver Lock in Geo-spatial Information Science, vol 23 n° 4 (December 2020)
[article]
Titre : Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? Type de document : Article/Communication Auteurs : Oliver Lock, Auteur ; Christopher Pettit, Auteur Année de publication : 2020 Article en page(s) : pp 275 - 292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] artefact
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données localisées des bénévoles
[Termes IGN] données massives
[Termes IGN] planification urbaine
[Termes IGN] réseau social
[Termes IGN] sentiment
[Termes IGN] Sydney (Nouvelle-Galles du Sud)
[Termes IGN] traitement du langage naturel
[Termes IGN] transport public
[Termes IGN] ville intelligenteRésumé : (auteur) We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion. Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city. With such pressures on existing public transportation systems, this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services. This research forms a case study of the use of passively collected forms of big data in cities – focusing on Sydney, Australia. Firstly, it examines social media data (Tweets) related to public transport performance. Secondly, it joins this to longitudinal big data – delay information continuously broadcast by the network over a year, thus forming hundreds of millions of data artifacts. Topics, tones, and sentiment are modeled using machine learning and Natural Language Processing (NLP) techniques. These resulting data, and models, are compared to opinions derived from a citizen survey among users. The validity of such data and models versus the intentions of users, in the context of systems that monitor and improve transport performance, are discussed. As such, key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques. Numéro de notice : A2020-787 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1815596 Date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.1080/10095020.2020.1815596 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96545
in Geo-spatial Information Science > vol 23 n° 4 (December 2020) . - pp 275 - 292[article]Mapping the fantastic great Southern continent, 1760–1777: A study in enlightenment geography / Vanessa Collingridge in Cartographic journal (the), Vol 57 n° 4 (November 2020)
[article]
Titre : Mapping the fantastic great Southern continent, 1760–1777: A study in enlightenment geography Type de document : Article/Communication Auteurs : Vanessa Collingridge, Auteur Année de publication : 2020 Article en page(s) : pp 335 - 352 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie ancienne
[Termes IGN] Australes (îles)
[Termes IGN] Australie
[Termes IGN] carte ancienne
[Termes IGN] Cook, James
[Termes IGN] dix-huitième siècle
[Termes IGN] information géographique
[Termes IGN] Océanie (géographie politique)Résumé : (auteur) This paper examines the relationship between the press and the construction of ‘enlightened’ public knowledge about – and imaginaries for – the emerging south in the period 1760–1777. It analyses the construction, nature, and forms of the putative Great Southern Continent in contemporary British newspapers and magazines, and offers some suggestions for how knowledge from navigators such as James Cook was given credence in the public domain back home. It draws parallels between the ‘new media’ of the eighteenth-century Enlightenment and those of the twenty-first century with their similar debates over who controls public (geographical) information, whom and how to trust, and why. Numéro de notice : A2021-835 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/00087041.2020.1884430 Date de publication en ligne : 18/05/2021 En ligne : https://doi.org/10.1080/00087041.2020.1884430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97879
in Cartographic journal (the) > Vol 57 n° 4 (November 2020) . - pp 335 - 352[article]Réservation
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