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A regional spatiotemporal analysis of large magnitude snow avalanches using tree rings / Erich Peitzsch in Natural Hazards and Earth System Sciences, Vol 21 n° 2 (February 2021)
[article]
Titre : A regional spatiotemporal analysis of large magnitude snow avalanches using tree rings Type de document : Article/Communication Auteurs : Erich Peitzsch, Auteur ; Jordi Hendrikx, Auteur ; Daniel Stahle, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 533 - 557 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] avalanche
[Termes IGN] Canada
[Termes IGN] cerne
[Termes IGN] croissance des arbres
[Termes IGN] dendrochronologie
[Termes IGN] données topographiques
[Termes IGN] échantillonnage
[Termes IGN] Etats-Unis
[Termes IGN] géomorphologie locale
[Termes IGN] magnitude
[Termes IGN] montagneRésumé : (auteur) Snow avalanches affect transportation corridors and settlements worldwide. In many mountainous regions, robust records of avalanche frequency and magnitude are sparse or non-existent. However, dendrochronological methods can be used to fill this gap and infer historical avalanche patterns. In this study, we developed a tree-ring-based avalanche chronology for large magnitude avalanche events (size ≥∼D3) using dendrochronological techniques for a portion of the US northern Rocky Mountains. We used a strategic sampling design to examine avalanche activity through time and across nested spatial scales (i.e., from individual paths, four distinct subregions, and the region). We analyzed 673 samples in total from 647 suitable trees collected from 12 avalanche paths from which 2134 growth disturbances were identified over the years 1636 to 2017 CE. Using existing indexing approaches, we developed a regional avalanche activity index to discriminate avalanche events from noise in the tree-ring record. Large magnitude avalanches, common across the region, occurred in 30 individual years and exhibited a median return interval of approximately 3 years (mean = 5.21 years). The median large magnitude avalanche return interval (3–8 years) and the total number of avalanche years (12–18) varies throughout the four subregions, suggesting the important influence of local terrain and weather factors. We tested subsampling routines for regional representation, finding that sampling 8 random paths out of a total of 12 avalanche paths in the region captures up to 83 % of the regional chronology, whereas four paths capture only 43 % to 73 %. The greatest value probability of detection for any given path in our dataset is 40 %, suggesting that sampling a single path would capture no more than 40 % of the regional avalanche activity. Results emphasize the importance of sample size, scale, and spatial extent when attempting to derive a regional large magnitude avalanche event chronology from tree-ring records. Numéro de notice : A2021-169 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.5194/nhess-21-533-2021 Date de publication en ligne : 05/02/2021 En ligne : https://doi.org/10.5194/nhess-21-533-2021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97108
in Natural Hazards and Earth System Sciences > Vol 21 n° 2 (February 2021) . - pp 533 - 557[article]Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery / Rajeev Bhattarai in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
[article]
Titre : Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery Type de document : Article/Communication Auteurs : Rajeev Bhattarai, Auteur ; Parinaz Rahimzadeh-Bajgiran, Auteur ; Aaron R. Weiskittel, Auteur Année de publication : 2021 Article en page(s) : pp 28 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Abies balsamea
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] défoliation
[Termes IGN] dégradation de la flore
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] insecte phyllophage
[Termes IGN] Nouveau-Brunswick (Canada)
[Termes IGN] Picea abiesRésumé : (auteur) Spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a valuable data source for seamless regional-scale mapping of forest composition. This study developed and evaluated new models to map the distribution and abundance of SBW host species at 20 m spatial resolution using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery in combination with several site variables for a total of 191 variables in northern New Brunswick, Canada using the Random Forest (RF) algorithm. We found Sentinel-2 multi-temporal single spectral bands and numerous spectral vegetation indices (SVIs) yielded the classification of SBW host species with an overall accuracy (OA) of 72.6% and kappa coefficient (K) of 0.65. Incorporating Sentinel-1 SAR data with Sentinel-2 variables coupled with elevation, only marginally improved the performance of the model (OA: 73.0% and K: 0.66). The use of Sentinel-1 SAR data with elevation resulted in a reasonable OA of 57.5% and K of 0.47. These spatially explicit up-to-date SBW host species maps are essential for identifying susceptible forests, monitoring SBW defoliation, and minimizing forest losses from insect impacts at landscape scale in the current SBW outbreak in the region. Numéro de notice : A2021-085 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.023 Date de publication en ligne : 15/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96845
in ISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 28 - 40[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021021 SL Revue Centre de documentation Revues en salle Disponible 081-2021022 DEP-RECF Revue Nancy Bibliothèque Nancy IFN Exclu du prêt GIS-based multicriteria evaluation for earthquake response: a case study of expert opinion in Vancouver, Canada / Blake Byron Walker in Natural Hazards, Vol 105 n° 2 (January 2021)
[article]
Titre : GIS-based multicriteria evaluation for earthquake response: a case study of expert opinion in Vancouver, Canada Type de document : Article/Communication Auteurs : Blake Byron Walker, Auteur ; Nadine Schuurman, Auteur ; David Swanlund, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2075 - 2091 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] allocation
[Termes IGN] analyse multicritère
[Termes IGN] cartographie collaborative
[Termes IGN] cartographie d'urgence
[Termes IGN] planification urbaine
[Termes IGN] prévention des risques
[Termes IGN] secours d'urgence
[Termes IGN] séisme
[Termes IGN] Vancouver (Colombie britannique)
[Termes IGN] zone à risqueRésumé : (auteur) GIS-based multicriteria evaluation (MCE) provides a framework for analysing complex decision problems by quantifying variables of interest to score potential locations according to their suitability. In the context of earthquake preparedness and post-disaster response, MCE has relied mainly on uninformed or non-expert stakeholders to identify high-risk zones, prioritise areas for response, or highlight vulnerable populations. In this study, we compare uninformed, informed non-expert, and expert stakeholders’ responses in MCE modelling for earthquake response planning in Vancouver, Canada. Using medium- to low-complexity MCE models, we highlight similarities and differences in the importance of infrastructural and socioeconomic variables, emergency services, and liquefaction potential between a non-weighted MCE, a medium-complexity informed non-expert MCE, and a low-complexity MCE informed by 35 local earthquake planning and response experts from governmental and non-governmental organisations. Differences in the observed results underscore the importance of accessible, expert-informed approaches for prioritising locations for earthquake response planning and for the efficient and geographically precise allocation of resources. Numéro de notice : A2021-203 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11069-020-04390-1 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1007/s11069-020-04390-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97164
in Natural Hazards > Vol 105 n° 2 (January 2021) . - pp 2075 - 2091[article]Analysing 18th century hydrographic data: a campaign in the Bay of Biscay, 1750-1751 / Helen Mair Rawsthorne (2021)
Titre : Analysing 18th century hydrographic data: a campaign in the Bay of Biscay, 1750-1751 Type de document : Article/Communication Auteurs : Helen Mair Rawsthorne , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2021 Conférence : Data for History 2021, 4th Data for History conference : Modelling Time, Places, Agents 19/05/2021 30/06/2021 Berlin virtuel Allemagne OA Abstracts only Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] campagne d'observations
[Termes IGN] carte ancienne
[Termes IGN] carte marine
[Termes IGN] données hydrographiques
[Termes IGN] sondage par points
[Termes IGN] Terre-Neuve, île de (Terre-Neuve-et-Labrador)
[Termes IGN] traitement de donnéesRésumé : (auteur) This paper features part of the work carried out for my Master’s thesis in Epistemology, History of Science and Technology. The project was completed during a six-month internship with the Région Nouvelle-Aquitaine as part of the Nouvelle-Aquitaine et Outre-Mers programme. In 2020, the French Service Hydrographique et Océanographique de la Marine (Shom) celebrated its 300th anniversary. The Shom is the French public authority for maritime and coastal geographical reference information. Such information is obtained through specific measurement techniques that have evolved throughout history. The Shom's predecessor, the Dépôt des Cartes et Plans de la Marine, was created in 1720 in order to collect, analyse and compile the documents produced by the maritime community to construct nautical maps. It was in the interest of the royal power of the time to collect mariners’ logbooks to monopolise the information contained inside them. They did this via the Grande Ordonnance de la Marine, established in 1681 and written by Colbert, secretary of the navy under the reign of Louis XIV, which required pilots of vessels to submit all logbooks to the Greffe de l’Amirauté. Then, in 1773 the Dépôt became the sole institution in charge of the production and publication of nautical charts in France. As well as simply collecting logbooks, the Dépôt began producing and enforcing rules and standards on how to log the information inside them. This information would then be regrouped by location and type, and used for the production or correction of nautical charts by Dépôt engineers. Upon discovering inaccuracies on nautical charts during voyages, mariners would often annotate the charts, which would later be subject to discussion and revision by the Dépôt upon their return. When significant errors or deficiencies were identified on published nautical charts, the Dépôt, along with the logistical assistance of the Ministre de la Marine, organised for hydrographic campaigns to be carried out to verify and improve existing nautical charts. In 1750 and 1751, a hydrographic campaign was conducted in the Bay of Biscay by a captain of the French Navy, chosen thanks to his practical navigation experience. The aim was to correct two charts of the region and to carry out landing soundings that could be added to new charts. During the mission, over 350 soundings were carried out in the Bay using a leadline to measure the depth of the water and to record samples of the seabed at different points. For every sounding point, some or all of the following information were recorded in manuscripts written on board the ship: the date, the time, the depth of the water, the nature of the seabed and the geographic position, either with bearings, with geographic coordinates or by dead reckoning. This study presents a methodology for the processing and analysis of the hydrographic data recorded during this campaign. The processing workflow involves numerous steps: the datafication of the information contained in the ship’s documents; the definition of the digitised data via the analysis of the accompanying historical archives of the campaign and the addition of metadata; the standardisation of the digitised data to comply with curent norms; the classification of the digitised data according to modern reference data. The newly interoperable historical data can then be compared and analysed alongside equivalent data collected at different moments in history that have undergone the same data processing. In this project, the historical data from the campaign, once processed, are compared to current data, collected and diffused by the Shom, allowing an analysis of the evolution and the continuities in the bathymetry and sedimentology in the Bay of Biscay. The methodology developed makes use of digital humanities tools, particularly digital cartography tools for visualising the mapping of the processed historical data. Numéro de notice : C2021-005 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://hal.science/hal-03239920v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97795 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 FOSTER - An R package for forest structure extrapolation / Martin Queinnec in Plos one, vol 16 n° 1 (January 2021)PermalinkPermalinkThe spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis / Matthew Quick in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)PermalinkAnalysis of the effect of climate warming on paludification processes: Will soil conditions limit the adaptation of Northern boreal forests to climate change? A synthesis / Ahmed Laamrani in Forests, vol 11 n°11 (November 2020)PermalinkEffects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data / Wai Yeung Yan in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkRiver ice segmentation with deep learning / Abhineet Singh in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkUsing climate-sensitive 3D city modeling to analyze outdoor thermal comfort in urban areas / Rabeeh Hosseinihaghighi in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkOpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkChloroplast haplotypes of Northern red oak (Quercus rubra L.) stands in Germany suggest their origin from Northeastern Canada / Jeremias Götz in Forests, vol 11 n° 9 (September 2020)PermalinkDecolonizing world heritage maps using indigenous toponyms, stories, and interpretive attributes / Mark Palmer in Cartographica, vol 55 n° 3 (Fall 2020)Permalink