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Arctic tsunamis threaten coastal landscapes and communities – survey of Karrat Isfjord 2017 tsunami effects in Nuugaatsiaq, western Greenland / Mateusz C. Strzelecki in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)
[article]
Titre : Arctic tsunamis threaten coastal landscapes and communities – survey of Karrat Isfjord 2017 tsunami effects in Nuugaatsiaq, western Greenland Type de document : Article/Communication Auteurs : Mateusz C. Strzelecki, Auteur ; Marek W. Jaskólski, Auteur Année de publication : 2020 Article en page(s) : pp 2521 - 2534 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse du paysage
[Termes IGN] Arctique
[Termes IGN] changement climatique
[Termes IGN] dégradation des sols
[Termes IGN] détection de changement
[Termes IGN] effondrement de terrain
[Termes IGN] érosion côtière
[Termes IGN] fjord
[Termes IGN] Groenland
[Termes IGN] inondation
[Termes IGN] littoral
[Termes IGN] paysage
[Termes IGN] risque naturel
[Termes IGN] toundra
[Termes IGN] tsunamiRésumé : (auteur) On the 17 June 2017, a massive landslide which mobilized 35–58 million m3 of material entered the Karrat Isfjord in western Greenland. It triggered a tsunami wave with a runup height exceeding 90 m close to the landslide, ca. 50 m on the opposite shore of the fjord. The tsunami travelled ca. 32 km along the fjord and reached the settlement of Nuugaatsiaq with ca. 1–1.5 m high waves which flooded the terrain up to 9 m a.s.l. (above sea level). Tsunami waves were powerful enough to destroy the community infrastructure, impact fragile coastal tundra landscape, and unfortunately injure several inhabitants and cause four deaths. Our field survey carried out 25 months after the event results in documentation of the previously unreported scale of damage in the settlement (ca. 48 % of infrastructure objects including houses and administration buildings were destroyed by the tsunami). We have observed a recognizable difference in the concentration of tsunami deposit accumulations between areas of the settlement overwashed by the wave and areas of runup and return flow. The key tsunami effects preserved in the coastal landscape were eroded coastal bluffs, gullied and dissected edges of cliffed coast in the harbour, and tundra vegetation compressed by boulders or icebergs rafted onshore during the event. Numéro de notice : A2020-612 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/nhess-20-2521-2020 Date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.5194/nhess-20-2521-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95979
in Natural Hazards and Earth System Sciences > vol 20 n° 9 (September 2020) . - pp 2521 - 2534[article]Improving drainage conditions of forest roads using the GIS and forest road simulator / Mehran Nasiri in Journal of forest science, vol 66 n° 9 (September 2020)
[article]
Titre : Improving drainage conditions of forest roads using the GIS and forest road simulator Type de document : Article/Communication Auteurs : Mehran Nasiri, Auteur Année de publication : 2020 Article en page(s) : pp 361 - 367 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] chemin forestier
[Termes IGN] drainage
[Termes IGN] écoulement des eaux
[Termes IGN] effondrement de terrain
[Termes IGN] hydrologie
[Termes IGN] Iran
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau de drainage
[Termes IGN] système d'information géographiqueRésumé : (auteur) In this study a new method of locating culverts is presented with the composition of achieved discharge from hydrological analysis and simulated forest roads in RoadEng 3D simulator to improve drainage condition. Locating culverts was performed on a small scale (1:20 000, using GIS) and large scale (1:2 000, road geometric design simulator). The small-scale study regarding the achieved discharge from streams shows that the installation of some culverts is not necessary. The large-scale study also showed that the geometric design of forest road has a significant effect on locating culverts and its accuracy. To improve drainage conditions 6 culverts and 2 waterfronts taking into account the geometric design of forest road, hydrological conditions and appropriate intervals (155 m) have been proposed. No installation or lack of accuracy to find the best location of culverts may result in the occurrence of creep and landslide, so the cost of destruction would be several times higher than the cost of technical buildings construction. Numéro de notice : A2020-693 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.17221/16/2020-JFS Date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.17221/16/2020-JFS Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96238
in Journal of forest science > vol 66 n° 9 (September 2020) . - pp 361 - 367[article]A novel algorithm to estimate phytoplankton carbon concentration in inland lakes using Sentinel-3 OLCI images / Heng Lyu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
[article]
Titre : A novel algorithm to estimate phytoplankton carbon concentration in inland lakes using Sentinel-3 OLCI images Type de document : Article/Communication Auteurs : Heng Lyu, Auteur ; Zhiqian Yang, Auteur ; Lei Shi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6512 - 6523 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] changement climatique
[Termes IGN] Chine
[Termes IGN] chlorophylle
[Termes IGN] corrélation
[Termes IGN] image Sentinel-OLCI
[Termes IGN] lac
[Termes IGN] plancton
[Termes IGN] réflectance
[Termes IGN] série temporelle
[Termes IGN] teneur en carboneRésumé : (auteur) Phytoplankton carbon, an important biogeochemical and ecological parameter, plays a critical role in the carbon cycle and in global warming reduction. Estimation of phytoplankton carbon in inland waters on a large scale using remote sensing is useful for understanding, evaluating, and monitoring the carbon dynamics, and, in particular, for determining the spatial–temporal variation of primary production in inland waters. In a correlation analysis of the phytoplankton carbon concentration and water components, the result revealed no significant correlation between the chlorophyll-a concentration and phytoplankton carbon concentration in inland waters. However, the absorption peak height of particles at 675 nm, which is defined as the absorption at 675 nm subtracted by that at 660 nm, was found to be closely correlated with the phytoplankton carbon concentration. Thus, the absorption peak height of particles at 675 nm could be used as an indicator of the phytoplankton carbon concentration. A semianalytical method based on the remote-sensing reflectance in Sentinel-3 Ocean and Land Color Instrument (OLCI) bands 8, 9, and 17 was developed to derive the absorption peak of particles at a wavelength of 675 nm. Finally, an algorithm for estimating the phytoplankton carbon concentration in inland waters using OLCI bands 8, 9, and 17 was constructed. From 2013 to 2018, eight field campaigns were conducted in inland lakes in different seasons, and the optical properties, optically active water components, and phytoplankton carbon concentrations were obtained. An assessment of its accuracy using an independent data set demonstrated that the algorithm performance is acceptable (mean absolute percentage error, 48.6%, and root mean square error, 0.36 mg/L). As a demonstration, the algorithm was successfully applied to map the phytoplankton carbon concentration in Taihu Lake and Chaohu Lake, China, using OLCI images acquired on December 5, 2017, and August 5, 2018 and December 8, 2... Numéro de notice : A2020-531 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2977080 Date de publication en ligne : 12/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2977080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95714
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6512 - 6523[article]Spatial simulation of rainstorm waterlogging based on a water accumulation diffusion algorithm / Jingwei Hou in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)
[article]
Titre : Spatial simulation of rainstorm waterlogging based on a water accumulation diffusion algorithm Type de document : Article/Communication Auteurs : Jingwei Hou, Auteur ; Yixian Du, Auteur Année de publication : 2020 Article en page(s) : pp 71 - 87 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] base de données localisées
[Termes IGN] canalisation
[Termes IGN] Chine
[Termes IGN] eau pluviale
[Termes IGN] écoulement des eaux
[Termes IGN] occupation du sol
[Termes IGN] réseau de drainage
[Termes IGN] simulation spatiale
[Termes IGN] utilisation du solRésumé : (auteur) This study presents a water accumulation diffusion algorithm to spatially simulate rainstorm-induced waterlogging for people’s lives and property safety. Taking part of Jinfeng District in Yinchuan City, China, as a study area, a storm water management model (SWMM) model is constructed with the aid of geographic information system (GIS) and remote sensing (RS) technologies. GIS is used to divide sub-catchments, generalize drainage system, set parameters, construct spatial geodatabase, and identify flood extents and depths. RS is used to obtain land-use/land-cover information. The water accumulation diffusion algorithm is then designed using the strategies of the dynamic interactions between pipes and surface and between central pixel and its neighbourhood pixels to transform water accumulation volume of sub-catchment into the submerged range and water accumulation depth. Positions, extents, depths, and volumes of water accumulation from pipe network and surface are simulated, respectively. The spatial simulation precisions of rainstorm waterlogging from the pipe network and surface are verified according to the measured and cyber rainstorm data, respectively. The results show that (1) the number of water accumulation nodes increases with the increase of rainfall intensity; (2) urban waterlogging is mainly distributed in the intersects of roads, low depressions and the aged drainage networks; and (3) spatial simulation of urban rainstorm waterlogging based on the GIS, RS, and SWMM techniques and the water accumulation diffusion algorithm is reliable. The results can provide decision-makings to predict rainstorm waterlogging, design drainage network, and construct a sponge city. Numéro de notice : A2020-566 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2019.1707719 Date de publication en ligne : 06/01/2020 En ligne : https://doi.org/10.1080/19475705.2019.1707719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95891
in Geomatics, Natural Hazards and Risk > vol 11 n° 1 (2020) . - pp 71 - 87[article]Water level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
[article]
Titre : Water level prediction from social media images with a multi-task ranking approach Type de document : Article/Communication Auteurs : P. Chaudhary, Auteur ; Stefano D'Aronco, Auteur ; João P. Leitão, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 252 - 262 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] inondation
[Termes IGN] niveau hydrostatique
[Termes IGN] régression
[Termes IGN] réseau social
[Termes IGN] surveillance hydrologique
[Termes IGN] vision par ordinateurRésumé : (auteur) Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DeepFlood, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with 11 cm root mean square error. Numéro de notice : A2020-549 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.003 Date de publication en ligne : 29/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95776
in ISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 252 - 262[article]Réservation
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