Descripteur
Documents disponibles dans cette catégorie (5469)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Integrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques / Hamid Karimi in Geocarto international, vol 36 n° 3 ([15/02/2021])
![]()
[article]
Titre : Integrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques Type de document : Article/Communication Auteurs : Hamid Karimi, Auteur ; Hossein Zeinivand, Auteur Année de publication : 2021 Article en page(s) : pp 320 - 339 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] carte hydrographique
[Termes IGN] combinaison linéaire ponderée
[Termes IGN] couche thématique
[Termes IGN] eau pluviale
[Termes IGN] écoulement des eaux
[Termes IGN] étang
[Termes IGN] Iran
[Termes IGN] modèle hydrographique
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] ruissellementRésumé : (auteur) Rainwater harvesting (RWH) is one of the major techniques that is investigated in the present study using Analytic Hierarchy Process (AHP) and Weighted Linear Combination (WLC) methods as two tools for decision-making, weighting and combining different thematic layers include land use, slope, drainage density and hydrological soil groups (HSG). The runoff map obtained by the distributed spatial-physical WetSpa model is considered as a useful layer that is integrated with other thematic layers in the geographic information system (GIS) environment for identifying RWH sites. Kakareza watershed (1132 km2) in Iran was selected as a study area to carry out the foregoing approach. The results showed that 256 km2 of the study area is good for RWH, 360 km2 is moderate and 516 km2 is poor. Thus, about 22.61% (256 km2) of Kakareza watershed is highly suitable for farm ponds. This article recommends the RWH suitable sites to a judicious decision for better water management in the area. Numéro de notice : A2021-141 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1608590 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/10106049.2019.1608590 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97037
in Geocarto international > vol 36 n° 3 [15/02/2021] . - pp 320 - 339[article]An improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards / Geraldo Moura Ramos Filho in Natural Hazards, Vol 105 n° 3 (February 2021)
![]()
[article]
Titre : An improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards Type de document : Article/Communication Auteurs : Geraldo Moura Ramos Filho, Auteur ; Victor Hugo Rabelo Coelho, Auteur ; Emerson da Silva Freitas, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2409 - 2429 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] crue
[Termes IGN] Indice de précipitations antérieures
[Termes IGN] indice de risque
[Termes IGN] inondation
[Termes IGN] méthode robuste
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] Sao Paulo
[Termes IGN] seuillage
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) This paper presents an improved method of using threshold of peak rainfall intensity for robust flood/flash flood evaluation and warnings in the state of São Paulo, Brazil. The improvements involve the use of two tolerance levels and the delineating of an intermediate threshold by incorporating an exponential curve that relates rainfall intensity and Antecedent Precipitation Index (API). The application of the tolerance levels presents an average increase of 14% in the Probability of Detection (POD) of flood and flash flood occurrences above the upper threshold. Moreover, a considerable exclusion (63%) of non-occurrences of floods and flash floods in between the two thresholds significantly reduce the number of false alarms. The intermediate threshold using the exponential curves also exhibits improvements for almost all time steps of both hydrological hazards, with the best results found for floods correlating 8-h peak intensity and 8 days API, with POD and Positive Predictive Value (PPV) values equal to 81% and 82%, respectively. This study provides strong indications that the new proposed rainfall threshold-based approach can help reduce the uncertainties in predicting the occurrences of floods and flash floods. Numéro de notice : A2020-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04405-x Date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1007/s11069-020-04405-x Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97167
in Natural Hazards > Vol 105 n° 3 (February 2021) . - pp 2409 - 2429[article]Assessment of mass-induced sea level variability in the Tropical Indian Ocean based on GRACE and altimeter observations / Shiva Shankar Manche in Journal of geodesy, vol 95 n° 2 (February 2021)
![]()
[article]
Titre : Assessment of mass-induced sea level variability in the Tropical Indian Ocean based on GRACE and altimeter observations Type de document : Article/Communication Auteurs : Shiva Shankar Manche, Auteur ; Rabindra K. Nayak, Auteur ; Prakash Chandra Mohanty, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse harmonique
[Termes IGN] changement climatique
[Termes IGN] données altimétriques
[Termes IGN] données GRACE
[Termes IGN] Indien (océan)
[Termes IGN] masse d'eau
[Termes IGN] modèle océanographique
[Termes IGN] niveau de la mer
[Termes IGN] surcharge océanique
[Termes IGN] variabilité
[Termes IGN] variation saisonnièreRésumé : (auteur) Assessment of mass-induced sea level (MISL) variability in the Tropical Indian Ocean (TIO) was studied using observations from the Gravity Recovery and Climate Experiment (GRACE) during 2003–2017 in conjunction with the steric effects in the sea level anomaly as measured by satellite altimeters. Two steric sea levels were estimated from the ocean model analysis and Argo gridded temperature and salinity fields. These datasets were consistent with each other and to the altimeter measured sea level records. They exhibited a coherent seasonal cycle with unique spatial patterns of amplitude maxima associated with annual and semi-annual harmonics. Steric component remained as a major contributor to the sea level variability at all the time scales. Addition of the GRACE measured MISL to the steric sea level improved the estimation of sea level (as measured by satellite altimeter) over most part of the TIO except over the northern part of the Arabian Sea. It was observed that the MISL had a significant contribution to the sea level variability at intra-seasonal and seasonal time scales and a minor contribution to the sea level inter-annual variability. During all the El Niño years, sea level underwent a large fluctuation coherent to the steric component. A linear barotropic vortex conservation model driven by ocean surface winds explained a major part of the observed MISL high-frequency variability in the Equatorial and southern TIO, and overestimated the observation in the northern TIO. Numéro de notice : A2021-137 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01471-2 Date de publication en ligne : 31/01/2021 En ligne : https://doi.org/10.1007/s00190-021-01471-2 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97010
in Journal of geodesy > vol 95 n° 2 (February 2021) . - n° 19[article]Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
![]()
[article]
Titre : Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach Type de document : Article/Communication Auteurs : Frank S. Marzano, Auteur ; Michele Iacobelli, Auteur ; Massimo Orlandi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 915 - 928 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Adriatique, mer
[Termes IGN] bathymétrie
[Termes IGN] chlorophylle
[Termes IGN] correction atmosphérique
[Termes IGN] couleur de l'océan
[Termes IGN] eaux côtières
[Termes IGN] image Sentinel-MSI
[Termes IGN] incertitude spectrale
[Termes IGN] matière organique
[Termes IGN] Méditerranée, mer
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Recent optical remote sensing satellite missions, such as Sentinel-2 with the MultiSpectral Imager (MSI) onboard, allow the estimation of coastal water key parameters with very high spatial resolutions (down to 10 m). In this article, multiple approaches are proposed for retrieving chlorophyll-a (Chl-a) and total suspended matter (TSM) along the Adriatic and Tyrrhenian coasts in Italy, using both empirical and model-based frameworks to design regressive and neural network (NN) estimation methods. The latter proves to be more accurate on a regional scale, where standard ocean color physical models exhibit high uncertainty in their local parameterization due to the complex spectral characteristics of the observed scene. Retrieval results are encouraging for Chl-a with a coefficient of determination R2 up to 0.72 with a root-mean-square error (RMSE) of 0.33 mg m−3 , using an empirical NN. The TSM algorithms exhibit higher uncertainty, mainly due to scarcity of in situ measurements and model parameterizations, with R2=0.52 and RMSE = 1.95 g/m 3 using NNs. The bio-optical model, used for the development of model-based algorithms, shows some inadequacies in representing the inherent and apparent optical properties for the case study areas, especially considering the different spectral features between the oligotrophic Tyrrhenian Sea and the eutrophic Adriatic Sea. This study confirms the potential of Sentinel-2 MSI products for coastal water monitoring, but it also highlights key issues to be further tackled such as the atmospheric correction impact, the need of reliable in situ measurements, and possible bathymetry effects near the shores. Numéro de notice : A2021-110 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2980941 Date de publication en ligne : 09/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2980941 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96912
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 915 - 928[article]A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
![]()
[article]
Titre : A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Zhice Fang, Auteur ; Yi Wang, Auteur ; Ling Peng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 321 - 347 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] effondrement de terrain
[Termes IGN] géomorphologie locale
[Termes IGN] pondération
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal récurrent
[Termes IGN] risque naturelRésumé : (auteur) This study introduces four heterogeneous ensemble-learning techniques, that is, stacking, blending, simple averaging, and weighted averaging, to predict landslide susceptibility in Yanshan County, China. These techniques combine several state-of-the-art classifiers of convolutional neural network, recurrent neural network, support vector machine, and logistic regression in specific ways to produce reliable results and avoid problems with the model selection. The study consists of three main steps. The first step establishes a spatial database consisting of 16 landslide conditioning factors and 380 historical landslide locations. The second step randomly selects training (70% of the total) and test (30%) datasets out of grid cells corresponding to landslide and non-slide locations in the study area. The final step constructs the proposed heterogeneous ensemble-learning methods for landslide susceptibility mapping. The proposed ensemble-learning methods show higher prediction accuracy than the individual classifiers mentioned above based on statistical measures. The blending ensemble-learning method achieves the highest overall accuracy of 80.70% compared to the other ensemble-learning methods. Numéro de notice : A2021-028 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808897 Date de publication en ligne : 15/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808897 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96704
in International journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 321 - 347[article]A dynamic bidirectional coupled surface flow model for flood inundation simulation / Chunbo Jiang in Natural Hazards and Earth System Sciences, Vol 21 n° 2 (February 2021)
PermalinkG-band radar for humidity and cloud remote sensing / Ken B. Cooper in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
PermalinkGeo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])
PermalinkGeomorphology and (palaeo-)hydrography of the Southern Atbai plain and western Eritrean Highlands (Eastern Sudan/Western Eritrea) / Stefano Costanzo in Journal of maps, vol 17 n° 2 (February 2021)
PermalinkA GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand / Narissara Nuthammachot in Geocarto international, vol 36 n° 2 ([01/02/2021])
PermalinkA GIS-based system for spatial-temporal availability evaluation of the open spaces used as emergency shelters: The case of Victoria, British Columbia, Canada / Yibing Yao in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)
PermalinkIWV retrieval from ground GNSS receivers during NAWDEX / Pierre Bosser in Advances in geosciences, vol 55 ([01/02/2021])
PermalinkLong-term tree species population dynamics in Swiss forest reserves influenced by forest structure and climate / Amanda S. Mathys in Forest ecology and management, vol 481 (February 2021)
PermalinkMonitoring the coastal changes of the Po river delta (Northern Italy) since 1911 using archival cartography, multi-temporal aerial photogrammetry and LiDAR data: implications for coastline changes in 2100 A.D. / Massimo Fabris in Remote sensing, Vol 13 n° 3 (February 2021)
PermalinkOptimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)
Permalink