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Spatial distribution of lead (Pb) in soil: a case study in a contaminated area of the Czech Republic / Nicolas Francos in Geomatics, Natural Hazards and Risk, vol 13 (2022)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 610 - 620 Titre : Spatial distribution of lead (Pb) in soil: a case study in a contaminated area of the Czech Republic Type de document : Article/Communication Auteurs : Nicolas Francos, Auteur ; Asa Gholizadeh, Auteur ; Eyal Ben-Dor, Auteur Année de publication : 2022 Article en page(s) : pp 610 - 620 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] autocorrélation spatiale [Termes IGN] contamination [Termes IGN] distribution spatiale [Termes IGN] image infrarouge [Termes IGN] interpolation spatiale [Termes IGN] krigeage [Termes IGN] plomb [Termes IGN] qualité du sol [Termes IGN] République Tchèque Résumé : (auteur) For decades, the Příbram district in the Czech Republic has been affected by industrial and mining activities, which are the main sources of heavy metal pollutants and negatively affect soil quality. A recent study examined visible–near-infrared (VNIR), shortwave-infrared (SWIR), and X-ray fluorescence (XRF) spectroscopy to model soil lead (Pb) content in a selected area located in Příbram. Following that study, and using the data, we examined the spatial distribution of Pb content in the soil, with a combination of traditional techniques (Moran’s I, hotspot analysis, and Kriging). One of the novel points of this work is the use of the Getis–Ord hotspot analysis before the execution of Kriging interpolation to better emphasize clustering patterns. The results indicated that Pb was a spatially dependent soil property and through extensive in-situ sampling, it was possible to generate an accurate interpolation model. The high-Pb hotspots coincided with topographic obstacles that were modeled using topographic profiles extracted from Google Earth, indicating that Pb content does not always exhibit a direct relationship with topographic height as a result of runoff, due to the contribution of topographic steps. This observation provides a new perspective on the relationship between Pb content and topographic patterns. Numéro de notice : A2022-872 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2039786 Date de publication en ligne : 23/02/2022 En ligne : https://doi.org/10.1080/19475705.2022.2039786 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102166 [article]

A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 15 p. Titre : A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing Type de document : Article/Communication Auteurs : Ran Pelta, Auteur ; Nimrod Carmon, Auteur ; Eyal Ben-Dor, Auteur Année de publication : 2019 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] étalonnage de modèle [Termes IGN] hydrocarbure [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] image proche infrarouge [Termes IGN] Israël [Termes IGN] Kappa de Cohen [Termes IGN] pétrole [Termes IGN] photo-interprétation [Termes IGN] pollution des sols [Termes IGN] réflectance du sol [Termes IGN] spectroscopie Résumé : (auteur) One of the most ubiquitous and detrimental types of environmental contamination in the world is crude oil pollution. When released into either the aquatic or terrestrial environments, this pollution can negatively impact flora and fauna, as well as human health. Hence, a rapid and affordable spatial assessment of the pollution is favored to limit the spill’s effects. Using airborne hyperspectral remote sensing (HRS) for crude oil detection in terrestrial areas has been investigated in previous studies, which mainly relied on heavily oiled artificial samples. These studies and others based their methodologies on the premise that the spectral features of petroleum hydrocarbon (PHC) are clearly observable, which might not be true in all cases. In this study, we aimed at assessing the true potential of using HRS for terrestrial oil spill mapping in a real disaster site in southern Israel, where laboratory and controlled conditions do not apply. Using the AISA SPECIM Fenix1 K sensor, we collected airborne image of the study site and analyzed the data with advanced data mining techniques. Various challenges and limitations arose from the airborne HRS image being taken two and a half years after the crude oil had been released into the environment and exposed to the surface. Here, no spectral features of PHC were detectable in the spectrum, preventing the use of PHC indices and spectral methods developed by others. Nevertheless, by using standardization techniques, vicarious band selection, dimension reduction, multivariate calibration, and supervised machine-learning, we were able to successfully distinguish between contaminated pixels from non-contaminated ones. Classification accuracy metrics of overall accuracy, sensitivity, specificity, and Kappa yielded good results of 0.95, 0.95, 0.95 and 0.9, respectively, for cross-validation, and 0.93, 0.91, 0.94 and 0.85, for the validation dataset. Classified image and test scenes also showed strong agreement with an orthophoto image taken several days after the disaster, when the pollution was clearly visible. Thus, we conclude that HRS technology can detect PHC traces in an oil spill site, even under the most challenging conditions. Numéro de notice : A2019-475 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.101901 Date de publication en ligne : 22/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.101901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93636 [article]

vol III-7 - July 2016 - [actes] XXIII ISPRS Congress, Commission 7, 12–19 July 2016, Prague, Czech Republic (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Lena Halounova  

Public [n° ou bulletin]  est un bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences / International society for photogrammetry and remote sensing (1980 -) (2012 - )   Titre : vol III-7 - July 2016 - [actes] XXIII ISPRS Congress, Commission 7, 12–19 July 2016, Prague, Czech Republic Type de document : Périodique Auteurs : Lena Halounova, Éditeur scientifique ; F. Sunar, Éditeur scientifique ; Markéta Potůčková, Éditeur scientifique ; L. Patková, Éditeur scientifique ; M. Yoshimura, Éditeur scientifique ; Uwe Soergel, Éditeur scientifique ; Eyal Ben-Dor, Éditeur scientifique ; J. Smit, Éditeur scientifique ; G. Bareth, Éditeur scientifique ; J. Zhang, Éditeur scientifique ; S. Kaasalainen, Éditeur scientifique ; Batuhan Osmanoglu, Éditeur scientifique ; M. Crespi, Éditeur scientifique ; Michele Crosetto, Éditeur scientifique ; Thomas Blaschke, Éditeur scientifique ; Maria Antonia Brovelli, Éditeur scientifique ; B. Zagajewski, Éditeur scientifique Congrès : ISPRS 2016, 23th international congress (12 - 19 juillet 2016; Prague, République tchèque), Auteur Année de publication : 2016 Langues : Français (fre) Numéro de notice : sans Affiliation des auteurs : non IGN Nature : Numéro de périodique En ligne : http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/index.html Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=27508 [n° ou bulletin]

Contient

• Spectral band selection for urban material classification using hyperspectral libraries / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-7 (July 2016)  

Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (Smile) in Hyperion Images / A. Dadon in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 6 (June 2010) . - pp 2603 - 2612 Titre : Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (Smile) in Hyperion Images Type de document : Article/Communication Auteurs : A. Dadon, Auteur ; Eyal Ben-Dor, Auteur ; A. Karnieli, Auteur Année de publication : 2010 Article en page(s) : pp 2603 - 2612 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] filtrage du bruit [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale Résumé : (Auteur) Earth Observing-1 Hyperion data were found to be relatively noisy and to contain significant cross-track spectral curvature nonlinearity disturbances, known as the smile/frown effect. A method for the correction of spectral curvature effects (smile) in Hyperion images, termed trend line smile correction (TLSC), is presented. The method is based on the assumption that there is a partial correlation between data spectral nonuniformity, due to the smile and eigenvalues gradient that mostly appears in the first minimum noise fraction (MNF) image (MNF-1). However, MNF-1 consists of both spatial and spectral information. Therefore, it is hypothesized that adaptation applied to MNF-1, according to exclusively spectrally derived parameters (e.g., atmospheric absorption features) can account specifically for the smile effect in the data. A set of normalization factors, calculated from the spectral derivative at the right-hand side of the O2 absorption feature (760 nm), MNF-1 and the moderate-resolution atmospheric transmittance radiative transfer model, are used to scale the initial MNF-1. The image is corrected after the inverse conversion of the MNF to radiance space. The methodology was tested on four different Hyperion scenes and consistently outperformed other tested methods by up to nine times. As a result, thematic mapping, using the TLSC-corrected reflectance data cube, was shown to be consistent with the geology maps of the study area. Numéro de notice : A2010-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2040391 En ligne : https://doi.org/10.1109/TGRS.2010.2040391 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30388 [article]

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A digital camera as a tool to measure colour indices and related properties of sandy soils in semi-arid environments / N. Levin in International Journal of Remote Sensing IJRS, vol 26 n° 24 (December 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 24 (December 2005) . - pp 5475 - 5492 Titre : A digital camera as a tool to measure colour indices and related properties of sandy soils in semi-arid environments Type de document : Article/Communication Auteurs : N. Levin, Auteur ; Eyal Ben-Dor, Auteur ; A. Singer, Auteur Année de publication : 2005 Article en page(s) : pp 5475 - 5492 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] caméra numérique [Termes IGN] composition chimique [Termes IGN] couleur du sol [Termes IGN] étalonnage des données [Termes IGN] fer [Termes IGN] image RVB [Termes IGN] particule [Termes IGN] sable [Termes IGN] spectromètre [Termes IGN] zone semi-aride Résumé : (Auteur) Soil colour carries important information regarding the soil's chemical and physical properties. However, common practices for measuring soil colour, either by Munsell charts or by field/laboratory spectrometers, are insufficient, due to the subjective and nonquantitative character of the Munsell charts, and to the high cost and inconvenience of field spectrometers. We present herein, a method to characterize the colour of soil samples, and related chemical and physical properties of the soil, using a digital camera, and an array of coloured plastic chips, that are used for calibration purposes. Using 370 samples of sandy soils, we have demonstrated that both RGB values from digital images and their derived soil indices, correlate highly with similar measurements performed by a field spectrometer. When checked against free iron oxide content and against the percentage of fine particles in a subsample set of 42 soils, the redness index as measured by the digital camera gave similar or better correlations than those obtained from a field spectrometer, against both free iron oxides and fine particle contents (R2 of 89% for the iron oxides, and of for the fine particles). We propose the use of a digital camera as a field analytical tool to determine precisely: soil colour, iron oxide and fine particle content. Further study in this direction, with other soil population and more soil properties, is strongly advised in order to launch this as a vastly applicable and generic method. Numéro de notice : A2005-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500099444 En ligne : https://doi.org/10.1080/01431160500099444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27658 [article]

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Topographic information of sand dunes as extracted from shading effects using Landsat images / N. Levin in Remote sensing of environment, vol 90 n° 2 (30/03/2004)  

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