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A water identification method basing on grayscale Landsat 8 OLI images / Zhitian Deng in Geocarto international, vol 35 n° 7 ([15/05/2020])
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Titre : A water identification method basing on grayscale Landsat 8 OLI images Type de document : Article/Communication Auteurs : Zhitian Deng, Auteur ; Yonghua Sun, Auteur ; Ke Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 700 - 710 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] Chine
[Termes descripteurs IGN] correction atmosphérique
[Termes descripteurs IGN] détection de contours
[Termes descripteurs IGN] eau de surface
[Termes descripteurs IGN] image Landsat-OLI
[Termes descripteurs IGN] image multibande
[Termes descripteurs IGN] Kappa de Cohen
[Termes descripteurs IGN] niveau de gris (image)
[Termes descripteurs IGN] Normalized Difference Water Index
[Termes descripteurs IGN] ressources en eauRésumé : (auteur) Accurate identification of water boundaries is of great significance to water resources surveys. Most water indexes have been designed for different districts and cannot be universally utilized in different regions and, in addition, they rely on atmospheric correction. A new water index, None-Radiation-Calibration Water Index (NRCWI), was constructed by Landsat OLI Band 3 (Green), Band 5 (NIR), and Band 6 (SWIR1), and was compared to the previous method, Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI). We evaluated the accuracy of four water index methods for classifying water in 30-m resolution Landsat 8 OLI imagery from the Bohai Sea Rim in China, which takes in a broad assortment of features including sea and coastline, lakes, rivers, man-made water features, and mountains (shadow water). The following outcomes were obtained: 1. The overall accuracy of NRCWI was 95.23%, which is higher than NDWI, MNDWI, AWEI; 2. The leakage error of NRCWI was 5.48%, the misclassification error was 6.15%, and it implies that the error of NRCWI was effected decrease; 3. NRCWI had the highest kappa coefficient in lakes, rivers, man-made waters, mountains, and other ground features, which means that the method can reach a high accuracy in case 2 water which is principally situated in the near shore, estuary and so on; 4. In the applicability study, the kappa values of NRCWI were 89.99% (OLI), 87.36% (ETM+), 87.33% (TM), and 81.20% (Sentinel-2 MSI). Overall, the NRCWI method performed the best, with the highest accuracy and the lowest leakage error, which may be useful in OLI, ETM+, and TM imagery. Numéro de notice : A2020-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1552324 date de publication en ligne : 14/06/2019 En ligne : https://doi.org/10.1080/10106049.2018.1552324 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95056
in Geocarto international > vol 35 n° 7 [15/05/2020] . - pp 700 - 710[article]A review of assessment methods for cellular automata models of land-use change and urban growth / Xiaohua Tong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
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Titre : A review of assessment methods for cellular automata models of land-use change and urban growth Type de document : Article/Communication Auteurs : Xiaohua Tong, Auteur ; Yongjiu Feng, Auteur Année de publication : 2020 Article en page(s) : pp 866 - 898 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] analyse de sensibilité
[Termes descripteurs IGN] analyse du paysage
[Termes descripteurs IGN] automate cellulaire
[Termes descripteurs IGN] changement d'occupation du sol
[Termes descripteurs IGN] croissance urbaine
[Termes descripteurs IGN] dynamique de la végétation
[Termes descripteurs IGN] dynamique spatiale
[Termes descripteurs IGN] Kappa de Cohen
[Termes descripteurs IGN] matrice
[Termes descripteurs IGN] modèle de simulation
[Termes descripteurs IGN] population urbaine
[Termes descripteurs IGN] propagation d'erreurRésumé : (auteur) Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using ‘cellular automata’, ‘land’ and ‘urban’ as keywords. Our review demonstrates that, during the past two decades, 89% of the publications include model assessment related to dataset, procedure and result using more than ten different methods. Among all methods, cell-by-cell comparison and landscape analysis were most frequently applied in the CA model assessment; specifically, overall accuracy and standard Kappa coefficient respectively rank first and second among all metrics. The end-state assessment is often criticized by modelers because it cannot adequately reflect the modeling ability of CA models. We provide five suggestions to the method selection, aiming to offer a background framework for future method choices as well as urging to focus on the assessment of input data and error propagation, procedure, quantitative and spatial change, and the impact of driving factors. Numéro de notice : A2020-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1684499 date de publication en ligne : 05/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1684499 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94880
in International journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 866 - 898[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020051 SL Revue Centre de documentation Revues en salle Disponible An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping / Qi Zhou in Transactions in GIS, Vol 23 n° 6 (November 2019)
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Titre : An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping Type de document : Article/Communication Auteurs : Qi Zhou, Auteur ; Xuecan Jia, Auteur ; Hao Lin, Auteur Année de publication : 2019 Article en page(s) : pp 1420 - 1443 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes descripteurs IGN] analyse des correspondances
[Termes descripteurs IGN] carte d'occupation du sol
[Termes descripteurs IGN] cartographie collaborative
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] jeu de données localisées
[Termes descripteurs IGN] Kappa de Cohen
[Termes descripteurs IGN] OpenStreetMap
[Termes descripteurs IGN] panel de référence
[Termes descripteurs IGN] précision de la classification
[Termes descripteurs IGN] zone tamponRésumé : (auteur) OpenStreetMap (OSM) provides free source data for land use and land cover (LULC) mapping of many regions globally. Earlier work has used just manual and subjective approaches to establish correspondence between paired OSM and reference datasets, an essential step for LULC mapping. This study proposes an approach to establish correspondence via three steps: (1) convert line feature(s) into polygon feature(s); (2) merge multiple polygon feature(s) into a single layer; and (3) establish correspondence and reclassify OSM and/or reference datasets. Study areas in Sheffield, London, Rome, and Paris were used for testing, and two measures (overall accuracy, OA and kappa index) were used for evaluation. Experiments were designed to verify this approach, with each pair of OSM and reference datasets initially compared after reclassification. Correspondence from one study area was then applied to another for further validation. Results show that OA was between 70 and 90% and the kappa index varied between 0.6 and 0.8. Evaluation also indicates that the correspondence obtained from one study area is applicable to another, and we illustrate the effectiveness of this approach. Numéro de notice : A2019-568 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12581 date de publication en ligne : 03/10/2019 En ligne : https://doi.org/10.1111/tgis.12581 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94420
in Transactions in GIS > Vol 23 n° 6 (November 2019) . - pp 1420 - 1443[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)
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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 descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] apprentissage dirigé
[Termes descripteurs IGN] étalonnage de modèle
[Termes descripteurs IGN] hydrocarbure
[Termes descripteurs IGN] image aérienne
[Termes descripteurs IGN] image hyperspectrale
[Termes descripteurs IGN] image infrarouge
[Termes descripteurs IGN] image proche infrarouge
[Termes descripteurs IGN] Israël
[Termes descripteurs IGN] Kappa de Cohen
[Termes descripteurs IGN] pétrole
[Termes descripteurs IGN] photo-interprétation
[Termes descripteurs IGN] pollution des sols
[Termes descripteurs IGN] réflectance du sol
[Termes descripteurs IGN] spectroscopieRésumé : (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
in International journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 15 p.[article]Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata / Tingting Xu in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)
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Titre : Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Jay Gao, Auteur ; Giovanni Coco, Auteur Année de publication : 2019 Article en page(s) : pp 1960 - 1983 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] analyse diachronique
[Termes descripteurs IGN] Auckland
[Termes descripteurs IGN] automate cellulaire
[Termes descripteurs IGN] base de données d'occupation du sol
[Termes descripteurs IGN] chaîne de Markov
[Termes descripteurs IGN] classification par réseau neuronal
[Termes descripteurs IGN] croissance urbaine
[Termes descripteurs IGN] étalement urbain
[Termes descripteurs IGN] Kappa de Cohen
[Termes descripteurs IGN] modèle de simulation
[Termes descripteurs IGN] morphologie urbaine
[Termes descripteurs IGN] optimisation (mathématiques)Résumé : (auteur) Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone. Numéro de notice : A2019-428 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1600701 date de publication en ligne : 05/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1600701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93561
in International journal of geographical information science IJGIS > vol 33 n° 10 (October 2019) . - pp 1960 - 1983[article]Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments / Leigh Tait in Remote sensing, vol 11 n° 19 (October 2019)
PermalinkDetecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
PermalinkTemporal accuracy in urban growth forecasting : a study using the SLEUTH model / Gargi Chaudhuri in Transactions in GIS, vol 18 n° 2 (April 2014)
PermalinkA multiresolution hierarchical classification algorithm for filtering airborne LiDAR data / Chuanfa Chen in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)
PermalinkGraph-based feature selection for object-oriented classification in VHR airborne imagery / T. Chen in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)
PermalinkConsistency of accuracy assessment indices for soft classification: simulation analysis / J. Chen in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 2 (March - April 2010)
PermalinkLand-cover change detection using one-class support vector machine / P. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 3 (March 2010)
PermalinkEvaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)
PermalinkAn improved fuzzy Kappa statistic that accounts for spatial autocorrelation / A. Hagen-Zanker in International journal of geographical information science IJGIS, vol 23 n° 1-2 (january 2009)
PermalinkA multi-directional ground filtering algorithm for airborne LIDAR / X. Meng in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 1 (January - February 2009)
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