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Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)
[article]
Titre : Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery Type de document : Article/Communication Auteurs : Jose Alan A. Castillo, Auteur ; Armando A. Apan, Auteur ; Tek N. Maraseni, Auteur ; Severino G. Salmo, Auteur Année de publication : 2017 Article en page(s) : pp 70 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] carte d'utilisation du sol
[Termes IGN] déboisement
[Termes IGN] estimation statistique
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] mangrove
[Termes IGN] modèle de simulation
[Termes IGN] Philippines
[Termes IGN] régression linéaire
[Termes IGN] rétrodiffusion
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82–0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8–28.5 Mg ha−1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery. Numéro de notice : A2017-730 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88428
in ISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 70 - 85[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017121 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017122 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017123 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt A simulation and visualization environment for spatiotemporal disaster risk assessments of network infrastructures / Magnus Heittzler in Cartographica, vol 52 n° 4 (Winter 2017)
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Titre : A simulation and visualization environment for spatiotemporal disaster risk assessments of network infrastructures Type de document : Article/Communication Auteurs : Magnus Heittzler, Auteur ; Juan Carlos Lam, Auteur ; Jürgen Hackl, Auteur ; Bryan T. Adey, Auteur ; Lorenz Hurni, Auteur Année de publication : 2017 Article en page(s) : pp 349 - 363 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] données spatiotemporelles
[Termes IGN] environnement de développement
[Termes IGN] évaluation
[Termes IGN] modèle de simulation
[Termes IGN] réseau technique
[Termes IGN] risque naturel
[Termes IGN] Suisse
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Emerging methodologies for risk assessments of civil infrastructure networks require the coupling of several spatiotemporal models that need to be executed multiple times with varying parametrizations to account for model uncertainty and to investigate “what-if” scenarios. These requirements led to the development of a software environment to support the simulation process and the visual analysis of its results. The simulation engine component of the environment makes it possible to define, couple, and execute models. An embedded infrastructure model facilitates the development of functionality to estimate and aggregate capacity measures of single objects affected by multiple hazards. The simulation manager component can be used to execute multiple instances of the simulation engine conveniently with varying parametrizations. The included visualization tool provides two complementary views. The ensemble view can be used to analyze the data at a highly aggregated level with information visualization techniques and the simulation view can be used to investigate simulations in greater detail via an interactive map window and a state dependency graph. The software environment is used in a risk assessment for the region of Chur, Switzerland, which comprises the simulation of multiple natural hazard scenarios that lead to impaired transport infrastructure capacities and thus to disrupted traffic flows. Numéro de notice : A2017-831 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart.52.4.2017-0009 En ligne : https://doi.org/10.3138/cart.52.4.2017-0009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89367
in Cartographica > vol 52 n° 4 (Winter 2017) . - pp 349 - 363[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2017041 SL Revue Centre de documentation Revues en salle Disponible The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data / Alby D. Rocha in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)
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Titre : The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data Type de document : Article/Communication Auteurs : Alby D. Rocha, Auteur ; Thomas A. Groen, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 61 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] complexité
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robuste
[Termes IGN] modèle de simulation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] précision
[Termes IGN] régression
[Termes IGN] validation des donnéesRésumé : (Auteur) The growing number of narrow spectral bands in hyperspectral remote sensing improves the capacity to describe and predict biological processes in ecosystems. But it also poses a challenge to fit empirical models based on such high dimensional data, which often contain correlated and noisy predictors. As sample sizes, to train and validate empirical models, seem not to be increasing at the same rate, overfitting has become a serious concern. Overly complex models lead to overfitting by capturing more than the underlying relationship, and also through fitting random noise in the data. Many regression techniques claim to overcome these problems by using different strategies to constrain complexity, such as limiting the number of terms in the model, by creating latent variables or by shrinking parameter coefficients. This paper is proposing a new method, named Naïve Overfitting Index Selection (NOIS), which makes use of artificially generated spectra, to quantify the relative model overfitting and to select an optimal model complexity supported by the data. The robustness of this new method is assessed by comparing it to a traditional model selection based on cross-validation. The optimal model complexity is determined for seven different regression techniques, such as partial least squares regression, support vector machine, artificial neural network and tree-based regressions using five hyperspectral datasets. The NOIS method selects less complex models, which present accuracies similar to the cross-validation method. The NOIS method reduces the chance of overfitting, thereby avoiding models that present accurate predictions that are only valid for the data used, and too complex to make inferences about the underlying process. Numéro de notice : A2017-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88407
in ISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 61 - 74[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017112 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017113 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods / Fatemeh Falah in Geocarto international, vol 32 n° 10 (October 2017)
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Titre : Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods Type de document : Article/Communication Auteurs : Fatemeh Falah, Auteur ; Samira Ghorbani Nejad, Auteur ; Omid Rahmati, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1069 - 1089 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse bivariée
[Termes IGN] ArcGIS
[Termes IGN] eau souterraine
[Termes IGN] géostatistique
[Termes IGN] Iran
[Termes IGN] modèle de simulation
[Termes IGN] ressources en eau
[Termes IGN] système d'information géographiqueRésumé : (Auteur) Groundwater is the most valuable natural resource in arid areas. Therefore, any attempt to investigate potential zones of groundwater for further management of water supply is necessary. Hence, many researchers have worked on this subject all around the world. On the other hand, the Generalized Additive Model (GAM) has been applied to environmental and ecological modelling, but its applicability to other kinds of predictive modelling such as groundwater potential mapping has not yet been investigated. Therefore, the main purpose of this study is to evaluate the performance of GAM model and then its comparison with three popular GIS-based bivariate statistical methods, namely Frequency Ratio (FR), Statistical Index (SI) and Weight-of-Evidence (WOE) for producing groundwater spring potential map (GSPM) in Lorestan Province Iran. To achieve this, out of 6439 existed springs, 4291 spring locations were selected for training phase and the remaining 2147 springs for model evaluation. Next, the thematic layers of 12 effective spring parameters including altitude, plan curvature, slope angle, slope aspect, drainage density, distance from rivers, topographic wetness index, fault density, distance from fault, lithology, soil and land use/land cover were mapped and integrated using the ArcGIS 10.2 software to generate a groundwater prospect map using mentioned approaches. The produced GSPMs were then classified into four distinct groundwater potential zones, namely low, moderate, high and very high classes. The results of the analysis were finally validated using the receiver operating characteristic (ROC) curve technique. The results indicated that out of four models, SI is superior (prediction accuracy of 85.4%) following by FR, GAM and WOE, respectively (prediction accuracy of 83.7, 77 and 76.3%). The result of groundwater spring potential map is helpful as a guide for engineers in water resources management and land use planning in order to select suitable areas to implement development schemes and also government entities. Numéro de notice : A2017-669 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.201 Date de publication en ligne : 07/06/2016 En ligne : https://doi.org/10.1080/10106049.2016.1188166 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87144
in Geocarto international > vol 32 n° 10 (October 2017) . - pp 1069 - 1089[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2017101 RAB Revue Centre de documentation En réserve L003 Disponible Modélisation géoprospective et simulation 3D immersive / Jean-Christophe Loubier in Revue internationale de géomatique, vol 27 n° 4 (octobre - décembre 2017)
[article]
Titre : Modélisation géoprospective et simulation 3D immersive Type de document : Article/Communication Auteurs : Jean-Christophe Loubier, Auteur ; Christine Voiron-Canicio, Auteur ; Dominique Genoud, Auteur ; Daniel Hunacek, Auteur ; Florian Sant, Auteur Année de publication : 2017 Article en page(s) : pp 547 - 566 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Alpes-maritimes (06)
[Termes IGN] analyse diachronique
[Termes IGN] approche participative
[Termes IGN] changement d'occupation du sol
[Termes IGN] littoral méditerranéen
[Termes IGN] modèle de simulation
[Termes IGN] participation du public
[Termes IGN] représentation cartographique 3D
[Termes IGN] territoireRésumé : (auteur) Cet article présente la démarche, et les premiers retours d’expérience, d’une recherche destinée à susciter les réflexions et les réactions de différents types d’acteurs sur les transformations futures de leur territoire de vie. La démarche se décompose en deux temps, tout d’abord, réalisation de différents scénarios de changements de l’occupation du sol, avec, associée à chacun d’eux, une représentation paysagère en 3D. Puis, travail en petits groupes dans un atelier participatif. La réflexion collective partagée porte sur les représentations paysagères en 3D des transformations de l’espace, choisies, testées, par les participants, à partir d’une application conçue à cet effet. Le territoire d’étude, situé sur le littoral des Alpes-Maritimes, est un territoire très menacé par l’urbanisation diffuse galopante. Numéro de notice : A2017-835 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3166/rig.2017.00042 En ligne : https://doi.org/10.3166/rig.2017.00042 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89377
in Revue internationale de géomatique > vol 27 n° 4 (octobre - décembre 2017) . - pp 547 - 566[article]Réservation
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