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A framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)
[article]
Titre : A framework to manage uncertainty in the computation of waste collection routes after a flood Type de document : Article/Communication Auteurs : Arnaud Le Guilcher , Auteur ; Sofiane Martel, Auteur ; Mickaël Brasebin , Auteur ; Yann Méneroux , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 4, 24th ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 4 Article en page(s) : pp 61 - 68 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] cadre conceptuel
[Termes IGN] calcul d'itinéraire
[Termes IGN] catastrophe naturelle
[Termes IGN] collecte des déchets
[Termes IGN] discrétisation spatiale
[Termes IGN] incertitude géométrique
[Termes IGN] inondation
[Termes IGN] programmation stochastique
[Termes IGN] variable aléatoireRésumé : (auteur) In this paper, we describe a framework to find a good quality waste collection tour after a flood, without having to solve a complicated optimization problem from scratch in limited time. We model the computation of a waste collection tour as a capacitated routing problem, on the vertices or on the edges of a graph, with uncertain waste quantities and uncertain road availability. Multiple models have been conceived to manage uncertainty in routing problems, and we build on the ideas of discretizing the uncertain parameters and computing master solutions that can be adapted to propose an original method to compute efficient solutions. We first introduce our model for the progressive removal of the uncertainty, then outline our method to compute solutions: our method first considers a low-dimensional set of random variables that govern the behaviour of the problem parameters, discretizes these variables and computes a solution for each discrete point before the flood, and then uses these solutions as a basis to build operational solutions when there are enough information about the parameters of the routing problem. We then give computational tools to implement this method. We give a framework to compute the basis of solutions in an efficient way, by computing all the solutions simultaneously and sharing information (that can lead to good quality solutions) between the different problems based on how close their parameters are, and we also describe how real solutions can be derived from this basis. Our main contributions are our model for the progressive removal of uncertainty, our multi-step method to compute efficient solutions, and our intrusive framework to compute solutions on the discrete grid of parameters. Numéro de notice : A2021-316 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2021-61-2021 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2021-61-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97946
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2021 (July 2021) . - pp 61 - 68[article]Groundwater vulnerability assessment of the chalk aquifer in the northern part of France / Lahcen Zouhri in Geocarto international, vol 36 n° 11 ([15/06/2021])
[article]
Titre : Groundwater vulnerability assessment of the chalk aquifer in the northern part of France Type de document : Article/Communication Auteurs : Lahcen Zouhri, Auteur ; Romain Armand, Auteur Année de publication : 2021 Article en page(s) : pp 1193 - 1216 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de sensibilité
[Termes IGN] aquifère
[Termes IGN] ArcGIS
[Termes IGN] carte hydrogéologique
[Termes IGN] craie
[Termes IGN] eau souterraine
[Termes IGN] Hauts-de-France (région 2016)
[Termes IGN] Oise (60)
[Termes IGN] utilisation du sol
[Termes IGN] vulnérabilitéRésumé : (auteur) This study explores the groundwater vulnerability of the chalk aquifer (northern part of France) using a well-known overlay and index DRASTIC method for intrinsic scenario and using land use (LU) parameter as additional factor. Different sources have allowed to compile data necessary to map the vulnerability of the aquifer under study, which used to generate the seven parameters of DRASTIC, namely: groundwater Depth, groundwater Recharge, lithology, Soil media, Topography, Impact of the vadose zone and hydraulic Conductivity. Applying the model in ArcGIS 10.2 platform leads to identify three classes of vulnerability: low, medium and high vulnerability. The highest DRASTIC indexes appear in areas where the groundwater depth is low and in more permeable unsaturated zones. The LU has a little effect on the distribution of vulnerability classes: this distribution is marked by the low vulnerability 44% against 6.5 of high vulnerability. Numéro de notice : A2021-434 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1637465 Date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1637465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97801
in Geocarto international > vol 36 n° 11 [15/06/2021] . - pp 1193 - 1216[article]Altimétrie laser et surveillance / Laurent Polidori in Géomètre, n° 2192 (juin 2021)
[article]
Titre : Altimétrie laser et surveillance Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2021 Article en page(s) : pp 18-18 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] altimétrie satellitaire par laser
[Termes IGN] biomasse forestière
[Termes IGN] calotte glaciaire
[Termes IGN] changement climatique
[Termes IGN] données ICEsat
[Termes IGN] écosystème forestier
[Termes IGN] fonte des glaces
[Termes IGN] précision altimétrique
[Termes IGN] surveillance forestièreRésumé : (Auteur) Un laser en orbite observe les forêts tropicales et les calottes polaires pour mesurer le réchauffement climatique. Numéro de notice : A2021-376 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 08/06/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97862
in Géomètre > n° 2192 (juin 2021) . - pp 18-18[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2021061 RAB Revue Centre de documentation En réserve L003 Disponible Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])
[article]
Titre : Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery Type de document : Article/Communication Auteurs : Sikdar M. M. Rasel, Auteur ; Hsing-Chung Chang, Auteur ; Timothy J. Ralph, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1075-1099 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] bande spectrale
[Termes IGN] biomasse
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Worldview
[Termes IGN] marais salé
[Termes IGN] modèle de simulation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] variableRésumé : (Auteur) Assessing large scale plant productivity of coastal marshes is essential to understand the resilience of these systems to climate change. Two machine learning approaches, random forest (RF) and support vector machine (SVM) regression were tested to estimate biomass of a common saltmarshes species, salt couch grass (Sporobolus virginicus). Reflectance and vegetation indices derived from 8 bands of Worldview-2 multispectral data were used for four experiments to develop the biomass model. These four experiments were, Experiment-1: 8 bands of Worldview-2 image, Experiment-2: Possible combination of all bands of Worldview-2 for Normalized Difference Vegetation Index (NDVI) type vegetation indices, Experiment-3: Combination of bands and vegetation indices, Experiment-4: Selected variables derived from experiment-3 using variable selection methods. The main objectives of this study are (i) to recommend an affordable low cost data source to predict biomass of a common saltmarshes species, (ii) to suggest a variable selection method suitable for multispectral data, (iii) to assess the performance of RF and SVM for the biomass prediction model. Cross-validation of parameter optimizations for SVM showed that optimized parameter of ɛ-SVR failed to provide a reliable prediction. Hence, ν-SVR was used for the SVM model. Among the different variable selection methods, recursive feature elimination (RFE) selected a minimum number of variables (only 4) with an RMSE of 0.211 (kg/m2). Experiment-4 (only selected bands) provided the best results for both of the machine learning regression methods, RF (R2= 0.72, RMSE= 0.166 kg/m2) and SVR (R2= 0.66, RMSE = 0.200 kg/m2) to predict biomass. When a 10-fold cross validation of the RF model was compared with a 10-fold cross validation of SVR, a significant difference (p = Numéro de notice : A2021-367 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1624988 Date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1624988 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97729
in Geocarto international > vol 36 n° 10 [01/06/2021] . - pp 1075-1099[article]Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)
[article]
Titre : Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 Type de document : Article/Communication Auteurs : Dimitris Poursanidis, Auteur ; Dimosthenis Traganos, Auteur ; Luisa Teixeira, Auteur ; Aurélie Shapiro, Auteur ; Lara Muaves, Auteur Année de publication : 2021 Article en page(s) : pp 275 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] écosystème
[Termes IGN] Google Earth Engine
[Termes IGN] habitat (nature)
[Termes IGN] image Sentinel-MSI
[Termes IGN] Mozambique
[Termes IGN] récif corallien
[Termes IGN] réserve naturelle
[Termes IGN] surveillance écologiqueRésumé : (auteur) The lack of detailed spatial information on coastal resources, notably shallow water coral reefs and associated benthic habitats, impedes our ability to protect and manage them in the face of global climate change and anthropogenic impacts. Here, we develop a semi-automated workflow in the cloud that uses freely available Sentinel-2 data from the European Space Agency (ESA) Copernicus programme to derive information on near-shore coral reef habitats in the Quirimbas National Park (QNP), a recently declared biosphere reserve in northern Mozambique. We use an end-to-end cloud-based framework within the Google Earth Engine cloud geospatial platform to process imagery from raw pixels to cloud-free composites which are corrected for glint and surface artefacts, water column and derived estimated depth and then classified into four benthic habitats. Using independent training and validation data, we apply three supervised classification algorithms: random forests (RF), support vector machine (SVM) and classification and regression trees (CART). Our results show that random forests are the most accurate supervised algorithm with over 82% overall accuracy. We mapped over 105 000 ha of shallow water habitat inside the protected area, of which 18% are dominated by coral and hardbottom; 27.5% are seagrass and submerged aquatic vegetation and another 23.4% are soft and sandy substrates, and the remaining area is optically deep water. We employ satellite-derived bathymetry to assess slope, bathymetric position, rugosity and underwater topography of these habitats. Finally, a spectral unmixing model provides further sub-pixel–level information of habitats with the potential to monitor changes over time. This effort provides the first, consistent and repeatable and also scalable coastal information system for an east African tropical marine protected area, which hosts shallow-water ecosystems which are of great significance to local communities and building resilience towards climate change. Numéro de notice : A2021-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/rse2.187 Date de publication en ligne : 29/11/2020 En ligne : https://doi.org/10.1002/rse2.187 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98679
in Remote sensing in ecology and conservation > vol 7 n° 2 (June 2021) . - pp 275 - 291[article]Detection of suitable sites for rainwater harvesting planning in an arid region using geographic information system / Hadeel Qays Hashim in Applied geomatics, vol 13 n° 2 (June 2021)PermalinkDirect analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia / Peter Kitin in Annals of Forest Science, vol 78 n° 2 (June 2021)PermalinkIdentifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing / Elliott White Jr in Remote sensing of environment, vol 258 (June 2021)PermalinkImproving tree biomass models through crown ratio patterns and incomplete data sources / Maria Menéndez-Miguélez in European Journal of Forest Research, vol 140 n° 3 (June 2021)PermalinkOn the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment / Salahuddin M. Jaber in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkProvisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change / Debojyoti Chakraborty in Annals of Forest Science, vol 78 n° 2 (June 2021)PermalinkRapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkThe social drift of trees. Consequence for growth trend detection, stand dynamics, and silviculture / Hans Pretzsch in European Journal of Forest Research, vol 140 n° 3 (June 2021)PermalinkAnalysing the impact of climate change on hydrological ecosystem services in Laguna del Sauce (Uruguay) using the SWAT model and remote sensing data / Celina Aznarez in Remote sensing, vol 13 n°10 (May-2 2021)PermalinkCanopy openness and exclusion of wild ungulates act synergistically to improve oak natural regeneration / Julien Barrere in Forest ecology and management, Vol 487 ([01/05/2021])Permalink