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Groundwater vulnerability assessment of the chalk aquifer in the northern part of France / Lahcen Zouhri in Geocarto international, vol 36 n° 11 ([01/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [01/07/2021] . - pp 1193 - 1216 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] vulnérabilité Résumé : (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 [article]

A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases / Chun Yang in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 38 - 56 Titre : A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases Type de document : Article/Communication Auteurs : Chun Yang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 38 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] Allemagne [Termes IGN] apprentissage profond [Termes IGN] approche hiérarchique [Termes IGN] classification automatique d'objets [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image aérienne [Termes IGN] jointure [Termes IGN] objet géographique [Termes IGN] occupation du sol [Termes IGN] optimisation (mathématiques) [Termes IGN] utilisation du sol Résumé : (Auteur) Land use as contained in geospatial databases constitutes an essential input for different applications such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to verify the land use information. For this purpose, a two-step strategy is applied. First, given high-resolution aerial images, the land cover information is determined. To achieve this, an encoder-decoder based convolutional neural network (CNN) is proposed. Second, the pixel-wise land cover information along with the aerial images serves as input for another CNN to classify land use. Because the object catalogue of geospatial databases is frequently constructed in a hierarchical manner, we propose a new CNN-based method aiming to predict land use in multiple levels hierarchically and simultaneously. A so called Joint Optimization (JO) is proposed where predictions are made by selecting the hierarchical tuple over all levels which has the maximum joint class scores, providing consistent results across the different levels. The conducted experiments show that the CNN relying on JO outperforms previous results, achieving an overall accuracy up to 92.5%. In addition to the individual experiments on two test sites, we investigate whether data showing different characteristics can improve the results of land cover and land use classification, when processed together. To do so, we combine the two datasets and undertake some additional experiments. The results show that adding more data helps both land cover and land use classification, especially the identification of underrepresented categories, despite their different characteristics. Numéro de notice : A2021-370 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.022 date de publication en ligne : 13/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97774 [article]

An incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 445 - 455 Titre : An incremental isomap method for hyperspectral dimensionality reduction and classification Type de document : Article/Communication Auteurs : Yi Ma, Auteur ; Zezhong Zheng, Auteur ; Yutang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 445 - 455 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] classification barycentrique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] échantillonnage de données [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] squelettisation [Termes IGN] utilisation du sol Résumé : (Auteur) Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We present in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature variation algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine. Numéro de notice : A2021-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.445 date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.7.445 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97829 [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)  

Public [article]  inApplied geomatics > vol 13 n° 2 (June 2021) . - pp 235 - 248 Titre : Detection of suitable sites for rainwater harvesting planning in an arid region using geographic information system Type de document : Article/Communication Auteurs : Hadeel Qays Hashim, Auteur ; Khamis Naba Sayl, Auteur Année de publication : 2021 Article en page(s) : pp 235 - 248 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algèbre de Boole [Termes IGN] analyse multicritère [Termes IGN] barrage [Termes IGN] combinaison linéaire ponderée [Termes IGN] eau pluviale [Termes IGN] Iraq [Termes IGN] MNS ASTER [Termes IGN] occupation du sol [Termes IGN] utilisation du sol [Termes IGN] zone aride Résumé : (auteur) Water is a key natural resource on earth, especially in arid and semi-arid regions with limited rainfall amounts. The impact of drought could be alleviated via constructing dams to ensure water storage and supply. The aim of the present study is to detect proper sites for planning rainwater harvesting (RWH) in the western desert of Iraq using both the Boolean overlay and the weighted linear combination (WLC) in the geographic information system (GIS). Potential sites of rainwater harvesting were identified using multi-criteria evaluation. Several criteria were used, including physical characteristics and climatological and socio-economic conditions to determine the proper location for RWH. Seven WLC parameters were used in the site selection process: runoff, slope, soil texture, land use/land cover (LULC), distance from irrigated lands, distance from residential areas, and distance from roads, while the Boolean overlay method used the stream order and distance from faults parameters. The results indicated that the final map can be classified into three classes of suitability, i.e., (i) highly suitable with 6% coverage (117 km2), (ii) moderately suitable with 4% coverage (78 km2), and (iii) least suitable with 90% coverage (1758 km2) of the basin area. It was indicated that only three earthen dams could be executed along streams. This low data-intensive and cost-effective methodology offered can be adopted in arid regions to embrace RWH as an efficient strategy to handle growing water scarcity. The proposed method could be adopted in many countries that have identical environmental and physical conditions to the western desert of Iraq, which is the case in most arid regions. Numéro de notice : A2021-411 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-020-00342-3 date de publication en ligne : 10/10/2020 En ligne : https://doi.org/10.1007/s12518-020-00342-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97736 [article]

Reference evapotranspiration (ETo) methods implemented as ArcMap models with remote-sensed and ground-based inputs, examined along with MODIS ET, for Peloponnese, Greece / Stavroula Dimitriadou in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 6 (June 2021) . - n° 390 Titre : Reference evapotranspiration (ETo) methods implemented as ArcMap models with remote-sensed and ground-based inputs, examined along with MODIS ET, for Peloponnese, Greece Type de document : Article/Communication Auteurs : Stavroula Dimitriadou, Auteur ; Konstantinos G. Nikolakopoulos, Auteur Année de publication : 2021 Article en page(s) : n° 390 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] ArcMap [Termes IGN] changement climatique [Termes IGN] climat méditerranéen [Termes IGN] évapotranspiration [Termes IGN] Grèce [Termes IGN] image Terra-MODIS [Termes IGN] modèle de Monteith [Termes IGN] système d'information géographique [Termes IGN] température [Termes IGN] utilisation du sol [Termes IGN] variation saisonnière Résumé : (auteur) The present study develops ArcMap models to implement the following three methods: FAO-56 Penman–Monteith (FAO PM), Hargreaves–Samani (HS) and Hansen, with the former used as a reference. Moreover, three models implementing statistical indices (RMSD, MB, NMB) are also created. The purpose is threefold, as follows: to investigate the variability in the daily mean reference evapotranspiration (ETo) for the Decembers and Augusts during 2016–2019, over Peloponnese, Greece. Furthermore, to investigate the agreement between the methods’ ETo estimates, and examine the former along with MODIS ET (daily) averaged products. The study area is a complex Mediterranean area. Meteorological data from sixty-two stations under the National Observatory of Athens (NOA), and MODIS Terra LST products, have been employed. FAO PM is found sensitive to wind speed and depicts interactions among climate parameters (T, evaporative demand and water availability) in the frame of climate change. The years 2016–2019 are four of the warmest since the preindustrial era. Hargreaves–Samani’s estimations for the Decembers of 2016–2019 were almost identical to MODIS ET, despite their different physical meaning. However, for the Augusts there are considerable discrepancies between the methods’ and MODIS’s estimates, attributed to the higher evaporative demand in the summertime. The GIS models are accurate, reliable, time-saving, and adjustable to any study area. Numéro de notice : A2021-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10060390 date de publication en ligne : 05/06/2021 En ligne : https://doi.org/10.3390/ijgi10060390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97959 [article]

The use of land cover indices for rapid surface urban heat island detection from multi-temporal Landsat imageries / Nagihan Aslan in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)  

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Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)  

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Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)  

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Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future / Tamás Gál in Computers, Environment and Urban Systems, vol 87 (May 2021)  

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A Voronoi-based method for land-use optimization using semidefinite programming and gradient descent algorithm / Vorapong Suppakitpaisarn in International journal of geographical information science IJGIS, vol 35 n° 5 (May 2021)  

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Electrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed / H.S. Virupaksha in Geocarto international, vol 36 n° 8 ([15/04/2021])  

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Aggregating land-use polygons considering line features as separating map elements / Sven Gedicke in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)  

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Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm / Siddhartho Shekhar Paul in Geocarto international, vol 36 n° 4 ([01/03/2021])  

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Attribution of the Australian bushfire risk to anthropogenic climate change / Geert Jan Van Oldenborgh in Natural Hazards and Earth System Sciences, vol 21 n° 3 (March 2021)  

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Suitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)  

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