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Assessment of groundwater potential using multi-criteria decision analysis and geoelectrical surveying / Marzieh Shabani in Geo-spatial Information Science, vol 25 n° 4 (December 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 4 (December 2022) . - pp 600 - 618 Titre : Assessment of groundwater potential using multi-criteria decision analysis and geoelectrical surveying Type de document : Article/Communication Auteurs : Marzieh Shabani, Auteur ; Zohreh Masoumi, Auteur ; Abolfazl Rezaei, Auteur Année de publication : 2022 Article en page(s) : pp 600 - 618 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse de sensibilité [Termes IGN] analyse multicritère [Termes IGN] bassin hydrographique [Termes IGN] carte thématique [Termes IGN] développement durable [Termes IGN] eau souterraine [Termes IGN] gestion de l'eau [Termes IGN] Iran [Termes IGN] processus de hiérarchisation analytique [Termes IGN] système d'information géographique Résumé : (auteur) A precise map of the dispersion of the groundwater potential across each watershed can help decision-makers to exert optimal water management in each region. In this research, the potential of groundwater resources in both the Zanjanrood Catchment and the Tarom Region, located in the northwest of Iran, has been studied. Seven effective criteria including slope, land-use, drainage density, spring density, lithology, lineament density, and rainfall are considered. Criteria were first weighted using the Analytical Hierarchical Process (AHP) method and then overlaid by the Technique for Order Preferences by Similarity to Ideal (TOPSIS) model. Finally, the spatial zoning map of groundwater potential was obtained in four categories. A sensitivity analysis was performed to determine the influence of each criterion on the obtained map. The model was verified using both the spatial distribution of the high-discharged production wells and the geophysical-based geoelectric field surveys. The results indicate that the high-discharged wells (>40 l/s) in both regions are dispersed predominantly in the very good zone and, in several cases, in the good zone. Besides, the results from the two-dimensional models of resistivity and induced polarization of geoelectrical field survey are inappropriate agreement with those from the TOPSIS method. Notably, there is no suitable potential zone of groundwater in the surrounding highlands to be used in the future for drinking purposes since the highlands water supply is a strategic supply for drinking. The strategies employed in this study, the results of GIS modeling, and the geoelectrical analysis can be considered for sustainable development of similar arid and semi-arid areas since groundwater is considered as the main supplier of water in such regions. Numéro de notice : A2022-891 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10095020.2022.2069052 Date de publication en ligne : 10/05/2022 En ligne : https://doi.org/10.1080/10095020.2022.2069052 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102238 [article]

A semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) / Masoud Azad in Applied geomatics, vol 14 n° 4 (December 2022)  

Public [article]  inApplied geomatics > vol 14 n° 4 (December 2022) . - pp 589 - 607 Titre : A semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) Type de document : Article/Communication Auteurs : Masoud Azad, Auteur ; Farshid Farnood Ahmadi, Auteur Année de publication : 2022 Article en page(s) : pp 589 - 607 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] extraction de la végétation [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction semi-automatique [Termes IGN] image aérienne [Termes IGN] Iran [Termes IGN] modèle numérique de terrain [Termes IGN] segmentation d'image [Termes IGN] seuillage [Termes IGN] Toronto [Termes IGN] zone urbaine Résumé : (auteur) In this article, a new feature detection approach based on integration of LiDAR data and visible images in the form of a semi-automatic method has been proposed. In this approach, a two-step method for feature detection was developed using object-based analysis in order to increase the level of automation and level of accuracy in the detection process. The first step is providing a method for integration of two data sources for detection process by maintaining independency between image data and LiDAR altimetric data. In this step, the feature detection process is started based on image data and for detecting areas that detection properly is not done, LiDAR altimetric data is used. In the second step, a new method for detection of vegetation is implemented. Of the characteristics of this method is that there is no need to use the infrared band in the image data and also there is no need for LiDAR intensity data. The implemented method in the recent step is based on the new indices developed for detection of vegetation using three visible bands (red, green, and blue). The results of applying the method on two sample data sets show that the proposed approach and developed indices have the lowest dependency on the type and region of imaging and about each input image data includes visible bands (red, green, and blue) along with LiDAR data (that both data have a high spatial resolution), feature detection process is done with acceptable accuracy. Only thresholds depend on image data and change about different images. The changes are very small. Therefore, using the mean of these thresholds, despite may not be optimal for all image data, but generally is useful and for different images is efficient. In the case of many accessible images from Iran, the thresholds determined optimally by the trial-and-error method, the changes were very small. About the image data of Toronto and Iran which great changes were expected in the thresholds, the optimal thresholds showed very small changes. The results of this research demonstrated that the proposed method can successfully detect urban features (include vegetation, road, and building) with different shapes. Evaluation process showed that the overall accuracy, kappa coefficient, producer’s accuracy, and user’s accuracy of the proposed method about vegetation are 97%, 92%, 96%, and 94%, respectively. Also, the producer’s accuracy, user’s accuracy, and kappa coefficient about the building class are 94%, 95%, and 91%, respectively. About the road class these parameters are 95%, 89%, and 91%. Numéro de notice : A2022-892 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-022-00455-x Date de publication en ligne : 10/08/2022 En ligne : https://doi.org/10.1007/s12518-022-00455-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102239 [article]

GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK / Morteza Pourreza in Forests, vol 13 n° 11 (November 2022)  

Public [article]  inForests > vol 13 n° 11 (November 2022) . - n° 1905 Titre : GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK Type de document : Article/Communication Auteurs : Morteza Pourreza, Auteur ; Fardin Moradi, Auteur ; Mohammad Khosravi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1905 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] cupressus (genre) [Termes IGN] diamètre des arbres [Termes IGN] hauteur de vol [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] Iran [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] point d'appui [Termes IGN] positionnement cinématique en temps réel [Termes IGN] structure-from-motion Résumé : (auteur) One of the main challenges of using unmanned aerial vehicles (UAVs) in forest data acquisition is the implementation of Ground Control Points (GCPs) as a mandatory step, which is sometimes impossible for inaccessible areas or within canopy closures. This study aimed to test the accuracy of a UAV-mounted GNSS RTK (real-time kinematic) system for calculating tree height and crown height without any GCPs. The study was conducted on a Cupressus arizonica (Greene., Arizona cypress) plantation on the Razi University Campus in Kermanshah, Iran. Arizona cypress is commonly planted as an ornamental tree. As it can tolerate harsh conditions, this species is highly appropriate for afforestation and reforestation projects. A total of 107 trees were subjected to field-measured dendrometric measurements (height and crown diameter). UAV data acquisition was performed at three altitudes of 25, 50, and 100 m using a local network RTK system (NRTK). The crown height model (CHM), derived from a digital surface model (DSM), was used to estimate tree height, and an inverse watershed segmentation (IWS) algorithm was used to estimate crown diameter. The results indicated that the means of tree height obtained from field measurements and UAV estimation were not significantly different, except for the mean values calculated at 100 m flight altitude. Additionally, the means of crown diameter reported from field measurements and UAV estimation at all flight altitudes were not statistically different. Root mean square error (RMSE Numéro de notice : A2022-838 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13111905 Date de publication en ligne : 12/11/2022 En ligne : https://doi.org/10.3390/f13111905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102039 [article]

Tidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran / Kourosh Shahryari Nia in Marine geodesy, vol 45 n° 6 (November 2022)  

Public [article]  inMarine geodesy > vol 45 n° 6 (November 2022) . - pp 645 - 669 Titre : Tidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran Type de document : Article/Communication Auteurs : Kourosh Shahryari Nia, Auteur ; Mohammad Ali Sharifi, Auteur ; Saeed Farzaneh, Auteur Année de publication : 2022 Article en page(s) : pp 645 - 669 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse harmonique [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données marégraphiques [Termes IGN] Iran [Termes IGN] marée océanique [Termes IGN] modèle de simulation [Termes IGN] niveau de la mer [Vedettes matières IGN] Altimétrie Résumé : (auteur) Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low. Numéro de notice : A2022-783 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2022.2116615 Date de publication en ligne : 28/08/2022 En ligne : https://doi.org/10.1080/01490419.2022.2116615 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101880 [article]

Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 3 (October 2022) . - pp 399 - 41 Titre : Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran Type de document : Article/Communication Auteurs : Sahand Seraj, Auteur ; Mahmoud Reza Delavar, Auteur Année de publication : 2022 Article en page(s) : pp 399 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] cartographie géologique [Termes IGN] classification floue [Termes IGN] entropie de Shannon [Termes IGN] forage [Termes IGN] granulométrie (pétrologie) [Termes IGN] hydrocarbure [Termes IGN] incertitude géométrique [Termes IGN] Iran [Termes IGN] prospection minérale [Termes IGN] sous ensemble flou [Termes IGN] système d'information géographique Résumé : (auteur) It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock, reservoir rock, trap and seal rock. In order to overcome such attributes with uncertainties, a number of soft computing methods are used. Information granules could be provided by the Rough Fuzzy Set Granulation (RFSG) with a thorough quality evaluation. This is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon systems. This paper is an endeavor to recommend a Geospatial Information System (GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular computing. The model used the RFSG for the attribute reduction by a Decision Logic language (DL-language). The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of uncertainty. In order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification, the fuzzy entropy and fuzzy cross-entropy are applied. The proposed RFSG model applied for 62 structures as the training data, average fuzzy entropy has been calculated as 0.85, whereas the average fuzzy cross-entropy has been calculated 0.18. As it can be discerned, just seven structures had cross-entropies greater than 0.1, while three structures were larger than 0.3. It is implied that the precision of the proposed model is about 89%. The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values. Numéro de notice : A2022-724 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10095020.2021.2020600 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1080/10095020.2021.2020600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101667 [article]

Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])  

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Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)  

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A boundary-based ground-point filtering method for photogrammetric point-cloud data / Seyed Mohammad Ayazi in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)  

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Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])  

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The influence of data density and integration on forest canopy cover mapping using Sentinel-1 and Sentinel-2 time series in Mediterranean oak forests / Vahid Nasiri in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

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A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])  

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Spatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran / Naeim Mijani in Transactions in GIS, vol 26 n° 2 (April 2022)  

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Flood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (March 2022)  

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Multi-parameter risk mapping of Qazvin aquifer by classic and fuzzy clustering techniques / Saman Javadi in Geocarto international, vol 37 n° 4 ([15/02/2022])  

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Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (2022)  

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