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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)
[article]
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] analyse spatiale
[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 analytiqueRésumé : (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
in Geo-spatial Information Science > vol 25 n° 4 (December 2022) . - pp 600 - 618[article]Bathymetry and benthic habitat mapping in shallow waters from Sentinel-2A imagery: A case study in Xisha islands, China / Wei Huang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)
[article]
Titre : Bathymetry and benthic habitat mapping in shallow waters from Sentinel-2A imagery: A case study in Xisha islands, China Type de document : Article/Communication Auteurs : Wei Huang, Auteur ; Jun Zhao, Auteur ; Bin Ai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4212412 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bathymétrie
[Termes IGN] carte thématique
[Termes IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] fond marin
[Termes IGN] habitat d'espèce
[Termes IGN] image hyperspectrale
[Termes IGN] image Sentinel-MSI
[Termes IGN] profondeur
[Termes IGN] réflectance spectraleRésumé : (auteur) Mapping of benthic habitats and bathymetry is crucial for sustainable development and assessment of climate change and human activities. In this study, Hyperspectral Optimization Process Exemplar (HOPE) was modified, renamed as M-HOPE, to simultaneously obtain bathymetry and benthic habitat in shallow waters in Xisha Island, China. A local lookup table (LUT) for benthic reflectance spectra was established. Validation using in situ measurements demonstrated good performance of M-HOPE with a R2 of 0.76 for bathymetry using the local LUT. Application of M-HOPE to Sentinel-2A imagery further proved good accuracy of M-HOPE derived bathymetry with a R2 of 0.86 against in situ observations and a R2 of 0.92 against ICESat-2 measurements. M-HOPE-derived benthic classification also agreed well with field observations with probability of detection (POD) >0.6 and false alarm ratio (FAR) Numéro de notice : A2022-907 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3229029 Date de publication en ligne : 14/12/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3229029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102338
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 12 (December 2022) . - n° 4212412[article]Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery / Thomas A. Lake in Remote sensing in ecology and conservation, vol 8 n° 6 (December 2022)
[article]
Titre : Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery Type de document : Article/Communication Auteurs : Thomas A. Lake, Auteur ; Ryan D. Briscoe Runquist, Auteur ; David A. Moeller, Auteur Année de publication : 2022 Article en page(s) : pp 875 - 889 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] espèce exotique envahissante
[Termes IGN] image Worldview
[Termes IGN] PlanetScope
[Termes IGN] série temporelleRésumé : (auteur) Effective management of invasive species requires rapid detection and dynamic monitoring. Remote sensing offers an efficient alternative to field surveys for invasive plants; however, distinguishing individual plant species can be challenging especially over geographic scales. Satellite imagery is the most practical source of data for developing predictive models over landscapes, but spatial resolution and spectral information can be limiting. We used two types of satellite imagery to detect the invasive plant, leafy spurge (Euphorbia virgata), across a heterogeneous landscape in Minnesota, USA. We developed convolutional neural networks (CNNs) with imagery from Worldview-2 and Planetscope satellites. Worldview-2 imagery has high spatial and spectral resolution, but images are not routinely taken in space or time. By contrast, Planetscope imagery has lower spatial and spectral resolution, but images are taken daily across Earth. The former had 96.1% accuracy in detecting leafy spurge, whereas the latter had 89.9% accuracy. Second, we modified the CNN for Planetscope with a long short-term memory (LSTM) layer that leverages information on phenology from a time series of images. The detection accuracy of the Planetscope LSTM model was 96.3%, on par with the high resolution, Worldview-2 model. Across models, most false-positive errors occurred near true populations, indicating that these errors are not consequential for management. We identified that early and mid-season phenological periods in the Planetscope time series were key to predicting leafy spurge. Additionally, green, red-edge and near-infrared spectral bands were important for differentiating leafy spurge from other vegetation. These findings suggest that deep learning models can accurately identify individual species over complex landscapes even with satellite imagery of modest spatial and spectral resolution if a temporal series of images is incorporated. Our results will help inform future management efforts using remote sensing to identify invasive plants, especially across large-scale, remote and data-sparse areas. Numéro de notice : A2023-033 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.288 En ligne : https://doi.org/10.1002/rse2.288 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102295
in Remote sensing in ecology and conservation > vol 8 n° 6 (December 2022) . - pp 875 - 889[article]A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples / Ali Jamali in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)
[article]
Titre : A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples Type de document : Article/Communication Auteurs : Ali Jamali, Auteur ; Masoud Mahdianpari, Auteur ; fariba Mohammadimanesh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103095 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Canada
[Termes IGN] carte thématique
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] réseau antagoniste génératif
[Termes IGN] zone humideRésumé : (auteur) Wetlands have long been recognized among the most critical ecosystems globally, yet their numbers quickly diminish due to human activities and climate change. Thus, large-scale wetland monitoring is essential to provide efficient spatial and temporal insights for resource management and conservation plans. However, the main challenge is the lack of enough reference data for accurate large-scale wetland mapping. As such, the main objective of this study was to investigate the efficient deep-learning models for generating high-resolution and temporally rich training datasets for wetland mapping. The Sentinel-1 and Sentinel-2 satellites from the European Copernicus program deliver radar and optical data at a high temporal and spatial resolution. These Earth observations provide a unique source of information for more precise wetland mapping from space. The second objective was to investigate the efficiency of vision transformers for complex landscape mapping. As such, we proposed a 3D Generative Adversarial Network (3D GAN) to best achieve these two objectives of synthesizing training data and a Vision Transformer model for large-scale wetland classification. The proposed approach was tested in three different study areas of Saint John, Sussex, and Fredericton, New Brunswick, Canada. The results showed the ability of the 3D GAN to stimulate and increase the number of training data and, as a result, increase the accuracy of wetland classification. The quantitative results also demonstrated the capability of jointly using data augmentation, 3D GAN, and Vision Transformer models with overall accuracy, average accuracy, and Kappa index of 75.61%, 73.4%, and 71.87%, respectively, using a disjoint data sampling strategy. Therefore, the proposed deep learning method opens a new window for large-scale remote sensing wetland classification. Numéro de notice : A2022-828 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103095 Date de publication en ligne : 08/11/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103095 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102012
in International journal of applied Earth observation and geoinformation > vol 115 (December 2022) . - n° 103095[article]Groundwater Potential zone mapping: Integration of multi-criteria decision analysis (MCDA) and GIS techniques for the Al-Qalamoun region in Syria / Imad Alrawi in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)
[article]
Titre : Groundwater Potential zone mapping: Integration of multi-criteria decision analysis (MCDA) and GIS techniques for the Al-Qalamoun region in Syria Type de document : Article/Communication Auteurs : Imad Alrawi, Auteur ; Jianping Chen, Auteur ; Arsalan Ahmed Othman, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 603 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse multicritère
[Termes IGN] carte hydrogéologique
[Termes IGN] carte thématique
[Termes IGN] eau souterraine
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] Syrie
[Termes IGN] système d'information géographiqueRésumé : (auteur) One of the most critical processes for the long-term management of groundwater resources is Groundwater Potential Zonation (GWPZ). Despite their importance, traditional groundwater studies are costly, difficult, complex, and time-consuming. This study aims to investigate GWPZ mapping for the Al-Qalamoun region, in the Western part of Syria. We combined the Multi-Influence Factor (MIF) and Analytic Hierarchy Process (AHP) methods with the Geographic Information Systems (GIS) to estimate the GWPZ. The weight and score factors of eight factors were used to develop the GWPZ including drainage density, lithology, slope, lineament density, geomorphology, land use/land cover, rainfall, and soil. According to the findings, about 46% and 50.6% of the total area of the Al-Qalamoun region was classified as suitable for groundwater recharge by the AHP and MIF methods, respectively. However, 54% and 49.4% of the area was classified as having poor suitability for groundwater recharge by the AHP and MIF methods, respectively. These areas with poor suitability can be utilized for gathering surface water. The validation of the results showed that the AHP and MIF methods have similar accuracy for the GWPZ; however, the accuracy and results depend on influencing factors and their weights assigned by experts. Numéro de notice : A2022-902 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11120603 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.3390/ijgi11120603 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102288
in ISPRS International journal of geo-information > vol 11 n° 12 (December 2022) . - n° 603[article]Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)PermalinkPremier atlas IGN des cartes de l’anthropocène / Jean-Pierre Maillard in XYZ, n° 173 (décembre 2022)PermalinkTesting of a new way of cadastral maps renewal in Slovakia / Peter Kyseľ in Geodetski vestnik, vol 66 n° 4 (December 2022 - February 2023)PermalinkUpdating and backdating analyses for mitigating uncertainties in land change modeling: a case study of the Ci Kapundung upper water catchment area, Java Island, Indonesia / Medria Shekar Rani in International journal of geographical information science IJGIS, vol 36 n° 12 (December 2022)PermalinkWall-to-wall mapping of forest biomass and wood volume increment in Italy / Francesca Giannetti in Forests, vol 13 n° 12 (December 2022)PermalinkAutomatic vectorization of fluvial corridor features on historical maps to assess riverscape changes / Samuel Dunesme in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)PermalinkBeyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? / Arthur Sanguet in Global ecology and conservation, vol 39 (November 2022)PermalinkExploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)PermalinkLessons learned from using historical maps to create a digital gazetteer of historical places / Mark Polczynski in International journal of cartography, vol 8 n° 3 (November 2022)PermalinkMapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)Permalink