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The "Incense Road" from Petra to Gaza: an analysis using GIS and Cost functions / Motti Zohar in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 2 (February 2020) . - pp 292 - 310 Titre : The "Incense Road" from Petra to Gaza: an analysis using GIS and Cost functions Type de document : Article/Communication Auteurs : Motti Zohar, Auteur ; Tali Erickson-Gini, Auteur Année de publication : 2020 Article en page(s) : pp 292 - 310 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] Arabie [Termes descripteurs IGN] bassin méditerranéen [Termes descripteurs IGN] carte archéologique [Termes descripteurs IGN] carte numérique [Termes descripteurs IGN] chemin le moins coûteux (algorithme) [Termes descripteurs IGN] empreinte [Termes descripteurs IGN] histoire [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] Petra [Termes descripteurs IGN] reconstruction de route [Termes descripteurs IGN] ressources en eau [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) As early as the fourth century BCE, the Nabateans established the Incense Road to facilitate the transport of aromatic substances (frankincense and myrrh) from the Arabian Peninsula to the Mediterranean basin. An important part of this road was the segment between Petra and Gaza. Although studied before, the accurate route of parts of this segment is still vague since evidence of Roman milestones are scarce and significant portions of the landscape have changed dramatically in modern times, essentially wiping out the tracks of ancient roads. In this study, we use Geographic Information Systems (GIS) and Least Cost Path (LCP) analyses for reconstructing the original path of the Incense Road as well as verifying the factors influencing its establishment. The implemented analyses support the archeological evidence of two travel phases between Petra and Oboda (Avdat): During the first phase the Nabateans used the Darb es-Sultan route; during the second phase, from the first century BCE onwards, they passed through the Ramon Crater. This is the first time such reconstruction is made in the southern Levant. It was found that slope degree and the distance to water resources are dominant factors in reconstructing the accurate path of the Incense Road. Numéro de notice : A2020-029 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : https://doi.org/10.1080/13658816.2019.1669795 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94482 [article]

Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey / Alkan Günlü in Geocarto international, Vol 35 n° 1 ([02/01/2020])  

Public [article]  inGeocarto international > Vol 35 n° 1 [02/01/2020] . - pp 17 - 28 Titre : Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; Ilker Erkanli, Auteur Année de publication : 2020 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] carbone [Termes descripteurs IGN] Fagus (genre) [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] Turquie Résumé : (auteur) The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values. Numéro de notice : A2020-017 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1499817 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1499817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94410 [article]

Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner / Kai Tan in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 129 - 139 Titre : Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner Type de document : Article/Communication Auteurs : Kai Tan, Auteur ; Jin Chen, Auteur ; Weiguo Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 129 - 139 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Chine [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données TLS (télémétrie) [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] littoral [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] rayonnement proche infrarouge [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémétrie laser terrestre [Termes descripteurs IGN] teneur en vapeur d'eau [Termes descripteurs IGN] vase Résumé : (Auteur) Estimations of the soil surface water contents and distributions play a key role in the ecological, environmental, and topographical investigations for intertidal mudflats. However, existing techniques have limitations. Long-range terrestrial laser scanners (TLSs) can record the co-located intensity value which refers to a measure of the backscattered laser from each scanned point. Most long-range TLSs emit near-infrared lasers that can be strongly absorbed by water. Thus, the intensity values can be used as proxies for water contents. In this study, the intensity data of long-range TLSs are corrected for the incidence angle and distance effects to quantitatively estimate the soil surface water contents of intertidal mudflats. A case study for a mudflat in Chongming Island, Shanghai, China, is conducted. Results indicate that compared with traditional techniques, the corrected intensity data of long-range TLSs are extremely effective data sources for a quick, accurate, and detailed estimation of water contents for large-area mudflats. The estimation root mean square error is approximately 3%. Furthermore, the 3D distributions of the water contents can be accurately mapped by combining the point cloud of the mudflats to potentially analyze the intrinsic association among water contents and topography, vegetation coverage, and habitation of creatures in mudflats. Numéro de notice : A2020-013 Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.isprsjprs.2019.11.003 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94402 [article]

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A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods / Yongjiu Feng in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 1 (January 2020) . - pp 74 - 97 Titre : A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods Type de document : Article/Communication Auteurs : Yongjiu Feng, Auteur ; Xiaohua Tong, Auteur Année de publication : 2020 Article en page(s) : pp 74 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] dynamique spatiale [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] modèle de Markov [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] Shanghai (Chine) [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) We develop a new geographical cellular automata (CA) modeling framework, named UrbanCA, through reconstructing the essential CA structure and incorporating nonspatial, spatial, and heuristic approaches. The new UrbanCA is featured by 1) the improvement of the CA modeling framework by reformulating relationships among CA components, 2) the development of two scaling parameters to adjust the effects of transition probability and neighborhood, 3) the incorporation of a variety of statistical and heuristic methods to construct transition rules, and 4) the inclusion of urban planning regulations and spatial heterogeneities to project future urban scenarios. To illustrate the effectiveness of UrbanCA, we calibrate a CA model using artificial bee colony (ABC) to simulate the past urban patterns and predict future scenarios in Shanghai of China. The results show that UrbanCA under different scaling parameters is comparable to CA-Markov (as a reference model) concerning the accuracy of the end-state and change simulations, and is better than CA-Markov regarding the driving factor’s ability to explain the modeling outcomes. UrbanCA provides more choices compared to existing CA software packages, and the models are readily calibrated elsewhere to simulate the dynamic urban growth and assess the resulting natural and socioeconomic impacts. Numéro de notice : A2020-008 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1648813 date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1080/13658816.2019.1648813 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94388 [article]

Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n°12 (December 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°12 (December 2019) . - pp 2406 - 2423 Titre : Extracting urban landmarks from geographical datasets using a random forests classifier Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Yuyang Cai, Auteur ; Yue Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2406 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] point de repère [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] représentation mentale spatiale [Termes descripteurs IGN] saillance [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] ville Résumé : (auteur) Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods. Numéro de notice : A2019-426 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1620238 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1620238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93559 [article]

Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

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New method for environmental monitoring in armed conflict zones: a case study of Syria / Samira Mobaied in Environmental Monitoring and Assessment, vol 191 n° 11 (November 2019)  

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Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])  

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Automatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Analysis of higher-order ionospheric effects on GNSS precise point positioning in the China area / Yaozong Zhou in Survey review, vol 51 n° 368 (September 2019)  

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Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey / Baris Suleymanoglu in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

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Evaluation of global geopotential models: a case study for India / Ropesh Goyal in Survey review, vol 51 n° 368 (September 2019)  

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Land-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images / Temulun Tangud in Geocarto international, vol 34 n° 11 ([15/08/2019])  

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Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 / Ali Mokhtari in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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