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Dynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)
[article]
Titre : Dynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Qimin Cheng, Auteur ; Fang Luo, Auteur Année de publication : 2022 Article en page(s) : pp 171 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] consommation
[Termes IGN] densité de population
[Termes IGN] éclairage public
[Termes IGN] électricité
[Termes IGN] étalement urbain
[Termes IGN] image DMSP-OLS
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] Pakistan
[Termes IGN] prise de vue nocturne
[Termes IGN] urbanisationRésumé : (auteur) This article proposes an estimation method for assessing urban sprawl using multispectral remote sensing data: SNPP-VIIRS, DMSP/OLS, Landsat 5-TM, and Landsat 8-OLI. This study focuses on the impacts of human activities, in terms of increased electrical-power consumption (EPC) due to urbanization. For this purpose, night-time light data are used to measure the EPC growth from 2000 to 2020. We also perform a suitability analysis using geographic information-systems techniques to propose a new urban town in Lahore to mitigate urbanization and EPC increase. We found an overall increase of 33% in urban area and an EPC increase of 21.6% in the last two decades. We also find that the best proposed site for the new urban town is in the northwest of Lahore. Numéro de notice : A2022-201 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00026R3 Date de publication en ligne : 01/03/2022 En ligne : https://doi.org/10.14358/PERS.21-00026R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100004
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 3 (March 2022) . - pp 171 - 179[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022031 SL Revue Centre de documentation Revues en salle Disponible Estimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image / Sinan Kaptan in Geocarto international, vol 37 n° 5 ([01/03/2022])
[article]
Titre : Estimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image Type de document : Article/Communication Auteurs : Sinan Kaptan, Auteur ; Hasan Aksoy, Auteur Année de publication : 2022 Article en page(s) : pp 1408 - 1425 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] correction géométrique
[Termes IGN] forêt inéquienne
[Termes IGN] houppier
[Termes IGN] image Landsat-OLI
[Termes IGN] occupation du sol
[Termes IGN] peuplement forestier
[Termes IGN] Turquie
[Termes IGN] utilisation du solRésumé : (Auteur) This study used the Landsat 8 OLI satellite image and the supervised classification method to estimate uneven-aged forest stand parameters and land use/cover. The spatial success of classification was also investigated. The overall success rates and Kappa values of the classification were, respectively, 74.7% and 0.75 for actual structural type, 84.6% and 0.80 for crown closure, and 88.35% and 0.81 for land use class, whereas the spatial success of classification on the forest cover type map was 36.91% for actual structural type, 64.74% for crown closure, and 41.78% for land use/cover class. The results revealed that the Landsat 8 OLI image can be used to identify stand parameters and land use/cover class. However, because the spatial success rates were below 50% for the actual structural type and land use/cover class of the stand types, it is not suitable for use in spatial classification determination for these classes. Numéro de notice : A2022-277 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1765888 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1080/10106049.2020.1765888 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100795
in Geocarto international > vol 37 n° 5 [01/03/2022] . - pp 1408 - 1425[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022051 RAB Revue Centre de documentation En réserve L003 Disponible Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])
[article]
Titre : Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Fumin Wang, Auteur ; Adam Sheka Kanu, Auteur ; Lingbo Yang, Auteur Année de publication : 2022 Article en page(s) : pp 1225 - 1236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image Sentinel-SAR
[Termes IGN] jeu de données localisées
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] polarisation
[Termes IGN] rizièreRésumé : (Auteur) Three Sentinel-1A datasets in vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisations, and the linear combination of VH and VV (VHVV) are evaluated for rice green leaf area index (LAI) estimation using four machine learning regression models [Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT)]. Results showed that for the entire growing season, VV outperformed VH, recording an R2 of 0.68 and an RMSE of 0.98 m2/m2 with the k-NN model. However, VHVV produced the most accurate estimates with GBDT (R2 of 0.82 and RMSE of 0.68 m2/m2), followed by that of VHVV with RF (R2 of 0.78 and RMSE of 0.90 m2/m2). Our findings have further confirmed that combining VH and VV data can achieve improved rice growth modelling, and that tree-based algorithms can better handle data dimensionality. Numéro de notice : A2022-274 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1773545 Date de publication en ligne : 05/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1773545 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100753
in Geocarto international > vol 37 n° 5 [01/03/2022] . - pp 1225 - 1236[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022051 RAB Revue Centre de documentation En réserve L003 Disponible Exploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China / Zhen Li in Sustainable Cities and Society, vol 78 (March 2022)
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Titre : Exploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China Type de document : Article/Communication Auteurs : Zhen Li, Auteur ; Dan Hu, Auteur Année de publication : 2022 Article en page(s) : n° 103392 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Bâti-3D
[Termes IGN] classification et arbre de régression
[Termes IGN] corrélation
[Termes IGN] données localisées 2D
[Termes IGN] hauteur du bâti
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-OLI
[Termes IGN] milieu urbain
[Termes IGN] morphologie urbaine
[Termes IGN] Pékin (Chine)
[Termes IGN] saison
[Termes IGN] température au solRésumé : (auteur) With rapid urbanization, urban three-dimensional morphology and its ecological effects have received more attention. However, thorough investigations into the multiple scale impact of the 2D/3D architectural morphology on urban land surface temperature (LST) remain limited. Taking Beijing as a case study area, we quantified the contributions of the 2D/3D architectural morphology indicators and revealed their marginal effects on multiple scales using the boosted regression trees (BRT) method. The results showed that (1) the building coverage ratio and building height were the most significant factors influencing the LST across all spatial scales and seasons, (2) the 3D shape index, 3D fractal, and 3D adjacency were found to be influential factors, with sum contributions varying from 6.0% to 37.7%, and (3) in summer, the 3D shape index showed a stepwise negative correlation with the LST. The 3D fractal and 3D adjacency exhibited both positive and negative correlations with the LST. When the spatial scale was 240 m, the regulation amplitudes for the 3D shape index, 3D fractal, and 3D adjacency were 2.0°C, 1.0°C and 1.0°C, respectively. These findings provide quantitative insights that can be used to improve urban thermal environments and achieve sustainable urban development by adjusting architectural morphology. Numéro de notice : A2022-242 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2021.103392 Date de publication en ligne : 28/12/2021 En ligne : https://doi.org/10.1016/j.scs.2021.103392 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100169
in Sustainable Cities and Society > vol 78 (March 2022) . - n° 103392[article]Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning / Jun Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)
[article]
Titre : Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning Type de document : Article/Communication Auteurs : Jun Xu, Auteur ; Jiasong Li, Auteur ; Hao Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 199 - 205 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification barycentrique
[Termes IGN] distance de Kullback-Leibler
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image Worldview
[Termes IGN] masque
[Termes IGN] occupation du sol
[Termes IGN] segmentation d'image
[Termes IGN] segmentation multi-échelle
[Termes IGN] séparateur à vaste margeRésumé : (auteur) In object-oriented information extraction from high-resolution remote sensing images, the segmentation and classification of images involves considerable manual participation, which limits the development of automation and intelligence for these purposes. Based on the multi-scale segmentation strategy and case-based reasoning, a new method for extracting high-resolution remote sensing image information by fully using the image and nonimage features of the case object is proposed. Feature selection and weight learning are used to construct a multi-level and multi-layer case library model of surface cover classification reasoning. Combined with image mask technology, this method is applied to extract surface cover classification information from remote sensing images using different sensors, time, and regions. Finally, through evaluation of the extraction and recognition rates, the accuracy and effectiveness of this method was verified. Numéro de notice : A2022-202 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00104R3 Date de publication en ligne : 01/03/2022 En ligne : https://doi.org/10.14358/PERS.20-00104R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100006
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 3 (March 2022) . - pp 199 - 205[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022031 SL Revue Centre de documentation Revues en salle Disponible Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques / J.O. Ondieki in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkFlood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (March 2022)PermalinkLand surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkMonitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images / Omer Gokberk Narin in Geocarto international, vol 37 n° 5 ([01/03/2022])PermalinkA novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)PermalinkSimultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)PermalinkAboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkA national fuel type mapping method improvement using sentinel-2 satellite data / Alexandra Stefanidou in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkSimulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkAn open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)Permalink