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Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation / Muhamad Farid Ramli in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 195 - 208 Titre : Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation Type de document : Article/Communication Auteurs : Muhamad Farid Ramli, Auteur ; Khairul Nizam Tahar, Auteur Année de publication : 2020 Article en page(s) : pp 195 - 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] Arecaceae [Termes IGN] croissance des arbres [Termes IGN] diamètre des arbres [Termes IGN] hauteur des arbres [Termes IGN] houppier [Termes IGN] image captée par drone [Termes IGN] Malaisie [Termes IGN] modèle numérique de surface [Termes IGN] orthoimage [Termes IGN] pas d'échantillonnage au sol [Termes IGN] point d'appui [Termes IGN] positionnement cinématique en temps réel [Termes IGN] QGIS [Termes IGN] SAGA GIS [Termes IGN] segmentation en régions Résumé : (auteur) The demand for tree height derivation is increasing year by year, especially for large plantation and forest area. The conventional method needs a long time to complete tree measurement for large forest area, especially when using a pole, measuring tape, rangefinder, clinometer, and tree climbing. This study aims to evaluate the height of oil palm tree based on crown diameter by using a multi-rotor Unmanned Aerial Vehicle (UAV). Digital Elevation Model (DEM) and orthophoto were generated by using Agisoft software, while oil palm tree crown diameter was delineated by using seed generation with Quantum Geographic Information System (QGIS) and Seeded Region Growing (SRG) segmentation methods in the System for Automated Geoscientific Analysis (SAGA). The study validates the results between the actual tree height and tree height estimated from UAV. The results showed that the orthophoto was successfully generated with a Ground Sampling Distance (GSD) of 2.95 cm and 129 tree crowns were successfully analyzed. The accuracy of the tree height as compared to the actual measurement was 57.7 cm. In conclusion, UAV images are capable of determining the tree height after going through the correct procedure to help foresters in their daily task. Numéro de notice : A2020-562 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1805366 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1080/10095020.2020.1805366 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95878 [article]

Local color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? / Istvan G. Lauko in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 222 - 236 Titre : Local color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? Type de document : Article/Communication Auteurs : Istvan G. Lauko, Auteur ; Adam Honts, Auteur ; Jacob Beihoff, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 222 - 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte de la végétation [Termes IGN] cartographie urbaine [Termes IGN] couleur (variable spectrale) [Termes IGN] densité de la végétation [Termes IGN] extraction de la végétation [Termes IGN] gestion urbaine [Termes IGN] image panoramique [Termes IGN] image Streetview [Termes IGN] indicateur environnemental [Termes IGN] indice de végétation [Termes IGN] Milwaukee [Termes IGN] paysage urbain [Termes IGN] rayonnement proche infrarouge Résumé : (auteur) Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems. These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy, but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover. Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View (GSV) images, made accessible by the Google Street View Image API. Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density, and it facilitates an analysis performed at the street-level. In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index. We deployed this image processing method and, using GSV images as a high-resolution GIS data source, we computed and mapped the green index of Milwaukee County, a 3,082 km2 urban/suburban county in Wisconsin. This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management, as well as for researchers investigating the correlation between environmental factors and human health outcomes. Numéro de notice : A2020-563 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1805367 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1080/10095020.2020.1805367 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95880 [article]

Measuring accessibility of bus system based on multi-source traffic data / Yufan Zuo in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 248 - 257 Titre : Measuring accessibility of bus system based on multi-source traffic data Type de document : Article/Communication Auteurs : Yufan Zuo, Auteur ; Zhiyuan Liu, Auteur ; Xiao Fu, Auteur Année de publication : 2020 Article en page(s) : pp 248 - 257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accessibilité [Termes IGN] approche holistique [Termes IGN] données multisources [Termes IGN] données spatiotemporelles [Termes IGN] modélisation spatio-temporelle [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] Shenzhen [Termes IGN] trafic routier [Termes IGN] transport collectif Résumé : (auteur) Accessibility is a representative indicator for evaluating the supply of bus system. Traditional studies have evaluated the accessibility from different aspects. Considering the interaction among land use, bus timetable arrangement and individual factors, a more holistic accessibility measurement is proposed to combine static and dynamic characteristics from multisource traffic data. The rationale of the proposed model is verified by a case study of bus system in Shenzhen, China, which is carried out to find the spatial and temporal discrepancy of service of bus system. It is found that the adjustment of bus schedule to time-varying travel demand can affect accessibility of bus system and that Land-use development, average bus speed and bus facilities all have positive effects on accessibility of bus system. These findings provide significant reference for transport planning and policy-making. The proposed model is not limited to accessibility measuring of bus system, but also applicable to other travel modes. Numéro de notice : A2020-564 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1783189 Date de publication en ligne : 24/07/2020 En ligne : https://doi.org/10.1080/10095020.2020.1783189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95881 [article]