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A novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December-1 2020)
[article]
Titre : A novel intelligent classification method for urban green space based on high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhiyu Xu, Auteur ; Yi Zhou, Auteur ; Shixin Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 3845 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace vert
[Termes IGN] image à haute résolution
[Termes IGN] image Gaofen
[Termes IGN] milieu urbain
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pékin (Chine)
[Termes IGN] phénologie
[Termes IGN] précision de la classification
[Termes IGN] urbanismeRésumé : (auteur) The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. Numéro de notice : A2020-792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12223845 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/rs12223845 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96565
in Remote sensing > vol 12 n° 22 (December-1 2020) . - n° 3845[article]Urban tree species identification and carbon stock mapping for urban green planning and management / MD Abdul Choudhury in Forests, vol 11 n°11 (November 2020)
[article]
Titre : Urban tree species identification and carbon stock mapping for urban green planning and management Type de document : Article/Communication Auteurs : MD Abdul Choudhury, Auteur ; Ernesto Marcheggiani, Auteur ; Francesca Despini, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : N° 1226 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] arbre urbain
[Termes IGN] cartographie écologique
[Termes IGN] déboisement
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données allométriques
[Termes IGN] données dendrométriques
[Termes IGN] Emilie-Romagne (Italie)
[Termes IGN] gestion urbaine
[Termes IGN] modèle de croissance végétale
[Termes IGN] photo-interprétation assistée par ordinateur
[Termes IGN] planification urbaine
[Termes IGN] puits de carbone
[Termes IGN] structure-from-motion
[Termes IGN] ville durableRésumé : (auteur) Recently, the severe intensification of atmospheric carbon has highlighted the importance of urban tree contributions in atmospheric carbon mitigations in city areas considering sustainable urban green planning and management systems. Explicit and timely information on urban trees and their roles in the atmospheric Carbon Stock (CS) are essential for policymakers to take immediate actions to ameliorate the effects of deforestation and their worsening outcomes. In this study, a detailed methodology for urban tree CS calibration and mapping was developed for the small urban area of Sassuolo in Italy. For dominant tree species classification, a remote sensing approach was applied, utilizing a high-resolution WV3 image. Five dominant species were identified and classified by applying the Object-Based Image Analysis (OBIA) approach with an overall accuracy of 78%. The CS calibration was done by utilizing an allometric model based on the field data of tree dendrometry—i.e., Height (H) and Diameter at Breast Height (DBH). For geometric measurements, a terrestrial photogrammetric approach known as Structure-from-Motion (SfM) was utilized. Out of 22 randomly selected sample plots of 100 square meters (10 m × 10 m) each, seven plots were utilized to validate the results of the CS calibration and mapping. In this study, CS mapping was done in an efficient and convenient way, highlighting higher CS and lower CS zones while recognizing the dominant tree species contributions. This study will help city planners initiate CS mapping and predict the possible CS for larger urban regions to ensure a sustainable urban green management system. Numéro de notice : A2020-757 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11111226 Date de publication en ligne : 21/11/2020 En ligne : https://doi.org/10.3390/f11111226 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96470
in Forests > vol 11 n°11 (November 2020) . - N° 1226[article]Using climate-sensitive 3D city modeling to analyze outdoor thermal comfort in urban areas / Rabeeh Hosseinihaghighi in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)
[article]
Titre : Using climate-sensitive 3D city modeling to analyze outdoor thermal comfort in urban areas Type de document : Article/Communication Auteurs : Rabeeh Hosseinihaghighi, Auteur ; Fatemeh Izadi, Auteur ; Rushikesh Padsala, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 688 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] albedo
[Termes IGN] arbre urbain
[Termes IGN] ArcGIS
[Termes IGN] changement climatique
[Termes IGN] CityEngine
[Termes IGN] climat urbain
[Termes IGN] distribution spatiale
[Termes IGN] gestion urbaine
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle de simulation
[Termes IGN] Montréal (Québec)
[Termes IGN] planification urbaine
[Termes IGN] températureRésumé : (auteur) With increasing urbanization, climate change poses an unprecedented threat, and climate-sensitive urban management is highly demanded. Mitigating climate change undoubtedly requires smarter urban design tools and techniques than ever before. With the continuous evolution of geospatial technologies and an added benefit of analyzing and virtually visualizing our world in three dimensions, the focus is now shifting from a traditional 2D to a more complicated 3D spatial design and assessment with increasing potential of supporting climate-responsive urban decisions. This paper focuses on using 3D city models to calculate the mean radiant temperature (Tmrt) as an outdoor thermal comfort indicator in terms of assessing the spatiotemporal distribution of heat stress on the district scale. The analysis is done to evaluate planning scenarios for a district transformation in Montreal/Canada. The research identifies a systematic workflow to assess and upgrade the outdoor thermal comfort using the contribution of ArcGIS CityEngine for 3D city modeling and the open-source model of solar longwave environmental irradiance geometry (SOLWEIG) as the climate assessment model. A statistically downscaled weather profile for the warmest year predicted before 2050 (2047) is used for climate data. The outcome shows the workflow capacity for the structured recognition of area under heat stress alongside supporting the efficient intervention, the tree placement as a passive strategy of heat mitigation. The adaptability of workflow with the various urban scale makes it an effective response to the technical challenges of urban designers for decision-making and action planning. However, the discovered technical issues in data conversion and wall surface albedo processing call for the climate assessment model improvement as future demand. Numéro de notice : A2020-728 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110688 Date de publication en ligne : 19/11/2020 En ligne : https://doi.org/10.3390/ijgi9110688 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96335
in ISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 688[article]Hierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds / Yongjun Wang in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)
[article]
Titre : Hierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds Type de document : Article/Communication Auteurs : Yongjun Wang, Auteur ; Tengping Jiang, Auteur ; Jing Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 26 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de filtrage
[Termes IGN] apprentissage profond
[Termes IGN] arbre hors forêt
[Termes IGN] arbre urbain
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] gestion urbaine
[Termes IGN] image captée par drone
[Termes IGN] optimisation (mathématiques)
[Termes IGN] reconnaissance d'objets
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] voxel
[Termes IGN] zone urbaineRésumé : (auteur) Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree. Numéro de notice : A2020-665 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9100595 Date de publication en ligne : 10/10/2020 En ligne : https://doi.org/10.3390/ijgi9100595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96142
in ISPRS International journal of geo-information > vol 9 n° 10 (October 2020) . - 26 p.[article]A preliminary exploration of the cooling effect of tree shade in urban landscapes / Qiuyan Yu in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)
[article]
Titre : A preliminary exploration of the cooling effect of tree shade in urban landscapes Type de document : Article/Communication Auteurs : Qiuyan Yu, Auteur ; Wenjie Ji, Auteur ; Ruiliang Pu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 102161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre urbain
[Termes IGN] coefficient de corrélation
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] ilot thermique urbain
[Termes IGN] image thermique
[Termes IGN] modèle numérique de surface
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] occupation du sol
[Termes IGN] ombre
[Termes IGN] paysage urbain
[Termes IGN] réflectance
[Termes IGN] semis de points
[Termes IGN] température au sol
[Termes IGN] ville durableRésumé : (auteur) Mitigating urban heat island (UHI) effects, especially under climate change, is necessary for the promotion of urban sustainability. Shade is one of the most important functions provided by urban trees for mitigating UHI. However, the cooling effect of tree shade has not been adequately investigated. In this study, we used a simple and straightforward method to quantify the spatial and temporal variation of tree shade and examined its effect on land surface temperature (LST). We used the hillshade function in a geographic information system to quantify the spatiotemporal patterns of tree shade by integrating sun location and tree height. Relationships between shade and LST were then compared in two cities, Tampa, Florida and New York City (NYC), New York. We found that: (1) Hillshade function combining the sun location and tree height can accurately capture the spatial and temporal variation of tree shade; (2) Tree shade, particularly at 07:30, has significant cooling effect on LST in Tampa and NYC; and (3) Shade has a stronger cooling effect in Tampa than in NYC, which is most likely due to the differences in the ratio of tree canopy to impervious surface cover, the spatial arrangements of trees and buildings, and their relative heights. Comparing the cooling effects of tree shade in two cities, this study provides important insights for urban planners for UHI mitigation in different cities. Numéro de notice : A2020-747 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.jag.2020.102161 Date de publication en ligne : 05/06/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102161 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96397
in International journal of applied Earth observation and geoinformation > vol 92 (October 2020) . - n° 102161[article]Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects / Jike Chen in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkAssessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery / Allison Lassiter in Plos one, vol 15 n° 5 (May 2020)PermalinkGeocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkCity-descriptive input data for urban climate models: Model requirements, data sources and challenges / Valéry Masson in Urban climate, vol 31 (March 2020)PermalinkContribution à la segmentation et à la modélisation 3D du milieu urbain à partir de nuages de points / Tania Landes (2020)PermalinkPressures and threats to nature related to human activities in European urban and suburban forests / Ewa Referowska-Chodak in Forests, vol 10 n° 9 (September 2019)PermalinkQuantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale / Elena Barbierato in European journal of remote sensing, vol 52 n° 4 (2019)PermalinkInvestigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data : A case study of Wuhan, Central China / Xin Huang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)PermalinkPermalinkDetection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach / Josselin Aval in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)Permalink