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occupation du sol
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Espace, organisation de l' Utilisation du sol Politique foncière Sol, Occupation du Sols -- Utilisation Sols -- Utilisation Terrains -- Utilisation Terrains, Utilisation des Utilisation du sol Espace (économie politique) >> Aménagement du territoire Paysage -- Évaluation Syndrome NIMBY >>Terme(s) spécifique(s) : Améliorations foncières Cadastres Décharges contrôlées Immobilier Photographie aérienne en utilisation du sol Politique forestière Promotion immobilière Propriété foncière Propriété immobilière -- Acquisition par l'Administration Terres publiques Zones d'aménagement différé Equiv. LCSH : Land use Domaine(s) : 330 |
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Heat wave-induced augmentation of surface urban heat islands strongly regulated by rural background / Shiqi Miao in Sustainable Cities and Society, vol 82 (July 2022)
[article]
Titre : Heat wave-induced augmentation of surface urban heat islands strongly regulated by rural background Type de document : Article/Communication Auteurs : Shiqi Miao, Auteur ; Wenfeng Zhan, Auteur ; Jiameng Lai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103874 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] Chine
[Termes IGN] climat tropical
[Termes IGN] couvert végétal
[Termes IGN] densité de la végétation
[Termes IGN] données environnementales
[Termes IGN] forêt
[Termes IGN] humidité de l'air
[Termes IGN] ilot thermique urbain
[Termes IGN] image Terra-MODIS
[Termes IGN] nuit
[Termes IGN] température au sol
[Termes IGN] zone humide
[Termes IGN] zone ruraleRésumé : (auteur) The impact of heat waves (HWs) on surface urban heat islands (SUHIs) has been widely studied, but the spatial pattern of SUHI responsiveness to HWs across various climates remains unclear, and the influence of HW intensity on SUHI responsiveness has not been systematically quantified. Using MODIS land surface temperature data, here we investigated the responsiveness of SUHI to HWs (quantified as ∆I) as well as its variations with HW intensity in 354 cities in seven climate zones across China. We find that during HW periods, the SUHI and surface urban cool island are augmented in the humid and arid regions of China, respectively. The inter-city heterogeneity in rural vegetation coverage accounts for such a spatial pattern. In eastern China, the ∆I peaks in the north subtropical climate (0.72 ± 0.54 K for daytime and 0.29 ± 0.23 K for the nighttime) probably for its specific rural farming method. With the intensification of HWs, the augmentation effect can be further enhanced for the north subtropical, warm temperate, and arid temperate climates during the day and for almost all the climates at night. These findings can help advance the understanding of the responsiveness of SUHI to extreme climatic events. Numéro de notice : A2022-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2022.103874 Date de publication en ligne : 13/04/2022 En ligne : https://doi.org/10.1016/j.scs.2022.103874 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100624
in Sustainable Cities and Society > vol 82 (July 2022) . - n° 103874[article]Graph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)
[article]
Titre : Graph-based block-level urban change detection using Sentinel-2 time series Type de document : Article/Communication Auteurs : Nan Wang, Auteur ; Wei Li, Auteur ; Ran Tao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112993 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multivariée
[Termes IGN] bâtiment
[Termes IGN] Chine
[Termes IGN] détection de changement
[Termes IGN] espace vert
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] segmentation d'image
[Termes IGN] série temporelle
[Termes IGN] zone urbaineRésumé : (auteur) Remote sensing technology has been frequently used to obtain information on changes in urban land cover because of its vast spatial coverage and timeliness of observation. Block-level change detection with high temporal resolution image data provides fine detail of urban changes, is suitable for urban management, and has gradually received widespread attention. High-dimensional features are required to express the heterogeneous structure of the blocks. High-dimensional high-frequency time series, namely, multivariate time series, are formed by arranging high-dimensional features chronologically. Classic change detection methods treat multivariate time series as univariate time series one by one. Few studies have analyzed the change in a multivariate time series by considering all variables as an entirety. Therefore, a graph-based segmentation for multivariate time series algorithm (MTS-GS) is proposed in this paper. Specifically, 1) we construct a similarity matrix to explore the changing patterns of multivariate time series for seasonal change, trend change, abrupt change, and noise disturbance; 2) a multivariate time series graph is defined based on the changing patterns; and 3) the corresponding graph segmentation algorithm is proposed in the paper to detect the abrupt and trend changes under noise and seasonal disturbances. Sentinel-2 images of the rapidly developing third-tier city of Luoyang, Henan province, China, are adopted to validate the algorithm. The F1-score in the spatial domain is 84.1%; the producer's and the user's accuracy in the temporal dimension are 81.8% and 80.1%, respectively. Seven change types are defined and extracted, showing the development pattern and the efficiency of land use in the city. Furthermore, the proposed MTS-GS can be used for pixel-level change detection and performs well under various time intervals and cloud covers. Numéro de notice : A2022-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112993 Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112993 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100699
in Remote sensing of environment > vol 274 (June 2022) . - n° 112993[article]Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)
[article]
Titre : Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images Type de document : Article/Communication Auteurs : Lingdong Mao, Auteur ; Zhe Zheng, Auteur ; Xiangfeng Meng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] détection d'objet
[Termes IGN] grande échelle
[Termes IGN] identification automatique
[Termes IGN] image à haute résolution
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Urban vacant land is a growing issue worldwide. However, most of the existing research on urban vacant land has focused on small-scale city areas, while few studies have focused on large-scale national areas. Large-scale identification of urban vacant land is hindered by the disadvantage of high cost and high variability when using the conventional manual identification method. Criteria inconsistency in cross-domain identification is also a major challenge. To address these problems, we propose a large-scale automatic identification framework of urban vacant land based on semantic segmentation of high-resolution remote sensing images and select 36 major cities in China as study areas. The framework utilizes deep learning techniques to realize automatic identification and introduces the city stratification method to address the challenge of identification criteria inconsistency. The results of the case study on 36 major Chinese cities indicate two major conclusions. First, the proposed framework of vacant land identification can achieve over 90 percent accuracy of the level of professional auditors with much higher result stability and approximately 15 times higher efficiency compared to the manual identification method. Second, the framework has strong robustness and can maintain high performance in various cities. With the above advantages, the proposed framework provides a practical approach to large-scale vacant land identification in various countries and regions worldwide, which is of great significance for the academic development of urban vacant land and future urban development. Numéro de notice : A2022-267 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.landurbplan.2022.104384 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1016/j.landurbplan.2022.104384 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100275
in Landscape and Urban Planning > vol 222 (June 2022) . - n° 104384[article]The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria / Alfred S. Alademomi in Applied geomatics, vol 14 n° 2 (June 2022)
[article]
Titre : The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria Type de document : Article/Communication Auteurs : Alfred S. Alademomi, Auteur ; Chukwuma J. Okolie, Auteur ; Olagoke E. Daramola, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 299 - 314 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] corrélation temporelle
[Termes IGN] détection de changement
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] Lagos
[Termes IGN] Normalized Difference Built-up Index
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] température au solRésumé : (auteur) In recent times, there has been renewed interest in understanding the dynamics of land cover change and its relationship with several environmental parameters. This study assesses the interrelationship between land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and land cover change in Amuwo-Odofin Local Government Area of Lagos State, Nigeria. Multi-temporal and multi-spectral Landsat imageries for years 2002, 2013, 2016, and 2019 served as the primary dataset. Using the parallelepiped classifier, the imageries were classified into five land cover classes — mixed vegetation, bare land, built-up area, water body, and wetland. The spectral indices (NDVI and NDBI) were computed and the LST was determined using a single-channel algorithm. Land cover transition matrices were calculated to examine the proportion of land cover change between classes, including the unchanged areas. Pearson’s correlation analysis enabled an analysis of the interdependence or interrelationship in the distribution of the parameters. From 2002 to 2019, the highest land cover transitions recorded were bare land to built-up area (12.64 km2), mixed vegetation to built-up area (21.55 km2), wetland to mixed vegetation (8.87 km2), and mixed vegetation to bare land (8.46 km2). There was a negative correlation between LST and NDVI, and between NDVI and NDBI. The distribution of the LST, NDVI, and NDBI varied correspondingly in accordance with land cover changes. The increase in built-up area could be the major driver of the observed changes in LST, NDBI, and NDVI, with an observed relationship that NDBI and LST values increase with increase in built-up areas. Numéro de notice : A2022-463 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-022-00434-2 Date de publication en ligne : 06/04/2022 En ligne : https://doi.org/10.1007/s12518-022-00434-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100790
in Applied geomatics > vol 14 n° 2 (June 2022) . - pp 299 - 314[article]Classification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)
[article]
Titre : Classification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon Type de document : Article/Communication Auteurs : Hermann Tagne, Auteur ; Arnaud Le Bris , Auteur ; David Monkam, Auteur ; Clément Mallet , Auteur Année de publication : 2022 Projets : TOSCA Parcelle / Le Bris, Arnaud Article en page(s) : pp 673 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Cameroun
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] série temporelleRésumé : (auteur) Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolutions. These images are in particular of utter interest for Land-Cover (LC) mapping at large scales. LC maps can now be computed on a yearly basis at the scale of a country with efficient supervised classifiers, assuming suitable training data are available. However, the efficient exploitation of large amount of Sentinel-2 imagery still remain challenging on unexplored areas where state-of-the-art classifiers are prone to fail. This paper focuses on Land-Cover mapping over Cameroon for the purpose of updating the Very High Resolution national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic vegetation classes (five) which enables providing robust focusing masks for higher resolution classifications. Two strategies are compared: (i) a LC map is calculated out of a year long time series and (ii) monthly LC maps are generated and merged into a single yearly map. Satisfactory accuracy scores are obtained (>94% in Overall Accuracy), allowing to provide a first step towards finer-grained map retrieval. Numéro de notice : A2022-426 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-3-2022-673-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-3-2022-673-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100731
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-3-2022 (2022 edition) . - pp 673 - 680[article]Fusion of optical, radar and waveform LiDAR observations for land cover classification / Huiran Jin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)PermalinkSmartphone digital photography for fractional vegetation cover estimation / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)PermalinkDetecting land use and land cover change on Barbuda before and after the Hurricane Irma with respect to potential land grabbing: A combined volunteered geographic information and multi sensor approach / Andreas Rienow in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkEstimation 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])PermalinkExtraction 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)PermalinkProbabilistic unsupervised classification for large-scale analysis of spectral imaging data / Emmanuel Paradis in International journal of applied Earth observation and geoinformation, vol 107 (March 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])PermalinkQuantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data / Tianyu Hu in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)PermalinkSemantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network / Ekrem Saralioglu in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkPermalink