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télédétection
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Télédétection aérospatiale Télédétection par satellite Télédétection satellitaire Télédétection spatiale Appareils enregistreurs >> Agriculture de précision Capteurs (technologie) Photogrammétrie aérienne Photographie aérienne >>Terme(s) spécifique(s) : Télédétection en sciences de la Terre Cartographie radar Traitement d'images -- Techniques numériques Images de télédétection Radar à antenne synthétique Radar en sciences de la Terre Reconnaissance aérienne Satellites artificiels en télédétection Satellites de télédétection des ressources terrestres SPOT (satellites de télédétection) Surveillance électronique Télédétection hyperfréquence Télémesure spatiale Thermographie Equiv. LCSH : Remote sensing Domaine(s) : 500; 600 |
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Feux de forêt : un drone traque les risques de reprise / Nathalie Da Cruz in Géomètre, n° 2205 (septembre 2022)
[article]
Titre : Feux de forêt : un drone traque les risques de reprise Type de document : Article/Communication Auteurs : Nathalie Da Cruz, Auteur Année de publication : 2022 Article en page(s) : pp 16 - 18 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] aide à la localisation
[Termes IGN] Gironde (33)
[Termes IGN] image captée par drone
[Termes IGN] image thermique
[Termes IGN] incendie de forêt
[Termes IGN] télédétection aérienne
[Termes IGN] température au solRésumé : (Auteur) Lors des incendies en Gironde, cet été, le cabinet de géomètres-experts Parallèle 45 a proposé aux autorités l’utilisation de son drone avec caméra thermique pour repérer les fumerons. Une aide précieuse appréciée des élus locaux et des sapeurs-pompiers. Numéro de notice : A2022-529 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/09/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101491
in Géomètre > n° 2205 (septembre 2022) . - pp 16 - 18[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 063-2022091 RAB Revue Centre de documentation En réserve L003 Disponible Historical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine / Luis Carrasco in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)
[article]
Titre : Historical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine Type de document : Article/Communication Auteurs : Luis Carrasco, Auteur ; Go Fujita, Auteur ; Kensuke Kito, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 277 - 289 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] cartographie historique
[Termes IGN] détection de changement
[Termes IGN] Google Earth
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] indice de végétation
[Termes IGN] Japon
[Termes IGN] phénologie
[Termes IGN] photographie aérienne
[Termes IGN] réflectance de surface
[Termes IGN] rizière
[Termes IGN] signature spectraleRésumé : (auteur) Mapping the expansion or reduction of rice fields is fundamental for food and water security, greenhouse gas emission accounting, and environmental management. The historical mapping of rice fields with satellite images is challenging because of the limited availability of remote sensing and training data from past decades. The use of phenology-based algorithms has been proposed for mapping rice fields because they can take advantage of rice fields’ characteristic spectral signature during the transplanting phase and do not need training data. However, in order to employ phenology-based algorithms effectively for the historical rice mapping of large areas, we need to incorporate automatized methods able to deal with non-usable data (e.g., cloud cover) and with spatial inconsistencies in the number of available images for each pixel. Here we propose the combination of a pixel-based, phenological algorithm with the temporal aggregation of all available Landsat images to produce national level historical maps of rice fields in Japan from the 1980s onwards. We used temporally aggregated metrics (median, percentiles, etc.), derived from spectral indices of a large number of images within the Google Earth Engine, to minimize the issue of inconsistent image availability and reduce the effects of outliers in phenology-based algorithms. We produced seven rice field maps, for the periods 1985–89, 1990–94, 1995–99, 2000–04, 2005–09, 2010–14, and 2015–19. The overall map accuracies ranged from 83% to 95% when validated with visually interpreted aerial photography. We detected a 23% decrease in the area of rice fields at a country level, although the changes varied greatly among prefectures. Here we present the first freely available historical rice field maps of Japan from the 1980s onwards, together with the source code, and a web application that enables the exploration of the maps and data relating to the derived rice field area changes. The application of temporal aggregation is promising for dealing with the gap-filling of large amounts of satellite data, reducing the issue of data outliers and providing an effective use of the historical Landsat archive for phenology-based crop detection algorithms. Our maps could greatly help researchers, conservationists and policymakers studying the drivers and consequences of rice field changes, and our methods could be extrapolated to map rice fields at large scales in other regions of the world. Numéro de notice : A2022-665 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.07.018 Date de publication en ligne : 08/08/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.07.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101527
in ISPRS Journal of photogrammetry and remote sensing > vol 191 (September 2022) . - pp 277 - 289[article]Landsat, le programme fête ses cinquante ans / Laurent Polidori in Géomètre, n° 2205 (septembre 2022)
[article]
Titre : Landsat, le programme fête ses cinquante ans Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 19-19 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Technologies spatiales
[Termes IGN] Landsat
[Termes IGN] programme spatial
[Termes IGN] satellite d'observation de la Terre
[Termes IGN] télédétection spatialeRésumé : (Auteur) Le programme de la Nasa propose un demi-siècle d’images de télédétection, les premières pouvant être considérées comme un « état zéro» environnemental de la planète. Numéro de notice : A2022-670 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/09/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101492
in Géomètre > n° 2205 (septembre 2022) . - pp 19-19[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 063-2022091 RAB Revue Centre de documentation En réserve L003 Disponible Incorporation of digital elevation model, normalized difference vegetation index, and Landsat-8 data for land use land cover mapping / Jwan Al-Doski in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 8 (August 2022)
[article]
Titre : Incorporation of digital elevation model, normalized difference vegetation index, and Landsat-8 data for land use land cover mapping Type de document : Article/Communication Auteurs : Jwan Al-Doski, Auteur ; Faez M. Hassan, Auteur ; Hussein Abdelwahab Mossa, Auteur ; Aus A. Najim, Auteur Année de publication : 2022 Article en page(s) : pp 507 - 516 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte d'utilisation du sol
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données auxiliaires
[Termes IGN] image Landsat-8
[Termes IGN] Malaisie
[Termes IGN] MNS ASTER
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] ombre
[Termes IGN] précision de la classificationRésumé : (Auteur) Ancillary data are crucial in land use land cover (LULC) mapping process. This study goal is to investigate if adding Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM) data as ancillary data to the Landsat-8 spectral imagery (acquired on 14 April 2016) in the support vector machine (SVM ) classification process improves LULC mapping accuracy in GuaMusang, Malaysia. ENVI software was used to preprocess a single Landsat-8 image, convert it to reflectance, and calculate NDVI. ASTER-GDEM data were used to generate the DEM. The logical channel method was used to combine NDVI and DEM with Landsat-8 bands and limit the impact of shadows during SVM classification. The SVM accuracy was tested and evaluated on ancillary data and Landsat-8 spectral-based collection. The results revealed that the user's accuracy and producer's accuracy improved by 15.1% and 2.1%, for primary forest and by 17.93% and 28.86% for secondary forest, respectively. The classification reliability of the majority of LULC categories has increased significantly. Compared to SVM spectral-based set, the overall accuracy and kappa coefficient of the SVM ancillary-based set improved by 8.77% and 0.12, respectively. In conclusion, this article demonstrated that integrating DEM and NDVI data improves Landsat-8 image classification precision. Numéro de notice : A2022-805 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00082R2 Date de publication en ligne : 01/08/2022 En ligne : https://doi.org/10.14358/PERS.21-00082R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102132
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 8 (August 2022) . - pp 507 - 516[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022081 SL Revue Centre de documentation Revues en salle Disponible Mainstreaming remotely sensed ecosystem functioning in ecological niche models / Adrián Regos in Remote sensing in ecology and conservation, vol 8 n° 4 (August 2022)
[article]
Titre : Mainstreaming remotely sensed ecosystem functioning in ecological niche models Type de document : Article/Communication Auteurs : Adrián Regos, Auteur ; João Gonçalves, Auteur ; Salvador Arenas-Castro, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 431 - 447 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carbone
[Termes IGN] écologie forestière
[Termes IGN] écosystème forestier
[Termes IGN] habitat animal
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] niche écologiqueRésumé : (auteur) Biodiversity is declining globally at unprecedented rates. Ecological niche mod-els (ENMs) are one of the most widely used toolsets to appraise global changeimpacts on biodiversity. Here, we identify a variety of advantages of incorporat-ing remotely sensed ecosystem functioning attributes (EFAs) into ENMs. Thedevelopment of ENMs that explicitly incorporate ecosystem functioning willallow a more holistic and integrative perspective of the habitat dynamics. Thesynergies between the increasingly available open-access satellite images andcloud-based platforms for planetary-scale geospatial analysis offer an unprece-dented opportunity to incorporate ecosystem processes and disturbances (suchas fires, insect outbreaks or droughts) that have been so far largely neglected inecological niche characterization and modelling. The most paradigmatic exam-ple of EFAs is the application of time series of spectral vegetation indicesrelated to primary productivity and carbon cycle. EFAs related to surface energybalance and water cycles derived from remote sensing products such as landsurface temperature or soil moisture enable a fine-scale characterization of thespecies’ niche—eventually improving the predictive performance of ENMs. Allthese advantages confirm that a new generation of ENMs based on such EFAswould offer great perspectives to increase our ability to monitor habitat suit-ability trends and population dynamics. However, despite the technicaladvances and increasing effort of remote sensing community to develop inte-grative EFAs, ENMs have yet to make full profit of the most recent develop-ments by integrating them in ENMs. A coordinated agenda for remote sensingexperts and ecological modellers will be essential over the coming years tobridge the gap between remote sensing and ecology disciplines and to take full(and timely) advantage of the fast-growing body of Earth observation data andremote sensing technologies—with special emphasis on the development andtesting of new variables related to key processes driving ecosystem functioning. Numéro de notice : A2022-715 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article DOI : 10.1002/rse2.255 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1002/rse2.255 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101614
in Remote sensing in ecology and conservation > vol 8 n° 4 (August 2022) . - pp 431 - 447[article]Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series / Maximilian Lange in Remote sensing of environment, vol 277 (August 2022)PermalinkRemote sensing and phytoecological methods for mapping and assessing potential ecosystem services of the Ouled Hannèche Forest in the Hodna Mountains, Algeria / Amal Louail in Forests, Vol 13 n° 8 (August 2022)PermalinkSmart city data science: Towards data-driven smart cities with open research issues / Iqbal H. Sarker in Internet of Things, vol 19 (August 2022)PermalinkMultiscale assimilation of Sentinel and Landsat data for soil moisture and Leaf Area Index predictions using an ensemble-Kalman-filter-based assimilation approach in a heterogeneous ecosystem / Nicola Montaldo in Remote sensing, vol 14 n° 14 (July-2 2022)PermalinkA framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkInvestigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 / Fahime Arabi Aliabad in Remote sensing, vol 14 n° 13 (July-1 2022)PermalinkInvestigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)PermalinkAnalysis of the land suitability for paddy fields in Tanzania using a GIS-based analytical hierarchy process / Ahmad Al-Hanbali in Geo-spatial Information Science, vol 25 n° 2 ([01/06/2022])PermalinkGIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkA phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images / Jing Zeng in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)PermalinkThe 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)PermalinkTowards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkVariance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data / Anjana N.J. Kukunuri in Geocarto international, vol 37 n° 10 ([01/06/2022])PermalinkAnalyzing spatio-temporal pattern of the forest fire burnt area in Uttarakhand using Sentinel-2 data / Shailja Mamgain in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)PermalinkLearning from the past: crowd-driven active transfer learning for semantic segmentation of multi-temporal 3D point clouds / Michael Kölle in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkVegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas / Benedikt Hiebl in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkAlternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation / Toshihiro Sakamoto in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)PermalinkA continuous change tracker model for remote sensing time series reconstruction / Yangjian Zhang in Remote sensing, vol 14 n° 9 (May-1 2022)PermalinkDevelopment of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model / Han Ma in Remote sensing of environment, vol 273 (May 2022)PermalinkSignificant loss of ecosystem services by environmental changes in the Mediterranean coastal area / Adriano Conte in Forests, vol 13 n° 5 (May 2022)Permalink