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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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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]Réservation
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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]Réservation
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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)
[article]
Titre : Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series Type de document : Article/Communication Auteurs : Maximilian Lange, Auteur ; Hannes Feilhauer, Auteur ; Ingolf Kühn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] bande spectrale
[Termes IGN] carte d'utilisation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] échantillonnage de données
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] prairie
[Termes IGN] série temporelleRésumé : (auteur) Information on grassland land-use intensity (LUI) is crucial for understanding trends and dynamics in biodiversity, ecosystem functioning, earth system science and environmental monitoring. LUI is a major driver for numerous environmental processes and indicators, such as primary production, nitrogen deposition and resilience to climate extremes. However, large extent, high resolution data on grassland LUI is rare. New satellite generations, such as Copernicus Sentinel-2, enable a spatially comprehensive detection of the mainly subtle changes induced by land-use intensification by their fine spatial and temporal resolution. We developed a methodology quantifying key parameters of grassland LUI such as grazing intensity, mowing frequency and fertiliser application across Germany using Convolutional Neural Networks (CNN) on Sentinel-2 satellite data with 20 m × 20 m spatial resolution. Subsequently, these land-use components were used to calculate a continuous LUI index. Predictions of LUI and its components were validated using comprehensive in situ grassland management data. A feature contribution analysis using Shapley values substantiates the applicability of the methodology by revealing a high relevance of springtime satellite observations and spectral bands related to vegetation health and structure. We achieved an overall classification accuracy of up to 66% for grazing intensity, 68% for mowing, 85% for fertilisation and an r2 of 0.82 for subsequently depicting LUI. We evaluated the methodology's robustness with a spatial 3-fold cross-validation by training and predicting on geographically distinctly separated regions. Spatial transferability was assessed by delineating the models' area of applicability. The presented methodology enables a high resolution, large extent mapping of land-use intensity of grasslands. Numéro de notice : A2022-468 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112888 Date de publication en ligne : 13/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100805
in Remote sensing of environment > vol 277 (August 2022) . - n° 112888[article]Remote 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)
[article]
Titre : Remote sensing and phytoecological methods for mapping and assessing potential ecosystem services of the Ouled Hannèche Forest in the Hodna Mountains, Algeria Type de document : Article/Communication Auteurs : Amal Louail, Auteur ; François Messner, Auteur ; Yamna Djellouli, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1159 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Algérie
[Termes IGN] analyse multicritère
[Termes IGN] carte thématique
[Termes IGN] entropie de Shannon
[Termes IGN] forêt méditerranéenne
[Termes IGN] image Landsat-8
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] protection de la biodiversité
[Termes IGN] relevé phytoécologique
[Termes IGN] service écosystémiqueRésumé : (auteur) Regardless of their biogeographic origins or degree of artificialization, the world’s forests are a source of a wide range of ecosystem services (ES). However, the quality and quantity of these services depend on the type of forest studied and its phytogeographic context. Our objective is to transpose the concept of ES, in particular, the assessment of forest ES, to the specific Mediterranean context of the North African mountains, where this issue is still in its infancy and where access to the data needed for assessment remains difficult. Our work presents an introductory approach, allowing us to set up methodological and scientific milestones based on open-access remote sensing data and already tested geospatial processing associated with phytoecological surveys to assess the ES provided by forests in an Algerian study area. Specifically, several indicators used to assess (both qualitatively and quantitatively) the potential ES of the Ouled Hannèche forest, a forest located in the Hodna Mountains, are derived from LANDSAT 8 OLI images from 2017 and an ALOS AW3D30 DSM. The qualitative ES typology is jointly based on an SVM classification of topographically corrected LANDSAT images and a geomorphic-type classification using the geomorphon method. NDVI is a quantitative estimator of many plant ecosystem functions related to ES. It highlights the variations in the provision of ES according to the types of vegetation formations present. It serves as a support for estimating spectral heterogeneity through Rao’s quadratic entropy, which is considered a relative indicator of biodiversity at the landscape scale. The two previous variables (the multitemporal NDVI and Rao’s Q), completed by the Shannon entropy method applied to the geomorphon classes as a proxy for topo-morphological heterogeneity, constitute the input variables of a quantitative map of the potential supply of ES in the forest determined by Spatial Multicriteria Analysis (SMCA). Ultimately, our results serve as a useful basis for land-use planning and biodiversity conservation. Numéro de notice : A2022-654 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13081159 Date de publication en ligne : 22/07/2022 En ligne : https://doi.org/10.3390/f13081159 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101502
in Forests > Vol 13 n° 8 (August 2022) . - n° 1159[article]Smart 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)PermalinkThe role of blue green infrastructure in the urban thermal environment across seasons and local climate zones in East Africa / Xueqin Li in Sustainable Cities and Society, vol 80 (May 2022)PermalinkCrop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information / Murali Krishna Gumma in Geocarto international, vol 37 n° 7 ([15/04/2022])Permalink