Descripteur
Termes IGN > télédétection > télédétection électromagnétique
télédétection électromagnétique |
Documents disponibles dans cette catégorie (898)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications / Amanda Veloso in Remote sensing of environment, vol 199 (15 September 2017)
![]()
[article]
Titre : Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications Type de document : Article/Communication Auteurs : Amanda Veloso, Auteur ; Stéphane Mermoz, Auteur ; Alexandre Bouvet, Auteur ; Thuy Le Toan, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 415 - 426 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] blé (céréale)
[Termes IGN] cultures
[Termes IGN] Glycine max
[Termes IGN] image optique
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] maïs (céréale)
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] surveillance agricole
[Termes IGN] tournesol
[Termes IGN] variation saisonnière
[Termes IGN] variation temporelleRésumé : (auteur) Crop monitoring information is essential for food security and to improve our understanding of the role of agriculture on climate change, among others. Remotely sensing optical and radar data can help to map crop types and to estimate biophysical parameters, especially with the availability of an unprecedented amount of free Sentinel data within the Copernicus programme. These datasets, whose continuity is guaranteed up to decades, offer a unique opportunity to monitor crops systematically every 5 to 10 days. Before developing operational monitoring methods, it is important to understand the temporal variations of the remote sensing signal of different crop types in a given region. In this study, we analyse the temporal trajectory of remote sensing data for a variety of winter and summer crops that are widely cultivated in the world (wheat, rapeseed, maize, soybean and sunflower). The test region is in southwest France, where Sentinel-1 data have been acquired since 2014. Because Sentinel-2 data were not available for this study, optical satellites similar to Sentinel-2 are used, mainly to derive NDVI, for a comparison between the temporal behaviors with radar data. The SAR backscatter and NDVI temporal profiles of fields with varied management practices and environmental conditions are interpreted physically. Key findings from this analysis, leading to possible applications of Sentinel-1 data, with or without the conjunction of Sentinel-2, are then described. This study points out the interest of SAR data and particularly the VH/VV ratio, which is poorly documented in previous studies. Numéro de notice : A2017-418 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2017.07.015 En ligne : https://doi.org/10.1016/j.rse.2017.07.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86311
in Remote sensing of environment > vol 199 (15 September 2017) . - pp 415 - 426[article]Improving the prediction of African savanna vegetation variables using time series of MODIS products / Miriam Tsalyuk in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
![]()
[article]
Titre : Improving the prediction of African savanna vegetation variables using time series of MODIS products Type de document : Article/Communication Auteurs : Miriam Tsalyuk, Auteur ; Maggi Kelly, Auteur ; Wayne M. Getz, Auteur Année de publication : 2017 Article en page(s) : pp 77 - 91 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] Afrique (géographie physique)
[Termes IGN] biomasse forestière
[Termes IGN] dégradation de la flore
[Termes IGN] Enhanced vegetation index
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] Namibie
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prédiction
[Termes IGN] savane
[Termes IGN] variationRésumé : (Auteur) African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems. Numéro de notice : A2017-537 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86575
in ISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 77 - 91[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017093 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017092 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform / Bangqian Chen in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
![]()
[article]
Titre : A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform Type de document : Article/Communication Auteurs : Bangqian Chen, Auteur ; Xiangming Xiao, Auteur ; Lianghao Pan, Auteur ; Russell Doughty, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 104 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] carte forestière
[Termes IGN] Chine
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] mangrove
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelleRésumé : (auteur) Due to rapid losses of mangrove forests caused by anthropogenic disturbances and climate change, accurate and contemporary maps of mangrove forests are needed to understand how mangrove ecosystems are changing and establish plans for sustainable management. In this study, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China. More specifically, these forests were mapped by identifying: (1) greenness, canopy coverage, and tidal inundation from time series Landsat data, and (2) elevation, slope, and intersection-with-sea criterion. The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of greenness, canopy coverage, and tidal inundation of mangrove forests, which are greatly affected by tide dynamics. In addition, the integration of Sentinel-1A VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresh water bodies, which is related to mangrove forests. This algorithm was developed using 6 typical Regions of Interest (ROIs) as algorithm training and was run on the Google Earth Engine (GEE) cloud computing platform to process 1941 Landsat images (25 Path/Row) and 586 Sentinel-1A images circa 2015. The resultant mangrove forest map of China at 30 m spatial resolution has an overall/users/producer’s accuracy greater than 95% when validated with ground reference data. In 2015, China’s mangrove forests had a total area of 20,303 ha, about 92% of which was in the Guangxi Zhuang Autonomous Region, Guangdong, and Hainan Provinces. This study has demonstrated the potential of using the GEE platform, time series Landsat and Sentine-1A SAR images to identify and map mangrove forests along the coastal zones. The resultant mangrove forest maps are likely to be useful for the sustainable management and ecological assessments of mangrove forests in China. Numéro de notice : A2017-419 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86313
in ISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 104 - 120[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017093 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017092 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements / Zhibin Ren in Annals of Forest Science, vol 74 n° 3 (September 2017)
![]()
[article]
Titre : Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements Type de document : Article/Communication Auteurs : Zhibin Ren, Auteur ; Ruiliang Pu, Auteur ; Haifeng Zheng, Auteur ; et al., Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] analyse structurale
[Termes IGN] attribut
[Termes IGN] données de terrain
[Termes IGN] données dendrométriques
[Termes IGN] écosystème urbain
[Termes IGN] flore urbaine
[Termes IGN] image Landsat-TM
[Termes IGN] indice foliaire
[Termes IGN] surveillance de la végétationRésumé : (Auteur)
Key message : We conducted spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating urban vegetation structural attributes including LAI, CC, and BA at an urban landscape level.
Context : Urban vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of urban vegetation structures is important for sustaining urban ecosystem service and improving the urban environment.
Aims : The purposes of this study were to evaluate the potential of estimating urban vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the urban structural attributes.
Methods : We first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect urban vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal urban vegetation structural maps were created using NDVI-based urban vegetation structure predictive models.
Results : The results show that NDVI can be used as a predictor for some selected urban vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural vegetation. The results also indicate that urban vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of urban vegetation all present a decreasing trend from suburban areas to urban central areas.
Conclusion : The experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution urban vegetation structural dataset (including CC, LAI, and BA).Numéro de notice : A2017-353 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1007/s13595-017-0654-x Date de publication en ligne : 05/07/2017 En ligne : https://doi.org/10.1007/s13595-017-0654-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85719
in Annals of Forest Science > vol 74 n° 3 (September 2017)[article]Evaluation of seasonal variations of remotely sensed leaf area index over five evergreen coniferous forests / Rong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
![]()
[article]
Titre : Evaluation of seasonal variations of remotely sensed leaf area index over five evergreen coniferous forests Type de document : Article/Communication Auteurs : Rong Wang, Auteur ; Jing M. Chen, Auteur ; Zhili Liu, Auteur ; Altaf Arain, Auteur Année de publication : 2017 Article en page(s) : pp 187 - 201 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aiguille
[Termes IGN] atmosphère terrestre
[Termes IGN] image Envisat-MERIS
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] phénologie
[Termes IGN] Pinophyta
[Termes IGN] placette d'échantillonnage
[Termes IGN] surface du sol
[Termes IGN] surveillance forestière
[Termes IGN] teneur en chlorophylle des feuilles
[Termes IGN] Tracing Radiation and Architecture of Canopies
[Termes IGN] variation saisonnièreRésumé : (Auteur) Seasonal variations of leaf area index (LAI) have crucial controls on the interactions between the land surface and the atmosphere. Over the past decades, a number of remote sensing (RS) LAI products have been developed at both global and regional scales for various applications. These products are so far only validated using ground LAI data acquired mostly in the middle of the growing season. The accuracy of the seasonal LAI variation in these products remains unknown and there are few ground data available for this purpose. We performed regular LAI measurements over a whole year at five coniferous sites using two methods: (1) an optical method with LAI-2000 and TRAC; (2) a direct method through needle elongation monitoring and litterfall collection. We compared seasonal trajectory of LAI from remote sensing (RS LAI) with that from a direct method (direct LAI). RS LAI agrees very well with direct LAI from the onset of needle growth to the seasonal peak (R2 = 0.94, RMSE = 0.44), whereas RS LAI declines earlier and faster than direct LAI from the seasonal peak to the completion of needle fall. To investigate the possible reasons for the discrepancy, the MERIS Terrestrial Chlorophyll Index (MTCI) was compared with RS LAI. Meanwhile, phenological metrics, i.e. the start of growing season (SOS) and the end of growing season (EOS), were extracted from direct LAI, RS LAI and MTCI time series. SOS from RS LAI is later than that from direct LAI by 9.3 ± 4.0 days but earlier than that from MTCI by 2.6 ± 1.9 days. On the contrary, for EOS, RS LAI is later than MTCI by 3.3 ± 8.4 days and much earlier than direct LAI by 30.8 ± 7.2 days. Our results suggest that the seasonal trajectory of RS LAI well captures canopy structural information from the onset of needle growth to the seasonal peak, but is greatly influenced by the decrease in leaf chlorophyll content, as indicated by MTCI, from the seasonal peak to the completion of needle fall. These findings have significant implications for improving existing RS LAI products and terrestrial productivity modeling. Numéro de notice : A2017-514 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86475
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 187 - 201[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Simultaneous estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from multiple-satellite data / Han Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkApplication of 3D triangulations of airborne laser scanning data to estimate boreal forest leaf area index / Titta Majasalmi in International journal of applied Earth observation and geoinformation, vol 59 (July 2017)
PermalinkEvaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery / Gabriel Navarro in International journal of applied Earth observation and geoinformation, vol 58 (June 2017)
PermalinkPan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents / Khan Rubayet Rahaman in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)
PermalinkBaltic sea ice concentration estimation using SENTINEL-1 SAR and AMSR2 microwave radiometer data / Juha Karvonen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
PermalinkPermalinkPermalinkPotential of satellite-derived ecosystem functional attributes to anticipate species range shifts / Domingo Alcaraz-Segura in International journal of applied Earth observation and geoinformation, vol 57 (May 2017)
PermalinkA comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration / Milad Mahour in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
PermalinkSpatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration / Yinghai Ke in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
Permalink