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
Extracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography / Lukas Roth in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
![]()
[article]
Titre : Extracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography Type de document : Article/Communication Auteurs : Lukas Roth, Auteur ; Helge Aasen, Auteur ; Achim Walter, Auteur ; Frank Liebisch, Auteur Année de publication : 2018 Article en page(s) : pp 161 - 175 Note générale : Bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] cultures
[Termes IGN] drone
[Termes IGN] Glycine max
[Termes IGN] image aérienne
[Termes IGN] image RVB
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] modélisation géométrique de prise de vue
[Termes IGN] orthoimage géoréférencée
[Termes IGN] segmentation d'image
[Termes IGN] simulation 3D
[Termes IGN] SuisseRésumé : (Editeur) Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI prediction accuracy was comparable with the one of a gap fraction-based handheld device ( of , RMSE of m 2m−2) and correlated well with destructive LAI measurements ( of , RMSE of m2 m−2). These results indicate that, if respecting the range (LAI ) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science. Numéro de notice : A2018-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.012 Date de publication en ligne : 07/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90402
in ISPRS Journal of photogrammetry and remote sensing > vol 141 (July 2018) . - pp 161 - 175[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018073 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018072 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Mapping rubber trees based on phenological analysis of Landsat time series data-sets / Janatul Aziera binti Abd Razak in Geocarto international, vol 33 n° 6 (June 2018)
![]()
[article]
Titre : Mapping rubber trees based on phenological analysis of Landsat time series data-sets Type de document : Article/Communication Auteurs : Janatul Aziera binti Abd Razak, Auteur ; Abdul Rashid bin M. Shariff, Auteur ; Noordin bin Ahmad, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 627 - 650 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre sempervirent
[Termes IGN] Arecaceae
[Termes IGN] carte agricole
[Termes IGN] hevea (genre)
[Termes IGN] image Landsat
[Termes IGN] Malaisie
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] phénologie
[Termes IGN] série temporelleRésumé : (Auteur) This study proposes a strategy for accurate mapping of rubber trees through the analysis of Landsat time series datasets. The phenological dynamics of rubber trees were derived from the Normalized Difference Vegetation Index (NDVI) to verify the three important phenological metrics of rubber trees; defoliation, foliation and their growing stages. A decade (2006–2015) ago, Landsat time series NDVIs were used to study the strength of relationship between rubber trees, evergreen trees and oil palm trees. Two important results that could discriminate these three types of vegetation were found; firstly, a weak relationship of NDVIs between rubber trees and evergreen trees during the defoliation period (r2 = 0.1358) and secondly between rubber trees and oil palm trees during the growing period (r2 = 0.2029). This analysis was verified using Support Vector Machine to map the distribution of the three types of vegetation. An accurate mapping strategy of rubber trees was successfully formulated. Numéro de notice : A2018-143 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1289559 Date de publication en ligne : 13/02/2017 En ligne : https://doi.org/10.1080/10106049.2017.1289559 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89701
in Geocarto international > vol 33 n° 6 (June 2018) . - pp 627 - 650[article]Error-regulated multi-pass DInSAR analysis for landslide risk assessment / Jung Rack Kim in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 4 (April 2018)
![]()
[article]
Titre : Error-regulated multi-pass DInSAR analysis for landslide risk assessment Type de document : Article/Communication Auteurs : Jung Rack Kim, Auteur ; HyeWon Yun, Auteur ; Stephan van Gasselt, Auteur ; YunSoo Choi, Auteur Année de publication : 2018 Article en page(s) : pp 189 - 202 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Corée du sud
[Termes IGN] effondrement de terrain
[Termes IGN] interferométrie différentielle
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modèle numérique de terrain
[Termes IGN] montagne
[Termes IGN] risque naturel
[Termes IGN] surveillance géologique
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] teneur en vapeur d'eauRésumé : (Auteur) Landslide risk assessment based on Differential Interferometric SAR analyses (DInSAR) is associated with a number of error effects. We here approach the problem of assessing landslide risks over mountainous areas, where DInSAR observations are often influenced by orographic effects and inaccurate base topographies by employing a dedicated error compensation. In order to obtain accurate information on surface deformation, we apply corrections for DInSAR interferograms using high-resolution base topography and water vapor information obtained from a satellite radiometer. We observe that the corrected DInSAR output is in accordance with the environmental context as inferred by geological and geomorphological settings. It is feasible to better quantify landslide monitoring schemes whenever high- accuracy atmospheric error maps and a methodology to effectively compensate for external errors in DInSAR interferograms are available. The approach in this study can be further upgraded for future SAR-based assessments and various error correction approaches for even more precise landslide risk assessments. Numéro de notice : A2018-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.4.189 Date de publication en ligne : 01/04/2018 En ligne : https://doi.org/10.14358/PERS.84.4.189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89688
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 4 (April 2018) . - pp 189 - 202[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2018041 RAB Revue Centre de documentation En réserve L003 Disponible Mapping forest characteristics at fine resolution across large landscapes of the southeastern united states using NAIP imagery and FIA field plot data / John Hogland in ISPRS International journal of geo-information, vol 7 n° 4 (April 2018)
![]()
[article]
Titre : Mapping forest characteristics at fine resolution across large landscapes of the southeastern united states using NAIP imagery and FIA field plot data Type de document : Article/Communication Auteurs : John Hogland, Auteur ; Nathaniel Anderson, Auteur ; Joseph St. Peter, Auteur ; Jason Drake, Auteur ; Paul Medley, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] composition floristique
[Termes IGN] densité du bois
[Termes IGN] Etats-Unis
[Termes IGN] image aérienne
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Pinus (genre)
[Termes IGN] surface terrière
[Termes IGN] télédétection aérienne
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) Accurate information is important for effective management of natural resources. In the field of forestry, field measurements of forest characteristics such as species composition, basal area, and stand density are used to inform and evaluate management activities. Quantifying these metrics accurately across large landscapes in a meaningful way is extremely important to facilitate informed decision-making. In this study, we present a remote sensing based methodology to estimate species composition, basal area and stand tree density for pine and hardwood tree species at the spatial resolution of a Forest Inventory Analysis (FIA) program plot (78 m by 70 m). Our methodology uses textural metrics derived at this spatial scale to relate plot summaries of forest characteristics to remotely sensed National Agricultural Imagery Program (NAIP) aerial imagery across broad extents. Our findings quantify strong relationships between NAIP imagery and FIA field data. On average, models of basal area and trees per acre accounted for 43% of the variation in the FIA data, while models identifying species composition had less than 15.2% error in predicted class probabilities. Moreover, these relationships can be used to spatially characterize the condition of forests at fine spatial resolutions across broad extents. Numéro de notice : A2018-109 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7040140 En ligne : https://doi.org/10.3390/ijgi7040140 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89538
in ISPRS International journal of geo-information > vol 7 n° 4 (April 2018)[article]Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data / Xiaojun Xu in Annals of Forest Science, vol 75 n° 1 (March 2018)
![]()
[article]
Titre : Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data Type de document : Article/Communication Auteurs : Xiaojun Xu, Auteur ; Huanqiang Du, Auteur ; Guomo Zhou, Auteur ; Fangjie Mao, Auteur ; Xuejian Li, Auteur ; Dien Zhu, Auteur ; Yanggguang Li, Auteur ; Lu Cui, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Chine
[Termes IGN] données de terrain
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] Phyllostachys edulis
[Termes IGN] réflectance végétale
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] teneur en chlorophylle des feuillesRésumé : (Auteur) We estimated the leaf area index (LAI) and canopy chlorophyll content (CC) of Moso bamboo forest by using statistical models based on MODIS data and field measurements. Results showed that the statistical model driven by MODIS data has the potential to accurately estimate LAI and CC, while the structure of the calibration models varied between on- and off-years because of the different leaf change and bamboo shoot production characteristics between these types of years. LAI and CC (gram per square meter of ground area) are important parameters for determining carbon exchange between Moso bamboo forest (Phyllostachys edulis (Carrière) J. Houz.) and the atmosphere. This study evaluated the ability of a statistical model driven by MODIS data to accurately estimate the LAI and CC in Moso bamboo forest, and differences in the LAI and CC between on-years (years with great shoot production) and off-years (years with less shoot production) were analyzed. The LAI and CC measurements were collected in Anji County, Zhejiang Province, China. Indicators of LAI and CC were calculated from MODIS data. Then, a regression analysis was used to build relationships between the LAI and CC and various indicators on the basis of leaf change and bamboo shoot production characteristics of Moso bamboo forest. LAI and CC were accurately estimated by using the regression analysis driven by MODIS-derived indicators with a relative root mean squared error (RMSEr) of 9.04 and 13.1%, respectively. The structure of the calibration models varied between on- and off-years. Long-term time series analysis from 2000 to 2015 showed that LAI and CC differed largely between on- and off-years. This study demonstrates that LAI and CC of Moso bamboo forest can be estimated accurately by using a statistical model driven by MODIS-derived indicators, but attention should be paid to differences in the calibration models between on-and off-years. Numéro de notice : A2018-311 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-018-0721-y Date de publication en ligne : 13/03/2018 En ligne : https://doi.org/10.1007/s13595-018-0721-y Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90431
in Annals of Forest Science > vol 75 n° 1 (March 2018)[article]Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery / Pablo J. Zarco-Tejada in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)
PermalinkPermalinkA comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere / Qing Chang in Geocarto international, vol 33 n° 1 (January 2018)
PermalinkDétection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018)
PermalinkPermalinkEstimation cohérente de l'indice de surface foliaire en utilisant des données terrestres et aéroportées / Ronghai Hu (2018)
PermalinkEstimation of surface roughness over bare agricultural soil from Sentinel-1 data / Mohammad Choker (2018)
PermalinkA hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)
PermalinkPermalinkPermalink