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Combination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)
[article]
Titre : Combination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan Type de document : Article/Communication Auteurs : Emal Wali, Auteur ; Masahiro Tasumi, Auteur ; Masao Moriyama, Auteur Année de publication : 2020 Article en page(s) : 17 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice foliaire
[Termes IGN] Japon
[Termes IGN] polarisation
[Termes IGN] régression linéaire
[Termes IGN] rizière
[Termes IGN] surveillance agricole
[Termes IGN] variable biophysique (végétation)Résumé : (auteur) This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage. Numéro de notice : A2020-223 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12010189 Date de publication en ligne : 05/01/2020 En ligne : https://doi.org/10.3390/rs12010189 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94936
in Remote sensing > vol 12 n° 1 (January 2020) . - 17 p.[article]Détermination conjointe des inondations et du type d’eau au moyen de l’imagerie multi-spectrale / Sabrine Amzil (2020)
Titre : Détermination conjointe des inondations et du type d’eau au moyen de l’imagerie multi-spectrale Type de document : Mémoire Auteurs : Sabrine Amzil, Auteur Editeur : Strasbourg : Institut National des Sciences Appliquées INSA Strasbourg Année de publication : 2020 Importance : 92 p. Format : 21 x 30 cm Note générale : bibliographie
Mémoire de soutenance de diplôme d'ingénieur INSA spécialité TopographieLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Amazone (fleuve)
[Termes IGN] Amazonie
[Termes IGN] image Aqua-MODIS
[Termes IGN] image en couleur
[Termes IGN] image multibande
[Termes IGN] image optique
[Termes IGN] image Sentinel-SAR
[Termes IGN] image Terra-MODIS
[Termes IGN] indice d'humidité
[Termes IGN] inondation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelleIndex. décimale : INSAS Mémoires d'ingénieur de l'INSA Strasbourg - Topographie, ex ENSAIS Résumé : (auteur) L’Amazonie, en Amérique du Sud, est connue pour ses plaines d’inondations et ses régimes saisonniers de précipitations très irréguliers à cause de plusieurs facteurs naturels et anthropiques. Les eaux amazoniennes se caractérisent non seulement par leurs grandes étendues mais également par la diversité des couleurs de ses fleuves et affluents. Ce projet de fin d’études vise à déterminer conjointement l’extension des inondations et les types d’eaux du bassin amazonien (eaux claires, laiteuses, noires, ...) par analyse de séries temporelles d’images multispectrales acquises par le capteur MODIS des satellites Aqua et Terra au cours de l’année 2017. La détection des inondations a été réalisée en se basant sur une combinaison d’indices spectraux NDVI, SWIb et AWEI après la recherche des valeurs seuils de chacun de ces indices. Tandis que la classification des types d’eaux s’effectue en fonction de la réponse de la valeur moyenne mensuelle du SWIb. Cette étude nous permet donc de mieux comprendre le bilan hydrologique et sédimentaire des zones d’inondation et fleuves amazoniens en se basant uniquement sur les apports de la télédétection optique. Note de contenu : Introduction
1- Etat de l'art
2- Création des méthodes de détection et classification des eaux
3- Evaluation et validation de la méthode
Conclusion et perspectivesNuméro de notice : 28577 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire ingénieur INSAS Organisme de stage : LEGOS (Toulouse) DOI : sans En ligne : http://eprints2.insa-strasbourg.fr/4187/1/M%C3%A9moire_PFE_AMZIL.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97884 Individual tree detection and classification for mapping pine wilt disease using multispectral and visible color imagery acquired from unmanned aerial vehicle / Takeshi Hoshikawa in Journal of The Remote Sensing Society of Japan, vol 40 n° 1 (2020)
[article]
Titre : Individual tree detection and classification for mapping pine wilt disease using multispectral and visible color imagery acquired from unmanned aerial vehicle Type de document : Article/Communication Auteurs : Takeshi Hoshikawa, Auteur ; Kazukiyo Yamamoto, Auteur Année de publication : 2020 Article en page(s) : pp 13 - 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] détection d'arbres
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] indice de végétation
[Termes IGN] maladie phytosanitaire
[Termes IGN] modèle de régression
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pinus (genre)
[Termes IGN] protection des forêts
[Termes IGN] régression logistique
[Termes IGN] semis de pointsRésumé : (auteur) Pine wilt disease is one of the most destructive disease of pine forests. It is important to detect and exterminate infected trees for preservation of the forest. We demonstrated a novel method combining individual tree detection (ITD) and classification by logistic regression using unmanned aerial vehicle (UAV) images for the mapping of infected trees. In the ITD phase, 50 % and 84 % of damaged trees were automatically detected from the 3D point cloud generated from the UAV images using the local maximum filter. These rates of detection were comparable to previous studies that used UAV imagery. Subsequently, five vegetation indices calculated from multispectral and visible color (RGB) images were used. Among the vegetation indices, normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and vegetation atmospherically resistant index (VARI) were preferable explanatory variable in the logistic regression to divide damaged and undamaged trees. The accuracy of these models ranged from 98 % to 100 % and the F-measure ranged from 94 % to 100 %. The best model, the logistic regression model using VARI as the explanatory variable, was then tested using five datasets to evaluate general performance. Each model showed explicitly high accuracy ranging from 95 % to 100 %. The best accuracy when considering the ITD and classification was 84 %. To map pine wilt disease, the proposed method is suitable for practical use due to its high-efficient and low-cost. Numéro de notice : A2020-405 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.11440/rssj.40.13 Date de publication en ligne : 31/01/2020 En ligne : https://doi.org/10.11440/rssj.40.13 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96090
in Journal of The Remote Sensing Society of Japan > vol 40 n° 1 (2020) . - pp 13 - 19[article]On the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)
Titre : On the joint exploitation of optical and SAR satellite imagery for grassland monitoring Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B3-2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 3, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 3 Importance : pp 591 - 598 Format : 21 x 30 cm Note générale : bibliographie
This research has been funded by the Agence pour le Développement Et la Maîtrise de l’Energie (ADEME) and the Centre National d’Etudes Spatiales (CNES).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] fusion de données
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prairie
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. Grasslands are such crucial surfaces, both in terms of economic and environmental issues and the automatic and frequent monitoring of their agricultural practices is required for many purposes. To address this problem, the deep-based SenDVI framework is presented. SenDVI proposes an object-based methodology to estimate NDVI values from Sentinel-1 SAR observations and contextual knowledge (weather, terrain). Values are regressed every 6 days for compliance with monitoring purposes. Very satisfactory results are obtained with this low-level multimodal fusion strategy (R 2 =0.84 on a Sentinel-2 tile). Finer analysis is however required to fully assess the relevance of each modality (Sentinel-1, Sentinel-2, weather, terrain) and feature sets and to propose the simplest conceivable framework. Results show that not all features are necessary and can be discarded while others have a mandatory contribution to the regression task. Moreover, experiments prove that accuracy can be improved by not saturating the network with non-essential information (among contextual knowledge in particular). This allows to move towards more operational solution. Numéro de notice : C2020-004 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B3-2020-591-2020 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-591-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95664 Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n° 1 (January 2020)
[article]
Titre : Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification Type de document : Article/Communication Auteurs : Viktor Myroniuk, Auteur ; Mykola Kutia, Auteur ; Arbi J. Sarkissian, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande infrarouge
[Termes IGN] carte forestière
[Termes IGN] changement climatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Google Earth Engine
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Landsat
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] image satellite
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] plaine
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] UkraineRésumé : (auteur) Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15% Numéro de notice : A2020-225 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs12010187 Date de publication en ligne : 05/01/2020 En ligne : https://doi.org/10.3390/rs12010187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94940
in Remote sensing > vol 12 n° 1 (January 2020) . - 24 p.[article]PermalinkA systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems / Dong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkAn implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkSpatiotemporal variation in the relationship between boreal forest productivity proxies and climate data / Clémentine Ols in Dendrochronologia, vol 58 (December 2019)PermalinkAccurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkDeep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkResidences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkAutomatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkEstimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images / Jie Wang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkCombining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes / Meng Zhang in Geocarto international, vol 34 n° 10 ([15/07/2019])Permalink