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Insights about Stagiaire - Irstea members on LinkedInShow prevIrstea - 112INRA French National Institute for Agricultural Research - 7EDF - 5ARVALIS - Institut du végétal - 3Education Nationale - 3CGI - 2Université Grenoble Alpes - 2Institut technologique FCBA - 2Centre National d'?tudes Spatiales - 2Artelia - 2Show nextBreakdown of top 10Show prevUniversité de Bordeaux - 32AgroParisTech - Institut des sciences et industries du vivant et de l'environnement - 29Université Joseph Fourier (Grenoble I) - 26Université Claude Bernard Lyon 1 - 22Université Blaise Pascal (Clermont-II) - Clermont-Ferrand - 21Université de Rennes I - 20University of Montpellier - 20Pierre and Marie Curie University - 20Paris-Sud University (Paris XI) - 18Université Paul Sabatier Toulouse III - 18Show nextBreakdown of top 10Show prevRenewables & Environment - 115Research - 104Environmental Services - 42Farming - 26Government Administration - 21Information Technology and Services - 20Chemicals - 18Mechanical or Industrial Engineering - 17Think Tanks - 15Show nextBreakdown of top 9Show prevFrance - 501Paris Area, France - 112Lyon Area, France - 78Bordeaux Area, France - 37Montpellier Area, France - 34Clermont-Ferrand Area, France - 21Show nextBreakdown of top 6Stagiaire profiles on LinkedInFind and connect to the top Stagiaire members on LinkedInLooking to hire?Get unbeatable access to Stagiaire candidates with LinkedIn Jobs.Stagiaire at IrsteaPast experienceCompany placeholder imageAnimateur Surveillant de Baignade at Accueil de Loisirs Municipal du Val-vertCompany placeholder imageAnimateur surveillant de baignade at Oceane voyagesCompany placeholder imageAnimateur Surveillant de Baignade at Accueil de loisirs Muncipal du Val-vertCompany placeholder imageAnimateur surveillant de baignade at Loisirs Provence MediterranéeEducationPolytech MontpellierPolytech Annecy-ChambéryCompany placeholder imageLycée Charles et Adrien DUPUYshow aboveshow belowStagiaire at IrsteaPast experienceStage étudiant at SEAALEducationCompany placeholder imagePolytech'Nice-SophiaCompany placeholder imageIncheon National University (South Korea)Ecole Nationale Polytechnique (ENP) - Algeriashow aboveshow belowStagiaire at IrsteaResponsable du P?le Qualité at Ville inventivePast experience?lève officier du personnel navigant at Armée de l'airEducationUniversité Paris 1 Panthéon-Sorbonneshow aboveshow belowStagiaire M2 at IrsteaCompany placeholder imageVice-président at Badminton Club MeylanPast experienceCompany placeholder imageStage étudiant at Abiolab-AsposanAuxiliaire de vacances at Société GénéraleEducationUniversité Grenoble AlpesUniversité Joseph Fourier (Grenoble I)Université Joseph Fourier (Grenoble I)SummaryBiologie - Environnement - Santé - Toxicologie - Ecotoxicologieshow aboveshow belowStagiaire en recherche appliquée at IrsteaPast experienceStagiaire en recherche at CIRADProjet d'élève ingénieur : Le service drone dans la détection des maladies en viticulture at Civic Drone (Ex-Workfly)Company placeholder imageStage en exploitation agricole laitière et maraichère at GAEC VoireuchonEducationUniversidad de TalcaMontpellier SupAgroCompany placeholder imageInstitut Emmanuel d'AlzonSummary2011 : Baccalauréat Scientifique mention AB
: Classe préparatoire aux grandes écoles BCPST
Septembre 2014 - Décembre 2015 :...show aboveshow belowAre you a Stagiaire?Create a free profile to get recognized for what you doStagiaire at IrsteaPast experienceCompany placeholder imageStagiaire at BRAKINACompany placeholder imageStage de fin de cycle at Laboratoire Eau Dépollution Ecosystème Santé (LEDES) de 2iE.Company placeholder imageStage de master 1 at Office National de l’Eau et de l’Assainissement (ONEA)Company placeholder imageStage de Bachelor-Mission de contr?le at Direction Générale de l’Entretien Routier (DGEREducationCompany placeholder imageUniversité Jean Moulin (Lyon III)Company placeholder imageInstitut international d'ingénierie de l'eau et de l'environnementCompany placeholder imageGraduate school of management ouagadougouSummaryAlarmée par les difficultés d'accès à l'eau potable et à des infrastructures sanitaires répondant aux normes mondiales par les populations...show aboveshow belowCompany placeholder imageEtudiant at EseoStagiaire at IrsteaEducationEcole supérieure d'Electronique de l'Ouest-ESEO ANGERSCompany placeholder imageLycée Chevrolliershow aboveshow belowStagiaire at IrsteaPast experienceStagiaire at Wageningen Environmental ResearchCompany placeholder imagePorteur de Projet at Jeu video SymbioseStagiaire at Ginger CEBTPEducation?cole Centrale de LyonUniversité Grenoble Alpesshow aboveshow belowStagiaire at IrsteaPast experienceStagiaire at OMPICCompany placeholder imagestagiaire at Institute of Plant Science Paris-Saclay (IPS2)EducationCompany placeholder imageUniversité Fran?ois Rabelais de ToursUniversité Paris Sud (Paris XI)SummaryAprès avoir obtenu une licence en biologie à l'université Paris Sud me permettant d'acquérir un raisonnement scientifique et de ma?triser...show aboveshow belowPerson placeholder imageStagiaire at IrsteaPast experienceStagiaire at Ecole Nationale Polytechnique (ENP) - AlgeriaCompany placeholder imageStagiaire at UNION PHARMACEUTIQUE CONSTANTINOISE (UPC) LIMITEDCompany placeholder imageStagiaire at SONATRACHCompany placeholder imageStagiaire at StidEstEducationENSTA ParisTech - ?cole Nationale Supérieure de Techniques AvancéesEcole Nationale Polytechnique (ENP) - Algeriashow aboveshow belowStagiaire at IrsteaPast experienceCompany placeholder imageMoniteur d'aviron at Rowing Club de MarseilleStagiaire at Orange FranceCompany placeholder imageStagiaire at Rowing Club de MarseilleEducationAix-Marseille UniversitéAix-Marseille Universitéshow aboveshow belowStagiaire at IrsteaPast experienceEmployé saisonnier at AQUABIOCompany placeholder imageStagiaire at Syndicat Intercommunal du Bassin Versant du DonCompany placeholder imageStagiaire at CNRS délégation Pays de la LoireCompany placeholder imageVolunteer at Exmoor Mires ProjectEducationCompany placeholder imageRennes 1, Science de la Terre et de l'environnement.show aboveshow belowPerson placeholder imageCompany placeholder imageStagiaire at IRSTEAPast experienceCompany placeholder imageStagiaire at ANSESEducationUniversité de Rennes Ishow aboveshow belowEn stage de fin d'étude at IrsteaStagiaire at IrsteaPast experienceCompany placeholder imageStagiaire technicien at Les Ciments de BizerteStagiaire ingénierie at TUNISAIRCompany placeholder imageStagiaire ingénierie at geftech tunisieEducationEcole nationale d'Ingénieurs de MonastirGrenoble INP - Génie industrielCompany placeholder imageInstitue Préparatoire aux Ecoles d'Ingénieurs d'El Manar (IPEIEM)SummaryJe suis en 3ème année génie mécanique à l'Ecole Nationale d'Ingénieurs de Monastir (ENIM) en spécialité Mécanique Numérique. show aboveshow belowPerson placeholder imageIngénieur stagiaire at IrsteaPast experienceIngénieur stagiaire at Grand Lyon, communauté urbaineEducationCompany placeholder imageEcole Nationale Supérieure d'Ingénieurs de Poitiers Company placeholder imageLycée Berthollet, AnnecyCompany placeholder imageLycée Champollion, Grenobleshow aboveshow belowStagiaire at IrsteaPast experienceCompany placeholder imageInondations en milieu urbain at Institut Jean Le Rond d'Alembert (IJLRDA)Internship in the DarkLight Experience at Massachusetts Institute of Technology (MIT)Company placeholder imageAnimatrice d'un atelier &Sciences au quotidien& at Ecole élémentaireStagiaire dans la division Optronique et Défense, pour les Boules optroniques GyroStabilisées at SagemEducationUniversité Pierre et Marie Curie (Paris VI)National University of SingaporeCompany placeholder imageLycée Marcelin Berthelot (94100)SummaryJe viens d'obtenir ma Licence d'ingénierie mécanique dans le Cursus Master en Ingénierie (CMI), à l'Université Pierre et Marie Curie, après...show aboveshow belowStagiaire at IrsteaPast experienceCompany placeholder imageAssistant administratif at AUVERGNE HABITATAssistant administratif at Crédit CoopératifCompany placeholder imageStudent Job at E. LeclercCompany placeholder imageStudent Job at E. LeclercEducationInstitut d'Etudes Politiques de GrenobleUniversit?t PotsdamCompany placeholder imageLycée Fénelonshow aboveshow belowstagiaire at IrsteaPast experienceCompany placeholder imageresponsable animatrice at ADAPEI DE LA LOIRECompany placeholder imageanimatrice at ASSAGAEducationInstitut d'Etudes Politiques de GrenobleLeopold-Franzens Universit?t InnsbruckCompany placeholder imageLycée Saint-Justshow aboveshow belowStagiaire at IrsteaPast experienceCompany placeholder imagePrésident at Associaiton Nature ProtectVolontaire at EduCARE India NGOCompany placeholder imageApprenti at Botaniste NaturopatheCompany placeholder imageOuvrier agricole at Sunrise FarmEducationBordeaux Sciences AgroUniversité LavalUniversity of Helsinkishow aboveshow belowStagiaire at IrsteaPast experienceCompany placeholder imageVoyage en autostop at Royaume UniStage at Université de Bretagne OccidentaleCompany placeholder imageStage at Ferme de CorailEducationUniversité de Bretagne OccidentaleUniversité de Bretagne OccidentaleSummaryEnthousiasmée par les différents enseignements que j’ai suivis durant mes études, j'ai un go?t prononcé pour la botanique et l’entomologie....show aboveshow belowPerson placeholder imageshow aboveshow belowstagiaire at IrsteaPast experienceStage- Veille concurrentielle at Now SocialEducationUniversité de Caen NormandieUniversité de Caen Normandieshow aboveshow belowStagiaire at IrsteaEducationUniversité Paul Valéry (Montpellier III)Université Nice Sophia AntipolisCompany placeholder imageLycée agricole et horticole, Antibesshow aboveshow belowStagiaire at IrsteaPast experienceIngénieur projets at EurofinsTechnicien - Département Contr?le Qualité Eau potable at Eau du Ponant SPLTechnicien Eau potable at Veolia WaterEducationUniversité de MontpellierUniversité de Bretagne-SudUniversité de Bretagne-Sudshow aboveshow belowstagiaire at IrsteaPast experienceCompany placeholder imagestage de Master 1 at Institut National de Recherche Agronomique (INRA)EducationUniversité de BordeauxUniversité de BordeauxUniversité Jean Monnet Saint-EtienneSummaryActuellement stagiaire à l'Irstea de Cestas, dans le cadre de mon Master 2 océanographie, spécialité biologie et écologie marines, de...show aboveshow belowShow moreShow lessReferencesSee all >4 Figures
21.97 · National Research Institute of Science and Technology for Environment and Agriculture39.53 · National Research Institute of Science and Technology for Environment and Agriculture17.42 · Maison de la Télédétection en Languedoc-RoussillonAbstractDeveloping and improving methods to monitor both natural and non-natural environments such as forest and urban in space and time is a timely challenge. To overcome this challenge, we created a software platform-TomoSAR. The kernel of this platform supports the entire processing from SAR, Interferometry, Polarimetry, to Tomography (so called TomoSAR). The objective of this paper is to introduce this platform about its design architecture and its capacity. We showed four examples to highlight the TomoSAR platform capacities. First, the useful of the interferometric coherence of TOPS Sentinel-1 for land cover classification was highlighted. Second, a TOPS Sentinel-1 differential inter-ferogram in a complex scenario volcano was successfully produced. Third, a TOPS Persistent Scatterers Interferome-try analysis for estimating subsidence in Ho Chi Minh City area was demonstrated. Finally, the capability of processing and modelling of 3D P-band tomography in volume forest scattering were reported.Discover the world's research13+ million members100+ million publications700k+ research projects
TOMOSAR PLATFORM: A NEW IRSTEA SERVICE AS DEMAND FOR SAR,INTERFEROMETRY, POLARIMETRY AND TOMOGRAPHYDinh HO TONG MINH, Yen-Nhi NGO, Nicolas BAGHDADI, Pierre MAURELIrstea, UMR TETIS, Montpellier, FranceEmail: Dinh.Ho-Tong-Minh@Irstea.frABSTRACTDeveloping and improving methods to monitor both natu-ral and non-natural environments such as forest and urbanin space and time is a timely challenge. To overcome thischallenge, we created a software platform - TomoSAR. Thekernel of this platform supports the entire processing fromSAR, Interferometry, Polarimetry, to Tomography (so calledTomoSAR). The objective of this paper is to introduce thisplatform about its design architecture and its capacity. Weshowed four examples to highlight the TomoSAR platformcapacities. First, the useful of the interferometric coher-ence of TOPS Sentinel-1 for land cover classification washighlighted. Second, a TOPS Sentinel-1 differential inter-ferogram in a complex scenario volcano was successfullyproduced. Third, a TOPS Persistent Scatterers Interferome-try analysis for estimating subsidence in Ho Chi Minh Cityarea was demonstrated. Finally, the capability of processingand modelling of 3D P-band tomography in volume forestscattering were reported.Index Terms—Synthetic Aperture Radar, TOPS Sentinel-1, Irstea, Comos SkyMED, TomoSAR platform, service asdemand, subsidence, Ho Chi Minh City, P-band tomography,coherence, land cover, volcano, interferometry1. INTRODUCTIONNumerous SAR space-borne sensors are currently operat-ing or will be launched in the near future. In this context,there is a need to reinforce the methodological and thematicdevelopments in relation to future space missions dedicatedto major scientific and societal issues: the Sentinel-1 andBIOMASS missions by European Space Agency (ESA).Both missions can be feasible to do tomography, particularlywith BIOMASS [1]. While interferometry SAR technique iswell known, the tomography is quite new and state-of-the-arttechnique which provides the unique information in verticaldirection and also the deformation of the objects. The ratio-nale of tomography is to employ multiple flight tracks, nearlyparallel to each other. The ensemble of all flight lines allowsus to form a 2D synthetic aperture, resulting in the possi-bility to focus the signal in the whole 3D space/time. WithBIOMASS tomographic capability, the forest biomass can beretrieval up to 500 t/ha with 10% error at 4-ha scale [1]. WithTOPS Sentinel-1, every city in the world can be monitoredwith millimeter level accuracy [2]. As a consequence, wecreated the TomoSAR platform. The main goal of this paperis to discuss about its design architecture and its capacity.2. TOMOSAR PLATFORM2.1. Concept designThe TomoSAR was designed as an end-to-end software plat-form for processing multi-sensor SAR images. The ker-nel of this platform supports the entire processing fromSAR, Interferometry, Polarimetry, to Tomography (so calledTomoSAR). The concept design is illustrated in Figure1. The TomoSAR platform is currently deployed as aservice as demand of Theia/GEOSUD (http://ids.equipex-geosud.fr/tomosar-services). Under scientific collaborations(e.g., a jointly journal paper), it maybe free of charge. If not,you pay as services as demand. The platform is based on theprevious works done by D. Ho Tong Minh in the frame ofhis research at the Politecnico di Milano [3, 4], and CESBIO[1, 5] and continuously developed by his group at Irstea [6, 7].2.2. TomoSAR architectureThe platform offers the entire processing from raw data tohigh level products such as digital elevation models (DEM),land cover, displacement, biomass and height maps.The development of the platform is conducted in the Clanguage using standard libraries like OpenMP to promoteparallelization and efficiency. Using C-code will allow theplatform to run in cross-platforms, e.g. Linux, Mac or Win-dows operate systems.In order to improve code reliability, the development issplitted in processors, each of them ensuring a specific taskof the processing chains. These processors are to addressthe following purposes: SAR, Interferometry (INTERF), Po-larimetry (POL) and Tomography (TOMO). The architectureis shown in Figure 2. The bottom is the TomoSAR enginewhich contains the dynamic link library functions of the plat-
Fig. 1. TomoSAR platform design.form. The top is the Display tools to facilitate the visuali-sation of the results. SAR-INTERF-POL-TOMO processorsare indeed a collection of stand alone programs. Using thisapproach, it makes the platform flexible and possible to con-trol by script languages such as Python, Perl, Matlab.Fig. 2. End-to-end TomoSAR platform architecture.2.2.1. SARThe SAR processor is to focalize synthetic aperture radar im-ages from raw signals to single look complex (SLC) SAR andmulti-look intensity data. This supports the traditional Range-Dopple algorithm to do range and azimuth compressions.2.2.2. Interferometry (INTERF)The Interferometry processor is to offer a complete tool forinterferogram generations, coherence estimation, 2-pass dif-ferential interferometry, terrain geocoding, adaptive phase fil-tering, phase unwrapping and height mapping. Particularly,the TOPSAR and stripmap modes are fully supported. In nearfuture, we will work for ScanSAR mode, e.g., ALOS-2 data.2.2.3. Polarimetry (POL)The Polarimetry processor is dedicated to thematic applica-tions by not only exploiting the phase as in interferometry butalso the polarimetric intensity. This implemented the toolswhich are useful to estimate parameters of interest such astemporal adaptive filtering, polarimetric decomposition andclassification.2.2.4. Tomography (TOMO)The Tomography processor is implemented to exploit themulti-polarimetry multi-temporal multi-baseline SAR data.The processor offers a collection of phase calibration programto remove phase artifacts from the original SAR data. Thephase calibration can be carried out by using stable targets(e.g., permanent/persistent scatterers [7]) mostly in urban andby unstable targets (e.g., distributed scatterers [4, 6]) mostlyin forest areas. After the phase calibration process, the pa-rameters of interest such as displacement history, velocity,3D layer, biomass and building height maps can be estimatedby the processor.3. DEMONSTRATIONSIn this section, we aim to show four examples to highlightthe TomoSAR platform capacities. This section is structuredas follows: in section 3.1, the interferometric coherence ofTOPS Sentinel-1 for land cover classification is reported byusing INTERF and POL in section 3.2, a TOPSSentinel-1 differential interogram is produced by INTERF in section 3.3, a Persistent Scatterers Interferom-etry analysis for estimating subsidence in Ho Chi Minh Cityarea is demonstrated by TOMO processor and finally in sec-tion 3.4, the capability of processing and modelling of 3Dtomography in volume forest scattering are shown by usingTOMO processor.3.1. TOPS coherence for land cover classificationThe potential of TOPS Sentinel-1 interferometric coherencedata for land cover classification was investigated at a studyarea closed to Auch city, France. A time-series of 21 TOPSSentinel-1 track 110 descending and 19 TOPS Sentinel-1track 30 ascending SAR images in 2015 were processed. Allimage data was coregistered and orthorectified into map co-ordinates using a SRTM DEM. A two-stage hybrid classifiermethod was employed, where the water-class was classi-
fied separately in the first classifier stage, and the remainingclasses were classified with an Maximum Likelihood classi-fier. The supervised samples are from the SPOT 6/7 images.The result is reported in Figure 3. The overall accuracy for8 classes was found to be 76% with kappa coefficient of 0.7.Interferometric coherence carries more land cover relatedinformation than the intensity (e.g., improving from 63% to79% in urban areas). This study confirms that the TOPSSentinel-1 could be exploited for land cover classification.3.1.1. Summary the processing using TomoSAR platformInterferometric procesingSince the data were from two different imaging geometriesdepending on the track ascending and descending, the imageswere processed in two separate sets. First, a master imagewas chosen for each track, and all images of each track werecoregistered with taking into account of TOPS mode to theirrespective master image. Deramping has been donne for allthe coregistered images by using deramping phase of mas-ter image. Interferograms were generated from each consec-utive pair by cross-correlating the already coregistered im-ages. Common band filtering was applied before interfero-gram generation in order to minimize the effects of the base-line geometry on coherence estimation. At this stage multi-looking (5 range looks) was performed in order to improveon the estimates of the interferometric phase and coherence.After multilooking the pixel size of the image data is approx-imately 13 m in both azimuth and slant range at the Sentinel-1 nominal look angle of 30. The resulting five-look inter-ferograms were flattened using high-quality orbit informationdistributed by ESA. The interferometric coherence was esti-mated using square estimator windows with 5x5 window sizesusing Gaussian weighing of the samples. In addition to thecoherence images with a temporal baseline of 12 days, coher-ence images with longer temporal baselines were also formed.The longtime coherence images are used to detect urban fea-tures that can remain interferometrically stable for months.Also, five-look intensity gamma images were generated andradiometrically calibrated for range spreading loss, antennagain, normalized reference area and the calibration constantthat depends on the parameters Sentinel-1 SAR header.Temporal filteringReliable estimates of the intensity from a distributed target re-quire that the estimated number of looks (ENL) is sufficientlylarge. Speckle filtering is often used to increase the ENL withloss of spatial resolution. In properly coregistered multitem-poral datasets it is possible to employ the technique of tem-poral filtering, which in principle increases radiometric reso-lution without degrading spatial resolution. The intensity andcoherence images were filtered separately with the temporalfilter. The temporally filtered images usually are markedlydiminished speckle with little or no reduction in spatial reso-lution.GeocodingAfter interferometric processing and filtering all the pro-cessed images were in the imaging geometries of the twomaster images. In order to create a unified dataset all imagedata had to be orthorectified into map coordinates. This wasaccomplished by creating a simulated SAR image from aSRTM DEM 30m, and using the simulated SAR image tocoregister the two image sets. After geocoding, all intensityimages are transformed to logarithm dB scale. Then all in-tensity and coherence images are scaled to values between0–255 (8 bits) and inputted into the classifier.3.2. TOPS InterferometryDifferent from the section 3.1 where the coherenc can be eas-ily estimated in the TOPS mode, the phase however is a bigchallenging. Compare to the conventional Stripmap whichexploits full Doppler history for each target in the azimuth di-rection, the TOPS uses only part of its [8]. As a consequence,the TOPS introduces an additional quadratic phase term in theazimuth direction. In case of a small mis-registration error(e.g., 1/1000 pixel) in azimuth between a pair of images, thisresidual term leads to a phase jump the interferometric phase[9]. A well-known solution of this phase jump is to use spec-tral diversity [10] and potentially enhancement by consideringa DEM available [11]. After solving the phase jump problem,the TOPS can be processed as in the Stripmap mode. An ex-ample of processing TOPS Sentinel-1 by TomoSAR platformis illustrated in Figure 4.3.3. TOPS Persistent Scatterer InterferometryRecent advances in the Persistent Scatter Interferometry, inthe work by [12] and [13], the estimation process of the de-formation rate can be done from not only PS (permenant scat-terers) but also DS (distributed scatterers) targets. The Max-imum Likelihood Estimation is used that jointly exploits allthe N (N - 1)/2 interferograms available from N images, inorder to squeeze the best estimates of the N - 1 interfero-metric phases. This step is known as name Phase Linkingor Phase Triangulation [13]. Such step is very powerful forDS-based phase calibration in forest SAR tomography frameworks, even with N=6 images [4, 6].3.3.1. Phase calibrationBasically, phase calibration is need to separate the contribu-tions of the decorrelation noises from the parameters of in-terest. After such step, the atmospheric phase screen, to-pographic residual and orbit errors should be removed fromthe original SLC data. Then the estimation of displacement
Fig. 3. Exploiting the interferometric coherence of TOPS Sentinel-1 for land cover application.
Fig. 4. TOPS Sentinel-1 differential interferogram (01 Feb.2015 – 21 Mar. 2015) using 3 bursts to coverage the wholeisland R?eunion. There is no phase jump problem even in thecomplex scenario (volcano) in this figure and it successfullycaptures the eruption in 04 February 2015 at the Piton de laFournaise.time-series of each measurement point can be done. In ourapproach, we exploit not only PS but also DS informationfor such analysis. In the following, after coregistration slaveSARs to the master SAR, the phase calibration processingchain can be described as follows:1) Apply the Phase Linking algorithm to each coherencematrix associated to each DS to squeeze optimized phases.2) Select the DS exhibiting a Phase Linking coherencevalue higher than a certain threshold and substitute the phasevalues of the original SAR images with their optimized val-ues.3) Select PS/DS candidates by combination of the backscat-ter variability and spectral diversity.4) Process the selected PS/DS jointly as an iterative fash-ion to improve the model parameters to achieve an optimal fitto the observed interferometric phases.5) Estimate atmostpheric phase screen, topographic resid-ual, and orbit errors.3.3.2. PS/DS resultsThe data-set considered here consists of 49 stripmap Cos-mos SkyMED (CSK) X-band and TOPS Sentinel-1 C-band23 images acquired from 2014 to 2016 in Ho Chi Minh City(HCMC). We found that after solving the phase jump prob-lem of the coregistration and phase deramping, the TOPS canbe processed as in the Stripmap mode.Assuming that most of the measured deformation corre-sponds to vertical displacement of the surface due to sub-sidence, we can then obtain vertical displacement throughstraightforward geometrical arguments. The assumption issupported by the fact that tectonics are found only in north-western and in central Vietnam [14]. Furthermore, we as-sume that there is no obvious seasonal variability sothat thesubsidence history can be approximated by a linear function.Such assumption is supported by the fact that in HCMC, thegroundwater abstraction is mainly from confined aquifer lay-ers (at 50-120 m depth), which are little affected by seasonalrecharge.In Figure 5, the averaged vertical velocity (mm/yr) map isshown. Zeros velocities (green colors) neg-ative velocities (red colors) represent subsidence. The com-parison Stripmap Cosmos Skymed and TOPS Sentinel-1 canbe made, in which the spatial distribution and the amplitudeshare the same trend between two datasets. The result fromTOPS Sentinel-1 and CSK datasets are consistent with a cor-relation coefficient R2= 0.72, which can be considered as asuccessful self-validation. Furthermore, the displacement his-tory of TOPS Sentinel-1 and CSK is also very similar over theoverlapped period of time.More discussions on the subsidence phenomena of HCMCcan be found in [7, 2].3.4. 3D TomographyAt Paracou forest site, the signal at P-band coming from uppervegetation layers (i.e.: ground contributions not included) wasfound strongly related to the forest biomass [4]. These resultsadvocate for the employment of 3D tomography methods tomap forest biomass in particular for tropical forest where highaccuracy in forest biomass is required to improve our knowl-edge of the forestcarbon stock and its change, essential ele-ments of the terrestrial carbon budget [1].For the Nouragues forest, it is necessary to take terraintopography factor into account, besides phase disturbancesand irregular baselines sampling issues in typical airbornetomographic processing. As compared to Paracou forest site,in Nouragues, the topography becomes a critical problemcaused by: a) the double bounce which tends to vanish whenthe topographic ground slope increases (i.e.&4-5°) [15]; b)the confusion between the signal coming from the canopy andfrom n and c) the weaker signal reachingthe ground [4]. These issues make the phase calibration beinga challenged task.3.4.1. Phase calibrationThe terrain topography correction is intended as the ability oftomography to focus at a certain height, then terrain contribu-tions are rejected. To do this, we aim to remove the groundphases defined by the optical paths from the ground layer tothe sensors from the SLC data stack. In other words, the
Fig. 5. Good agreement between 23 images TOPS Sentinel-1 C-band and 49 images Stripmap CosmosSkyMED X-band in HoChi Minh City. Both are effective to detect the subsidence phenomena in .ground phases are determined not only by terrain height zgbut also by the phase disturbances ηderiving from the plat-form motion. In formula, ?gr ound =Kzzg+η, where : Kzis the height-to-phase factor. In the following, we describe theprocessing chain to retrieve ground phases.In the first step, thanks to the outperformance of HHchannel in observing the contributions from the groundlevel, an initial guess for ground phases has been carriedout by exploiting Capon spectra for HH channel. In formula,?initial =Kzz0g, where z0g=arg max{SCapon(z;H H)}.We then correct the original SLC data stack using this initialphase. After this step, the multi-polarimetric multi-baselinecovariance matrix Wcan be approximated by retaining thefirst 2 terms of the Sum of Kronecker Products [16]. Informula,W≈CG?RG+CV?RV(1)where Rand Care referred to interferometric and polari-metric information, Gand Vare associated withground-only and volume-only contributions, respectively.Finally, the ground phase can be obtained by:?ground =∠RG+?initial (2)To do terrain topography correction, a phase calibration isthen performed by removing the retrieved ground phases fromthe original data SLC data stack. By this way, we get twoadvantages: 1) the removal of the propagation disturbances,which allows a correct focusing along the vertical direction byexploiting the Fourier Transform and 2) the removal of terraintopography, resulting in the contributions from the terrain tobe automatically focused at 0 m, independent of the actual to-pography. After phase calibration, the TomoSAR processingsuch as tomographic imaging is carried out according to theprocedure proposed in [4].3.4.2. Tomographic resultsThe tomographic data-set considered here consists of 5fully polarimetric SLC images at P-band acquired over theNouragues forest in August 2009 [17].Figure 6 presents a tomographic profile of a range sec-tion for Ground and Volume contributions, HH and HV po-larizations. The top panels and the white lines denote the Li-DAR height measurements, for comparison with the tomog-raphy profile. After terrain topography correction, based onthe ground profile on the top left panel, most of phase centerare located on the ground, showing the excellent performanceof the retrieval of the ground contributions. On the right pan-els, contributions from the ground level beneath the forest areobserved. However, significant scattering contributions arealso observed at the canopy level in HH polarization, whereasthe volume scattering contribution is dominating in HV polar-ization. These results show that the scattering mechanisms intropical forest are quite different from boreal forests where for
Fig. 6. Tomographic profile of a range section at the Nouragures forest for Ground and Volume contributions (left panels), andfor HH and HV polarizations (right panels). The power level for each channel is normalized in such a way that the level rangesfrom 0 (dark blue) to 1 (dark red). The top panels and the white line denote the LiDAR height measurements.all polarizations, the dominating contribution was observed tobe associated with the ground level.More discussions on the 3D tomograpgy can be found in[4, 6].4. ACKNOWLEDGMENTSSentinel-1 and TropiSAR data were provided by EuropeanSpace Agency. Land cover SPOT 6/7 reference was providedby CESBIO. Cosmos SkyMED images were provided byAgenzia Spaziale Italiana under project COSMO-SkyMed-Open ID2265. REFERENCES[1] D. Ho Tong Minh, S. Tebaldini, F. Rocca, T. Le Toan,L. Villard, and P. Dubois-Fernandez, “Capabilitiesof BIOMASS tomography for investigating tropicalforests,” Geoscience and Remote Sensing, IEEE Trans-actions on, vol. 53, no. 2, pp. 965–975, Feb 2015.[2] D. Ho Tong Minh, Q. V. Vuong, V. T. Le, and Yen-NhiNgo, “Comparison stripmap Cosmos Skymed X-bandand TOPS Sentinel-1 C-band in estimating ground sub-sidence using Irstea TomoSAR platform: Ho Chi Minhcity case study,” in ESA Living Planet Symposium 2016;Proceedings of, May 2016, pp. 1–8.[3] D. Ho Tong Minh, S. Tebaldini, F. Rocca, T. Koleck,P. Borderies, C. Albinet, L. Villard, A. Hamadi, andT. Le Toan, “Ground-based array for tomographic imag-ing of the tropical forest in P-band,” Geoscience and Re-mote Sensing, IEEE Transactions on, vol. 51, no. 8, pp., Aug 2013.[4] D. Ho Tong Minh, T. Le Toan, F. Rocca, S. Tebaldini,M. Mariotti d’Alessandro, and L. Villard, “Relating P-band synthetic aperture radar tomography to tropicalforest biomass,” Geoscience and Remote Sensing, IEEETransactions on, vol. 52, no. 2, pp. 967–979, Feb 2014.[5] D. Ho Tong Minh, S. Tebaldini, F. Rocca, andT. Le Toan, “The impact of temporal decorrelation onbiomass tomography of tropical forests,” Geoscienceand Remote Sensing Letters, IEEE, vol. 12, no. 6, pp., June 2015.[6] D. Ho Tong Minh, T. Le Toan, F. Rocca, S. Tebaldini,L. Villard, M. Rejou-Mechain, J. Chave, and K. Scipal,“SAR tomography for the retrieval of forest biomass andheight : cross-validation at two tropical forest sites inFrench Guiana,” Remote Sensing of Environment, vol.175, pp. 138–147, Mar. 2016.[7] D. Ho Tong Minh, V. T. Le, and T. Le Toan, “Map-ping ground subsidence phenomena in ho chi minh citythrough the radar interferometry technique using alospalsar data,” Remote Sensing, vol. 7, pp. ,July 2015.[8] F. D. Zan and A. M. M. Guarnieri, “Topsar: Terrainobservation by progressive scans,” IEEE Transactionson Geoscience and Remote Sensing, vol. 44, no. 9, pp., Sept 2006.[9] P. Prats-Iraola, R. Scheiber, L. Marotti, S. Wollstadt,and A. Reigber, “Tops interferometry with terrasar-x,”IEEE Transactions on Geoscience and Remote Sensing,vol. 50, no. 8, pp. , Aug 2012.[10] R. Scheiber and A. Moreira, “Coregistration of in-terferometric SAR images using spectral diversity,”
IEEE Transactions on Geoscience and Remote Sensing,vol. 38, no. 5, pp. 2179 –2191, Sep. 2000.[11] R. Scheiber, M. Joger, P. Prats-Iraola, F. D. Zan, andD. Geudtner, “Speckle tracking and interferometric pro-cessing of terrasar-x tops data for mapping nonstation-ary scenarios,” IEEE Journal of Selected Topics in Ap-plied Earth Observations and Remote Sensing, vol. 8,no. 4, pp. , April 2015.[12] F. Rocca, “Modeling interferogram stacks,” Geoscienceand Remote Sensing, IEEE Transactions on, vol. 45,no. 10, pp. , Oct. 2007.[13] A. Ferretti, A. Fumagalli, F. Novali, C. Prati, F. Rocca,and A. Rucci, “A new algorithm for processing interfer-ometric data-stacks: SqueeSAR,” Geoscience and Re-mote Sensing, IEEE Transactions on, vol. 49, no. 9, pp., Sept 2011.[14] C. Lepvrier, N. V. Vuong, H. Maluski, P. T. Thi, andT. V. Vu, “Indosinian tectonics in vietnam,” ComptesRendus Geoscience, vol. 340, no. 2-3, pp. 94 – 111,2008, lorogenese triasique indosinienne en Asie del’Est.[15] Mariotti d’Alessandro, M., S. Tebaldini, and F. Rocca,“Phenomenology of ground scattering in a tropical for-est through polarimetric synthetic aperture radar tomog-raphy,” Geoscience and Remote Sensing, IEEE Transac-tions on, vol. 51, no. 8, pp. , 2013.[16] S. Tebaldini, “Algebraic synthesis of forest scenariosfrom multibaseline PolInSAR data,” Geoscience andRemote Sensing, IEEE Transactions on, vol. 47, no. 12,pp. 4132 –4142, dec. 2009.[17] P. C. Dubois-Fernandez, T. Le Toan, S. Daniel, H. Oriot,J. Chave, L. Blanc, L. Villard, M. W. J. Davidson, andM. Petit, “The TropiSAR airborne campaign in FrenchGuiana: Objectives, description, and observed temporalbehavior of the backscatter signal,” Geoscience and Re-mote Sensing, IEEE Transactions on, vol. 8, no. 50, pp., Aug. 2012.
CitationsCitations0ReferencesReferences16ABSTRACT: The objective of this paper is to provide a better understanding of Persistent Scatterers Interferometry (PSI) capabilities in subsidence estimations of TOPSAR Sentinel-1 data. This work has presented an advanced PSI analysis, to provide unprecedented spatial extent and continuous temporal coverage of the subsidence in Ho Chi Minh City by using 49 stripmap Cosmos SkyMED (CSK) X-band and TOPS Sentinel-1 C-band 23 images acquired from 2014 to 2016. The analysis was carried out by using the Irstea TomoSAR platform, which supports the entire processing from SAR, Interferom-etry, Polarimetry, to Tomography (so called TomoSAR). The study shows that the performance of stripmap CSK and TOPS Sentinel-1 is quite similar and effective to detect the subsidence phenomena. Subsidence is most severe in the Holocene silt loam areas along Sai Gon river and in the Southwest of the city, with the maximum value up to-30 mm/yr, similar with the previous study using ALOS PALSAR.Conference Paper · May 2016 · IEEE Transactions on Geoscience and Remote Sensing+1 more author...ABSTRACT: Developing and improving methods to monitor forest carbon in space and time is a timely challenge, especially for tropical forests. The next European Space Agency Earth Explorer Core Mission BIOMASS will collect synthetic aperture radar (SAR) data globally from employing a multiple baseline orbit during the initial phase of its lifetime. These data will be used for tomographic SAR (TomoSAR) processing, with a vertical resolution of about 20 m, a resolution sufficient to decompose the backscatter signal into two to three layers for most closed-canopy tropical forests. A recent study, conducted in the Paracou site, French Guiana, has already shown that TomoSAR significantly improves the retrieval of forest aboveground biomass (AGB) in a high biomass forest, with an error of only 10% at 1.5-ha resolution. However, the degree to which this TomoSAR approach can be transferred from one site to another has not been assessed. We test this approach at the Nouragues site in central French Guiana (ca 100 km away from Paracou), and develop a method to retrieve the top-of-canopy height from TomoSAR. We found a high correlation between the backscatter signal and AGB in the upper canopy layer (i.e. 20–40 m), while lower layers only showed poor correlations. The relationship between AGB and TomoSAR data was found to be highly similar for forests at Nouragues and Paracou. Cross validation using training plots from Nouragues and validation plots from Paracou, and vice versa, gave an error of 16–18% of AGB using 1-ha plots. Finally, using a high-resolution LiDAR canopy model as a reference, we showed that TomoSAR has the potential to retrieve the top-of-canopy height with an error to within 2.5 m. Our analyses show that the TomoSAR-AGB retrieval method is accurate even in hilly and high-biomass forest areas and suggest that our approach may be generalizable to other study sites, having a canopy taller than 30 m. These results have strong implications for the tomographic phase of the BIOMASS spaceborne mission. Full-text · Article · Mar 2016 +1 more author...ABSTRACT: The rapidly developing urbanization since the last decade of the 20th century has led to extensive groundwater extraction, resulting in subsidence in Ho Chi Minh City, Vietnam. Recent advances in multi-temporal spaceborne SAR interferometry, especially with a persistent scatters interferometry (PSI) approach, has made this a robust remote sensing technique for measuring large-scale ground subsidence with millimetric accuracy. This work has presented an advanced PSI analysis, to provide an unprecedented spatial extent and continuous temporal coverage of the subsidence in Ho Chi Minh City from 2006 to 2010. The study shows that subsidence is most severe in the Holocene silt loam areas along the Sai Gon River and in the southwest of the city. The groundwater extraction resulting from urbanization and urban growth is mainly responsible for the subsidence. Subsidence in turn leads to more flooding and water nuisance. The correlation between the reference leveling velocity and the estimated PSI result is R 2 = 0.88, and the root mean square error is 4.3 (mm/year), confirming their good agreement. From 2006 to 2010, the estimation of the average subsidence rate is -8.0 mm/year, with the maximum value up to -70 mm/year. Remote Sens. 4 After four years, in regions along Sai Gon River and in the southwest of the city, the land has sunk up to -12 cm. If not addressed, subsidence leads to the increase of inundation, both in frequency and spatial extent. Finally, regarding climate change, the effects of subsidence should be considered as appreciably greater than those resulting from rising sea level. It is essential to consider these two factors, because the city is inhabited by more than 7.5 million people, where subsidence directly impacts urban structures and infrastructure. Full-text · Article · Jul 2015 ABSTRACT: Terrain observation by progressive scan (TOPS)
antenna beam steering is utilized for European Space Agency’s (ESA’s) Sentinel-1 synthetic aperture radar (SAR) sensor for the interferometric wide swath (IW) and extra wide swath (EW) modes. As a consequence of the azimuth steering, the resulting signal characteristics have to be accounted for in SAR interferometric (InSAR) processing. This paper assesses the performance of speckle tracking and spectral diversity (SD) [also referred to as split spectrum or multi-aperture interferometry (MAI)] when applied to TOPS data acquired over nonstationary scenarios, such as glaciers. The characteristics of the TOPS signal, especially the azimuth-variant Doppler centroid, are discussed with particular consideration of along-track surface motion between the interferometric acquisitions. The TOPS specific coregistration requirements are formulated, followed by an analysis of the theoretical estimation accuracy as a function of the estimation window size. A refined adaptive coregistration approach based on D is suggested. Experimental TerraSAR-X TOPS data acquired over the Lambert glacier, Antarctica, are used to validate the proposed speckle tracking and SD methodologies. Full-text · Article · Oct 2014 +1 more author...ABSTRACT: This paper aims at characterizing the scattering mechanisms occurring at the ground level in a tropical forest illuminated by a P-band synthetic aperture radar (SAR). The analysis is carried out based on the multibaseline, fully polarimetric, data set collected by ONERA over Paracou, French Guyana, in the frame of the European space agency campaign TropiSAR. The favorable baseline distribution of this data set results in the possibility of removing most contributions from the vegetation layer by tomographic techniques, thus allowing the generation of a new fully polarimetric single look complex SAR image relative to scattering contributions from the ground level only. Such a ground layer image is then analyzed by considering the variation of its polarimetric signature with respect to terrain local slope and Radar look angle. Two major conclusions are drawn: 1) double bounce scattering from trunk-ground interactions is observed to be the dominant scattering mechanism at the ground level on flat terrains, whereas it rapidly tends to vanish as the topographic slope increases, and 2) the characteristic parameter that rules trunk-ground scattering is not the tree height, but rather the available free path facing the tree, as a result of the presence of nearby trees, undulating topography, or understory preventing double bounce scattering from taking place whenever the ground bounce occurs too far away from the considered tree. The mean free path length resulting from the analysis of this data-set is found to be L ? 7 m. Finally, we discuss how the concept of free path length can be accounted for in simple terms by assuming an equivalent extinction model characterized by a variation along the horizontal dimension.Article · Aug 2013 ABSTRACT: The TropiSAR campaign has been conducted in August 2009 in French Guiana with the ONERA airborne radar system SETHI. The main objective of this campaign was to collect data to support the Phase A of the 7th Earth Explorer candidate mission, BIOMASS. Several specific questions needed to be addressed to consolidate the mission concept following the Phase 0 studies, and the data collection strategy was constructed accordingly. More specifically, a tropical forest data set was required in order to provide test data for the evaluation of the foreseen inversion algorithms and data products. The paper provides a description of the resulting data set which is now available through the European Space Agency website under the airborne campaign link. First results from the TropiSAR database analysis are presented with two in-depth analyses about both the temporal radiometric variation and temporal coherence at P-band. The temporal variations of the backscatter values are less than 0.5 dB throughout the campaign, and the coherence values are observed to stay high even after 22 days. These results are essential for the BIOMASS mission. The observed temporal stability of the backscatter is a good indicator of the expected robustness of the biomass estimation in tropical forests, from cross-polarized backscatter values as regarding environmental changes such as soil moisture. The high temporal coherence observed after a 22-day period is a prerequisite for SAR Polarimetric Interferometry and Tomographic applications in a single satellite configuration. The conclusion then summarizes the paper and identifies the next steps in the analysis. Full-text · Article · Aug 2012 +1 more author...ArticleFebruary 2015 · IEEE Geoscience and Remote Sensing Letters · Impact Factor: 2.10Conference PaperJuly 2015Conference PaperJuly 2014+4 more authors…Conference PaperJuly 2014+3 more authors…Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.This publication is from a journal that may support self archiving.
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