Applications of ISES for vegetation and land use

Remote sensing relative to applications involving vegetation cover and land use is reviewed to consider the potential benefits to the Earth Observing System (Eos) of a proposed Information Sciences Experiment System (ISES). The ISES concept has been proposed as an onboard experiment and computational resource to support advanced experiments and demonstrations in the information and earth sciences. Embedded in the concept is potential for relieving the data glut problem, enhancing capabilities to meet real-time needs of data users and in-situ researchers, and introducing emerging technology to Eos as the technology matures. These potential benefits are examined in the context of state-of-the-art research activities in image/data processing and management

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Electromagnetic Scattering Models for InSAR Correlation Measurements of Vegetation and Snow

Interferometric Synthetic Aperture Radar (InSAR) has proved successful and efficient in measuring the vertical structure of the distributed targets such as vegetation and snow, which are dominated by volume scattering. In particular, the InSAR correlation measurement has been utilized to retrieve the target vertical structural information. One existing and well-known electromagnetic scattering model of the InSAR correlation was first brought forward focusing on the single-pass InSAR observation of a sparse random medium like vegetation. However, the lack of the adaption of this InSAR scattering model for repeat-pass InSAR observation of vegetation as well as for single-pass InSAR observation of snow by considering its dense medium characteristics, essentially constrain fully exploiting InSAR\u27s capability of measuring sparse and dense medium characteristics. In this work, the well-known InSAR scattering model will be adapted to accommodate the two scenarios: 1) repeat-pass InSAR observation of vegetation and 2) single-pass InSAR observation of snow and considering its dense medium characteristics. Theoretical model derivations as well as parameter retrieval approaches are demonstrated for both of the applications, respectively. Both of the simulated and ground validation results are also presented. The InSAR scattering models along with the parameter retrieval analysis described in this work will expand InSAR\u27s capability as well as the range of vegetation and snow characteristics that can be retrieved by single-pass and/or repeat-pass InSAR systems

JERS-1 SAR and LANDSAT-5 TM image data fusion: An application approach for lithological mapping

Satellite image data fusion is an image processing set of procedures utilise either for image optimisation for visual photointerpretation, or for automated thematic classification with low error rate and high accuracy. Lithological mapping using remote sensing image data relies on the spectral and textural information of the rock units of the area to be mapped. These pieces of information can be derived from Landsat optical TM and JERS-1 SAR images respectively. Prior to extracting such information (spectral and textural) and fusing them together, geometric image co-registration between TM and the SAR, atmospheric correction of the TM, and SAR despeckling are required. In this thesis, an appropriate atmospheric model is developed and implemented utilising the dark pixel subtraction method for atmospheric correction. For SAR despeckling, an efficient new method is also developed to test whether the SAR filter used remove the textural information or not. For image optimisation for visual photointerpretation, a new method of spectral coding of the six bands of the optical TM data is developed. The new spectral coding method is used to produce efficient colour composite with high separability between the spectral classes similar to that if the whole six optical TM bands are used together. This spectral coded colour composite is used as a spectral component, which is then fused with the textural component represented by the despeckled JERS-1 SAR using the fusion tools, including the colour transform and the PCT. The Grey Level Cooccurrence Matrix (GLCM) technique is used to build the textural data set using the speckle filtered JERS-1 SAR data making seven textural GLCM measures. For automated thematic mapping and by the use of both the six TM spectral data and the seven textural GLCM measures, a new method of classification has been developed using the Maximum Likelihood Classifier (MLC). The method is named the sequential maximum likelihood classification and works efficiently by comparison the classified textural pixels, the classified spectral pixels, and the classified textural-spectral pixels, and gives the means of utilising the textural and spectral information for automated lithological mapping

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Program for Arctic Regional Climate Assessment (PARCA)

The Program for Arctic Regional Climate Assessment (PARCA) is a NASA-sponsored initiative with the prime objective of understanding the mass balance of the Greenland ice sheet. In October 1998, PARCA investigators met to review activities of the previous year, assess the program's progress, and plan future investigations directed at accomplishing that objective. Some exciting results were presented and discussed, including evidence of dramatic thinning of the ice sheet near the southeastern coast. Details of the investigations and many of the accomplishments are given in this report, but major highlights are given in the Executive Summary of the report

Investigation of the microwave signatures of the Baltic Sea ice

It is essential for winter shipping in the Baltic Sea to get reliable and up-to-date information of its rapidly changing ice conditions. Spaceborne synthetic aperture radar (SAR) images are the only way to produce this information operationally in fine scale independent of daylight and nearly independent of weather conditions. Currently, classification algorithms for the RADARSAT-1 and ENVISAT SAR images utilize mainly the image structure and only limited information on sea ice geophysics and empirical statistics of backscattering signatures of various ice types are utilized. Therefore, interpretation of the classification results is often difficult. Both classification results and their interpretation should very likely improve with the addition of this information. Spaceborne microwave radiometer data are not suitable for the operational Baltic Sea ice monitoring aiding ship navigation due to their coarse spatial resolution, but they can provide an independent data source on the sea ice conditions for validation of the SAR classification algorithms. Both SAR and radiometer data based sea ice products can also be utilized in the geophysical studies of the Baltic Sea ice. In order to support development of operational classification algorithms for SAR and radiometer data, basic research on the microwave remote sensing of the Baltic Sea ice has been conducted in this work. The research work included the following topics: (1) statistics of C- and X-band backscattering signatures of various ice types, (2) statistics of L- and C-band polarimetric discriminants of various ice types, (3) radar incidence angle dependence of backscattering coefficient (σ°) in RADARSAT-1 SAR images, (4) dependence between standard deviation and measurement length for σ° signatures and its usability in sea ice classification, (5) comparison between SAR σ° time series and results from a thermodynamic snow/ice model, and (6) statistics of passive microwave signatures of various ice types. Additionally, a comprehensive literature review of the previous work on the microwave remote sensing of the Baltic Sea ice is presented. The main results of this work include the following. It is not possible to discriminate open water and various ice types using the level of σ°, co- or cross-polarization ratio, or standard deviation of σ°. C-band VH-polarized σ° at high incidence angle provides slightly better ice type discrimination accuracy than any other combination of C- and X-band radar parameters. VH-polarization is more suitable for estimating the degree of ice deformation than co-polarizations. Snow wetness has a large effect on the σ° statistics. Notably, when snow cover is wet then the σ° contrasts between various ice types are smaller than in the dry snow case. Incidence angle dependence of the C-band HH-polarized σ° was derived for level ice and deformed ice. It is utilized in the operational SAR classification algorithms developed by Finnish Institute of Marine Research. The method for deriving the σ° incidence angle dependence is applicable for any SAR sensor. There is a large variation of level ice σ° with changing weather conditions. A 1-D high-resolution thermodynamic snow/ice model generally helps to interpret changes in the σ° time series. The modeled snow and ice surface temperature, cases of snow melting, and evolution of snow and ice thickness are related to the changes in σ°. It was found out that the standard deviation of σ° for various ice types depends on the length of measurement. This may be utilized in the SAR image classification. It is not possible to resolve concentrations of thin new ice and all other ice types combined in the Baltic Sea using radiometer data as has been done for the Arctic seasonal ice zones.Talvimerenkulku Itämerellä tarvitsee luotettavaa ja ajantasaista informaatiota Itämeren nopeasti muuttuvista jääoloista. Synteettisen apertuurin tutkan (SAR) kuvat ovat ainoa tapa tuottaa operatiivisesti tarvittavaa jääinformaatiota riippumatta päivänvalon määrästä ja lähes riippumatta sääolosuhteista. RADARSAT-1 ja ENVISAT SAR-tutkakuvien luokittelualgoritmit perustuvat tällä hetkellä lähinnä kuvien rakenteeseen, eikä merijään geofysiikkaa ja empiiristä tilastotietoa eri jäätyyppien sirontavasteista hyödynnetä kuin rajallisesti. SAR-kuvien luokittelutulosten tulkitseminen on siten usein vaikeaa. Sekä itse luokittelutulokset, että niiden tulkinta parantuisivat, jos luokittelualgorimit hyödyntäisivät edellä mainittua tietoa. Satelliittiradiometrien kuvat eivät sovellu Itämeren jään operatiiviseen monitorointiin niiden karkean spatiaalisen resoluution vuoksi. Niillä kuitenkin voitaisiin validoida SAR-kuvien luokittelualgoritmeja, koska ne ovat SAR-kuvista riippumaton datalähde Itämeren jääoloista. Tässä työssä on suoritettu seuraavaa perustutkimusta Itämeren jään mikroaaltokaukokartoituksessa, minkä tarkoituksena on tukea SAR- ja radiometrikuvien operatiivisten luokittelualgoritmien kehitystyötä: (1) eri jäätyyppien C- ja X-kanavien sirontakertoimien (σ°) statistiikka, (2) eri jäätyyppien L- ja C-kanavien polarimetristen diskriminanttien statistiikka, (3) σ°:n mittauskulmariippuvuus RADARSAT-1 SAR-kuvissa, (4) σ°:n keskihajonnan ja mittausmatkan välinen riippuvuus ja hyödyntäminen jäätyyppiluokittelussa, (5) SAR-kuvien sirontakerroinaikasarjojen vertailu merijään termodynamiikkamalliin, ja (6) eri jäätyyppien kirkkauslämpötilojen statistiikka. Työssä saavutettiin seuraavia merkittäviä tuloksia. Eri jäätyyppien ja avoveden luokittelu ei ole mahdollista käyttäen sirontakerrointa, yhdensuuntais- ja ristipolarisaatiosuhdetta tai σ° keskihajontaa. C-kanavan VH-polarisaation σ° suurella mittauskulmalla luokittelee eri jäätyypit hieman paremmin kuin mikään muu C- ja X-kanavan tutkaparametrikombinaatio. Merijään deformoitumisasteen estimointiin sopii paremmin VH-polarisaation σ° kuin yhdensuuntaispolarisaation. Lumipeitteen kosteudella on suuri vaikutus sirontakerroinstatistiikkaan; erityisesti, kun lumipeite on märkä on sirontakerroinkontrasti eri jäätyyppien välillä pienempi kun lumipeite on kuiva. C-kanavan HH-polarisaation σ°:n mittauskulmariippuvuus määritettiin tasaiselle ja deformoituneelle jäälle. Mittauskulmariippuvuuden laskentamenetelmää voidaan käyttää mille tahansa SAR-tutkakuvalle. Muuttuvat sääolosuhteet aiheuttavat suuria muutoksia tasaisen jään σ°:ssa. Merijään termodynamiikkamalli yleensä auttaa selittämään muutoksia σ°:n aikasarjassa. σ°:n muutokset ovat yhteydessä termodynamiikkamallilla laskettuihin lumen ja jään parametreihin. σ°:n keskihajonnan havaittiin riippuvan etäisyydestä. Tätä riippuvuutta voitaneen hyödyntään SAR-kuvien luokittelussa. Itämerellä satelliittiradiometridatalla pystytään määrittämään vain merijään kokonaiskonsetraatio, toisin kuin arktisten merien kausiluontoisilla merijääalueilla, missä myös eri jäätyyppien konsentraatioiden määrittäminen on mahdollista.reviewe

Approches tomographiques structurelles pour l'analyse du milieu urbain par tomographie SAR THR : TomoSAR

SAR tomography consists in exploiting multiple images from the same area acquired from a slightly different angle to retrieve the 3-D distribution of the complex reflectivity on the ground. As the transmitted waves are coherent, the desired spatial information (along with the vertical axis) is coded in the phase of the pixels. Many methods have been proposed to retrieve this information in the past years. However, the natural redundancies of the scene are generally not exploited to improve the tomographic estimation step. This Ph.D. presents new approaches to regularize the estimated reflectivity density obtained through SAR tomography by exploiting the urban geometrical structures.La tomographie SAR exploite plusieurs acquisitions d'une même zone acquises d'un point de vue légerement différent pour reconstruire la densité complexe de réflectivité au sol. Cette technique d'imagerie s'appuyant sur l'émission et la réception d'ondes électromagnétiques cohérentes, les données analysées sont complexes et l'information spatiale manquante (selon la verticale) est codée dans la phase. De nombreuse méthodes ont pu être proposées pour retrouver cette information. L'utilisation des redondances naturelles à certains milieux n'est toutefois généralement pas exploitée pour améliorer l'estimation tomographique. Cette thèse propose d'utiliser l'information structurelle propre aux structures urbaines pour régulariser les densités de réflecteurs obtenues par cette technique