TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe

Sea ice segmentation using Tandem-X pursuit mono static and alternative bistatic modes

Accepted manuscript version. Source at https://doi.org/10.1109/IGARSS.2017.8126964In this paper we investigate interferometric pairs of SAR images acquired by Tandem-X with the monostatic pursuit and the alternative bistatic modes for sea ice segmentation. The individual SAR images are modelled as non-Gaussian, and from the modelled data different features are extracted, stacked together and clustered. The interferometric coherence is regarded as an additional feature and utilized for clustering. In addition to complementing the information extracted from the individual images, the interferometric coherence is found to be capable of discriminating between open water and sea ice, as well as between different ice types

Remote sensing of sea ice properties and dynamics using SAR interferometry

Landfast ice is attached to the coastline and islands and stays immobile over most of the ice season. It is an important element of polar ecosystems and plays a vital role as a marine habitat and in life of local people and economy through offshore technology. Landfast ice is routinely used for on-ice traffic, tourism, and industry, and it protects coasts from storms in winter from erosion. However, landfast ice can break or experience deformation in order of centimeters to meters, which can be dangerous for the coastline and man-made structures, beacons, on-ice traffic, and represents a safety risk for working on the ice and local people. Therefore, landfast ice deformation and stability are important topics in coastal engineering and sea ice modeling. In the framework of this dissertation, InSAR (SAR Interferometry) technology has been applied for deriving landfast ice displacements (publication I), and mapping sea ice morphology, topography and its temporal change (publication III). Also, advantages of InSAR remote sensing in sea ice classification compared to backscatter intensity were demonstrated (publications II and IV). In publication I, for the first time, Sentinel-1 repeat-pass InSAR data acquired over the landfast ice areas were used to study the landfast ice displacements in the Gulf of Bothnia. An InSAR pair with a temporal baseline of 12 days acquired in February 2015 was used. In the study, the surface of landfast ice was stable enough to preserve coherence over the 12-day period, enabling analysis of the interferogram. The advantage of this long temporal baseline is in separating the landfast ice from drift ice and detecting long-term trends in deformation maps. The interferogram showed displacements of landfast ice on the order of 40 cm. The main factor seemed to be compression by drift ice, which was driven against the landfast ice boundary by strong winds from southwest. Landfast ice ridges can hinder ship navigation, but grounded ridges help to stabilize the ice cover. In publication III, ridge formation and displacements in the landfast ice near Utqiaġvik, Alaska were examined. The phase signatures of two single-pass bistatic X-band SAR (Synthetic Aperture Radar) image pairs acquired by TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) satellite on 13 and 24 January 2012 were analyzed. Altogether six cases were identified with ridge displacement in four and formation in two cases under onshore compression. The ridges moved approximately 0.6 and 3.7 km over the study area and ridge formation reached up to 1 meter in upward. The results well corresponded with the locations identified as convergence zones retrieved from the drift algorithm generated by a SAR-based sea ice-tracking algorithm, backscatter intensity images and coastal radar imagery. This method could potentially be used in future to evaluate sea ice stability and ridge formation. A bistatic InSAR pair acquired by the TanDEM-X mission in March 2012 over the Bothnian Bay was used in two further studies (publications II and IV). The potential of X-band InSAR imagery for automated sea ice classification was evaluated. The first results were presented in publication II and the data were further elaborated in publication IV. The backscatter intensity, coherence magnitude and InSAR-phase features, as well as their different combinations, were used as the informative features in classification experiments. In publication II, the purpose was to assess ice properties on the scale used in ice charting, with ice types based on ice concentration and sea ice morphology, while in publication IV, a detailed small-scale analysis was performed. In addition, the sampling design was different in these publications. In publication II, to achieve the best discrimination between open water and several sea-ice types, RF (Random Forests) and ML (Maximum likelihood) classifiers were employed. The best overall accuracies were achieved by combining backscatter intensity & InSAR-phase using RF approach and backscatter intensity & coherence-magnitude using ML approach. The results showed the advantage of adding InSAR features to backscatter intensity for sea ice classification. In the further study (publication IV), a set of state-of-the-art classification approaches including ML, RF and SVM (Support Vector Machine) classifiers were used to achieve the best discrimination between open water and several sea-ice types. Adding InSAR-phase and coherence magnitude to backscatter intensity improved the OA (Overall Accuracy) compared to using only backscatter intensity. The RF and SVM algorithms gave somewhat larger OA compared to ML at the expense of a somewhat longer processing time. Results of publications II and IV demonstrate InSAR features have potential to improve sea ice classification. InSAR could be used by operational ice services to improve mapping accuracy of automated sea ice charting with statistical and machine learning classification approaches.Viime vuosikymmeninä satelliittivälitteisestä SAR-tutkasta on tullut erittäin tärkeä työkalu merijään kaukokartoituksessa. Tämän tutka perustuu sähkömagneettisten aaltojen sirontaan kiinnostavasta kohteesta takaisin tutkaan, mitä seuraa signaalin voimakkuuden mittaaminen. SAR-tutkat käyttävät synteettistä antennia, joka perustuu satelliitin liikkeeseen, mikä mahdollistaa tarkkojen, korkean erotuskyvyn kuvien tuottamisen. SAR-anturit mittaavat myös signaalin vaihetta, jota käytetään interferometria tekniikassa pinnan topografian ja siirtymien laskemiseen eri sovelluksissa, kuten maan muodonmuutoksissa, tarkassa kartoituksessa, maanjäristyksen arvioinnissa ja tulivuorenpurkauksien tarkkailussa. Interferometri tekniikkaa käytettiin tässä opinnäytetyössä pienten jäänsiirtymien analysointiin kiintojäävyöhykkeellä, joka on kiinni rantaviivassa ja saarissa eikä juuri liiku tuulien tai virtausten mukana. Kiintojääalueilla on pohjaan tarttuneita jäävalleja, jotka edistävät kiintojääpeitteen vakautumista. Kiintojäällä on tärkeä rooli merellisenä elinympäristönä, maankäytön kysymyksissä sekä paikallisten ihmisten elämässä ja meritekniikassa. Kiintojää voi murtua liikahdella useita metrejä, mikä voi olla vaarallista rakenteille, majakoille ja jäällä liikkujille. Tässä väitöskirjassa Sentinel-1A ja TanDEM-X satelliitteja ja interferometri tekniikkaa on käytetty arktisilla alueilla ja Itämerellä mittaamaan kiintojään muodonmuutoksia ja siirtymiä sekä niihin liittyviä mekanismeja. Lisäksi on tutkittu automaattista merijääluokitusta interferometrian apuohjelmiston avulla, mikä laajentaa operatiivisten merijääpalvelujen tutkahavaintojen käyttöä. Sentinel-1A:n avulla voitiin tarkastella 12 päivän pituisia muutoksia Pohjanlahden kiintojäävyöhykkeellä, kun interferometria tekniikka mittasi voimakkaan tuulen aiheuttaman 40 cm:n siirtymiä. Pohjoisella jäämerellä voitiin tunnistaa jäävallien siirtymiä ja muodostumia. Vallit siirtyivät noin 0,6 ja 3,7 km matkoja ja muodostuessaan ne kasvoivat metrin korkeuteen. Interferometri tekniikan lisääminen tutkakuvauksen analyysiin osoitti potentiaalin parantaa automaattisen merijääkartoituksen kartoituksen tarkkuutta tilastollisilla ja koneoppimiseen perustuvan luokittelun menetelmillä. Tulevaisuuden työnä merijään luokituksessa ja vallitutkimuksissa olisi suositeltavaa käyttää erilaisia ja useampia tutkakuvauksen geometrioita sekä erilaisia jääolosuhteita eri sääolosuhteiden vallitessa

Radarkaugseire rakendused metsaüleujutuste ja põllumajanduslike rohumaade jälgimiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Käesolev doktoritöö keskendub radarkaugseire rakenduste arendamisele kahes keerukas looduskeskkonnas: üleujutatud metsas ja põllumajanduslikel rohumaadel. Uurimistöö viidi läbi Tartu Observatooriumis, Tartu Ülikoolis, Ventspilsi Kõrgkoolis ja Aalto Ülikoolis. Töö esimene osa käsitleb X-laineala polarimeetrilise radarisignaali käitumist regulaarselt üleujutatavas metsas Soomaa näitel ning teine osa põllumajanduslike rohumaade seisundi ja polarimeetriliste ning interferomeetriliste tehisava-radari parameetrite vahelisi seoseid. 2012 kevadel Soomaa testalal TerraSAR-X andmetega läbi viidud eksperiment näitas, et topelt-peegeldusele tundlik HH-VV polarimeetriline kanal pakub tõesti kontrastsemat tagasihajumisepõhist üleujutatud metsa eristust üleujutamata metsast kui traditsiooniline HH polarimeetriline kanal. HH-VV kanali eelis HH kanali ees on seda suurem, mida madalam on mets ning raagus tingimustes lehtmetsas oli HH-VV kanali eelis HH kanali ees suurem kui okasmetsas. Lisaks on üleujutusele tundlik HH ja VV kanali polarimeetriline faasivahe, mida on soovitatud ka varasemates töödes kasutada täiendava andmeallikana üleujutuste kaardistamisel. Käesolevas doktoritöös mõõdeti polarimeetrilise X-laineala tehisava-radari HH/VV faasivahe suurenemine üleujutuste tõttu erineva kõrgusega okas- ja lehtmetsas. 2013 a vegetatsiooniperioodil korraldati Rannu test-alal välimõõtmistega toetatud eksperiment uurimaks X- ja C-laineala polarimeetrilise ning X-laineala interferomeetrilise tehisava-radari parameetrite undlikkust rohumaade tingimuste muutustele. Ilmnes, et ühepäevase vahega kogutud X-laineala tehisava-radari interferomeetriliste paaride koherentsus korreleerus rohu kõrgusega. Koherentsus oli seda madalam, mida kõrgem oli rohi - leitud seost on võimalik potentsiaalselt rakendada niitmise tuvastamiseks. TerraSAR-X ja RADARSAT-2 polarimeetriliste aegridade analüüsi tulemusel leiti kaks niitmisele tundlikku parameetrit: HH/VV polarimeetriline koherentsus ja polarimeetriline entroopia. Niitmise järel langes HH/VV polarimeetriline koherentsus järsult ning polarimeetriline entroopia tõusis järsult. Rohu tagasikasvamise faasis hakkas HH/VV polarimeetriline koherentsus aeglaselt kasvama ning entroopia aeglaselt kahanema. Täheldatud efekt oli tugevam TerraSARX X-laineala aegridadel kui RADARSAT-2 C-riba tehisava-radari mõõtmistel ning seda selgemini nähtav mida rohkem biomassi niitmise järgselt maha jäi. Leitud HH/VV polarimeetrilise koherentsuse ja polarimeetrilise entroopia käitumine vastas taimkatte osakestepilve radarikiirguse tagasihajumismudelile. Mudeli järgi põhjus- 60 tas eelnimetatud parameetrite iseloomulikku muutust rohukõrte kui dipoolide orientatsiooni ja korrastatuse muut niitmise tõttu, mis on kooskõlas meie välimõõtmiste andmetega.This thesis presents research about the application of radar remote sensing for monitoring of complex natural environments, such as flooded forests and agricultural grasslands. The study was carried out in Tartu Observatory, University of Tartu, Ventspils University College, and Aalto University. The research consists of two distinctive parts devoted to polarimetric analysis of images from a seasonal flooding of wetlands, and to polarimetric and interferometric analysis of a summer-long campaign covering eleven agricultural grasslands. TerraSAR-X data from 2012 were used to assess the use of the double-bounce scattering mechanism for improving the mapping of flooded forest areas. The study confirmed that the HH–VV polarimetric channel that is sensitive to double-bounce scattering provides increased separation between flooded and unflooded forest areas when compared to the conventional HH channel. The increase in separation increases with decreasing forest height, and it is more pronounced for deciduous forests due to the leaf-off conditions during the study. The phase difference information provided by the HH–VV channel may provide additional information for delineating flooded and unflooded forest areas. Time series of X-band (TanDEM-X and COSMO-SkyMed) and C-band (RADARSAT-2) data from 2013 were analyzed in respect to vegetation parameters collected during a field survey. The one-day repeat-pass X-band interferometric coherence was shown to be correlated to the grassland vegetation height. The coherence was also found to be potentially useful for detecting mowing events. The polarimetric analysis of TanDEM-X and RADARSAT-2 data identified two parameters sensitive to mowing events - the HH/VV polarimetric coherence magnitude and the H2α entropy. Mowing of vegetation consistently caused the coherence magnitude to decrease and the entropy to increase. The effect was more pronounced in case of X-band data. Additionally, the effect was stronger with more vegetation left on the ground after mowing. The effect was explained using a vegetation particle scattering model. The changes in polarimetric variables was shown to be caused by the change of orientation and the randomness of the vegetation