Video Sequence Alignment

The task of aligning multiple audio visual sequences with similar contents needs careful synchronisation in both spatial and temporal domains. It is a challenging task due to a broad range of contents variations, background clutter, occlusions, and other factors. This thesis is concerned with aligning video contents by characterising the spatial and temporal information embedded in the high-dimensional space. To that end a three- stage framework is developed, involving space-time representation of video clips with local linear coding, followed by their alignment in the manifold embedded space. The first two stages present a video representation techniques based on local feature extraction and linear coding methods. Firstly, the scale invariant feature transform (SIFT) is extended to extract interest points not only from the spatial plane but also from the planes along the space-time axis. Locality constrained coding is then incorporated to project each descriptor into a local coordinate system produced by a pooling technique. Human action classification benchmarks are adopted to evaluate these two stages, comparing their performance against existing techniques. The results shows that space-time extension of SIFT with a linear coding scheme outperforms most of the state-of-the-art approaches on the action classification task owing to its ability to represent complex events in video sequences. The final stage presents a manifold learning algorithm with spatio-temporal constraints to embed a video clip in a lower dimensional space while preserving the intrinsic geometry of the data. The similarities observed between frame sequences are captured by defining two types of correlation graphs: an intra-correlation graph within a single video sequence and an inter-correlation graph between two sequences. A video retrieval and ranking tasks are designed to evaluate the manifold learning stage. The experimental outcome shows that the approach outperforms the conventional techniques in defining similar video contents and capture the spatio-temporal correlations between them

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Exploring the Intersection of Multi-Omics and Machine Learning in Cancer Research

Cancer biology and machine learning represent two seemingly disparate yet intrinsically linked fields of study. Cancer biology, with its complexities at the cellular and molecular levels, brings up a myriad of challenges. Of particular concern are the deviations in cell behaviour and rearrangements of genetic material that fuel transformation, growth, and spread of cancerous cells. Contemporary studies of cancer biology often utilise wide arrays of genomic data to pinpoint and exploit these abnormalities with an end-goal of translating them into functional therapies. Machine learning allows machines to make predictions based on the learnt data without explicit programming. It leverages patterns and inferences from large datasets, making it an invaluable tool in the modern era of large scale genomics. To this end, this doctoral thesis is underpinned by three themes: the application of machine learning, multi-omics, and cancer biology. It focuses on employment of machine learning algorithms to the tasks of cell annotation in single-cell RNA-seq datasets and drug response prediction in pre-clinical cancer models. In the first study, the author and colleagues developed a pipeline named Ikarus to differentiate between neoplastic and healthy cells within single-cell datasets, a task crucial for understanding the cellular landscape of tumours. Ikarus is designed to construct cancer cell-specific gene signatures from expert-annotated scRNA-seq datasets, score these genes, and distribute the scores to neighbouring cells via network propagation. This method successfully circumvents two common challenges in single-cell annotation: batch effects and unstable clustering. Furthermore, Ikarus utilises a multi-omic approach by incorporating CNVs inferred from scRNA-seq to enhance classification accuracy. The second study investigated how multi-omic analysis could enhance drug response prediction in pre-clinical cancer models. The research suggests that the typical practice of panel sequencing — a deep profiling of select, validated genomic features — is limited in its predictive power. However, incorporating transcriptomic features into the model significantly improves predictive ability across a variety of cancer models and is especially effective for drugs with collateral effects. This implies that the combined use of genomic and transcriptomic data has potential advantages in the pharmacogenomic arena. This dissertation recapitulates the findings of two aforementioned studies, which were published in Genome Biology and Cancers journals respectively. The two studies illustrate the application of machine learning techniques and multi-omic approaches to address conceptually distinct problems within the realm of cancer biology.Die Krebsbiologie und das maschinelle Lernen sind zwei scheinbar konträre, aber intrinsisch verbundene Forschungsbereiche. Insbesondere die Krebsbiologie ist auf zellul ̈arer und molekularer Ebene hoch komplex und stellt den Forschenden vor eine Vielzahl von Herausforderungen. Zu verstehen wie abweichendes Zellverhalten und die Umstrukturierung genetischer Komponente die Transformation, das Wachstum und die Ausbreitung von Krebszellen antreiben, ist hierbei eine besondere Herausforderung. Gleichzeitig bestrebt die Krebsbiologie diese Abnormalitäten zu nutzen zu machen, Wissen aus ihnen zu gewinnen und sie so in funktionale Therapien umzusetzen. Maschinelles Lernen ermöglicht es Vorhersagen auf der Grundlage von gelernten Daten ohne explizite Programmierung zu treffen. Es erkennt Muster in großen Datensätzen, erschließt sich so Erkenntnisse und ist deswegen ein unschätzbar wertvolles Werkzeug im modernen Zeitalter der Hochdurchsatz Genomforschung. Aus diesem Grund ist maschinelles Lernen eines der drei Haupthemen dieser Doktorarbeit, neben Multi-Omics und Krebsbiologie. Der Fokus liegt hierbei insbesondere auf dem Einsatz von maschinellen Lernalgorithmen zum Zweck der Zellannotation in Einzelzell RNA-Sequenzdatensätzen und der Vorhersage der Arzneimittelwirkung in präklinischen Krebsmodellen. In der ersten, hier präsentierten Studie, entwickelten der Autor und seine Kollegen eine Pipeline namens Ikarus. Diese kann zwischen neoplastischen und gesunden Zellen in Einzelzell-Datensätzen unterscheiden. Eine Aufgabe, die für das Verst ̈andnis der zellulären Landschaft von Tumoren entscheidend ist. Ikarus ist darauf ausgelegt, krebszellenspezifische Gensignaturen aus expertenanotierten scRNA-seq-Datensätzen zu konstruieren, diese Gene zu bewerten und die Bewertungen über Netzwerkverbreitung auf benachbarte Zellen zu verteilen. Diese Methode umgeht erfolgreich zwei häufige Herausforderungen bei der Einzelzellannotation: den Chargeneffekt und die instabile Clusterbildung. Darüber hinaus verwendet Ikarus, durch das Einbeziehen von scRNA-seq abgeleiteten CNVs, einen Multi-Omic-Ansatz der die Klassifikationsgenauigkeit verbessert. Die zweite Studie untersuchte, wie Multi-Omic-Analysen die Vorhersage der Arzneimittelwirkung in präklinischen Krebsmodellen optimieren können. Die Forschung legt nahe, dass die übliche Praxis des Panel Sequenzierens - die umfassende Profilierung ausgewählter, validierter genomischer Merkmale - in ihrer Vorhersagekraft begrenzt ist. Durch das Einbeziehen transkriptomischer Merkmale in das Modell konnte jedoch die Vorhersagefähigkeit bei verschiedenen Krebsmodellen signifikant verbessert werden, ins besondere für Arzneimittel mit Nebenwirkungen. Diese Dissertation fasst die Ergebnisse der beiden oben genannten Studien zusammen, die jeweils in Genome Biology und Cancers Journalen veröffentlicht wurden. Die beiden Studien veranschaulichen die Anwendung von maschinellem Lernen und Multi-Omic-Ansätzen zur Lösung konzeptionell unterschiedlicher Probleme im Bereich der Krebsbiologie

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Visual Concept Detection in Images and Videos

The rapidly increasing proliferation of digital images and videos leads to a situation where content-based search in multimedia databases becomes more and more important. A prerequisite for effective image and video search is to analyze and index media content automatically. Current approaches in the field of image and video retrieval focus on semantic concepts serving as an intermediate description to bridge the “semantic gap” between the data representation and the human interpretation. Due to the large complexity and variability in the appearance of visual concepts, the detection of arbitrary concepts represents a very challenging task. In this thesis, the following aspects of visual concept detection systems are addressed: First, enhanced local descriptors for mid-level feature coding are presented. Based on the observation that scale-invariant feature transform (SIFT) descriptors with different spatial extents yield large performance differences, a novel concept detection system is proposed that combines feature representations for different spatial extents using multiple kernel learning (MKL). A multi-modal video concept detection system is presented that relies on Bag-of-Words representations for visual and in particular for audio features. Furthermore, a method for the SIFT-based integration of color information, called color moment SIFT, is introduced. Comparative experimental results demonstrate the superior performance of the proposed systems on the Mediamill and on the VOC Challenge. Second, an approach is presented that systematically utilizes results of object detectors. Novel object-based features are generated based on object detection results using different pooling strategies. For videos, detection results are assembled to object sequences and a shot-based confidence score as well as further features, such as position, frame coverage or movement, are computed for each object class. These features are used as additional input for the support vector machine (SVM)-based concept classifiers. Thus, other related concepts can also profit from object-based features. Extensive experiments on the Mediamill, VOC and TRECVid Challenge show significant improvements in terms of retrieval performance not only for the object classes, but also in particular for a large number of indirectly related concepts. Moreover, it has been demonstrated that a few object-based features are beneficial for a large number of concept classes. On the VOC Challenge, the additional use of object-based features led to a superior performance for the image classification task of 63.8% mean average precision (AP). Furthermore, the generalization capabilities of concept models are investigated. It is shown that different source and target domains lead to a severe loss in concept detection performance. In these cross-domain settings, object-based features achieve a significant performance improvement. Since it is inefficient to run a large number of single-class object detectors, it is additionally demonstrated how a concurrent multi-class object detection system can be constructed to speed up the detection of many object classes in images. Third, a novel, purely web-supervised learning approach for modeling heterogeneous concept classes in images is proposed. Tags and annotations of multimedia data in the WWW are rich sources of information that can be employed for learning visual concepts. The presented approach is aimed at continuous long-term learning of appearance models and improving these models periodically. For this purpose, several components have been developed: a crawling component, a multi-modal clustering component for spam detection and subclass identification, a novel learning component, called “random savanna”, a validation component, an updating component, and a scalability manager. Only a single word describing the visual concept is required to initiate the learning process. Experimental results demonstrate the capabilities of the individual components. Finally, a generic concept detection system is applied to support interdisciplinary research efforts in the field of psychology and media science. The psychological research question addressed in the field of behavioral sciences is, whether and how playing violent content in computer games may induce aggression. Therefore, novel semantic concepts most notably “violence” are detected in computer game videos to gain insights into the interrelationship of violent game events and the brain activity of a player. Experimental results demonstrate the excellent performance of the proposed automatic concept detection approach for such interdisciplinary research

Kliimamuutuse mõju hindamine rannaniidu taimekooslusele mesokosmi katse ja mehitamata õhusõidukiga kogutud andmete põhjal

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.Semi-natural grasslands are an essential part of the cultural landscape of Europe. Semi-natural grasslands are commonly characterised by a very high biodiversity, including rare species. Beyond the high biodiversity value, semi-natural grasslands worldwide provide many ecosystem services, including: carbon sequestration and storage, nutrient cycling, regulation of soil quality, habitats for migrating birds, erosion control, and flood regulation. Within the realm of semi-natural grasslands, coastal meadows are particularly important. However, coastal grasslands are threatened by a range of factors such as coastal squeeze, transformation into monoculture ponds, pollution, and climate change. Coastal areas are threatened at a range of spatial scales as a result of sea-level rise, and can include higher flooding frequency in coastal areas, salt water intrusion in aquifers, and potential declines in the extent of coastal wetlands. A warmer climate also implies a modification in precipitation patterns affecting runoff into the sea. In coastal areas, both water levels and salinity have a strong impact on species distribution and therefore on the structure and composition of aquatic and coastal floral and faunal communities. Consequently, plant communities in coastal meadows are expected to undergo changes in their composition and structure. The current thesis explores different methodologies to assess plant community distribution, above-ground biomass, and the effects of management type, duration, and intensity on sward structure using UAV-derived multispectral data and aerial photogrammetry. In addition, the keystone of this thesis is a mesocosm experiment that was used to assess shifts in species richness and abundance in plant community types in Estonian coastal meadows related to future change scenarios of water level and salinity for the Baltic Sea. a. Unmanned Aerial Vehicle (UAV) The use of UAV demonstrated to be able to identify plant community extent and distribution in high biodiversity value coastal meadows in West Estonia. Species diversity and biomass significantly influence the quality of data and this should be accounted for when planning the sample collection to achieve better results. This study has shown that UAVs are useful tools of mapping grasslands at a plant community level. Also, UAV showed to be possible to reveal the structure of the grassland and how it is affected by the management history. For example, the grassland turns more homogeneous under long-term monospecific grazing, b. Mesoscosm Experiment The mesocosm experiment in the present study revealed different temporal changes of wetland communities to altered salinity and water conditions, highlighting the response of plant species to environmental variables. These changes were not significant according to alteration of water level and salinity in the Open Pioneer community, but they were over time. On the other hand, Lower Shore and Upper Shore had significant changes according to time and treatments. These could be explained by dynamic differences in the communities, since Open Pioneer was more variable. c. Conclusions Both methodologies, remote sensing and the mesocosm experiment, are evidently important to evaluate the structure and function of Estonian coastal meadows. The mapping of the extent and structure of coastal plant communities allows an evaluation of the current state of the ecosystem. The mesocosm experiment helps to understand changes in plant community composition under altered conditions of water level and salinity in Estonian coastal meadows and consequently, understand how species richness, abundance, and biomass will respond to those changes. This information is important when considering the protection and potential management of these areas taking into account the species diversity of fauna and flora as well as that of livestock.Uuring viidi läbi kahel tasandil: uuringukohtades Lääne-Eestis ja katsekeskkonnas. Esimesel juhul valiti Silma looduskaitsealal, Matsalu rahvuspargis ja Vormsi saarel ranniku taimekoosluste ja maapealse biomassi kaardistamiseks kokku üheksa rannaniiduala (I, II). Teine osa hõlmab mesokosmi katset (III), mille käigus kasutati katse seadmiseks ja eksperimenteerimiseks Silma looduskaitsealalt kogutud proove. Vaatamata oma suhteliselt väikesele pindalale (45 228 km2) iseloomustab Eestit mitmekesine geoloogia, pinnamood ja kliima. Läänemere rannaniidud on tekkinud ja need säilivad maa isostaatilise tõusu, setete kogunemise ja alade vähese intensiivsusega majandamise – karjatamise või niitmise – tõttu. Eesti rannikumärgaladel on ebatavaline hüdroloogiline režiim. Kuna loodete ulatus on väga väike (~0,02 m), põhjustab rannaniitude üleujutusi valdavalt tsüklonaalne aktiivsus Põhja-Atlandil ja Fennoskandias. Üleujutuste sagedus ja ulatus on ebaregulaarne ning varieerub kogu rannikumaastikul, sõltudes tuule kiirusest ja suunast. Hiljutised hinnangud suhtelise meretaseme tõusu kohta kolmelt mõõnamõõturilt piki Eesti rannikut on järgmised: Tallinnas 1,5–1,7 mm a-1, Narva-Jõesuus 1,7–2,1 mm a-1 ja Pärnus 2,3–2,7 mm a-1 (Ward et al., 2014). Taimekoosluse klassifitseerimiseks ja biomassi prognoosimiseks analüüsiti üheksat rannikumärgala kolmes kohas Silma looduskaitsealal, kahes kohas Matsalu rahvuspargis ja neljas kohas Vormsi saarel. Neis kohtades esinevad kõik väitekirjas käsitletud taimekooslused. Uurimiskohtade taimekooslused liigitati vastavalt Burnside´i jt fütosotsioloogilisele klassifikatsioonile (2007): pilliroostik, võsasoo, madal rannik, kõrgrannik, pioneerliikidega paljakud, kõrgrohustu, võsa ja metsamaa. Võsasoo ning võsa ja metsamaa jäeti nende marginaalse esinemise tõttu uurimusest välja. Uurimistöö käigus tehti kaks erinevat analüüsi, kasutades UAV-ga kogutud multispektraal- ja rgb-fotosid. UAV multispektraalseid pilte kasutati taimekoosluste kaardistamiseks Silma looduskaitsealal Põhja-Tahu, Lõuna-Tahu ja Kudani rannaniidul (I). Järgnevalt kasutati multispektraalseid ja rgb-pilte kõrge ruumilise eraldusvõimega kaartide koostamiseks maapealse biomassi tuvastamiseks kõigis üheksas uuringukohas (II). Taimekoosluste kaardistamiseks (I) ja maapealse biomassi prognoosimiseks (II) kasutati otsustusmetsa klassifikatsiooni. Seejärel analüüsiti maapealse biomassi kaartide abil majandamisviisi ja intensiivsuse mõju rannaniitude heinamaade struktuurile (II). Teavet rannaniitude kasutusviisi kohta saadi maaomanikega isiklikult suheldes. Uurimistöö teises osas valiti mesokosmi katse jaoks kolm taimekooslust: pioneerliikidega paljakud, madal rannik ja kõrgrannik. Need kooslused valiti sealsete võtmeliikide spetsiifilise autökoloogilise kasvukohaeelistuse tõttu (nt soolsus ja mulla veesisaldus). Katsest välja jäetud pilliroostikus ja võsasoos domineerivad üleujutust taluvad liigid; kõrgrohustu kujutab endast maismaa ja märgalade ökosüsteemi vahelist kooslust, ning võsa on täielikult maismaa. Silma looduskaitsealal varuti Põhja-Tahu alalt 2018. aasta juunis kolmest valitud taimekooslusest 15 mätast (suurus 50 x 70 cm, paksus 30 cm). Mesokosmi katse varustus koosnes mahutitest (90L, mõõtmed 56 x 79 x 32 cm), mis olid täidetud 2:1:1 mullaseguga, mis koosneb pestud sõmera struktuuriga liivast, savist ja kompostist, mis on väga sarnane märgala põhjasubstraadiga. Mahutid numereeriti ja varustati vastava tähisega. Mahutid asusid kogu katse jooksul samal kohal. Katse käiku hinnati alalise gradueeritud 50 cm2 kvadraadi abil, mis jaotati 25 kvadraadiks (10 x 10 cm), ja määrati kindlaks muutused esinevate taimeliikide arvukuses pinnakatte pindala järgi (katteprotsent). Katse kestis kolme aastat veetaseme ja soolsuse tingimustes, mis tuletati kliimamuutuste prognoosidest 2100. aastaks. Liikide arvukus ja liigirikkus arvutati 2018., 2019. ja 2020. aastaks iga taimekoosluse kohta eraldi. Liigirikkuse erinevusi aastati ja kasvutingimuste suhtes hinnati Kruskal Wallise testiga, mis põhineb Bonferroni kohandustega Dunni testil, et tuvastada liigirikkuse erinevusi igal aastal. Liigilise arvukuse esitamiseks kasutati arvukuse kõveraid. Taimekoosluse koostise erinevuste uurimiseks kasutati permutatsioonilist mitmemõõtmelist analüüsi Bray-Curtise erinevusega. Aasta ristmõju analüüsis käsitleti töötlemisviisi fikseeritud mõju ja valimeid juhusliku mõjuna. Tulemused ja järeldused Rannaniitudel hinnati taimekoosluste levikut, maismaa biomassi ja taimestiku vertikaalset struktuuri. Fleissi kapa kordaja 0,89 põhjal kaardistati põhjalikult taimekooslused (I). Otsustusmetsa klassijärgsed vead näitavad, et homogeensema struktuuriga piirkondi on kergem klassifitseerida kui keerulise struktuuriga koosluseid. Otsustusmetsa algoritmi jõudlusanalüüs näitas, et biomassi hindamisel saadi parim tulemus, kui multispektraalne info kombineeriti fotogramm-meetriliselt loodud digitaalse maastikumudeliga (DTM, ingl digital terrain model) (II). Tulemused viitavad sellele, et mitme anduri kombinatsiooni saab kasutada ökosüsteemi omaduste mõõtmiseks, mida ainult spektraalinformatsiooni analüüsides ei pruugi tuvastada. Siinse uuringu maapealse biomassi prognooside suur täpsus näitab, et rannaniitude jälgimisel on kaugseire UAV-ga sobiv meetod. Struktuurianalüüsi tulemused näitasid, mil määral mõjutab biomassi jaotust karjatamise kestus ja heterogeensus. Pidevalt majandatavatele rohumaadele on iseloomulikud suuremad ja homogeensemad alad (II). Üldine lineaarne mudelianalüüs ja Mann-Whitney u-testid näitasid, kuidas taimtoidulised liigid mõjutavad rohumaa struktuuri. Rohumaad, millel karjatatakse erinevaid taimtoidulisi, on mitmekesisema struktuuriga kui veiste karjamaa (II). Mesokosmi katse tulemused näitasid, et kõigis kolmes Läänemere ranniku märgalade koosluses ilmnesid aja jooksul vee- ja soolsusrežiimis märkimisväärsed muutused, mis tõi esile taimeliikide reaktsiooni keskkonnamuutuste suhtes (III). Pioneerliikidega paljakutel suurenes liigirikkus ja taimkate kõigi keskkonnamuutuste korral, sellega võrreldes esines madalal rannikul ja kõrgrannikul nii veetaseme kui ka soolsusega seotud muutusi vähemal määral. Pioneerliikidega paljakuid mõjutab enamasti soolsus, seda isegi peamiselt sõmerast, keskmise fraktsiooniga ja peenest liivast koosnevas pinnases, mis säilitab vähem toitaineid kui peenema fraktsiooniga muld. Spergularia marina ja Glaux maritima aitasid kaasa liigirikkuse suurenemisele mulla suurenenud ja vähenenud soolsuse tingimustes. Üldiselt ei ilmnenud madala ranniku ja kõrgranniku taimekooslustes soolsuse muutumise korral olulisi muutusi võrreldes kontrollkatsega. Nendes kooslustes on liike, mis kasvavad nii soolases kui ka mittesoolases keskkonnas. Veetaseme muutus mõjutas pioneerliikidega paljakute taimekooslust sarnaselt soolsuse muutmisega. Selle koosluse liigirikkus suurenes kõrgema veetaseme korral, võrreldes kontrollkatsega. Kõrgema veetasemega kohanenud liike nagu Eleocharis palustris esines kõrgenenud veetaseme korral kolmandal aastal rohkem; alanenud vees leidus katse lõpus rohkem väiksema veevajadusega liike, nagu Glaux maritima ja Centaurium littorale. Madalal rannikul registreeriti madalama veetaseme korral liigirikkuse muutus, võrreldes kontrollkatsega. Aja jooksul toimuv liikide varieeruvus ilmnes vähese pinnakatvusega liikide puhul, nt ahenesid Carex flacca ja Triglochin palustris´e kasvukohad. Madal rannik asub veetasemelt pioneerliikidega paljaku ja kõrgranniku vahel ning see võib seletada, miks sealsed liigid taluvad mulla mitmesuguseid niiskustingimusi. Kõrgranniku koosluses vähenes kõrgenenud veetaseme korral liikide arv ja sellest tulenevalt ka liigirikkus; sealjuures laienesid vähese pinnakatvuse ja madala veetasemega kohastunud liikide Stellaria graminea ja Viola canina kasvukohad. See uurimus näitas, et ökoloogilistes uuringutes võib erinevate metoodikate kombinatsioon osutuda tõhusaks. Vaid vähestes uuringutes kombineeritakse ökosüsteemiprotsesside mõistmiseks erinevaid lähenemisviise, nt kaugseiret ja katseplatvorme, antud töös mesokosmi katset. Uued tehnoloogilised edusammud kaugseire vallas võivad lahendada küsimusi, millele vastuse leidmine traditsiooniliste ökoloogiliste meetodite abil oleks keeruline või ebapraktiline. Samas on traditsioonilise lähenemisviisiga, nt mesokosmi katsega saadud teadmised uue tehnoloogilise potentsiaali rakendamiseks väga vajalikud. Uurimus näitas, et UAV on sobiv vahend rannikurohumaade struktuuri ja taimekoosluste leviku täpse eraldusvõimega kaartide koostamiseks. Teisest küljest aitab mesokosmi katse mõista taimekoosluse koostise muutusi eri veetaseme ja soolsuse tingimustes.Publication of this thesis is supported by the Estonian University of Life Sciences and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund