When a devastating event such as a forest fire occurs, multiple actions have to be taken. The first priority is to ensure people's safety during the fire, then the fire has to be kept under control and finally extinguished. After all of this, what remains is a damaged area in the forest. The objective of this thesis is to evaluate medium and high-resolution satellite imagery for the classification of different burn severities in a wildfire damaged forest. The classification can then be used to plan where to focus restoration efforts after the fire to achieve a safe and economically beneficial usage of the affected area. Trängslet fire in Dalarna and Lillhärdal fire in Härjedalen, the two of the 2018 forest fire sites in Sweden were chosen for this study. Satellite imagery over both study areas at medium spatial resolution from Sentinel-2 were acquired pre-fire in early July, 2018 and post-fire on October 2, 2018 while imagery at high spatial resolution from Pleiades were acquired on September 13, 2018. Image processing, analysis and classification were performed using Google Earth Engine (GEE) and PCI Geomatica. To ensure the quality of the classifications, field data were collected during a field trip to the Lillhärdal area using Open Data Kit (ODK). ODK was used since it is an application that can collect/store georeferenced information and images. The result that this thesis found is that while both the medium and high-resolution classifications achieved accurate results, the Sentinel-2 classification is the most suited method in most cases since it is an easy and automated classification using differential Normalized Burn Ratio (dNBR) compared to the Pleiades classification where a lot of manual work has to be put in. There are however cases where the Pleiades classification would be preferable, such as when the affected area usually is obscured by clouds and Sentinel-2 thus finds it hard to achieve good images and when a good spatial resolution is required to more easily display the classification with the original image. The most accurate result according to the data collected at the site in Lillhärdal also showed that the Pleiades classification had a precise match of 61.54% and a plausible match of 92.31%. This can be compared to the Sentinel-2 classification that had a precise match of 48.72% and a plausible match of 94.87%. These percentages are based on the visual analysis of collected images at the Lillhärdal site compared to the classifications. This thesis could have been improved if more information regarding the groundwork that had been done after the fire, but before the acquiring of the satellite imagery, were available. The result would also most likely be better if a satellite with better spatial resolution than Sentinel-2 but still with near infrared and short-wave infrared bands would have been used. The reason being that dNBR, which gave a good result, only needs those two bands.

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The efficient and timely monitoring of forest dynamics is of paramount importance and requires accurate, high-resolution and time-tagged predictions at global scale. Despite numerous methodologies have been proposed in the literature, existing approaches often compromise on accuracy, resolution, temporal fidelity or coverage. To tackle these challenges and limitations, the main objective of this doctoral thesis is the investigation of the potential of artificial intelligence (AI) for the regression of bio-physical forest parameters from spaceborne Earth Observation (EO) data. This work explores for the first time the combined use of TanDEM-X single-pass interferometric products and convolutional neural networks for canopy height estimation at country scale. To achieve this, a novel deep learning framework is proposed, leveraging the capability of deep neural networks to effectively capture the complex spatial relationships between forest properties and satellite data, as well as ensuring the adaptability to different environmental conditions. The design and the understanding of the model is driven by explainable AI principles and by considerations on large-scale forest dynamics, with a great emphasis set on the challenges related to the variable acquisition geometry of the TanDEM-X mission, and by relying on the use of LVIS-derived LiDAR measurements as reference data. Moreover, several investigations are conducted on the adaptability of the developed framework for transferring knowledge to related domains, such as digital terrain model regression and above-ground biomass density estimation. Finally, the capability of the proposed approach to be extended to the use of other EO sensors is also evaluated, with a particular emphasis on the ESA Sentinel-1 and Sentinel-2 missions. The developed deep learning framework sets a solid groundwork for the generation of large-scale products of bio-physical forest parameters from spaceborne EO data. The approach achieves cutting-edge performance, significantly advancing the current state of forest assessment and monitoring technologies.

<p dir="ltr">This study analyzes one subset of twelve lakes within Kosciusko County, Indiana between 2015 and 2021 to provide a quantitative understanding of the mechanisms which influence onset and occurrence of HABs. Analysis of water samples, balanced by imagery from satellite remote sensing platforms, are used to quantify the biogeochemical state of these water systems and better understand the mechanisms involved in formation of HABs. Parameters studied include in-situ measurements (e.g., water temperature), laboratory measurements (e.g., microcystin, nitrogen, and phosphorous concentrations), and satellite derived responses (chlorophyll-a). Results indicate no single parameter is correlated with cyanotoxin concentrations, but instead multiple parameters have a synergistic effect on algal bloom growth and toxicity.</p>

Der Amazonas befindet sich im Wandel. Seine Regenwälder sind zunehmend durch die expandierende Landwirtschaft bedroht. Brandrodungen und die meist extensive Weidewirtschaft verantworten großflächige Ökosystemschäden und hohe Treibhausgasemissionen. Erdbeobachtungssysteme wie die Landsat und Sentinel-2 Satelliten ermöglichen eine großflächige Analyse dieser Entwicklungen und sind unerlässlich zur Evaluierung von Maßnahmen zum Schutze des Amazonas. Allerdings sind in den Kerntropen Fernerkundungsanalysen aufgrund des Bewölkungsgrades sehr herausfordernd. Diese Arbeit zielt daher auf eine verbesserte Erkennung landwirtschaftlicher Prozesse, wie sie an Entwaldungsfronten und speziell in der Region Novo Progresso, Pará, Brasilien, typisch sind. Dazu wurde zunächst der EO Time Series Explorer entwickelt, um verschiedene Dimensionen dichter Multisensorzeitserien interaktiv zur Erstellung von Referenzdaten in Wert zu setzen. Mit den Clear Observation Sequences (COS) wurde darauf basierend ein neuer Ansatz zur Erfassung hoch-dynamischer landwirtschaftlicher Prozesse entwickelt, etwa Feuer mit geringer Brandlast oder Bodenbearbeitungsmaßnahmen. Darauf aufbauend wurde schließlich der Landnutzungswandel in der Region Novo Progresso zwischen 2014 und 2020 untersucht. Die Ergebnisse zeigen einen alarmierenden Anstieg der Entwaldung und eine Zunahme landwirtschaftlicher Feuer seit der Präsidentschaft von Jair Bolsonaro. Differenziert nach Landnutzungszonen und Betriebsgrößen wird deutlich, dass Schutzgebiete weniger wirksam sind und insbesondere größere Landwirtschaftsbetriebe die Entwaldung vorantreiben. Diese Arbeit zeigt den hohen Wert einer synergetischen Nutzung unterschiedlicher Satellitenzeitserien für die fernerkundliche Analyse landwirtschaftlicher Prozesse. Eine weitere Verdichtung der Zeitserien mit räumlich und spektral höherauflösenden Sensoren bietet weiteres Verbesserungspotential bei der Beschreibung landwirtschaftlicher Dynamiken. / The Amazon is in transition, and its rainforests are increasingly threatened by agricultural expansion. A slash-and-burn agriculture and mostly extensive cattle grazing are responsible for large-scale ecosystem damage and high levels of greenhouse gas emission. Earth observation systems such as the Landsat and Sentinel-2 satellites enable large-scale analysis of these developments and are essential for evaluating measures to protect the Amazon. However, cloud cover makes remote sensing analysis challenging in the core tropics. The present work aims to improve the detection of agricultural processes typical of deforestation frontiers, focusing specifically on the Novo Progresso region, Pará, Brazil. To that end, the EO Time Series Explorer was developed to interactively visualize the different dimensions of dense multi-sensor time series and to create reference data. Based on this software tool, the Clear Observation Sequences (COS) approach was developed to capture highly dynamic agricultural processes such as low-load fires or tillage operations. Finally, the investigation of land-use changes in the Novo Progresso region between 2014 and 2020 shows an alarming increase in deforestation and agricultural fires since Jair Bolsonaro’s accession to the presidency. Analysis by land-use zone and property size shows that protected areas have become less effective and that larger properties are driving deforestation. This work demonstrates the value of synergistic use of satellite time series for remote sensing analysis of agricultural processes. Further densification of time series using higher spatial and spectral resolution sensors promises to further improve the description of agricultural dynamics.

Supraglacial lakes in the ablation zone of the Greenland Ice Sheet (GrIS) often drain rapidly (in hours to days) by hydraulically-driven fracture (“hydrofracture”) in the summer. Hydrofracture can deliver large meltwater volumes to the ice-bed interface and open-up surface-to-bed connections, thereby routing surface meltwater to the subglacial system, altering basal water pressures and, consequently, the velocity profile of the GrIS. The study of rapidly draining lakes is thus important for developing coupled hydrology and ice-dynamics models, which can help predict the GrIS’s future mass balance. Remote sensing is commonly used to identify the location, timing and magnitude of rapid lake-drainage events for different regions of the GrIS and, with the increased availability of high-quality satellite data, may be able to offer additional insights into the GrIS’s surface hydrology. This study uses new remote-sensing datasets and develops novel analytical techniques to produce improved knowledge of rapidly draining lake behaviour in west Greenland over recent years. While many studies use 250 m MODerate-resolution Imaging Spectroradiometer (MODIS) imagery to monitor intra- and inter-annual changes to lakes on the GrIS, no existing research with MODIS calculates changes to individual and total lake volume using a physically-based method. The first aim of this research is to overcome this shortfall by developing a fully-automated lake area and volume tracking method (“the FAST algorithm”). For this, various methods for automatically calculating lake areas and volumes with MODIS are tested, and the best techniques are incorporated into the FAST algorithm. The FAST algorithm is applied to the land-terminating Paakitsoq and marine-terminating Store Glacier regions of west Greenland to investigate the incidence of rapid lake drainage in summer 2014. The validation and application of the FAST algorithm show that lake areas and volumes (using a physically-based method) can be calculated accurately using MODIS, that the new algorithm can identify rapidly draining lakes reliably, and that it therefore has the potential to be used widely across the GrIS to generate novel insights into rapidly draining lakes. The controls on rapid lake drainage remain unclear, making it difficult to incorporate lake drainage into models of GrIS hydrology. The second aspect of this study therefore investigates whether various hydrological, morphological, glaciological and surface-mass-balance controls can explain the incidence of rapid lake drainage on the GrIS. These potential controlling factors are examined within an Exploratory Data Analysis statistical technique to elicit statistical similarities and differences between the rapidly and non-rapidly draining lake types. The results show that the lake types are statistically indistinguishable for almost all factors, except lake area. It is impossible, therefore, to elicit an empirically-supported, deterministic method for predicting hydrofracture in models of GrIS hydrology. A frequent problem in remote sensing is the need to trade-off high spatial resolution for low temporal resolution, or vice versa. The final element of this thesis overcomes this problem in the context of monitoring lakes on the GrIS by adapting the FAST algorithm (to become “the FASTER algorithm”) to use with a combined Landsat 8 and Sentinel-2 satellite dataset. The FASTER algorithm is applied to a large, predominantly land-terminating region of west Greenland in summers 2016 and 2017 to track changes to lakes, identify rapidly draining lakes, and ascertain the extra quantity of information that can be generated by using the two satellites simultaneously rather than individually. The FASTER algorithm can monitor changes to lakes at both high spatial (10 to 30 m) and temporal (~3 days) resolution, overcoming the limitation of low spatial or temporal resolution associated with previous remote sensing of lakes on the GrIS. The combined dataset identifies many additional rapid lake-drainage events than would be possible with Landsat 8 or Sentinel-2 alone, due to their low temporal resolutions, or with MODIS, due to its inferior spatial resolution.

Throughout the world, there is a great demand to map out the increasing environmental changes and life habitats on Earth. The vast majority of Earth Observations today, are collected using satellites. The Global Watch Center (GWC) initiative was started with the purpose of producing a global situational awareness of the premises for all life on Earth. By collecting, studying and analyzing vast amounts of data in an automatic, scalable and transparent way, the GWC aims are to work towards reaching the United Nations (UN) Sustainable Development Goals (SDG). The GWC vision is to make use of qualified accessible data together with leading organizations in order to lay the foundation of the important decisions that have the biggest potential to make an actual difference for the common awaited future. As a show-case for the initiative, the UN strategic department has recommended a specific use-case, involving mapping large accumulation of waste in areas greatly affected, which they believe will profit the initiative very much. This Master Thesis aim is, in an automatic and scalable way, to detect and classify dumpsites in Kampala, the capital of Uganda, by using available satellite imagery. The hopes are that showing technical feasibility and presenting interesting remarks will aid in spurring further interest in coming closer to a realization of the initiative. The technical approach is to use a lightweight version of Automatic Target Recognition. This is conventionally used in military applications but is here used, to detect and classify features of large accumulations of solid-waste by using techniques from the field of Image Analysis and Data Mining. Choice of data source, this study's area of interest as well as choice of methodology for Feature Extraction and choice of the Machine Learning algorithm Support Vector Machine will all be described and implemented. With a classification precision of 95 percent will technical results be presented, with the ambition to promote further work and contribute to the GWC initiative with valuable information for later realization.

Les missions spatiales d'observation de la Terre de nouvelle génération telles que Sentinel-2 (préparé par l'Agence Spatiale Européenne ESA dans le cadre du programme Copernicus, auparavant appelé Global Monitoring for Environment and Security ou GMES) ou Venµs, conjointement développé par l'Agence Spatiale Française (Centre National d 'Études Spatiales CNES) et l'Agence Spatiale Israélienne (ISA), vont révolutionner la surveillance de l'environnement d' aujourd'hui avec le rendement de volumes inédits de données en termes de richesse spectrale, de revisite temporelle et de résolution spatiale. Venµs livrera des images dans 12 bandes spectrales de 412 à 910 nm, une répétitivité de 2 jours et une résolution spatiale de 10 m; les satellites jumeaux Sentinel-2 assureront une couverture dans 13 bandes spectrales de 443 à 2200 nm, avec une répétitivité de 5 jours, et des résolutions spatiales de 10 à 60m. La production efficace de cartes d'occupation des sols basée sur l'exploitation de tels volumes d'information pour grandes surfaces est un défi à la fois en termes de coûts de traitement mais aussi de variabilité des données. En général, les méthodes classiques font soit usage des approches surveillées (trop coûteux en termes de travaux manuels pour les grandes surfaces), ou soit ciblent des modèles locaux spécialisés pour des problématiques précises (ne s'appliquent pas à autres terrains ou applications), ou comprennent des modèles physiques complexes avec coûts de traitement rédhibitoires. Ces approches existantes actuelles sont donc inefficaces pour l'exploitation du nouveau type de données que les nouvelles missions fourniront, et un besoin se fait sentir pour la mise en œuvre de méthodes précises, rapides et peu supervisées qui permettent la généralisation à l'échelle de grandes zones avec des résolutions élevées. Afin de permettre l'exploitation des volumes de données précédemment décrits, l'objectif de ce travail est la conception et validation d'une approche entièrement automatique qui permet l'estimation de la couverture terrestre de grandes surfaces avec imagerie d'observation de la Terre de haute résolution spatiale, spectrale et temporelle, généralisable à des paysages différents, et offrant un temps de calcul opérationnel avec ensembles de données satellitaires simulés, en préparation des prochaines missions. Cette approche est basée sur l'intégration d'algorithmes de traitement de données, tels que les techniques d'apprentissage de modèles et de classification, et des connaissances liées à l'occupation des sols sur des questions écologiques et agricoles, telles que les variables avec un impact sur la croissance de la végétation ou les pratiques de production. Par exemple, la nouvelle introduction de température comme axe temporel pour un apprentissage des modèles ultérieurs intègre un facteur établi de la croissance de la végétation à des techniques d'apprentissage automatiques pour la caractérisation des paysages. Une attention particulière est accordée au traitement de différentes questions, telles que l'automatisation, les informations manquantes (déterminées par des passages satellitaires, des effets de réflexion des nuages, des ombres ou encore la présence de neige), l'apprentissage et les données de validation limitées, les échantillonnages temporels irréguliers (différent nombre d'images disponible pour chaque période et région, données inégalement réparties dans le temps), la variabilité des données, et enfin la possibilité de travailler avec différents ensembles de données et nomenclatures. / The new generation Earth observation missions such as Sentinel-2 (a twin-satellite initiative prepared by the European Space Agency, ESA, in the frame of the Copernicus programme, previously known as Global Monitoring for Environment and Security or GMES) and Venµs, jointly developed by the French Space Agency (Centre National d'Études Spatiales, CNES) and the Israeli Space Agency (ISA), will revolutionize present-day environmental monitoring with the yielding of unseen volumes of data in terms of spectral richness, temporal revisit and spatial resolution. Venµs will deliver images in 12 spectral bands from 412 to 910 nm, a repetitivity of 2 days, and a spatial resolution of 10 m; the twin Sentinel-2 satellites will provide coverage in 13 spectral bands from 443 to 2200 nm, with a repetitivity of 5 days, and spatial resolutions of 10 to 60m. The efficient production of land cover maps based on the exploitation of such volumes of information for large areas is challenging both in terms of processing costs and data variability. In general, conventional methods either make use of supervised approaches (too costly in terms of manual work for large areas), target specialised local models for precise problem areas (not applicable to other terrains or applications), or include complex physical models with inhibitory processing costs. These existent present-day approaches are thus inefficient for the exploitation of the new type of data that the new missions will provide, and a need arises for the implementation of accurate, fast and minimally supervised methods that allow for generalisation to large scale areas with high resolutions. In order to allow for the exploitation of the previously described volumes of data, the objective of this thesis is the conception, design, and validation of a fully automatic approach that allows the estimation of large-area land cover with high spatial, spectral and temporal resolution Earth observation imagery, being generalisable to different landscapes, and offering operational computation times with simulated satellite data sets, in preparation of the coming missions.

Detecting water under vegetation is critical to tracking the status of geological ecosystems like wetlands. Researchers use different methods to estimate water presence, avoiding costly on-site measurements. Optical satellite imagery allows the automatic delineation of water using the concept of the Normalised Difference Water Index (NDWI). Still, optical imagery is subject to visibility conditions and cannot detect water under the vegetation, a typical situation for wetlands. Synthetic Aperture Radar (SAR) imagery works under all visibility conditions. It can detect water under vegetation but requires deep network algorithms to segment water presence, and manual annotation work is required to train the deep models. This project uses DEEPAQUA, a cross-modal knowledge distillation method, to eliminate the manual annotation needed to extract water presence from SAR imagery with deep neural networks. In this method, a deep student model (e.g., UNET) is trained to segment water in SAR imagery. The student model uses the NDWI algorithm as the non-parametric, cross-modal teacher. The key prerequisite is that NDWI works on the optical imagery taken from the exact location and simultaneously as the SAR. Three different deep architectures are tested in this project: UNET, SegNet, and UNET++, and the Otsu method is used as the baseline. Experiments on imagery from Swedish wetlands in 2020-2022 show that cross-modal distillation consistently achieved better segmentation performances across architectures than the baseline. Additionally, the UNET family of algorithms performed better than SegNet with a confidence of 95%. The UNET++ model achieved the highest Intersection Over Union (IOU) performance. However, no statistical evidence emerged that UNET++ performs better than UNET, with a confidence of 95%. In conclusion, this project shows that cross-modal knowledge distillation works well across architectures and removes tedious and expensive manual work hours when detecting water from SAR imagery. Further research could evaluate performances on other datasets and student architectures. / Att upptäcka vatten under vegetation är avgörande för att hålla koll på statusen på geologiska ekosystem som våtmarker. Forskare använder olika metoder för att uppskatta vattennärvaro vilket undviker kostsamma mätningar på plats. Optiska satellitbilder tillåter automatisk avgränsning av vatten med hjälp av konceptet Normalised Difference Water Index (NDWI). Optiska bilder fortfarande beroende av siktförhållanden och kan inte upptäcka vatten under vegetationen, en typisk situation för våtmarker. Synthetic Aperture Radar (SAR)-bilder fungerar under alla siktförhållanden. Den kan detektera vatten under vegetation men kräver djupa nätverksalgoritmer för att segmentera vattennärvaro, och manuellt anteckningsarbete krävs för att träna de djupa modellerna. Detta projekt använder DEEPAQUA, en cross-modal kunskapsdestillationsmetod, för att eliminera det manuella annoteringsarbete som behövs för att extrahera vattennärvaro från SAR-bilder med djupa neurala nätverk. I denna metod tränas en djup studentmodell (t.ex. UNET) att segmentera vatten i SAR-bilder semantiskt. Elevmodellen använder NDWI, som fungerar på de optiska bilderna tagna från den exakta platsen och samtidigt som SAR, som den icke-parametriska, cross-modal lärarmodellen. Tre olika djupa arkitekturer testas i detta examensarbete: UNET, SegNet och UNET++, och Otsu-metoden används som baslinje. Experiment på bilder tagna på svenska våtmarker 2020-2022 visar att cross-modal destillation konsekvent uppnådde bättre segmenteringsprestanda över olika arkitekturer jämfört med baslinjen. Dessutom presterade UNET-familjen av algoritmer bättre än SegNet med en konfidens på 95%. UNET++-modellen uppnådde högsta prestanda för Intersection Over Union (IOU). Det framkom dock inga statistiska bevis för att UNET++ presterar bättre än UNET, med en konfidens på 95%. Sammanfattningsvis visar detta projekt att cross-modal kunskapsdestillation fungerar bra över olika arkitekturer och tar bort tidskrävande och kostsamma manuella arbetstimmar vid detektering av vatten från SAR-bilder. Ytterligare forskning skulle kunna utvärdera prestanda på andra datamängder och studentarkitekturer.

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