The Island of Tongatapu, Tonga, was struck by a tsunami on January 15, 2022. Internet was cut off from the island, which made remote sensing a valuable tool for the assessment of damages. Through land cover classification, change vector analysis and log-ratio image differencing, damages caused by the tsunami were assessed remotely in this thesis. Damage assessment is a vital part of both assessing the need for humanitarian aid after a tsunami, but also lays the foundation for preventative measurements and reconstruction. The objective of this thesis was to assess damage in terms of square kilometers and create damage maps. It was also vital to assess the different methods and evaluate their accuracy. Results from this study could theoretically be combined with other damage assessments to evaluate different aspects of damage. It was also important to evaluate which methods would be good to use in a similar event. In this study Sentinel-1, Sentinel-2 and high-resolution Planet Imagery were used to conduct a damage assessment. Evaluating both moderate and high-resolution imagery in combination with SAR yielded plausible, but flawed results. Land cover was computed for moderate and high-resolution imagery using three types of classifiers. It was found that the Random Forest classifier outperforms both CART and Support Vector Machine classification for this study area.  Land cover composite image differencing for pre-and-post tsunami Sentinel-2 images achieved an accuracy of around 85%. Damage was estimated to be about 10.5 km^2. Land cover classification with high-resolution images gave higher accuracy. The total estimated damaged area was about 18 km^2. The high-resolution image classification was deemed to be the better method of urban damage assessment, with moderate-resolution imagery working well for regional damage assessment.  Change vector analysis provided plausible results when using Sentinel-2 with NDVI, NDMI, SAVI and BSI. NDVI was found to be the most comprehensive change indicator when compared to the other tested indices. The total estimated damage using all tested indices was roughly 7.6 km^2. Using the same method for Sentinel-1's VV and VH bands, the total damage was estimated to be 0.4 and 2.6 km^2 respectively. Log ratio for Sentinel-1 did not work well compared to change vector analysis. Issues with false positives occurred. Both log-ratios of VV and VH gave a similar total estimated damage of roughly 5.2 km^2.  Problems were caused by cloud cover and ash deposits. The analysis could have been improved by being consistent with the choice of dates for satellite images. Also, balancing classification samples and using high-resolution land cover classification on specific areas of interest indicated by regional methods. This would circumvent problems with ash, as reducing the study area would make more high-resolution imagery available.

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This study aims at increasing the amount of data available on recent past Swedish glacier front variations, at improving the knowledge on the present behavior of these glaciers, and at contributing to the defnition of the guideline for future of glacier front observations in Sweden. To do so, the study proposes Sentinel-2 based yearly front variation measurements for 22 Swedish glaciers between 2015 and 2022, calculated based on the multicentreline approach of the MaQiT tool. It also assesses the uncertainty of Sentinel-2 based mapping by comparing it to 0.48 m spatial resolution aerial imagery based mapping and to field based mapping conducted on four northern Sweden glaciers during the end of the summer 2022: Kaskasatj SE, Kebnepakteglaciären, Mårmaglaciären, and Storglaciären. The fieldwork included handheld GNSS, UAV photogrammetry, and total station survey in order to compare the three methods in the mapping of glacier fronts. This study also compares the measured glaciers front variations to climatic factors and glaciers boundary conditions. The resulting glacier front variations in Sweden between 2015 and 2022, averaged over all glaciers studied, is −10.28 m yr−1. Small glaciers retreated on average 0.51 % of their length per year, while large glaciers retreated on average 0.35 % per year. This study highlights the importance of recording yearly front positions of a large amount of glaciers, and therefore encourages for the future the use of satellite imagery to observe all Swedish glaciers fronts on a yearly basis. It also supports the conduction of regular UAV photogrammetry surveys to provide high resolution mapping of a sample of glacier fronts chosen for their vicinity with the Tarfala Research Station, the Swedish field centre for glaciological and alpine research.

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Water quality monitoring is crucial globally due to the vital role of freshwater in providing drinking water, irrigation, and ecosystem services. Highly polluted water poses risks to both ecosystems and human health. Current water quality monitoring methods deployed in the field are often expensive, labor-intensive, and invasive. To overcome these issues, this degree project investigated the use of remote sensing to assess critical water quality parameters in the Swedish river Indalsälven. The research questions focus on determining the accuracy of predicting chemical oxygen demand (COD), river color, turbidity, and total phosphorus (TP) using satellite data and machine learning algorithms. The findings revealed that COD can be predicted with a cross-validated coefficient of determination (R²CV) of 0.7, indicating a robust predictive capability. The study suggests that while approximate quantitative prediction of COD in oligotrophic rivers is feasible using Sentinel-2 imagery, predictions for the other parameters remain challenging in the context of Indalsälven. Improvements in prediction accuracy were achieved through optimized band combinations, reduced datasets encompassing satellite data collected within two days of field measurements, and suitable pre-processing methods. / Airborne Monitoring of Water Quality in Remote Regions

The main objectives of this master thesis are to evaluate and compare chosen classification algorithm for the tree species classification. With usage of satellite imagery Sentinel-2 and Landsat 8 is examined whether the better spatial resolution affects the quality of the resulted classification. According to past case studies and literature was chosen supervised algorithms Support Vector Machine, Neural Network and Maximum Likelihood. To achieve the best possible results of classification is necessary to find a suitable choice of parameters and rules. Based on literate was applied different settings which were subsequently evaluated by cross validation. All results are accompanied by tables, charts and maps which comprehensively and clearly summarize the answers to the main objectives of the thesis.

The aim of this diploma thesis is a proposal of a methodology used for an assessment of the measure of defoliation based on the multispectral satellite images from missions Landsat and Sentinel-2. The first part of the thesis is dedicated to the introduction of the problematics of remote sensing using multispectral sensors and the basics of research into forest vegetation. Following on this part, there is a chapter considering possibilities of monitoring defoliation using resources of remote sensing, and the closely connected problematics of the health condition of forest vegetation. After that comes a description of the used data (the satellite images and the data of ground investigation by VÚLHM) and logically compounded process of transformation of the data from satellite images on the levels of defoliation. Outcomes of the thesis include analysis of the ability of single spectral bands and vegetation indices to predict defoliation of Norway spurce (Picea abeis) and Scots pine (Pius sylvestris) vegetation. The assessment of the measure of defoliation is demonstrated on single band in near-infrared region with used of linear regression model.

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Classification of selected agricultural crops from time series of Sentinel-2 and PlanetScope imagery in Kutnohorsko model area Abstract The thesis is focused on the analysis of spectral characteristics of selected agricultural crops druring agriculutural season from time series of Sentinel -2 (A and B) and PlanetScope sensors in the model area situated around the settlements of Kolín and Kutná Hora. It is based on the assumption that the use of multiple dates of image data acquired crops in different phenological phases of the crops allows better identification of crop species (Lu et al., 2004). The aim of the thesis was to analyse the characteristics of the seasonal course of spectral features of selected agricultural crops (sugar beet, spring barley, winter barley, maize, spring wheat, winter wheat, winter rape) and to determine the period of the year suitable for the differentiation of individual crops. Another aim of the thesis was to classify these crops in the model area from time series of two above-mentioned sensors and to compare the accuracy of the pixel and object-oriented classification approach for multitemporal composites and the accuracy for monotemporal image from the term when the individual crops are clearly distinguishable. The training and validation datasets and the classification mask...

Our planet is traversing the age of human-induced climate change and biodiversity loss. Projected global warming of 1.5 ºC above pre-industrial levels and related greenhouse gas emission pathways will bring about detrimental and irreversible impacts on the interconnected natural and human ecosystem. A global warming of 2 ºC could further exacerbate the risks across the sectors of biodiversity, energy, food, and water. Time- and cost-effective solutions and strategies are required for strengthening humanity’s response to the present environmental and societal challenges. Coastal seascape ecosystems including seagrasses, corals, mangrove forests, tidal flats, and salt marshes have been more recently heralded as nature-based solutions for mitigating and adapting to the climate-related impacts. This is due to their ability to absorb and store large quantities of carbon from the atmosphere. Focusing on seagrass habitats, although occupying only 0.2% of the world’s oceans, they can sequestrate up to 10% of the total oceanic carbon pool, all the while providing important food security, biodiversity, and coastal protection. But seagrass ecosystems, as all of their blue carbon seascape neighbors, are losing 1.5% of their extent per year due to anthropogenic activities. This has adverse implications for global carbon stocks, coastal protection, and marine biodiversity. Seagrass and seascape recession necessitates their science and policy-based management, protection, conservation which will ensure that our planet will remain within its sustainable boundaries in the age of climate change. The present PhD Thesis and research aim is to develop algorithms for seagrass mapping and monitoring leveraging the recent emergences in remote sensing technology―new satellite image archives, machine learning frameworks, and cloud computing―with field data from multiple sources. The main PhD findings are the demonstration of the suitability of Sentinel-2, RapidEye, and PlanetScope satellite imagery for regional to large-scale seagrass mapping; the introduction and incorporation of machine learning frameworks in the context of seagrass remote sensing and data analytics; the development of a semi-analytical model to invert the bottom reflectance of seagrasses; the design and implementation of multi-temporal satellite image approaches in coastal aquatic remote sensing; and the introduction, design and application of a scalable cloud-based tool to scale up seagrass mapping across large spatial and temporal dimensions. The approaches of the present PhD cover the gaps of the existing scientific literature of seagrass mapping in terms of the lack of spatial and temporal scalability and adaptability; the infancy in seagrass and seascape-related artificial intelligence endeavours; the restrictions of local server and mono-temporal approaches; and the absence of new methodological developments and applications using new (mainly open) satellite image archives. I anticipate and envisage that the near-future steps after the completion of my PhD will address the scalability of the designed cloud-native, data-driven mapping tool to standardise, automate, commercialise and democratise mapping and monitoring of seagrass and seascape ecosystems globally. The synergy of the developed momentum around the global seascape with the technological potential of Earth Observation can contribute to humanity’s race to adapt to and mitigate the climate change impacts and avoid cross tipping points in climate patterns, and biodiversity and ecosystem functions.