This thesis addresses the problem of improving the delineation of skylines, also referred to as skyline detection, in occluded and challenging environments where existing skyline delineation methods may struggle or fail. Delineated skylines can be used in monocular camera localization methods by comparing delineated skylines to digital elevation model data to estimate a position based on known terrain. This is particularly useful in GPS-denied environments in which active sensing is either impractical or undesirable for various reasons, so that passive sensing using monocular cameras is necessary and/or strategically advantageous. This thesis presents a novel method of skyline delineation using deep learning-based semantic segmentation of monocular camera images to detect natural skylines of distant landscapes in the presence of occlusions. Skylines are extracted from semantic segmentation predictions as the boundary between pixel clusters labeled as terrain to those labeled as sky, with additional segmentation classes representing the known set of potential occlusions in a given environment. Additionally, each pixel in the detected skyline contours are assigned a confidence score based on local intensity gradients to reduce the potential impacts of erroneous skyline contours on position estimation. The utility of these delineated skylines is demonstrated by obtaining orientation and position estimates using existing methods of skyline-based localization. In these methods, the delineated natural skyline is compared to rendered skylines using digital elevation model data and the position estimate is obtained by finding the closest match. Results from the proposed skyline delineation method using semantic segmentation, with accompanying localization demonstration, is presented on two distinct data sets. The first is obtained from the Perseverance Rover operating in the Jezero Crater region of Mars, and the second is obtained from an uncrewed surface vessel operating in the Gulf of Koper, Slovenia. / Denna avhandling tar upp problemet med att förbättra avgränsningen av skylines, även kallad skylinedetektion, i tilltäppta och utmanande miljöer där befintliga skylineavgränsningsmetoder kan kämpa eller misslyckas. Avgränsade skylines kan användas i monokulära kameralokaliseringsmetoder genom att jämföra avgränsade skylines med digitala höjdmodelldata för att uppskatta en position baserat på känd terräng. Detta är särskilt användbart i GPS-nekas miljöer där aktiv avkänning är antingen opraktisk eller oönskad av olika skäl, så att passiv avkänning med användning av monokulära kameror är nödvändig och/eller strategiskt fördelaktig. Denna avhandling presenterar en ny metod för skylineavgränsning med användning av djupinlärningsbaserad semantisk segmentering av monokulära kamerabilder för att detektera naturliga skylines av avlägsna landskap i närvaro av ocklusioner. Horisonter extraheras från semantiska segmenteringsförutsägelser som gränsen mellan pixelkluster märkta som terräng till de märkta som himmel, med ytterligare segmenteringsklasser som representerar den kända uppsättningen potentiella ocklusioner i en given miljö. Dessutom tilldelas varje pixel i de detekterade skylinekonturerna ett konfidenspoäng baserat på lokala intensitetsgradienter för att minska den potentiella påverkan av felaktiga skylinekonturer på positionsuppskattning. Användbarheten av dessa avgränsade skylines demonstreras genom att erhålla orienterings- och positionsuppskattningar med hjälp av befintliga metoder för skylinebaserad lokalisering. I dessa metoder jämförs den avgränsade naturliga horisonten med renderade silhuetter med hjälp av digitala höjdmodelldata och positionsuppskattningen erhålls genom att hitta den närmaste matchningen. Resultat från den föreslagna metoden för skylineavgränsning med semantisk segmentering, med tillhörande lokaliseringsdemonstration, presenteras på två distinkta datamängder. Den första kommer från Perseverance Rover som verkar i Jezero Crater-regionen på Mars, och den andra erhålls från ett obemannat ytfartyg som verkar i Koperbukten, Slovenien.

The increased temperatures in the Arctic accelerate the loss of land based ice stored in glaciers. The Greenland Ice Sheet is the largest ice mass in the Northern Hemisphere and holds ~10% of all the freshwater on Earth, equivalent to ~7 metres of global sea level rise. A few decades ago, the mass balance of the Greenland Ice Sheet was poorly known and assumed to have little impact on global sea level rise. The development of regional climate models and remote sensing of the ice sheet during the past decade have revealed a significant mass loss. To monitor how the Greenland Ice Sheet will affect sea levels in the future requires understanding the physical processes that govern its mass balance and movement. In the southeastern and central western regions, mass loss is dominated by the dynamic behaviour of ice streams calving into the ocean. Changes in surface mass balance dominate mass loss from the Greenland Ice Sheet in the central northern, southwestern and northeastern regions. Little is known about what the hydrological system looks like beneath the ice sheet; how well the hydrological system is developed decides the water’s impact on ice movement. In this thesis, I have focused on radar sounding measurements to map the subglacial topography in detail for a land-terminating section of the western Greenland Ice Sheet. This knowledge is a critical prerequisite for any subglacial hydrological modelling. Using the high-resolution ice thickness and bed topography data, I have made the following specific studies: First, I have analysed the geological setting and glaciological history of the region by comparing proglacial and subglacial spectral roughness. Second, I have analysed the subglacial water drainage routing and revealed a potential for subglacial water piracy between adjacent subglacial water catchments with changes in the subglacial water pressure regime. Finally, I have looked in more detail into englacial features that are commonly observed in radar sounding data from western Greenland. In all, the thesis highlights the need not only for accurate high-resolution subglacial digital elevation models, but also for regionally optimised interpolation when conducting detailed hydrological studies of the Greenland Ice Sheet. / De ökade temperaturerna i Arktis påskyndar förlusten av landbaserad is lagrad i glaciärer och permafrost. Grönlands inlandsis är den största ismassan på norra halvklotet och lagrar ca 10% av allt sötvatten på jorden, vilket motsvarar ca 7 meter global havsnivåhöjning. För ett par decennier sedan var inlandsisens massbalans dåligt känd och antogs ha liten inverkan på dagens havsnivåhöjning. Utvecklingen av regionala klimatmodeller och satellitbaserad fjärranalys av inlandsisen har under de senaste decenniet påvisat en betydande massförlust. För att förutse vilken inverkan inlandsisen har på framtida havsnivåhöjningar krävs en förståelse för de fysikaliska processerna som styr dess massbalans och isrörelse. I de sydöstra och centrala västra delarna av inlandsisen domineras massförlusten av dynamiska processer i isströmmar som kalvar ut i havet. Massförlusten i de centrala norra, sydvästra och nordöstra delarna domineras av isytans massbalans. Ytterst lite är känt om hur det hydrologiska systemet ser ut under inlandsisen; hur väl det hydrologiska systemet är utvecklat avgör vattnets påverkan på isrörelsen. I denna doktorsavhandling har jag använt markbaserade radarmätningar för att kartlägga den subglaciala topografin för en del av den västra landbaserade inlandsisen. Denna kunskap är en viktig förutsättning för att kunna modellera den subglaciala hydrologin. Med hjälp av rumsligt högupplöst data över istjockleken och bottentopografin har jag gjort följande specifika studier: Först har jag analyserat de geologiska och glaciologiska förhållandena i regionen genom att jämföra proglacial och subglacial spektralanalys av terrängens ytojämnheter. Sedan har jag analyserat den subglaciala vattenavrinningen och påvisat en potential för att avrinningsområdena kan ändras beroende på vattentryckförhållandena på botten. Slutligen har jag tittat mer i detalj på englaciala radarstrukturer som ofta observerats i radardata från västra Grönland. Sammanfattningsvis belyser avhandlingen behovet av inte bara noggranna rumsligt högupplösta subglaciala digitala höjdmodeller, utan även regionalt optimerad interpolering när detaljerade hydrologiska studier ska utföras på Grönlands inlandsis.

A sustainable society is a society that satisfies its resource requirements without endangering the sustainability of these resources. The mineral endowment on the African continent is estimated to be the first or second largest of world reserves. Therefore, it is recognised that the African continent still heavily depends on mineral exports as a key contributor to the gross domestic product (GDP) of various countries. These mining activities, however, do introduce primary and secondary environmental degradation factors. They attract communities to these mining areas, light and heavy industrial establishments occur, giving rise to artisanal activities. This study focussed on satellite RS products as an aid to a mine’s operations and the monitoring of its environment. Effective operational mine management and control ensures a more sustainable and profitable lifecycle for mines. Satellite based RS holds the potential to observe the mine and its surrounding areas at high temporal intervals, different spectral wavelengths and spatial resolutions. The combination of SAR and optical information creates a spatial platform to observe and measure the mine’s operations and the behaviour of specific land cover and land use classes over time and contributes to a better understanding of the mining activities and their influence on the environment within a specific geographical area. This study will introduce an integrated methodology to collect, process and analyse spatial information over a specific targeted mine. This methodology utilises a medium resolution land cover base map, derived from Landsat 8, to understand the predominant land cover types of the surrounding area. Using very high resolution mono- and stereoscopic satellite imagery provides a finer scale analysis and identifies changes in features at a smaller scale. Combining these technologies with the synthetic aperture radar (SAR) applications for precise measurement of surface subsidence or upliftment becomes a spatial toolbox for mine management. This study examines a combination of satellite remote sensing products guided by a systematic workflow methodology to integrate spatial results as an aid for mining operations and environmental monitoring. Some of the results that can be highlighted is the successful land cover classification using the Landsat 8 satellite. The land cover that dominated the Kolomela mine area was the “SHRUBLAND/GRASS” class with a 94% coverage and “MINE” class of 2.6%. Sishen mine had a similar dominated land cover characteristic with a “SHRUBLAND/GRASS” class of 90% and “MINE” class of 4.8%. The Pléiades time-series classification analysis was done using three scenes each acquired at a different time interval. The Sishen and Kolomela mine showed especially changes from the bare soil class to the asphalt or mine class. The Pléiades stereoscopic analysis provided volumetric change detection over small, medium, large and recessed areas. Both the Sishen and Kolomela mines demonstrated height profile changes in each selected category. The last category of results focused on the SAR technology to measure within millimetre accuracy the subsidence and upliftment behaviour of surface areas over time. The Royal Bafokeng Platinum tailings pond area was measured using 74 TerraSAR-X scenes. The tailings wall area was confirmed as stable with natural subsidence that occurred in its surrounding area due to seasonal changes of the soil during rainy and dry periods. The Chuquicamata mine as a large open pit copper mine area was analysed using 52 TerraSAR-X scenes. The analysis demonstrated significant vertical surface movement over some of the dumping sites. It is the wish of the researcher that this dissertation and future research scholars will continue to contribute in this scientific field. These contributions can only assist the mining sector to continuously improve its mining operations as well as its monitoring of the primary as well as the secondary environmental impacts to ensure improved sustainability for the next generation. / Environmental Sciences / M. Sc. (Environmental Science)

<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>