The Swedish mining and minerals sector is one of the most prominent greenhouse gas emitters in the country. At the same time, it also provides Sweden and other nations with vast amounts of important metals and minerals, which are not easily replaced. However, in light of the increasing urgency to reduce global greenhouse gas concentrations, it is imperative that the combustion of fossil fuels is decreased. This quantitative case study uses a document review and interviews to investigate the prerequisites and technical potential for decarbonising the Dannemora iron ore mine in eastern Sweden. Furthermore, a comparative life cycle inventory based on methods from the Greenhouse Gas Protocol is performed in order to calculate potential savings of both energy and greenhouse gases from exchanging conventional underground mining equipment with more sustainable alternatives. Results show that emissions from underground activities in the Dannemora mine could potentially be reduced by up to 90 %, and energy consumption by up to 64 %. It was however also shown that no viable emission-free explosives currently exist that could safely replace conventional alternatives. Further research needs to be conducted in order to investigate the effects of Scope 3 emissions from the production of battery electric mining vehicles, as this would have an effect on overall GHG and energy savings.

In recent decades, the night lights data of the Earth’s surface derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been used to detect the human settlements and human activities, because the DMSP/OLS data is able to supply the information about the urban areas  and non-urban areas on the Earth which means it is more suitable for urban studies than usual satellite imagery data.   The urban development is closed linked to the human society development. Therefore, studies of urban development will help people to understand how the urban changed and predict the urban change. The aim of this study was to detect Beijing’s urban development from 1992 to 2012, and find the contributions to the urban sprawl from socio-economic factors. Based on this objective, the main dataset used in this thesis was night lights images derived from the DMSP/OLS which was detected from  1992 to 2012. Due to the lacking of on-board calibration on OLS, and the over-glow of the lights resources, the information about the night lights cannot be extracted directly. Before any process, the night lights images should be calibrated. There is a method to calibrate the night light images which is called intercalibration. It is a second order regression model based method to find the related digital number values. Therefore, intercalibration was employed, and the threshold values were determined to extract urban areas in this study. Threshold value is useful for diffusing the over-glow effect, and finding the urban areas from the DMSP/OLS data. The methods to determine the threshold value in this thesis are empirical threshold method, sudden jump detection method, statistic data comparison method and k-mean clustering method. In addition, 13 socio-economic factors which included gross domestic product, urban population, permanent population, total energy consumption and so on were used to build the regression model. The contributions from these factors to the sum of the Beijing’s lights were found based on modeling.   The results of this thesis are positive. The intercalibration was successful and all the DMSP/OLS data used in this study were calibrated. And then, the appropriate threshold values to extract the urban areas were figured out. The achieved urban areas were compared to the satellite images and the result showed that the urban areas were useful. During the time certain factors used in this study, such as mobile phone users, possession of civil vehicles, GDP, three positively highest contributed to urban development were close to 23%, 8% and 9%, respectively.

Permafrost carbon stores have been suggested to react to warming trends with increased terrestrial loading to its coastal waters. Presently, the warming has been seen to be especially high in the East Siberian Arctic and the fate of the major release of terrestrial matter to these coasts is yet to be detailed. Our work is focused on the East Siberian Shelf (ESS) – which is the largest continental shelf in the world. It receives substantial inputs of terrestrial organic matter both from the large Russian rivers and from eroding coastlines. The largest of its rivers, the Lena, discharges in Buor-Khaya Bay, which is also a hot spot for coastal erosion. Previous studies of molecular and δ¹3C and Δ¹⁴C composition of terrestrial organic matter received by Arctic coastal waters have suggested a different propensity of different terrestrial source materials towards bacterial degradation. This detailed isotopic and molecular marker survey which is the basis for this thesis reveals clearly distinguished source patterns both between surface water (POC) and sea floor (SOC) as well as with distance away from the sources. The heavy terrestrial dominance over marine/planktonic sources are clearly detected in gradients of high POC and SOC levels, as well as depleted δ13C -OC and high HMW/LMW n-alkane ratios. Furthermore, data suggests that terr-OC was substantially older yet less degraded in the surface sediment than in the surface waters. This unusual vertical degradation trend was only recently found also for the coastal and central East Siberian Sea. It seems that the riverine terr-OC component comprising mainly annual thaw layer surface soil dominates the buoyant surface water POC and is readily degraded. This is in contrast to the coastline-erosion terr-OC which is associated with minerals and therefore ballasted to the sediments where it makes up the key OC component and seems relatively protected from degradation. The study area of this work is a region with strong terrestrial influence hosting many of the important carbon cycling processes, and data reveal two important OC contributors of different origin, mineral associated coastal erosion matter and riverine borne surface soil matter. These two components may well represent different propensities to contribute to a positive feedback to climate warming by converting OC from coastal and inland permafrost into CO₂.

Wetlands have been heavily affected by human-alteration, this is done to drain thewetland so that the ground can be used for other purposes such as forestry oragriculture. With high accuracy data available now, it is possible to use differentmethods automatically to identify these ditches in wetlands or other areas. Fourdifferent methods were applied to two areas to delineate ditches. To compare the different methods, the results were quantified by comparing with amanually created map of the ditches. Drainage density and an agreement index wereused. The results indicate that the methods of impoundment index and map gullydepth are best able to distinguish the ditches in wetlands. The former gave a betterresult on areas inside wetlands while the latter gave a better result with non-wetlandareas. The other two methods make mistakes and misjudgements that givemisleading results, they ignore ditches partially or completely, or finds ditches inareas without them. Even so, all methods are at least a clear improvement over thecurrently available property map's water flows, but not in the same class as the timeconsumingmanual method. / Våtmarker har påverkats mycket av människan genom dikning, detta görs för attdränera våtmarken så att marken kan användas för andra syften som skogsbruk ellerjordbruk. Med höjddata i hög noggrannhet som nu finns tillgänglig är det möjligt attanvända olika metoder för att urskilja dessa diken i våtmarker. Fyra olika metodermed applicerades på två olika områden för att undersöka möjligheten att urskiljadiken. För att jämföra de olika metoderna kvantifieras resultaten genom att jämföras meden manuellt skapad karta över diken. Dränerings densitet och ettöverensstämmelseindex användes. Resultaten pekar på att metoderna fördämningsindex (impoundment index) och ravindjupskartering (map gully depth) klarar bäst avatt urskilja diken i våtmarker. Där den föregående ger ett gav ett bättre resultat inomvårmarker och den senare gav ett bättre resultat generellt över området. De tvåandra metoderna gör missar och felbedömningar som ger missvisande resultat.Oavsett det, så är karteringen av dikena i nuläget en klar förbättring överFastighetskartans vattenflöden, men inte i samma klass som den tidskrävandemanuella metoden.

Life on early earth has long been the topic of discussion for many researchers: how did it come to be? Which cells came first? Where can we find them? The most ancient rocks on our planet may hold some of the answers to these questions, but many may only be answered in laboratories. Chemical and morphological traces can be found from Archaean deposits, tantalisingly similar to modern day prokaryotes. Often, they are interpreted as the fossilised remains of bacteria or archaea. However, the caveat remains the abiotic mechanisms with which many similar traces and markers can be formed. The purpose of this thesis was to look into the similarities and differences in abiotic and biological formation of filamentous structures in rocks and observe whether there are chemical or morphological factors that allow for distinguishing between the two. Various laboratory methods were used: chemical gardens to form filamentous abiotic structures and experimental mineralisation of a filamentous methanogen in carbonate, phosphate, and silicate in order to compare and contrast the various mineralisation mechanisms in the fidelity of preservation of the microbes. In the former experiment, analysis with electron paramagnetic resonance (EPR) spectroscopy was carried out to identify potential chemical biomarkers. A combination of scanning and transmission electron microscopy, energy dispersive X-ray (EDX) analysis, X-ray diffraction (XRD) and Raman spectroscopy were also used to analyse the minerals and precipitates formed in both sets of experiments. The results of this research indicate that morphology of filamentous structures and the chemical signatures in biominerals may not be reliable as biogenic indicators. Furthermore, the work on experimental mineralisation reveals the possible biases in the rock record of microbial preservation which is highly dependent on the structure of the cell wall, chemistry of the environment, and the mineral formed. Finally, this work has important outcomes for the search for biomarkers on earth and on other planets and for the recognition of pseudofossils versus microbial fossils in the rock record.

The senseFly eBeePlus fixed-wing drone is a market available UAV compatible with a range of sensors that includes the Parrot Sequoia+ multispectral camera. Commercial applications of the drone predominantly focus on agriculture, environmental management, and engineering applications. The Sequoia 4-band multispectral sensor with bands optimised for plant health analysis, has a spectral range that coincides with the absorption features of iron. Previous studies with the use of hyperspectral sensors on multicopter UAVs have proven successful in the detection and delineation of hydroxides and sulphates associated with weathering of sulphides at the surface. This study aims to evaluate the ability of the eBeePlus drone equipped with a Parrot Sequoia+ sensor to effectively detect and delineate surficial sulphide mineral expressions by testing its capability on a known nickel-copper mineralisation occurrence at Illukunnguaq, on the north-western coast of Disko Island, West Greenland. Formally hosting a 28-tonne nickeliferous pyrrhotite massive sulphide boulder, many companies have sought this region for a possible extension of the mineralisation or another local mineral occurrence. Iron-feature band ratios and Spectral Angle Mapping (SAM) are two methods tested to first characterise the known occurrence, then search the wider region for other features with a similar signature as the Illukunnguaq dyke. To assist the evaluation and fine tune the Sequoia sensor, it will be compared against the trialled and trusted Rikola hyperspectral sensor, proven to map iron features. In addition, eigen maxima as one of many geomorphological indices that utilise the co-product Digital Surface Model (DSM) of the spectral survey, is employed to assess whether the Illukunnguaq dyke and other features are structurally mappable.  Results show that the Sequoia multispectral sensor, albeit less spectrally resolved than the Rikola hyperspectral sensor was able to detect surficial sulphide mineral expressions both by applying iron-feature band ratios and SAM. The latter was performed using laboratory measured and open-access library spectra. To fine-tune the tools compatible with the Sequoia sensor, in-depth investigations into iron-feature band ratio index values and best-fit library spectra for SAM was conducted. Confidence was increased by the blind detection of another known exposure and permitted a regional search to find additional features with spectral similarities to the Illukunnguaq dyke for future ground truthing. This study demonstrates that the eBeePlus drone can be used for mineral exploration when iron-sulphides are a part of the mineral system and outcropping at the surface. Leading field programs with detailed multispectral mapping can improve the efficiency of geologists by generating or verifying targets prior to ‘boots-on-the-ground’ geological sampling or mapping.

In the current context of the energy transition, it has been argued by researchers and authors that the demand for raw materials for the necessary green technologies can’t be met without the input of primary raw materials. These materials can only be supplied through the mining cycle: exploration, mining, and processing. The mining cycle, however, can pose risks to the environment, which could be in contradiction with the motivation behind the implementation of green technologies. It is then society’s duty to strive for a constant reduction of the environmental impact of the mining cycle, or else, we would be in a paradoxical situation where, by mining materials to power the energy transition, if not done with care, we could be also risking the environment.  While this megatrend of the energy transition occurs, Unmanned Aerial Vehicles (UAVs) also known as drones, have reached a significant level of development which together with the miniaturization of geoscientific sensors, has opened the door to interesting fast, agile, and non-invasive ways of obtaining geological information. This has bridged gaps between the traditional scales of airborne and ground surveying and holds the potential of contributing to a less environmentally harmful mining cycle.  This thesis work intends to be a useful reference for anyone interested in working with UAVs in geosciences, especially for the exploration and management of raw materials from an entrepreneurial point of view. Here, a brief review of the current state of the art through the recent scientific literature on applications of drones in the mining cycle, including but not limited to geophysics and hyperspectral imaging is presented. Using this state of the art as a point of departure, semi-structured interviews with different stakeholders in the mining cycle were conducted to answer the research questions. The concept of value, ubiquitously present in the business research literature, was used to analyze the benefits that the use of UAVs can bring to the raw materials industry and the efforts to reduce its environmental footprint. The opportunities for entrepreneurs to be the conduit to deploy such benefits in society were also analyzed.  The work ends with a summary of the qualitative research findings, highlighting how drones constitute an optimization tool that can be used in all the stages of the mining cycle. Additionally, it highlights that UAV gravity and electromagnetic methods, together with better data processing software for hyperspectral imaging, are currently some of the most sought out and/or needed solutions by users.

The Cettiidae, a family of primarily small, insectivorous, Asiatic and Austronesian, mountain birds have been the subject of acoustic analysis in the past. However, until this point, an in-depth review of the songs of the entire family had yet to be undertaken. In an effort to resolve this shortcoming, the songs of 29 Cettiidae species were examined through the usage of acoustic analysis software, with specific factors such as bandwidth, frequency, and strophe duration being statistically recorded. In total 286 individuals and over 800 strophes were analyzed, with the collected data being displayed in various PCA plots. These PCA graphs were then compared to both a dated phylogenetic tree specifically created for this study, and a Mahalanobis distance vs. genetic distance plot, created using the acoustic data as well as Cytochrome b genetic data. Based on these plots, several notable trends could be observed across the entire family. While largescale divergence from the norm was noted in several pairwise comparisons of species, as well as large scale conservation within clades such as the island Horornis species, examples of convergent evolution of their songs was rather scant. It was also noted that despite the strong divergence of certain species, each genus occupied its own area of multivariate space within the PCAs. Strong statistical divergence between island and continental species was also noted in both the PCAs and the Mahalanobis graph. Meanwhile, the statistical analysis of these species unfortunately provided no clues as to the ancestral state of their songs. However, a visual analysis of every species song, mapped on the dated phylogenetic tree, suggested that two distinct linages of simple and complex songs could be traced back approximately 10 million years. This allows for speculation as to the songs of now long extinct Cettiidae species as far back as the Miocene.

The mining industry generates billions of tonnes of waste annually, which is often stored in tailings storage facilities (TSF). This waste is generated from the extraction of ore from surface or underground mines, as well as from metallurgical processing and low-grade stockpiles. TSF can have significant environmental impacts, as they can cause acid mine drainage resulting in the leaching and transport of heavy metals into ground and surface waters. With increasing demand for critical raw material, recent studies have shown that the valorisation of mine waste can be a potential secondary source of critical raw materials. The valorisation of mine waste is possible when the waste is accurately characterised.A novel method that uses multispectral satellite remote sensing and machine learning to estimate the mineral resource in a defunct TSF in the Witwatersrand Basin, South Africa is proposed in this research. Four machine learning models: 1) random forest (RF); 2) adaptive boosting (AB); 3) extra trees (ET); and 4) k-nearest neighbours are developed using supervised machine learning. The models are trained using training data acquired from a TSF with known gold concentration located 3 kilometres from the TSF and deployed on the TSF to predict the gold grades. The results of the machine learning model predictions indicates that machine learning models had high performances for predicting gold grades in the TSF. The AB, RF and ET, models performed best. Their performances were evaluated using the coefficient of determination (R2) value. The R2 values for the machine learning models were 0.95, 0.92, 0.87 and 0.70 for AB, ET, RF and kNN respectively. The mean gold grade predicted was 0.44 g/t by all machine learning models. This was compared to a 2D surficial geostatistical model which estimated 0.35g/t gold in the TSF using ordinary kriging and a 2D vertically averaged geostatistical model with an estimated 0.4 g/t mean gold grade. The short-wave infrared (SWIR) - band 11 at a 20 m spatial resolution had the highest correlation with the reflectance of gold in the TSF. This study demonstrated the value of leveraging multi-spectral remote sensing data and machine learning to perform mineral resource estimation in defunct TSF.

The way of representing Earth has changed; two dimensional (2D) maps have turned into three Dimensional (3D) representations. There are many studies in order to create 3D city maps as well as areas where these are applied such as 3D cadastral, 3D visualization and flood simulation, etc. These can be created with the aid of different data sources using photogrammetric Digital Surface Model (DSM) derived from image matching and from Light Detection and Ranging (LiDAR) point clouds or both of them combining orthophotos and building footprints. Several software has been developed to ease and speed up this process. In this study, a current state-of-the art in the 3D city modeling with particular interest to commercial software was analyzed. DSMs from image matching (Satellite and Airborne) were used in order to create a 3D city model of Trento, Italy. The objectivewas to examine the degree of automation and the computation time of two available software: Feature Manipulation Engine (FME) and Building Reconstruction (BREC). Other problem such as the quality of the DSM needed would be discussed for each software and the results would be compared to those achieved using LiDAR data. Recommendations and possible problems would also be addressed. In order to create a 3D city model, the ALDPAT software (Airborne LiDAR Data Processing and Analysis Tool) has been used to separate the ground regions (Digital Terrain Model, DTM) from the man-made objects and trees (Normal Digital Surface Model, nDSM). FME and BREC software have been used to reconstruct the 3D city model. The output results from FME and BREC have been analyzed qualitatively and quantitatively. The comparison between the models generated from photogrammetric and LiDAR DSMs have been performed. The results show that buildings generated by satellite images have poorest quality compared with buildings from LiDAR and airborne data. In particular, the performed tests will be shown that among the Level of Details (LoD), a LoD1 and LoD2 3D city models can be generated using a DSM by image matching. A deeper study should be done in order to analyze the level of detail qualitatively.