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Radio Occultation (RO) is a remote sensing technique which uses Global Navigation Satellite System (GNSS) signals tracked by a Low-Earth Orbit (LEO) satellite to sound the earth's atmosphere both in low (troposphere, stratosphere) and high (ionosphere) altitudes. GNSS-RO provides global coverage and SI traceable measurements of atmospheric data with high-vertical resolution. Refractivity, dry temperature, pressure and water vapour profiles retrieved from RO measurements have a relevant contribution in Numerical Weather Prediction (NWP) systems and in climate-monitoring. Due to the partial propagation through the ionosphere, a systematic bias is added to the lower atmospheric data product. Most of this contribution is removed by a linear combination of data for two frequencies. In climatology studies, one can apply a second-order correction - so called κ-correction - which relies on a priori information on the conditions in the ionosphere. However, both approaches do not remove high-order terms in the error due to horizontal gradient and earth's geomagnetic fields. The remaining residual ionospheric error (RIE) and its systematic bias in RO atmospheric data is a well-known issue and its mitigation is an open research topic. In this licentiate dissertation, the residual ionospheric error after the standard correction is evaluated with computational simulations using a wave optics propagator (WOP). Multiple Phase Screen (MPS) method is used to simulate occultation events in different ionospheric scenarios, e.g. quiet and disturbed conditions. Electron density profiles (EDP) assumed in simulations are either defined by analytical equations or measurements. The disturbed cases are modelled as small-scale irregularities within F-region in two different ways: as sinusoidal fluctuations; and by using a more complex approach, where the irregularities follow a single-slope power-law that yields moderate to strong scintillation in the signal amplitude. Possible errors in MPS simulations assuming long segment of orbit and ionosphere are also evaluated. The results obtained with the sinusoidal disturbances show minor influence in the RIE after the standard correction, with the major part of the error due to the F-region peak. The implementation of the single-slope power-law is validated and the fluctuations obtained in simulation show good agreement to the ones observed in RO measurements. Finally, an alternative to overcome limitations in MPS simulations considering occultations with long segment of orbit and ionosphere is introduced and validated. The small-scale irregularities modelled in F-region with the power-law can be added in simulations of a large dataset subjected to κ-correction, in order to evaluate the RIE bending angle and the consequences in atmospheric parameters, e.g. temperature. / NRPF-3, Rymdstyrelsen, 241/15

Översvämningar är naturfenomen som väntas bli vanligare i framtiden i och med de klimatförändringar världen står inför. För att skydda urbaniserade områden har EU tagit fram ett direktiv där områden med stor översvämningsrisk ska lokaliseras, riskbedömas och förebyggande åtgärder ska utföras. Kristinehamns kommun har tidigare haft omfattande problem av översvämningar och har därför utfört karteringar för både skyfall och höga vattennivåer i Vänern för alla urbaniserade områden i kommunen. Tidigare analyser bygger på LiDAR-data (höjddata från flygburen laserskanning) från 2011 och sedan dess har ett större område byggts om från industriområde till ett bostadsområde, vilket gör analyserna i området obsoleta.  Syftet med studien är att i samråd med Kristinehamns kommun ta fram en ny kartering för höga nivåer i Vänern i det aktuella området och undersöka hur utbredningen skiljer sig från föregående analys. Projektet utvärderar även några konsekvenser dessa nivåer skulle kunna medföra för människor i det nybyggda bostadsområdet. Resultatet kan också jämföras med de karteringar som finns sen tidigare. På grund av detta har samma åtta scenarier som i föregående analys undersökts. Eftersom ingen ny laserskanning utförts i området startades projektet med insamling av nya höjddata med hjälp av UAV (Unmanned Aerial Vehicle).  Med flygfotografierna tagna med UAV kunde en högupplöst höjdmodell av området skapas. Höjdmodellen subtraherades sedan med värdet för respektive översvämningsnivå. Därefter omklassificerades och extraherades modellerna som slutligen resulterade i översvämnings-data för alla scenarier.  Dessa användes sedan som grund till överlagringsanalyser för att identifiera översvämningshotatde objekt i området.  Resultaten visar att översvämningsutbredningen skiljer sig betydligt från föregående analys för nivåer över 46,00 meter (RH2000). Vattnet begränsas till största del utanför fastighetsgränserna i området vilket gör att bostäder och boenden inte blir omfattat påverkade. Däremot kommer framkomligheten till några bostäder påverkas vid de högsta nivåerna när vägar översvämmas. Dagvattenledningar kan också komma att förvärra översvämningarnas utbredning då ledningarna förväntas bli mättade och snarare trycka upp vatten än föra bort det. / Flooding is a natural phenomenon that will become more frequent in the future due to climate changes. To protect urbanised areas from floods the EU has developed a directive where areas that are at a high risk of flooding are to be identified, the risk assessed and preventative measures will be put into place. Kristinehamn municipality has previously experienced extensive problems during flooding events and has already preformed analyses modelling both pluvial and fluvial flooding. The modelling is based on elevation data from LiDAR-data created in 2011. Since then, a bigger industrial area in the municipality has been rebuilt into a residential area which makes the current analyses in the area obsolete.     The aim of this study is to create a new model for fluvial flooding and in the event of high water levels in Lake Vänern, and to compare the results with the results from the previous analyses. This is done in collaboration with Kristinehamn municipality.  The study also aims to evaluate the consequences that high water levels could have on the people living in the newly built residential area. To make the results comparable, the same levels as the previous analyses will be used. Since no new LiDAR-data are available the study started by collecting new elevation data from UAV.  Based on  UAV images a high-resolution elevation model was created. The level for each flooding scenario was subtracted from the elevation model and the data were then reclassified to create different depths. This resulted in a model for each flooding scenario. In the last step the data for water and low points were extracted from the models, and the results were later used to analyse affected objects. The results show that since the reconstruction, less consequences can be expected for the area at water levels exceeding 46.00 meters (RH2000). Nearly exclusively areas planned to maintain roads and parks are affected as the water will gather outside the property boundaries. Therefore, the effect on people living in the area is limited. However, the accessibility of the area is affected since roads will be flooded in the highest water level scenario. The results also show that the storm drains may worsen the impact of a flooding event due to backflow and water being pushed up rather than being drained away.

The seagrass extent has been declining globally. The human activities that are most likely to cause seagrass loss are those which affect the water quality and clarity. However, turbidity following coastal erosion is often left out from marine ecosystem vulnerability indices. This study quantified the coastal erosion for Tsimipaika Bay in northwestern Madagascar by using change detection analysis of satellite imageries. The annual coastal erosion data was then used to create an index for seagrass vulnerability to turbidity following coastal erosion. Considering that the height of seagrass species plays an important role in their survival following turbidity, the seagrass vulnerability index (SVI) was based on two factors; seagrass species height and their distance to the nearest possible erosion place. The results for the coastal erosion showed that the amount of erosion was particularly high in 1996, 2001 and 2009 for Tsimipaika Bay. The highest erosion occurred in 2001 with a land loss area of about 6.2 km2 . The SVI maps revealed that 40% of the seagrass communities had minimum mean SVI values in 2001 and 50% had the maximum mean SVI during the year 2009. This study showed that it is possible to use coastal erosion to measure seagrass vulnerability; however, the index requires configuration such as including the total amount of annual coastal erosion and incorporating bathymetric data. The entire project was built and automated in Jupyter Notebook using Python programming language, which creates a ground for future studies to develop and modify the project.

For intelligent systems, the ability to adapt a sensor's sensing capabilities offers promise for reducing numbers, weight, and volume of sensors required. This basic idea is in line with a recent assertion by the well-known roboticist Rodney Brooks, that versatile robots could be used to perform various tasks instead of requiring a large number of specialized robots.In the current work, we consider the concept of a "smart" sensor which could dynamically adapt itself to replace multiple static sensors--within the application area of ingestible smart pills, where small sensors might be required to detect problems such as bleeding or tumours.\\ Simulations were used to evaluate some basic strategies for how to adapt the sensor and their effectiveness was compared; as well, a hardware prototype using LEDs to indicate system switching was prepared.

The use of GNSS has made huge progress over the last few decades and in many cases replaced the use of total stations. A current problem for the GNSS-technique is dense vegetation, which prevents the receivers from making reliable calculations for the satellite signals. In this study a new receiver from Trimble that is said to be able to measure in these particular environments is compared to its predecessor. By assignment from ÅF Karlstad we have therefore conducted a comparison of the new receiver (R12) and its predecessor (R10), to see if R12 to a greater extent than R10 can replace the use of a total station.   The comparison was carried out by repeatedly measuring six different points, totally or partly obstructed by vegetation. To achieve uniform points these where measured with a Realtime Updated Free Station (RUFRIS) and the altitude was balanced from nearby fixed points.   The results where then analyzed by calculating average distance from the known points as well as the maximum dispersion within each respective moment of measurement. The results show that Trimble R12 can conduct reliable measurements in environments where the R10 is not useable. At one of the points the R12 achieved fixed solution where the R10 failed to do so, which is clearly reflected in the results. The results further show a more even and gathered result compared to the R10.   The conclusion of the project is that the R12 constantly performs a better result than the R10 and also enables measurements in environments previously not measurable with GNSS.

KNAS (Continuous Habitat Mapping of Protected Areas) is a Metria AB project that produces vegetation and habitat mapping in protected areas in Sweden. Vegetation and habitat mapping is challenging due to its heterogeneity, spatial variability and complex vertical and horizontal structure. Traditionally, multispectral data is used due to its ability to give information about horizontal structure of vegetation. LiDAR data contains information about vertical structure of vegetation, and therefore contributes to improve classification accuracy when used together with spectral data. The objectives of this study are to integrate LiDAR and multispectral data for KNAS and to determine the contribution of LiDAR data to the classification accuracy. To achieve these goals, two object-based classification schemes are proposed and compared: a spectral classification scheme and a spectral-LiDAR classification scheme. Spectral data consists of four SPOT-5 bands acquired in 2005 and 2006. Spectral-LiDAR includes the same four spectral bands from SPOT-5 and nine LiDAR-derived layers produced from NH point cloud data from airborne laser scanning acquired in 2011 and 2012 from The Swedish Mapping, Cadastral and Land Registration Authority. Processing of point cloud data includes: filtering, buffer and tiles creation, height normalization and rasterization. Due to the complexity of KNAS production, classification schemes are based on a simplified KNAS workflow and a selection of KNAS forest classes. Classification schemes include: segmentation, database creation, training and validation areas collection, SVM classification and accuracy assessment. Spectral-LiDAR data fusion is performed during segmentation in eCognition. Results from segmentation are used to build a database with segmented objects, and mean values of spectral or spectral-LiDAR data. Databases are used in Matlab to perform SVM classification with cross validation. Cross validation accuracy, overall accuracy, kappa coefficient, producer’s and user’s accuracy are computed. Training and validation areas are common to both classification schemes. Results show an improvement in overall classification accuracy for spectral-LiDAR classification scheme, compared to spectral classification scheme. Improvements of 21.9 %, 11.0 % and 21.1 % are obtained for the study areas of Linköping, Örnsköldsvik and Vilhelmina respectively.

Semi-natural grasslands are amongst the most biodiverse ecosystems in Europe, though their importance they are experiencing a declining trend. To monitor and assess the health of these ecosystems is generally costly, personnel demanding and time-consuming. With satellite imagery and machine learning becoming more accessible, this can offer a cheap and effective way to gain ecological information about semi-natural grasslands.This thesis explores the possibilities to predict plant species richness in semi-natural grasslands with high resolution satellite imagery through machine learning. Five different machine learning models were employed with various subsets of spectral- and geographical features to see how they performed and why. The study area was in southern Sweden with satellite and survey data from the summer of 2019.Geographical features were the features that influenced the machine learning models most. This can be explained by the geographical spread of the semi-natural grasslands, as well as difficulties in finding correlations in the relatively noisy satellite data. The most important spectral features were found in the red edge- and the short-wave infrared spectrums. These spectrums represent leaf chlorophyll content and water content in vegetation, respectively. The most accurate machine learning model was Random Forest when it was trained using with all the spectral- and geographical features. The other models; Logistic Regression, Support Vector Machine, Voting Classifier and Neural Network, showed general inabilities to interpret feature subsets containing the spectral data.This thesis shows that with deeper knowledge about the satellite-biodiversity relationship and how to apply it with machine learning have the possibilities of cheaper, more efficient and standardized monitoring of ecologically valuable areas such as semi-natural grasslands.

When natural disasters such as earthquakes happen, there is a need for an efficient method to support humanitarian aid organizations in the decision making process. One such decision is placement of Foreign Field Hospitals to assist with medical help.To support such a decision lots of different information and data needs to be gathered and combined. The main objectives of this thesis are to collect existing data published shortly after the earthquake in Haiti 2010 as well as data published up to two months after the earthquake. The data is then to be evaluated according to adequacy for analysis and the result of the analysis to be compared to the actual placements of the field hospitals after the 2010 earthquake.The method used in this analysis is Multi Criteria Evaluation (MCE). Data regarding population, elevation, roads, land use, damage, climate, water, health facility locations and airport location are collected and weighted relative with the Analytic Hierarchy Process (AHP) with weights retrieved from a questionnaire sent out to Non-Governmental Organizations (NGOs) and countries involved in the disaster relief. The result obtained from the MCE is a final suitability map depicting areas that are suitable according to the different factors.The data availability for the thesis project is an issue, due to lack of data published shortly after the earthquake. Some of the data used in the analysis do not have the sufficient detail level. Still, an analysis can be performed where suitable areas are obtained.The suitable locations found in the analysis agree well in most cases with where the actual FFHs are placed, however a few locations are not in proximity to where the suitable areas lie. A few of the locations were located in areas exposed to frequently floods. Even though the data availability and quality leaves things to desire, the analysis method shows promising results for future research. The approach could help aggregating information from different sources and provide support in pre-dispatch organization, already having a set of suitable locations to arrive to.

The Swedish Environmental Protection Agency (Naturvårdsverket) is the public agency in Sweden with responsibility to overview the conditions of the environment and the policies related to the environmental monitoring data. Nowadays, observation data are stored in several different data models in this organization, leading to difficulties in finding, understanding and consequently using data in terms of analysis and management of environmental issues. One common model that uniformly structures observation data could largely make it easier for decision makers to find the required information. The aim of this study is to build an interoperable data model for environmental monitoring observation in Naturvårdsverket based on OGC-SWE standard formats. The proposed solution relies on Sensor Web architecture, which is the set of data model definitions andweb service specifications. Also, this methodology is based on open source components; therefore it is cost-effective for the users. The Service Oriented Architecture (SOA) is used to create a uniform model by using communication protocols such as Extensible Markup Language (XML) and Simple Object Access Protocol (SOAP). The primary findings of the thesis is that when the observation is encoded into the standard format from the beginning, then it is easier to parse these documents and find the required information for the end users without knowing how these information are gathered and stored. The client scan send a request to the Sensor Observation Service (SOS) and receive the observation that is structured based on Observation and Measurements (O&M).