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.

This chapter presents a concise synthesis of recent research efforts, emphasizing the combined use of ecosystem services and landscape metric concepts for quantifying provision, quality, and accessibility to ecosystem services as indicators of socio-ecological well-being in deprived urban areas in the Majority World. Such analyses are challenging due to the common lack of official and reliable data related to socioeconomic, demographic, ecological, and land use/land cover variables. The recommended analytical steps leverage freely available earth observation products with global coverage, requiring no proprietary software and enabling barrier-free application. Integration of readily available data sets is possible during image classification, post-processing, and ensuing spatio-ecological evaluation. The study highlights the importance of differentiating between ecosystem functions and services and separating land use from land cover to ensure accurate attributions. Additionally, incorporating spatial and temporal aspects, as well as considering beneficiaries, is essential for assessing ecosystem services. Local stakeholder and community interactions are advised to gain a comprehensive understanding of the local context. Future research should explore challenges associated with sustainable management of ecosystem service provision areas in densely populated informal settlements. This includes prioritizing specific services, developing tailored valuation approaches, and quantifying the influences of landscape configuration and composition. Addressing discrepancies between actual and intended land use remains critical for advancing the understanding of ecosystem services in urban environments. This approach underscores the importance of leveraging remote sensing data and fostering local stakeholder engagement for effective ecosystem service management in deprived urban areas.

This thesis aims to establish analytical frameworks to map urban growth patterns with spaceborne remote sensing data and to evaluate environmental impacts through Landscape Metrics and Ecosystem Services. Urbanization patterns at regional scale were evaluated in China's largest urban agglomerations and at metropolitan scale in Shanghai, Stockholm and Beijing using medium resolution optical satellite data. High-resolution data was used to investigate changes in Shanghai’s urban core. The images were co-registered and mosaicked. Tasseled Cap transformations and texture features were used to increase class separabilities prior to pixel-based Random Forest and SVM classifications. Urban land cover in Shanghai and Beijing were derived through object-based SVM classification in KTH-SEG. After post-classification refinements, urbanization indices, Ecosystem Services and Landscape Metrics were used to quantify and characterize environmental impact. Urban growth was observed in all studies. China's urban agglomerations showed most prominent urbanization trends. Stockholm’s urban extent increased only little with minor environmental implications. On a regional/metropolitan scale, urban expansion progressed predominately at the expense of agriculture. Investigating urbanization patterns at higher detail revealed trends that counteracted negative urbanization effects in Shanghai's core and Beijing's urban-rural fringe. Beijing's growth resulted in Ecosystem Services losses through landscape structural changes, i.e. service area decreases, edge contamination or fragmentation. Methodological frameworks to characterize urbanization trends at different scales based on remotely sensed data were developed. For detailed urban analyses high-resolution data are recommended whereas medium-resolution data at metropolitan/regional scales is suggested. The Ecosystem Service concept was extended with Landscape Metrics to create a more differentiated picture of urbanization effects.​

The unprecedented growth of urban areas all over the globe is nowadays maybe most apparent in China having undergone rapid urbanization since the late 1970s. The need for new residential, commercial and industrial areas leads to new urban regions challenging sustainable development and the maintenance and creation of a high living standard as well as the preservation of ecological functionality. Therefore, timely and reliable information on land-cover changes and their consequent environmental impacts are needed to support sustainable urban development.The objective of this research is the analysis of land-cover changes, especially the development of urban areas in terms of speed, magnitude and resulting implications for the natural and rural environment using satellite imagery and the quantification of environmental impacts with the concepts of ecosystem services and landscape metrics. The study areas are the cities of Shanghai and Stockholm and the three highly-urbanized Chinese regions Jing-Jin-Ji, the Yangtze River Delta and the Pearl River Delta. The analyses are based on classification of optical satellite imagery (Landsat TM/ETM+ and HJ-1A/B) over the past two decades. The images were first co-registered and mosaicked, whereupon GLCM texture features were generated and tasseled cap transformations performed to improve class separabilities. The mosaics were classified with a pixel-based SVM and a random forest decision tree ensemble classifier. Based on the classification results, two urbanization indices were derived that indicate both the absolute amount of urban land and the speed of urban development. The spatial composition and configuration of the landscape was analysed by landscape metrics. Environmental impacts were quantified by attributing ecosystem service values to the classifications and the observation of value changes over time.

ivThe results from the comparative study between Shanghai and Stockholm show a decrease in all natural land-cover classes and agricultural areas, whereas urban areas increased by approximately 120% in Shanghai, nearly ten times as much as in Stockholm where no significant land-cover changes other than a 12% urban expansion could be observed. From the landscape metrics analysis results, it appears that fragmentation in both study regions occurred mainly due to the growth of high density built-up areas in previously more natural environments, while the expansion of low density built-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted in ecosystem service value losses of ca. 445 million US dollars in Shanghai, mostly due to a decrease in natural coastal wetlands. In Stockholm, a 4 million US dollar increase in ecosystem service values could be observed that can be explained by the maintenance and development of urban green spaces. Total urban growth in Shanghai was 1,768 km2 compared to 100 km2 in Stockholm. Regarding the comparative study of urbanization in the three Chinese regions, a total increase in urban land of about 28,000 km2 could be detected with a simultaneous decrease in ecosystem service values corresponding to ca. 18.5 billion Chinese Yuan Renminbi. The speed and relative urban growth in Jing-Jin-Ji was highest, followed by the Yangtze River Delta and the Pearl River Delta. The increase in urban land occurred predominately at the expense of cropland. Wetlands decreased due to land reclamation in all study areas. An increase in landscape complexity in terms of land-cover composition and configuration could be detected. Urban growth in Jing-Jin-Ji contributed most to the decrease in ecosystem service values, closely followed by the Yangtze River Delta and the Pearl River Delta.

In the past 20 years, China has experienced rapid urbanization as a consequence of economic reforms and population growth.  Urbanization is still proceeding at staggering speed. Therefore, the development of effective analytical methods to monitor the unprecedented growth of Chinese cities and the resulting environmental impacts are crucial for urban planning and sustainable development. The overall objective of this research is to investigate urban land cover change between 1990 and 2010 and the resulting effects upon ecosystem services by analysis of multitemporal Landsat 5 and HJ1-A/B images in three highly urbanized regions.

This study aims at providing a new method to efficiently analyze detailed urban ecological conditions at the example of Shanghai, one of the world's most densely populated megacities. The main objective is to develop a method to effectively analyze high-resolution optical satellite data for mapping of ecologically important urban space and to evaluate ecological changes through the emerging ecosystem service supply and demand concept. Two IKONOS and GeoEye-1 scenes were used to determine land use/land cover change in Shanghai's urban core from 2000 to 2009. After preprocessing, the images were segmented and classified into seven distinct urban land use/land cover classes through SVM. The classes were then transformed into ecosystem service supply and demand budgets for regulating, provisioning and cultural services, and ecological integrity based on ecosystem functions. Decreases in continuous urban fabric and industrial areas in the favor of urban green sites and high-rise areas with commercial/residential function could be observed resulting in an increase of at least 20% in service supply budgets. Main contributor to the change is the decrease in continuous urban fabric and industrial areas. The overall results and outcome of the study strengthen the suggested application of the proposed method for urban ecosystem service budget mapping with hitherto for that purpose unutilized high-resolution data. The insights and results from this study might further contribute to sustainable urban planning, prove common grounds for interurban comparisons, or aid in enhancing ecological intraurban functionality by analyzing the distribution of urban ecospace and lead to improved accessibility and proximity to ecosystem services in urban areas.

This study aims at providing a new method to efficiently analyse detailed urban ecological conditions at the example of Shanghai, one of the world’s most densely populated megacities. The main objective is to develop a method to effectively analyse high-resolution optical satellite data for mapping of ecologically important urban space and to evaluate ecological changes through the emerging ecosystem service supply and demand budget concept. Two IKONOS and GeoEye-1 scenes were used to determine land use/land cover change in Shanghai's urban core from 2000 to 2009. After pre-processing, the images were segmented and classified into seven distinct urban land use/land cover classes through SVM. The classes were then transformed into ecosystem service supply and demand budgets based on ecosystem functions. Decreases of continuous urban fabric and industrial areas in the favour of urban green sites and high-rise areas with commercial/residential function could be observed resulting in an increase of at least 20% in service supply budgets. Main contributors to the change are mainly the decrease of continuous urban fabric and industrial areas. The overall results and outcome of the study strengthen the suggested application of the proposed method for urban ecosystem service budget mapping with hitherto for that purpose unutilized high-resolution data. The insights and results from this study might further contribute to sustainable urban planning, prove common grounds for inter-urban comparisons or aid in enhancing ecological intra-urban functionality by analysing the distribution of urban eco-space and lead to improved accessibility and proximity to ecosystem services in urban areas

The degree of urbanization and resulting effects on Ecosystem Services is investigated in the Yangtze River Delta in China within a 20 year time frame from 1990 to 2010. A Random forest classifier is used to classify the Landsat mosaic from 1990 and the HJ-1A/B mosaic dating from 2010. Urban Land Index (UI) and Urban Expansion Index (UX) are used to represent the intensity and rapidity of urbanization. Post-Classification Change Detection is then performed and Ecosystem Service value losses for the land-cover classes water, wetland, forest and cropland that transitioned to urban areas are calculated according to a valuation scheme adapted to the Chinese market. The results showed that Yangtze River Delta experienced significant urbanization during 1990 to 2010. Urban areas increased alongside a major decrease in cropland resulting in a substantial loss of 4.2 billion CNY in Ecosystem Services.

The two main objectives of this study are to evaluate the potential use and synergetic effects of ESA Sentinel-1A C-band SAR and Sentinel-2A MSI data for classification and mapping of ecologically important urban and peri-urban space and to introduce spatial characteristics into ecosystem service analyses based on remotely sensed data. Image resolutions between 5 m and 20 m provided by the Sentinel satellites introduce a new relevant spatial scale in-between high and medium resolution data at which not only urban areas but also their important hinterlands can be effectively and efficiently mapped. Sentinel-1/2 data fusion facilitates both the capture of ecologically relevant details while at the same time also enabling large-scale urban analyses that draw surrounding regions into consideration. The combined use of Sentinel-1A SAR in Interferometric Wide Swath mode and simulated Sentinel-2A MSI (APEX) data is being evaluated in a classification of the Zürich metropolitan area, Switzerland. The SAR image was terrain-corrected, speckle-filtered and co-registered to the simulated Sentinel-2 image. After radiometric and spatial resampling, the fused image stack was segmented and classified by SVM. After post-classification, landscape elements were investigated in terms of spatial characteristics and topological relations that are believed to influence ecosystem service supply and demand, i.e. area, contiguity, perimeter-toarearatio and distance. Based on the classification results, ecosystem service supplies and demands accounting for spatial and topological patch characteristics were attributed to 14 land cover classes. The quantification of supply and demand values resulted in a positive ecosystem service budget for Zürich. The spatially adjusted service budgets and the original budgets are similar from a landscape perspective but deviate up to 50% on thepatch level. The introduction of spatial and topological patch characteristics gives a more accurate impression of ecosystem service supply and demands and their distributions, thus enabling more detailed analyses in complexurban surroundings. The method and underlying data are considered suitable for urban land cover and ecosystem service mapping and the introduction of spatial aspects into relative ecosystem service valuation concepts is believed to add another important aspect in currently existing approaches.

The objective of the study is to evaluate the potential use and synergetic effects of novel ESA Sentinel-1A C-band SAR and Sentinel-2A MSI data for mapping of ecologically important urban and peri-urban space. Image resolutions between 5 m and 20 m provided by the Sentinel satellites introduce a new relevant spatial scale in-between high and medium resolution data at which not only urban areas but also their important hinterlands are expected to be effectively and efficiently mapped. The fusion of Sentinel-1/2 facilitates both the capture of ecologically relevant details but at the same time also enables large-scale urban analyses that draw surrounding regions into consideration. The combined use of Sentinel-1A SAR in Interferometric Wide Swath mode and simulated Sentinel-2A MSI (APEX) data is being evaluated in classification of a metropolitan area over Zürich, Switzerland. The SAR image was pre-processed using Range-Doppler terrain correction. A 5x5 adaptive Lee speckle filter was applied to the VH and VV intensity bands before co-registration to the simulated Sentinel-2 image. After radiometric and spatial resampling, the fused images were segmented by the KTH-SEG algorithm before being classified by SVM. After reclassification under masks and sieve-filtering, the resulting landscape patches were investigated in terms of spatial characteristics and topological relations that are deemed to be influential for ecosystem service provision. Based on the classification result, ecosystem service supply and demand values that account for spatial and topological patch characteristics were attributed to 14 different land cover classes. The method and underlying data were found suitable for urban land-cover and ecosystem service mapping. The introduction of spatial aspects into ecosystem service providing areas is believed to add another important aspect in currently existing valuation approaches.

This study investigates land cover changes, magnitude and speed of urbanization and evaluates possible impacts on the environment by the concepts of landscape metrics and ecosystem services in China's three largest and most important urban agglomerations: Jing-Jin-Ji, the Yangtze River Delta and the Pearl River Delta. Based on the classifications of six Landsat TM and HJ-1A/B remotely sensed space-borne optical satellite image mosaics with a superior random forest decision tree ensemble classifier, a total increase in urban land of about 28,000 km(2) could be detected alongside a simultaneous decrease in natural land cover classes and cropland. Two urbanization indices describing both speed and magnitude of urbanization were derived and ecosystem services were calculated with a valuation scheme adapted to the Chinese market based on the classification results from 1990 and 2010 for the predominant land cover classes affected by urbanization: forest, cropland, wetlands, water and aquaculture. The speed and relative urban growth in Jing-Jin-Ji was highest, followed by the Yangtze River Delta and Pearl River Delta, resulting in a continuously fragmented landscape and substantial decreases in ecosystem service values of approximately 18.5 billion CNY with coastal wetlands and agriculture being the largest contributors. The results indicate both similarities and differences in urban-regional development trends implicating adverse effects on the natural and rural landscape, not only in the rural-urban fringe, but also in the cities' important hinterlands as a result of rapid urbanization in China.

Sustainable development in metropolitan regions is challenging in the light of continuous urbanization. Remote sensing provides timely and reliable information on urban areas and their changing patterns. This study's objectives are to evaluate the contribution of Sentinel-2A (S-2A) data to urban ecosystem service mapping and to investigate spatial ecosystem service characteristics with landscape metrics through a novelmethod. Service pattern changes between 2005 and 2015 are mapped for Beijing, China. Landscape metrics are used to qualitatively evaluate urban ecosystem service provision bundle changes. S-2A and Landsat TM data are segmented and classified with SVM, distinguishing three artificial and four natural classes based on ecosystem function. Spatial characteristics influencing ecosystem services are quantified with seven landscape metrics. Beijing's urban development is characterized by reduction in agricultural areas in the urban fringe in favor of built-up areas, urban green space, and golf courses. A transformation of old suburban agglomerations into urban green space can be observed. The planar increase in urban areas is accompanied by the creation of managed urban green space. Service bundles based on land cover classes and spatial characteristics decreased more than 30% for bundles that represent food supply, noise reduction, waste treatment, and global climate regulation. Temperature regulation/moderation of climate extremes, recreation/place values/social cohesion, and aesthetic benefits/cognitive development are least affected. This new approach of extending the ecosystem service concept through integration of spatial characteristics of ecosystem service provisional patches through landscape metrics is believed to give a more realistic appraisal of ecosystem services in urban areas.

This study investigates urbanization and its potential environmental consequences in Shanghai andStockholm metropolitan areas over two decades. Changes in land use/land cover are estimated fromsupport vector machine classifications of Landsat mosaics with grey-level co-occurrence matrix fea-tures. Landscape metrics are used to investigate changes in landscape composition and configurationand to draw preliminary conclusions about environmental impacts. Speed and magnitude of urbaniza-tion is calculated by urbanization indices and the resulting impacts on the environment are quantified byecosystem services. Growth of urban areas and urban green spaces occurred at the expense of croplandin both regions. Alongside a decrease in natural land cover, urban areas increased by approximately 120%in Shanghai, nearly ten times as much as in Stockholm, where the most significant land cover changewas a 12% urban expansion that mostly replaced agricultural areas. From the landscape metrics results,it appears that fragmentation in both study regions occurred mainly due to the growth of high densitybuilt-up areas in previously more natural/agricultural environments, while the expansion of low densitybuilt-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted inecosystem service value losses of approximately 445 million US dollars in Shanghai, mostly due to thedecrease in natural coastal wetlands while in Stockholm the value of ecosystem services changed very lit-tle. Total urban growth in Shanghai was 1768 km2and 100 km2in Stockholm. The developed methodologyis considered a straight-forward low-cost globally applicable approach to quantitatively and qualitativelyevaluate urban growth patterns that could help to address spatial, economic and ecological questions inurban and regional planning.

The potential of high-resolution optical satellite images for mapping of ecologically important urban space is investigated in this study. Both a GeoEye-1 and a Landsat 8 scene over central Shanghai were first segmented by two different algorithms and then classified into seven urban classes by SVM. Shadows in the pan-sharpened GeoEye-1 image were masked out and replaced by the corresponding pan-sharpened classified Landsat 8 image. Largest confusions occurred between sealed and permeable but non-vegetated surfaces, and between low-rise residential and high-rise commercial buildings. Based on the classification result, ecosystem service balances, supply and demand was modelled for each particular land cover class. Classification accuracies of 88% and 91% could be reached, indicating the suitability of the underlying data and method for this application domain. The KTH-SEG segmentation algorithm slightly outperformed the one implemented in eCognition. The highest supply of ecosystem services was found in water bodies whereas high-rise built-up areas revealed largest demands.

The increasing need for reliable identification of wet soils in nature preservation and forestry requires more detailed and accurate maps.  Previous maps are mostly based on aerial photos which may result in an incomplete and generalized representation of wetlands. Using high resolution LiDAR-data and modern GIS software, topographic indices can be generated which has the potential to model wetlands in a more realistic and detailed manner.

This study evaluated the widely used Topgraphic Wetness Index (TWI) and the more recently developed Depth to Water Index (DTW). Five TWI-raster with a resolution of 2-20 m was created. Four DTW-raster with 2 m resolution was created with Flow Initiation Threshold varying between 1-8 ha. A vegetation map (GSD-Vegetationsdata) was used as reference since it was judged to be the best available option for comparison.

The results show that the index-maps overall identify more wetland area than the vegetation map. The DTW-index generates a clearer and realistic map compared to the TWI. The methods used for index-based mapping of soil moisture has some uncertainties. However, the DTW-index has good potential for further development. It was concluded that index-based maps (primarily DTW) can be useful for identification of soil moisture, especially if combined with other sources of map data. 

In Europe, overgrown lakes and watercourses are creating increasing problems, which are partly due to climate change and human impact. One of the main reasons for the overgrowth of lakes and watercourses is eutrophication.

The aim of the study is to evaluate the possibility of automatically identifying overgrown lakes and watercourses using remote sensing and GIS. An analysis of vegetation changes in Lake Sottern in Uppland county, Sweden is conducted, where overgrowth creates problems and where clearing machines are used to manage the vegetation, primarily consisting of reeds, water lilies, and other aquatic vegetation. By applying various classification algorithms, band combinations and vegetation indices, changes in the lake's condition are investigated by classifying Sottern into two main classes: water and aquatic vegetation.

The study is based on remote sensing data from the optical satellite constellation Sentinel-2 and a high-resolution reference image from Google Earth Pro. Data were collected during the growing season, from May to October, for the years 2021 and 2022 to analyze if and how aquatic vegetation changes over time.

The results show that Maximum Likelihood Classification (MLC) emerges as the most effective algorithm for identifying aquatic vegetation, especially when combined with a "False color" band combination consisting of bands 8 (NIR), 4 (red), and 3 (green). MLC shows higher accuracy compared to Random Trees (RT) and Support Vector Machine (SVM). By applying the Normalized Difference Vegetation Index (NDVI), the study provides a deeper understanding of how vegetation changes over time. By combining the results from these methods, it is possible to draw conclusions about how aquatic vegetation changes over time in lakes like Sottern, where a clear increase in aquatic vegetation occurs between May and June, while the decrease in aquatic vegetation is not as consistent.

The company Team Exact delivers measurement technical services, and the main business is aimed at the construction and land industry. The company uses UAS and offers services to customers and delivers products such as orthophotos and DEMs that can be used for mapping, volume calculations and planning. Team Exact uses the consulting company SkyMap’s web-based platform for photogrammetric processing of UAV-generated aerial images. DEM needs to achieve good positional uncertainty, to achieve HMK standard level 3, it is required that the basis for construction documents has a positional uncertainty of 0.02–0.05 m / 0.03–0.07 m (level / height). Team Exact achieves good positional uncertainty in horizontal coordinates but has varying results in height reproduction. The study thus aims to find methods to ensure the height within a study area with varying topography, terrain and ground surfaces. Factors to be investigated are ground control points, RTK data, flight paths, camera settings and conceivable measures in varying topography, as well as seeing trends in how the height representation differs on different ground surfaces.

A coordinate network was established over the study area with three established coordinate reference points, the points were measured with static NRTK measurement 1 minute. The network was levelled with the total station and then control points, profiles, surfaces, and ground control points were measured. The study investigated the location uncertainty with 0, 5, 9 and 12 ground control points. The UAV used in the study is equipped with an RTK module and was therefore expected to provide positioning data that was worth investigating. The placement of the ground support points was planned with four constants in the outer corner of the study area and a fifth constant at the highest level of the study area. The remaining points were placed in an even distribution over the area’s peaks and valleys.

The evaluated flight methods were rooted in previous studies. Common settings across all methods were the study area delimitation, 40 m flight altitude and the flight speed of 3 m/s. Remaining were floating parameters that were of value to investigate. The study adjusted the parameters regarding flight path, coverage, camera angle and camera settings. In total, there were three flight methods where the four different ground support combinations were examined, which gave 12 processes to evaluate. The evaluation was performed against 77 control points where the RMSE value for height and plane was examined. The control points were evenly distributed over the surface and soil types. A further analysis was performed with volume calculations between the reference terrain models and the generated terrain models.

Flight method 3 gave the best results where the photogrammetry setting Double Grid was used and the overlap was 80/60 % and the camera was tilted to -70 °. The sensor sensitivity was set to ISO100, the shutter had an aperture value of f/5 and the shutter speed was set to 1/500s. The results of the study indicate that flight method 3, which was levelled with 12 ground support points, generated the best results on a positional uncertainty in horizontal coordinates of 0,015 m and 0,035 m in height.

Mapping urbanization and ensuing environmental impacts using satellite data combined with landscape metrics has become a hot research topic. The objectives of the study are to analyze the spatio-temporal evolution of urbanization patterns of Kigali, Rwanda over the last three decades (from 1984 to 2015) using multitemporal Landsat data and to assess the associated environmental impact using landscape metrics. Landsat images, Normalized Difference Vegetation Index (NDVI), Grey Level Co-occurrence Matrix (GLCM) variance texture and digital elevation model (DEM) data were classified using a support vector machine (SVM). Eight landscape indices were derived from classified images for urbanization environment impact assessment. Seven land cover classes were derived with an overall accuracy exceeding 88% with Kappa Coefficients around 0.8. As most prominent changes, cropland was reduced considerably in favour of built-up areas that increased from 2,349 ha to 11,579 ha between 1984 and 2015. During those 31 years, the increased number of patches in most land cover classes illustrated landscape fragmentation, especially for forest. The landscape configuration indices demonstrate that in general the land cover pattern remained stable for cropland but it was highly changed in built-up areas. Satellite-based analysis and quantification of urbanization and its effects using landscape metrics are found to be interesting for grassroots and provide a cost-effective method for urban information production. This information can be used for e.g. potential design and implementation of early warning systems that cater for urbanization effects.

Rapid urbanization in developing countries often results in uncontrolled urban growth. In order to support sustainable urban development, reliable and up-to-date information on urban land cover changes and their environmental impact is needed. In this study, we aim at evaluating the potential of Sentinel-2 (S-2) Multi-spectral Instrument (MSI) data for urban land cover change monitoring and for analyzing resulting impacts on Ecosystem Services (ES) provision in Kigali, Rwanda. Land cover classification into eight distinct urban classes (84% overall accuracies, 0.8 Kappa) was performed on data from 2016 and 2021 using a hybrid approach combining Random Forest with a U-Net-based impervious surface segmentation that improved the delineation of urban areas. The bi-temporal land cover maps were then analyzed regarding landscape structure using Landscape Metrics (LM). Ecosystem service bundles were derived for both years and their changes were summarized. Service providing areas were further evaluated in terms of changes in spatial attributes and structure of patches. ES were aggregated into eight bundles and grouped into provisioning, regulating and supporting services. The bundles were further analyzed using a matrix spatially linking landscape units with service supply and demand budgets. The results illustrated that three urban development scenarios can be distinguished including infill through housing and infrastructures development in core urban areas, urban sprawl in fringe zones and the development of urban patches at distant locations intercepted by cropland. The results revealed that the changes in LM negatively affected ES supply mainly through a decrease in cropland and forest. The expansion of built-up areas resulted in a high demand for provisioning and regulating services, especially food and water provision, surface runoff mitigation and erosion control. This is the first study demonstrating that detailed monitoring of urbanization and resulting environmental impacts can be performed with open access S-2 MSI data in Sub-Saharan Africa. Moreover, the framework developed in this study has the potential to be transferred to other Sub-Saharan cities.

On January 8^th, 2005 one of the most devastating storms in Sweden’s history occurred, where hundreds of thousands became powerless and seven people lost their lives. The storm Gudrun hit central Götaland worst and nearly nine years’ professional felling of forests was estimated to have fallen in some areas. Previous studies of the storm were carried out on behalf of the Swedish Forest Agency, where the results show that the proportion of windthrown forest area was 11 % in the worst affected municipality of Ljungby. About 80 % of all damaged forests were spruce, 18 % were pine and 2 % were deciduous.                       

The aim of this thesis is to investigate the amount of windthrown forest after the storm Gudrun through analysis of satellite remote sensing data. The proportion of windthrown coniferous and deciduous forest was calculated and the results were compared to the reports written on behalf of the Swedish Forest Agency. Furthermore, the proportion of reforested areas in 2016 was calculated. A change analysis based on satellite data from Landsat 5 from 2004 and 2005 was performed which included a forest mask created by supervised MLC classification. The forest mask was used to exclude uninteresting areas in the analyses. The result was then used for the analysis of the proportion of windthrown coniferous and deciduous forest and for the analysis of reforested areas in 2016. In the latter, a forest mask based on Landsat 8 data was used.

The results from the analyses show that about 15.8 % of the forest was windthrown, of which 78 % were coniferous and 13 % were deciduous forest. By 2016, 25% of the windthrown areas had been reforested. The accuracy of the results is generally high, but despite this, it substantially differs from the results of earlier studies. The reason for this could be differences in satellite images and programs and additional error sources in conjunction with the analyses.

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. 

Karlstad Municipality has what is known as a solar radiation map with the purpose of providing an overview of how much solar irradiance that is received by individual roof planes. It serves as a basis for decisions regarding the installation of solar panels on building roofs. The map is interactive and covers the entire municipality. Unfortunately, it lacks detail outside the city of Karlstad and would benefit from an update.

The study aims to explore methods for extracting two-dimensional roof planes according to LOD2 (Level of Detail 2) using building footprints and point cloud data. The roof planes could later form the foundation for an interactive solar map. Three methods for roof segmentation were examined, each based on different software: ArcGIS Pro, Whitebox Tools, and TerraScan. The study area, located in the Våxnäs industrial area in Karlstad consisted of 68 buildings with varying roof types and complexities. An additional dimension to the study involved comparing two different input datasets for each segmentation method: a high-resolution photogrammetric point cloud generated from UAV (Unmanned Aerial Vehicle) images and a lower-resolution point cloud collected with airborne laser scanning.

In total, six different results were evaluated based on completeness and appearance. The highest mean completeness for each method was: 99,6 % for the TerraScan-based method, 90,2 % for the Whitebox Tools-based method and 82,0 % for the ArcGIS Pro-based method. Regarding input datasets, the two best methods showed an increase of approximately 6 percentage points in mean completeness for the UAV dataset, while the least effective method showed a decrease of 10 percentage points. In terms of practicality, the TerraScan method provided a solid basis for a solar map. The Whitebox Tools method could most likely be usable if a better generalization algorithm in post-processing is cultivated.

The study also discusses differences, potential sources of error, and mentions some additional methods that were not fully developed due to inadequate results. Remaining challenges include addressing gaps of missing data in point clouds before roof plane segmentation.

River Klarälven is one of Sweden's longest rivers and is unique in Scandinavia because of its characteristic meander course.  The uniqueness in the case of Klarälven is that the riverbed is trapped between two mountain sides. The meandering course of Klarälven means major changes in its path due to extensive erosion along some reaches.

The purpose of this study is to compare and analyze how some reaches of River Klarälven have changed over time. The data consists of a historical map from 1883 as well as orthophotos from 1961 and 2014. The study is limited to a 25 km meandering reach north of the town Ekshärad in northern Värmland.

Methods involved are georeferencing of the historical map and screen digitizing of all data. Subsequently, the results were reviewed and compared by overlay analysis divided into two periods and all periods at once.

The result shows that extensive erosion and deposit have taken place. In the river's outer curves where the velocity of the water is highest is also the place where most erosion occurs. In the inner curves where the water velocity is slower, the eroded material is deposited. The largest differences were measured between the years 1883 and 1961. However, erosion changes on a smaller scale also occurred between 1961 and 2014.

Africa, as a major climate change hotspot, faces severe impacts, including extreme temperatures. Notably, urban areas are unequally affected by these impacts. The urban poor are particularly vulnerable to extreme temperatures, because of the environmental and physical characteristics of their neighbourhoods, and their limited resources to develop coping strategies. Limited knowledge exists of the spatial patterns of thermal inequalities within neighbourhoods. Our overall scientific objective is to explore the potential of Earth Observation (EO) to study how and why urban dwellers in the Global South (focusing on Africa) with different levels of deprivation are divergently exposed to varying temperatures and extreme heat, and to quantify the urban population exposed to such conditions. We make use of several state-of-the-art EO/AI models, and employ innovative in situ data collection methods together with local stakeholders through Citizen Science. We rely as far as possible on open or low-cost satellite imagery (e.g., Sentinel-1/2, Landsat, ECOSTRESS) for scalability and transferability, and we implement Machine Learning (ML) methods, including Deep Learning (DL). Results highlight significant local differences in thermal exposure, emphasizing the need to understand and communicate these spatial patterns to support the development of cost-effective adaptation strategies.