We present a combined method for estimating a new global Moho model named KTH15C, containing Moho depth and Moho density contrast (or shortly Moho parameters), from a combination of global models of gravity (GOCO05S), topography (DTM2006) and seismic information (CRUST1.0 and MDN07) to a resolution of 1 degrees x 1 degrees based on a solution of Vening Meinesz-Moritz' inverse problem of isostasy. This paper also aims modelling of the observation standard errors propagated from the Vening Meinesz-Moritz and CRUST1.0 models in estimating the uncertainty of the final Moho model. The numerical results yield Moho depths ranging from 6.5 to 70.3 km, and the estimated Moho density contrasts ranging from 21 to 650 kg/m(3), respectively. Moreover, test computations display that in most areas estimated uncertainties in the parameters are less than 3 km and 50 kg/m(3), respectively, but they reach to more significant values under Gulf of Mexico, Chile, Eastern Mediterranean, Timor sea and parts of polar regions. Comparing the Moho depths estimated by KTH15C and those derived by KTH11C, GEMMA2012C, CRUST1.0, KTH14C, CRUST14 and GEMMA1.0 models shows that KTH15C agree fairly well with CRUST1.0 but rather poor with other models. The Moho density contrasts estimated by KTH15C and those of the KTH11C, KTH14C and VMM model agree to 112, 31 and 61 kg/m(3) in RMS. The regional numerical studies show that the RMS differences between KTH15C and Moho depths from seismic information yields fits of 2 to 4 km in South and North America, Africa, Europe, Asia, Australia and Antarctica, respectively.

Disasters, whether natural or man-made have become an issue of mounting concern all over the world. Natural disasters such as floods, earthquakes, landslides, cyclones, tsunamis and volcanic eruptions are yearly phenomena that have devastating effect on infrastructure and property and in most cases, results in the loss of human life. Floods are amongst the most prevalent natural disasters. The frequency with which floods occur, their magnitude, extent and the cost of damage are escalating all around the globe. Accra, the capital city of Ghana experiences the occurrence of flooding events annually with dire consequences. Past studies demonstrated that remote sensing and geographic information system (GIS) are very useful and effective tools in flood risk assessment and management.  This thesis research seeks to demarcate flood risk areas and create a flood risk map for the Greater Accra Metropolitan Area using remote sensing and Geographic information system. Multi Criteria Analysis (MCA) is used to carry out the flood risk assessment and Sentinel-1A SAR images are used to map flood extend and to ascertain whether the resulting map from the MCA process is a close representation of the flood prone areas in the study area.  The results show that the multi-criteria analysis approach could effectively combine several criteria including elevation, slope, rainfall, drainage, land cover and soil geology to produce a flood risk map. The resulting map indicates that over 50 percent of the study area is likely to experience a high level of flood.  For SAR-based flood extent mapping, the results show that SAR data acquired immediately after the flooding event could better map flooding extent than the SAR data acquired 9 days after.  This highlights the importance of near real-time acquisition of SAR data for mapping flooding extent and damages.  All parts under the study area experience some level of flooding. The urban land cover experiences very high, and high levels of flooding and the MCA process produces a risk map that is a close depiction of flooding in the study area.  Real time flood disaster monitoring, early warning and rapid damage appraisal have greatly improved due to ameliorations in the remote sensing technology and the Geographic Information Systems.

Satellite altimetry, satellite gravimetry, and in-situ subsurface salinity and temperature profiles are used to investigate the total, barystatic, and steric sea level variations in the Baltic Sea, respectively. To estimate the steric sea level, the density variations are weighted in deeper layers to prevent overestimation of their contribution. We show that the sum of barystatic and steric components exhibits excellent cross correlation (0.9) with satellite altimetry sea level variations and also explains up to 84% of total signal variability from 2002 to 2019. Considering the dominance of barystatic sea level variations in the basin and the limitation of satellite gravimetry in resolving the mass change in water-land transition zones (known as the leakage problem), the mismatch is likely attributed to the inadequate accuracy of the barystatic datasets. The total sea level and its contributors are further decomposed into seasonal, interannual, and decadal temporal components. It is shown that despite its insignificant contributions to seasonal and interannual changes, the steric sea level plays an important role in decadal variations. Additionally, we show that the interannual variations of the barystatic sea level are governed by the North Atlantic Oscillation in the basin. The sea level variation in the North Sea is also examined to deduce the water exchange patterns on different time scales. A drop in the North Sea level can be seen from 2005 to 2011 which is followed by the Baltic Sea level with a ~3-year lag, implying the outflow from the Baltic Sea to the North Sea.

Open Data Cubes are platforms that contain open source satellite data and provide analysis tools for governments or organizations. The Swedish version is known as Swedish Space Data Lab (SSDL) and this master thesis was a part of it, providing the first analysis tools of the SSDL. Within a smaller project in the SSDL a drought analysis was done for the region of Mälardalen. The thesis work consisted on developing data analysis methods using packages for machine learning and statistical analysis in Python and Jupyter Notebooks. The drought analysis consisted of a two-year comparison between 2018 and 2019 due to limitations on the data availability. It was found that first year was drier than the second. However, longer time series would be needed in order to observe trends related to possible changes in the climate.

We address the problem of estimating atmosphere parameters (temperature, water vapour content) from data captured by an airborne thermal hyperspectral imager, and propose a method based on direct optimization. The method also involves the estimation of object parameters (temperature and emissivity) under the restriction that the emissivity is constant for all wavelengths. Certain sensor parameters can be estimated as well in the same process. The method is analyzed with respect to sensitivity to noise and number of spectral bands. Simulations with synthetic signatures are performed to validate the analysis, showing that estimation can be performed with as few as 10-20 spectral bands at moderate noise levels. More than 20 bands does not improvethe estimates. The proposedmethod is alsoextended to incorporateadditionalknowledge,for examplemeasurements ofatmospheric parameters and sensor noise.

When we digitize data from a hyperspectral imager, we do so in three dimensions; the radiometric dimension, the spectral dimension, and the spatial dimension(s). The output can be regarded as a random variable taking values from a discrete alphabet, thus allowing simple estimation of the variable’s entropy, i.e., its information content. By modeling the target/background state as a binary random variable and the corresponding measured spectra as a function thereof, wecan compute theinformation capacity ofa certainsensoror sensor configuration. This can be used as a measure of the separability of the two classes, and also gives a bound on the sensor’s performance. Changing the parameters of the digitizing process, bascially how many bits and bands to spend, will affect the information capacity, and we can thus try to find parameters where as few bits/bands as possible gives us as good class separability as possible. The parameters to be optimized in this way (and with respect to the chosen target and background) are spatial, radiometric and spectral resolution, i.e., which spectral bands to use and how to quantize them. In this paper, we focus on the band selection problem, describe an initial approach, and show early results of target/background separation.

Remote Sensing (RS) technology provides valuable information about Earth by enabling an overview of the planet from above, making it a much-needed resource for many applications. Given the abundance of RS data and continued urbanisation, there is a need for efficient approaches to leverage RS data and its unique characteristics for the assessment and management of urban areas. Consequently, employing Deep Learning (DL) for RS applications has attracted much attention over the past few years. In this thesis, novel datasets consisting of satellite RS images over urban areas in Sweden were compiled from Sentinel-2 multispectral, Sentinel-1 Synthetic Aperture Radar (SAR) and Urban Atlas 2018 Land Use/Land Cover (LULC) data. Then, DL was applied for multiband and multiclass semantic image segmentation of LULC. The contributions of complementary spectral, temporal and SAR data and spectral indices to LULC classification performance compared to using only Sentinel-2 data with red, green and blue spectral bands were investigated by implementing DL models based on the fully convolutional network-based architecture, U-Net, and performing data fusion. Promising results were achieved with 25 possible LULC classes. Furthermore, almost all DL models at an overall model level and all DL models at an individual class level for most LULC classes benefited from complementary satellite RS data with varying degrees of classification improvement. Additionally, practical knowledge and insights were gained from evaluating the results and are presented regarding satellite RS data characteristics and semantic segmentation of LULC in urban areas. The obtained results are helpful for practitioners and researchers applying or intending to apply DL for semantic segmentation of LULC in general and specifically in Swedish urban environments.

Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity.

      Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.

Lack of land for waste disposal is one of the main problems facing urban areas in developing countries. The Sulaimaniyah Governorate, located in Northern Iraq, is one of the main cities of the country in the Kurdistan Region, covering an area of 2400 km2. Currently, there is no landfill site in the study region that meets the scientific and environmental requirements, and the inappropriate dumping of solid waste causes adverse effects to the environment, economic and urban aesthetic. To overcome this phenomenon, it is crucial to suggest a landfill site, even in countries that recycle or burn their waste to protect the environment. Landfill sites should be carefully selected taking into account all regulations and other restrictions. The integration of geographic information systems and the multi-criteria decision analysis were used in this study to select suitable landfill locations in the region. To this end, thirteen layers prepared according to their importance including slope, geology, land use, urban area, villages, rivers, groundwater, slope, elevation, soil, geology, road, oil and gas, land use, archaeology and power lines. Two different methods (simple additive weighting and analytic hierarchy process) were implemented in a geographical information system to obtain the suitability index map for candidate landfill sites, where all these conditions satisfied the scientific and environmental criteria adopted in this study. The comparison of the maps resulting from these two different methods demonstrates that both methods produced consistent results.

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. 

This paper presents two incoherent change detection methods for wavelength-resolution synthetic aperture radars (SAR) image stacks based on masking techniques. The first technique proposed is the Simple Masking Detection (SMD). This method uses the statistical behavior of pixels-sets in the image stack to create a binary mask, which is used to remove pixels that are not related to changes in a surveillance image from the same interest region. The second technique is the Multiple Concatenated Masking Detection (MCMD), which produces a more selective mask than the SMD by concatenating multiple masks from different image stacks. The MCMD can be used in specific applications where multiple stacks share common patterns of target deployments. Both proposed techniques were evaluated using 24 incoherent SAR images obtained by the CARABAS II system. The experimental results revealed that the proposed detection methods have better performance in terms of probability of detection and false alarm rate when compared with other change detection techniques, especially for high detection probabilities scenarios.

This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59 & x0025; of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.

Floddeltan och sjöar stöder den biologiska mångfalden och erbjuder viktiga ekosystemtjänster som färskvattenförsörjning, översvämningsbekämpning och fiske. Klimatförändringar och mänskliga aktiviteter har dock påverkat deltan och sjöar globalt och förändrat de tjänster de tillhandahåller. Eftersom förekomsten av ytvatten i delta och sjöar och vattennivåerna svarar på klimatförändringar och antropogena aktiviteter måste vi övervaka deras variationer för att förstå de potentiella drivkrafterna för effektiva vattenförvaltningsstrategier. Viktiga deltan som Selenga River Delta (SRD) i Ryssland saknar dock en detaljerad analys av vattenförekomsten. När det gäller sjöarnas vattennivå har antalet mätstationer minskat på grund av installations- och underhållskostnader. Till exempel har Sverige ~ 100 000 sjöar som är källor till sötvatten och vattenkraft, men endast 38 sjöar har långa och kontinuerliga in situ-register över vattennivån.

Eftersom satellitdata är tillförlitliga alternativ till konventionella metoder för att övervaka deltan och sjöar, har jag använt satellitobservationer för att kvantifiera förändringar i ytvattenförekomst i SRD och vattennivåer i svenska sjöar och identifiera deras huvudsakliga drivkrafter. Jag har vidare utvecklat och utforskat en ny metod för uppskattning av sjövattennivån baserad på Differential Interferometric Synthetic Aperture Radar (D-InSAR) genom att beräkna sexdagars fasskillnader i 30 svenska sjöar.

För att uppnå dessa mål har jag övat på, samt tillämpat en Maximum Likelihood-klassificering på Landsat-bilder från 1987 till 2020 och kvantifierade ytvattenförekomst och dess förändringar i SRD. Jag har nått slutsatsen att ytvattenförekomsten i 51 % av deltat upplevt en minskning. Eftersom Selengafloden är den enda flod som rinner ut i SRD, korrelerade förändringen i ytvattenförekomsten i SRD med flodutsläpp, men inte med flodens suspenderade sedimentkoncentration, sjövattennivån i SRD:s utlopp eller evapotranspiration över deltat.

I Sverige använde jag satellithöjdmätningsdata från ERS-2, ENVISAT, JASON-1,2,3, SARAL och Sentinel-3A/B för att kvantifiera vattennivån i 144 sjöar från 1995-2022. Jag fann att 52 % av sjöarna visade ökande trender (mestadels i norr) och 43 % minskande trender (mestadels i söder). Vattenståndstrender och variationer korrelerade inte starkt med hydroklimatiska förändringar (nederbörd och temperatur) men skilde sig åt i reglerade sjöar jämfört med oreglerade sjöar både i norra och södra Sverige.

Resultaten av D-InSAR-metoden för uppskattning av sjövattennivån visade att i två svenska sjöar (Hjälmaren och Solnen) med vattennivåförändringar mindre än en fullständig SAR-fas, korrelerar fasförändringarna med in situ-vattennivåförändringar med ett minsta rotmedelkvadratfel på 0,43 cm i vissa pixlar. I 30 andra sjöar ackumulerade jag fasförändringarna för varje pixel genom hela antalet interferogram för att konstruera vattennivåer. Denna metod replikerade riktningen för vattennivåförändringar som visas av höga Pearsons korrelationer i minst en pixel i varje sjö.

Denna avhandling belyser vikten av satellitdata för att uppskatta ytvattenförekomst och sjövattennivå och sätter fokus på framtiden för jordobservationer genom avancerade rymduppdrag som ytvatten och havstopografi (SWOT) och NASA-ISRO Synthetic Aperture Radar (NISAR). Resultaten understryker behovet av att kontinuerligt övervaka sjövattennivåer och förekomst för att anpassa sig till klimatförändringar och förstå effekterna av vattenreglerande system.

It is essential to track lake water level fluctuations, however, the number of conventional gauging stations is declining worldwide due to impractical installation and maintenance procedures. Satellite altimetry is a substitute for traditional gauges. Nevertheless, altimetry sensors cannot identify small lakes owing to poor spatial coverage. Their application is limited to lakes falling exactly below the path of the altimeter. Differential Interferometric Synthetic Aperture Radar (D-InSAR) is commonly used to track land deformation and water surface changes, with the latter being comparatively limited and focused mainly on wetlands. We here explore the potential of D-InSAR to track water level changes in two Swedish lakes, focusing on the shoreline in search of potential double-bounce backscattering and analyzing pixel phase changes and coherence. We use Sentinel-1A and Sentinel-1B data from 2019, generate six-day interferograms, and exclude those when corresponding to in-situ water level changes exceeding one phase cycle. We find that D-InSAR is sensitive to minor water level changes, obtaining Lin's correlations of up to 0.63 and 0.89 (RMSE = 9 & 4 mm, respectively). These results evidence the potential of future L-band SAR missions with larger wavelengths, such as NISAR, to track water level changes in lakes and aid water tracking missions such as the SWOT.

Lakes are valuable water resources that support aquatic and terrestrial ecosystems and supply fresh water for the agricultural, industrial, and urban sectors worldwide. Although water levels should be tracked to monitor these services, conventional gauging is unfeasible in most lakes. This study explores the potential, advantages, and limitations of using Differential Interferometric Synthetic Aperture Radar (D-InSAR) to estimate small water level changes in lakes (i.e., less than the full cycle of the SAR signal) and overall long-term direction of change. We validated the method across the shores of 30 Swedish lakes with gauged observations during 2019. We used Sentinel-1A/B images with a six-day temporal separation to construct consecutive interferograms and accumulated the phase changes in pixels of high coherence to build time series of water levels. We find that the accumulated phase change replicates the magnitude of water levels in seven lakes in Southern Sweden, where water level changes seldom exceed a complete SAR phase (i.e., 1.8 cm in the vertical direction), evident from the Concordance Correlation Coefficients (0.30 < CCC < 0.55). Furthermore, D-InSAR can estimate the long-term direction of water level change (i.e., increase or decrease) in all 30 lakes. We elaborate on the possible explanation for this last finding. The novel methodology could be used to validate future altimetry missions such as SWOT in lakes worldwide and can be improved with upcoming SAR missions with longer wavelengths.

Skattning av skogliga variabler är inte längre begränsade till datainsamling genom manuell fältmätning. Numera kan skattningar göras genom insamling av data från såväl laserskanning (Airborne Laser Scanning, ALS) som mobilapplikationen Katam Forest (KF) som företaget Katam utvecklat. Dessutom kan varje enskilt träd skattas genom att kombinera Katam Treemap (KT), som använder sig av fotogrammetri för att mäta trädhöjd och identifiera träd, med KF (KF+KT) . I denna studie jämfördes skattningar av grundytevägd medeldiameter (DGV) och grundyta (GY) utförda med manuell fältmätning, ALS, KF samt KF+KT, i tre olika grandominerande bestånd. KF+KT beräknades felaktigt varför inga slutsatser kan dras om metoden. KF visade ett lägre relativt RMSE än ALS, 7,8 % jämfört med 9,6 % vid skattning av DGV. För GY var relativ RMSE 22,5 % vid KF, och 23 % vid ALS. KF bedöms med fördel kunna användas likvärdigt med manuell fältmätning.

The term green infrastructure captures the need to conserve biodiversity and to sustain landscapes’ different ecosystem services. Maintaining green infrastructures through protected areas, management and landscape restoration requires knowledge in geography, spatial data about biophysical, anthropogenic and immaterial values, spatial comprehensive planning, and thus geographical information systems (GIS). To understand land use planning practices and planning education regarding GIS in Sweden we interviewed 43 planners and reviewed 20 planning education programmes. All planners used GIS to look at data but did not carry out spatial analyses of land covers. BSc programmes included more GIS than MSc programmes but very few taught analyses for spatial planning. As key spatial planning actors, municipalities’ barriers and bridges for improved GIS use for collaborative learning about green infrastructures are discussed. A concluding section presents examples of how GIS can support spatial planning for green infrastructures.

Phosphorus flowing via rivers into the Baltic Sea is a major source of nutrients, and in some cases the limiting factor for the growth of algae which causes the phenomenon known as eutrophication. Remote sensing of phosphorus, here using Landsat TM-data, can help to give a better understanding of the process of eutrophication. Since Landsat TM-data is used, this could form a basis for further spatio-temporal analysis in the Baltic Sea region. A method originally described and previously applied for a Chinese river is here transferred and applied to three different rivers flowing into the Baltic Sea. The results show that by measuring the proxy variables of Secchi Depth and Chloryphyll-a the remote sensing model is able to explain 41% of the variance in total- phosphorus for the rivers Dalälven, Norrström and Gavleån without any consideration taken to CDOM, turbidity or other local features.

In this article, the effect a moving target has on the signal-to-interference-plus-noise-ratio (SINR) for high time-bandwidth noise radars is investigated. To compensate for cell migration we apply a computationally efficient stretch processing algorithm that is tailored for batched processing and suitable for implementation onto a real-time radar processor. The performance of the algorithm is studied using experimental data. In the experiment, pseudorandom noise, with a bandwidth of 100 MHz, is generated and transmitted in real-time. An unmanned aerial vehicle (UAV), flown at a speed of 11.5 m/s, is acting as a target. For an integration time of 1 s, the algorithm is shown to yield an increase in SINR of roughly 13 dB, compared to no compensation. It is also shown that coherent integration times of 2.5 s can be achieved.

The utilisation of continuous random waveforms for radar, that is, noise radar, has been extensively studied as a candidate for low probability of intercept operation. However, compared with the more traditional pulse-Doppler radar, noise radar systems are significantly more complicated to implement, which is likely why few systems exist. If noise radar systems are to see the light of day, system design, implementation, limitations etc., must be investigated. Therefore, the authors examine and detail the implementation of a real-time noise radar system on a field programmable gate array. The system is capable of operating with 100% duty cycle, 200 MHz bandwidth, and 268 ms integration time while processing a range of about 8.5 km. Additionally, the system can perform real-time moving target compensation to reduce cell migration. System performance is primarily limited by the memory bandwidth of the off-chip dynamic random access memory.

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results.

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.

Critical infrastructures (CIs) are becoming increasingly sophisticated with embedded cyber-physical systems (CPSs) that provide managerial automation and autonomic controls. Yet these advances expose CI components to new cyber-threats, leading to a chain of dysfunctionalities with catastrophic socio-economical implications. We propose a comprehensive architectural model to support the development of incident management tools that provide situation-awareness and cyber-threats intelligence for CI protection, with a special focus on smart-grid CI. The goal is to unleash forensic data from CPS-based CIs to perform some predictive analytics. In doing so, we use some AI (Artificial Intelligence) paradigms for both data collection, threat detection, and cascade-effects prediction. 

Critical infrastructures (CIs) are becoming increasingly sophisticated with embedded cyber-physical systems (CPSs) that provide managerial automation and autonomic controls. Yet these advances expose CI components to new cyber-threats, leading to a chain of dysfunctionalities with catastrophic socio-economical implications. We propose a comprehensive architectural model to support the development of incident management tools that provide situation-awareness and cyber-threats intelligence for CI protection, with a special focus on smart-grid CI. The goal is to unleash forensic data from CPS-based CIs to perform some predictive analytics. In doing so, we use some AI (Artificial Intelligence) paradigms for both data collection, threat detection, and cascade-effects prediction. 

A land cover map of two arctic catchments near the Abisko Scientific Research Station was obtained based on a classification from a Sentinel-2 satellite image and a ground survey performed in July 2022. The two contiguous catchments, Miellajokka and Stordalen, are covered by various ecotypes, from boreal forest to alpine tundra and peatland. Two classification algorithms, support vector machine and random forest, were tested and gave very similar results. The percentage of correctly classified pixels was over 88% in both cases. The developed workflow relies solely on open-source software and acquired ground observations. Space organization was directed by the altitude as demonstrated by the intersection of the land cover with the topography. Comparison between this new land cover map and previous ones based on data acquired between 2008 and 2011 shows some trends in vegetation cover evolution in response to climate change in the considered area. This land cover map is key input data for permafrost modeling and, hence, for the quantification of climate change impacts in the studied area.

This paper presents an assessment of the excess heat recovery potential from EU metro stations. The assessment is a sub-study on low temperature recovery opportunities, explored in the H2020 ReUseHeat project, and consists of spatial mapping of 1994 underground stations with quantitative estimates of sensible and latent heat, monthly and annually, attainable in rejected platform ventilation exhaust air. Being a low-temperature source, the assessment conceptually anticipates recovery of attainable heat with compressor heat pumps to facilitate the temperature increase necessary for utilisation in district heating systems. Further, the paper explores the influence on useful excess heat volumes from low-temperature heat recoveries when distributed at different temperature levels. The findings, which distinguishes available (resource) and accessible (useful) excess heat potentials, indicate an annual total EU28 available potential of ~21 PJ, characterised by a certain degree of seasonal temporality, and corresponding accessible potentials of ~40 PJ per year at 3^rd generation distribution, and of ~31 PJ at anticipated 4^th generation conditions. Despite lower accessible volumes, utilisation in 4^th generation systems are naturally more energy efficient, since relatively less electricity is used in the recovery process, but also more cost-effective, since heat pumps, at lower temperatures, can be operated at capacities closer to design conditions and with less annual deviations.

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.

The utilization of remote sensing observations to monitor essential climate variables (ECVs) has become increasingly important in studying their regional and global impacts, as defined by the Global Climate Observing System (GCOS). Understanding the Earth’s surface conditions, including soil moisture runoff, snow, temperature, precipitation, water vapour, radiation, groundwater and sea surface height (SSH), can positively impact the environment and ecosystems. Here, the authors present an overview of how global navigation satellite systems (GNSS) can be employed for environmental monitoring, with a particular focus on sea surface height monitoring. This includes examination of the advantages and disadvantages of utilizing a network of permanent GNSS stations for monitoring sea level rise along shorelines.

The objective of this research was to evaluate the synergistic effects of multitemporal European remote sensing satellite 1 (ERS-1) synthetic aperture radar (SAR) and Landsat thematic mapper (TM) data for crop classification using a per-field artificial neural network (ANN) approach. Eight crop types and conditions were identified: winter wheat, corn (good growth), corn (poor growth), soybeans (good growth), soybeans (poor growth), barley/oats, alfalfa, and pasture. With the per-field approach using a feed-forward ANN, the overall classification accuracy of three-date early- to mid-season SAR data improved almost 20%, and the best classification of a single-date (5 August) SAR image improved the overall accuracy by about 26%, in comparison to a per-pixel maximum-likelihood classifier (MLC). Both single-date and multitemporal SAR data demonstrated their abilities to discriminate certain crops in the early and mid-season; however, these overall classification accuracies (<60%) were not sufficiently high for operational crop inventory and analysis, as the single-parameter, high-incidence-angle ERS-1 SAR system does not provide sufficient differences for eight crop types and conditions. The synergy of TM3, TM4, and TM5 images acquired on 6 August and SAR data acquired on 5 August yielded the best per-field ANN classification of 96.8% (kappa coefficient = 0.96). It represents an 8.3% improvement over TM3, TM4, and TM5 classification alone and a 5% improvement over the per-pixel classification of TM and 5 August SAR data. These results clearly demonstrated that the synergy of TM and SAR data is superior to that of a single sensor and the ANN is more robust than MLC for per-field classification. The second-best classification accuracy of 95.9% was achieved using the combination of TM3, TM4, TM5, and 24 July SAR data. The combination of TM3, TM4, and TM5 images and three-date SAR data, however, only yielded an overall classification accuracy of 93.89% (kappa = 0.93), and the combination of TM3, TM4, TM5, and 15 June SAR data decreased the classification accuracy slightly (88.08%; kappa = 0.86) from that of TM alone. These results indicate that the synergy of satellite SAR and Landsat TM data can produce much better classification accuracy than that of Landsat TM alone only when careful consideration is given to the temporal compatibility of SAR and visible and infrared data.

The overall objective of this research is to evaluate multitemporal Sentinel-1A SAR and Sentinel-2A MSI data for global urban services using innovative methods and algorithms, namely KTH-Pavia Urban Extractor, a robust algorithm for urban extent extraction and KTHSEG, a novel object-based classification method for detailed urban land cover mapping. Ten cities around the world in different geographical and environmental conditions were selected as study areas. Large volume of Sentinel-1A SAR and Sentinel-2A MSI data were acquired during vegetation season in 2015 and 2016. The preliminary urban extraction results showed that urban areas and small towns could be well extracted using multitemporal Sentinel-1A SAR data with the KTH-Pavia Urban Extractor. For urban land cover mapping, multitemporal Sentinel-1A SAR data alone yielded an overall classification accuracy of 60% for Stockholm. Sentinel-2A MSI data as well as the fusion of Sentinel-1A SAR and Sentinel-2A MSI data, however, produced much higher classification accuracies, both reached 80%.

The overall objectives of this research are to investigate spaceborne SAR data, optical data and fusion of SAR and optical data for urbanization monitoring in China, and to assess the impact of urbanization on the environment for sustainable development. Effective segmentation and classification methods for urban extent extraction and land cover mapping were developed. Several change detection algorithms and approaches using SAR and optical data were evaluated. Further, synergistic effects of multisensor SAR data as well as ASAR and HJ-1B data are examined. The results show that the developed methods were effective for urban extent extraction, land cover mapping and change detection. The fusion of multisensor spaceborne SAR as well as fusion of ASAR and HJ-1 data were beneficial for urban land cover mapping. The spatiotemporal patterns of urbanization in China were analyzed. The results show that rapid urbanization in Yangtze River Delta, Jingjinji and Pearl River Delta has a significant impact on the environment in terms of landscape fragmentation and ecosystem services.

Based on photo-interpretation procedures, the technique of sequential masking can be used to differentiate image features using a series of multitemporal images. In this study, a set of nine ERS-1 SAR images is analyzed using this technique to determine the earliest dates for identifying different crop types in an agricultural area of southern Ontario, Canada. SAR temporal backscatter profiles of crops were generated from calibrated radar imagery. Based on these temporal backscatter profiles, per-field classifications using the sequential-masking technique were performed on the early- and mid-season multitemporal SAR data. It was found that using only three images, acquired on May 31, June 16 and July 5, it is possible to differentiate winter wheat, alfalfa/hay, barley/oats, soybeans and corn with an overall validation accuracy of 88.5% and a Kappa coefficient of 0.85.

Multi-temporal radar backscatter characteristics of crops and their underlying soils were analysed for an agricultural area in south-western Ontario, Canada using nine dates of ERS-1 SAR imagery acquired during the 1993 growing season. From the calibrated data, SAR temporal backscatter profiles were generated for each crop type. The results indicate that small changes in incidence-angle can have strong impacts on radar backscatter. Thus, attention must be given to local incidence-angle effects when using ERS-1 SAR data,especially when comparing backscatter coefficients of the same area from different scenes or different areas within the same scene.

This research investigates the capability of the multitemporal RADARSAT Fine-Beam C-HH SAR imagery for landuse/land-cover mapping and change detection in therural-urban fringe of the Greater Toronto Area (GTA). Five-date RADARSAT fine-beamSAR images were acquired during May to August in 2002. One scene of Landsat TM imagery was acquired in 1988 for change detection. The major landuse/land-coverclasses were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and three types of agricultural lands. These ten classes were chosen to characterize the complex landuse/land-cover types in the rural-urban fringe of the GTA. The results demonstrated that, for identifying landuse/land-cover classes, five-date raw SAR imagery yielded very poor result due to speckles. Much better results were achieved with combined Mean, Standard Deviation and Correlation texture images using artificial neural networks (ANN) and with raw images using object-based classification. The change detection procedure was able to identify the areas of significant changes, for example, major new roads, new low-density and high-density built up areas and golf courses, even though the overall accuracy of the change detection was rather low. 

The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying I I land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density, builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.

The objective of this research is to evaluate Quickbird multi-spectral (MS) data, multi-temporal RADARSAT Fine-Beam C-HH synthetic aperture radar (SAR) data and fusion of Quickbird MS and RADARSAT SAR for urban land-use/land-cover mapping. One scene of Quickbird multi-spectral imagery was acquired on 18 July 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August 2002. Quickbird MS images and RADARSAT SAR data were classified using an object-based and rule-based approach. The results demonstrated that the object-based and knowledge-based approach was effective in extracting urban land-cover classes. For identifying 16 land-cover classes, object-based and rule-based classification of Quickbird MS data yielded an overall classification accuracy of 87.9% (kappa: 0.868). For identifying 11 land-cover classes, object-based and rule-based classification of RADARSAT SAR data yielded an overall accuracy: 86.6% (kappa: 0.852). Decision level fusion of Quickbird classification and RADARSAT SAR classification was able to take advantage of the best classifications of both optical and SAR data, thus significantly improving the classification accuracies of several land-cover classes (25% for pasture, 19% for soybeans, 17% for rapeseeds) even though the overall classification accuracy of 16 land-cover classes increased only slightly to 89.5% (kappa: 0.885).

This research investigates multitemporal dual-orbit RADARSAT-2 polarimetric SAR data for urban land cover classification using an object-based support vector machine (SVM). Six-date RADARSAT-2 high-resolution SAR data in both ascending and descending orbits were acquired in the rural-urban fringe of the Greater Toronto Area during the summer of 2008. The major landuse/land-cover classes include high-density residential area, low-density residential area, industrial and commercial area, construction site, park, golf course, forest, pasture, water and two types of agricultural crops. The results show that multitemporal SAR data improve urban land cover classification and the best classification result is achieved using data from all six-dates. However, similar accuracies could be achieved using only three-date data from both ascending and descending orbits with relatively longer temporal span. Combinations of SAR data with relatively short temporal span are observed to yield lower classification accuracy. Similarly, combinations of SAR data from either ascending or descending orbit alone yield lower accuracy than the combinations of ascending and descending data. The results indicate that the combination of both the ascending and descending spaceborne SAR data with appropriate temporal span are suitable for urban land cover mapping.