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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.

We present a method to monitor methane at atmospheric concentrations with errors inthe order of tens of parts per billion. We use machine learning techniques and periodic calibrationswith reference equipment to quantify methane from the readings of an electronic nose. The resultsobtained demonstrate versatile and robust solution that outputs adequate concentrations in a varietyof different cases studied, including indoor and outdoor environments with emissions arising fromnatural or anthropogenic sources. Our strategy opens the path to a wide-spread use of low-costsensor system networks for greenhouse gas monitoring.

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.

Buildings are not only central to the day-to-day activities but also serve as critical indicators of urban development and transformation. The automatic extraction of building footprints from high-resolution Remote Sensing Imagery (RSI) has emerged as an important and popular tool in urban studies. It helps to enhance the understanding and management of urban sprawl, urban planning, population estimation, resource allocation, and post-disaster damage assessment. In this context, having an automated and robust building detection model is crucial. Deep Learning (DL) model and Object-Based Image Analysis (OBIA) techniques are the main and commonly used for automated building detection. This study investigates the efficacy of a pre-trained DL model and a rule-based model OBIA techniques in building detection across varied resolutions and geographic settings. Employing orthophotos from Luleå, Gävle, and Stockholm, the research assesses the adaptability and robustness of these methods under image properties and urban densities. The DL model was initially trained on 0.25m resolution data of Sweden by Lantmäteriet (Sweden mapping agency). The rule-based model was developed by applying OBIA techniques on behalf of this study. Models were analyzed through six feature agreement statistics including Critical Success Index (CSI), Precision, and Detection Probability (POD). The findings reveal that the DL model consistently outperformed the OBIA approach across all study areas, particularly at the original 25 cm resolution. Gävle showed superior precision with a CSI of 0.8139 for the DL model against a CSI of 0.7493 for OBIA at 25 cm. The evaluation was improved by considering 50*50 sq. m subsets and building sizes. These evaluations highlight that building size and urban density significantly influence detection accuracy. Larger (> 2500 sq. m) buildings and less dense areas tend to yield higher accuracy across both detection methods. The DL model exhibited high CSI values fo

Mapping wetland types in northern-latitude regions with Earth Observation (EO) data is important for several practical and scientific applications, but at the same time challenging due to the variability and dynamic nature in wetland features introduced by differences in geophysical conditions. The objective of this study was to better understand the ability of Sentinel-1 radar data, Sentinel-2 optical data and terrain derivatives derived from Copernicus digital elevation model to distinguish three main peatland types, two upland classes, and surface water, in five contrasting landscapes located in the northern parts of Alaska, Canada and Scandinavia. The study also investigated the potential benefits for classification accuracy of using regional classification models constructed from region-specific training data compared to a global classification model based on pooled reference data from all five sites. Overall, the results show high promise for classifying peatland types and the three other land cover classes using the fusion approach that combined all three EO data sources (Sentinel-1, Sentinel-2 and terrain derivatives). Overall accuracy for the individual sites ranged between 79.7% and 90.3%. Class specific accuracies for the peatland types were also high overall but differed between the five sites as well as between the three classes bog, fen and swamp. A key finding is that regional classification models consistently outperformed the global classification model by producing significantly higher classification accuracies for all five sites. This suggests for progress in identifying effective approaches for continental scale peatland mapping to improve scaling of for example, hydrological- and greenhouse gas-related processes in Earth system models., Funding: Swedish Research Council Formas [2017-01944, 2018-01794, 2018-00570]; European Space Agency

Monitoring carbon stock has emerged as a critical environmental problem among several worldwide organizations and collaborations in the context of global warming and climate change. This study seeks to provide a remote sensing solution based on three types of data, to explore the feasibility and reliability of estimating aboveground biomass (AGB) in order to improve the efficiency of monitoring carbon stock. The study attempted to investigate the potential of using Google Earth Engine (GEE), and the combinations of different datasets from Sentinel-1 (SAR), Sentinel-2 multispectral imagery, and LiDAR data to estimate AGB, by using the random forest algorithm (RF). Two models were proposed: the first one (Model 1) detected the AGB temporal changes from 2016 to 2021 in Southern Sweden; while the second one (Model 2) focused on Hultsfred municipality and studied the influence of different variables including the canopy height. Besides, six experimental groups of variables were tested to determine the performance of using different types of remote sensing data. We validated these two models with the observed AGB, and the findings showed that the combination of SAR polarization, multisprectral bands, vegetation indices able to estimate AGB for Model 1. In addition, Model 2 showed that further using the canopy height data can further improve the estimation. We also found out that the spectral bands from Sentinel-2 contributed the most to AGB estimation for Model 1 in terms of: bands B3 (Green), B4 (Red), B5 (Red edge), B11 (SWIR), B12 (SWIR); and, vegetation indices of RVI, DVI, and EVI. On the other hand, for Model 2, B1(Ultra blue), B4 (Red), EVI, SAVI, and the canopy height are the most crucial variables for estimating AGB. Besides, the radar backscatter values using VV and VH modes from Sentienl-1 were both important for Models 1 and 2. For Model 1, the experimental group with the best accuracy was the group that used all variable combinations from Sentinel-1 and 2, a

Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning-based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes.

Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accurac, Validerad;2023;Nivå 1;2023-02-17 (joosat);Licens fulltext: CC BY License

Water resources play an important role in society and fulfill various functions such as providing drinking water, supporting industrial production and enhancing the overall landscape. Water bodies, such as rivers and lakes, are particularly important in this context. However, as societies and economies develop, the demand for water increases significantly. This also leads to the release of domestic, agricultural and industrial wastewater, which often exceeds the self-purification capacity of water bodies. Consequently, rivers and lakes are getting more and more polluted, endangering the safety of drinking water and causing ecological damage, affecting human health and biodiversity. Water quality monitoring plays a crucial role in evaluating the state of water bodies. Traditional monitoring methods involve labor-intensive field sampling and expensive construction and maintenance of automatic stations. Although these methods provide accurate results, they are limited to specific sampling points and struggle to meet the demands of monitoring water quality across entire surfaces of rivers and lakes. This degree project aim at developing a method that can predict absorbance in water with the aim of remote sensing. Along with multispectral imaging and machine learning this work proves that this is possible. The result from multivariate analysis is an optimal model that can predict absorbance at 420 nm with RSQ of 0,996 and RMSE of 0,00081.

The thesis investigates above-ground biomass (AGB) with terrestrial laser scanning (TLS) for estimating AGB in a study area in Valls Hage, Gävle. The study used TLS for field measurements to collect highly detailed point clouds of two tree species for AGB estimation and comparison against validation data. TLS-derived data were validated using a non-destructive method involving direct field measurements using tape measures and a Trimble SX12 for extracting diameter at breast height (DBH), tree height, and crown diameter. Wood density was obtained from the literature. Data processing for segmentation, filtering, and generation of the quantitative structure model (QSM) was performed by using SimpleForest tool in Computree software. A statistical analysis was performed using linear regression, and AGB was estimated using QSM-derived volume multiplied by wood density. The finding in the results for the comparison of AGB estimation between TLS QSM and field validation from DBH-based tree-specific allometric equation had an RMSE of 154 kg, with a near-perfect agreement of 0.997 %, and RMSE of 189 kg, with the agreement of 0.990% for TLS QSM and TLS validation DBH-based tree specific equation. The comparison between TLS-derived DBH and field validation was accurate, leaving with insignificant differences, while the tree height had noticeable differences, and crown diameter had relatively low differences. The challenges during data processing were highlighted and the importance of TLS data for accurate AGB estimation, with the potential for refinement and integrating internal tree structure information to improve allometric models for future studies.

Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high-resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame-by-frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high-resolution up-to-date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up-to-date support for conservation and ecosystem management.

River deltas and lakes support biodiversity and offer crucial ecosystem services such as freshwater provision, flood control, and fishing. However, climate change and human activities have affected deltas and lakes globally, altering the services they provide. Since delta and lake surface water occurrence and water levels respond to climate change and anthropogenic activities, we need to monitor their variations to understand the potential drivers for effective water management strategies. However, important deltas like the Selenga River Delta (SRD) in Russia lack a detailed analysis of water occurrence. Regarding lake water level, there has been a decline in the number of gauging stations globally, due to installation and maintenance costs. For example, Sweden has ~100,000 lakes which are sources of freshwater and hydro-power, but only 38 lakes have long and continuous in-situ records of water level. As satellite data are reliable alternatives for conventional methods to monitor deltas and lakes, I employed Earth Observations (EO) to quantify changes in surface water occurrence in the SRD and water levels in Swedish lakes and identify their main drivers. I also developed and explored a novel methodology for lake water level estimation based on Differential Interferometric Synthetic Aperture Radar (D-InSAR) by calculating the six-day phase differences in 30 Swedish lakes. To achieve these objectives, I trained and applied a Maximum Likelihood classification to Landsat images from 1987 to 2020 and quantified surface water occurrence and its changes in the SRD. I found that surface water occurrence in 51% of the delta experienced a decrease. As the Selenga River is the only river flowing into the SRD, the change in surface water occurrence in the SRD correlated with river discharge, but not with the river suspended sediment concentration, the lake water level in the outlet of the SRD, or evapotranspiration over the delta. In Sweden, I used satellite altimetry data fro, 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 Sve

High-resolution synthetic aperture radar (SAR) sensors are now commonly used for flood detection. Automated detection tends to be limited to rural areas owing to the complicated backscattering mechanisms occurring in urban areas. Flooding can be identified in urban areas by searching for increased SAR backscatter in a postflood image due to double scattering between water and adjacent buildings, compared with a preflood image where double scattering is between unflooded ground and buildings. For co-polarized data, if f is the angle between the building wall and the satellite direction of travel, double scattering is strongest for f = 0 deg and falls off as f increases. Theoretical studies estimating the ratio of flooded-to-unflooded double scatterer (DS) radar cross section (RCS) using X-band SAR data, found that the ratio was high at f = 0 deg but only small at f > 10 deg. Ostensibly, this implies that few DSs might be detected in an urban area. However, experiments on real images have called into question the veracity of the modeling. We describe an empirical study to examine the relationship between the flooded-to-unflooded DS RCS ratio and f in Sentinel-1 (S-1) C-band data. We use high-resolution light detection and ranging and aerial photographs so that f can be measured accurately and is based on S-1 images from flood events that occurred in the United Kingdom during the storms of winter 2019 to 2020. Results indicate that vertical-vertical polarization is better than vertical-horizontal at distinguishing flooded from unflooded DS; that the theoretical model used underestimates the number of DS with high RCS ratios in the f range 10 deg to 30 deg; and that sufficient DS ground heights can be determined to estimate an accurate local average flood level, although in high density housing there are less of these due to the presence of adjacent buildings.

Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView 2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for ma, Validerad;2023;Nivå 1;2023-09-05 (hanlid)

Urban area expansion and the increase in the built-up area are major threats to agriculture, the natural environment, and ecology throughout the world. The population of the city of Larkana is rapidly increasing due to natural growth and uncontrolled migration from the surrounding areas, which have resulted in a haphazard increase in the built-up area over the fertile agricultural land and affected the city’s thermal environment. This research aims to evaluate the impacts of urban area expansion on agricultural land and the thermal environment of the study area by assessing the change in land use/ land cover (LULC) from 1990 to 2020 and land surface temperature (LST). For this purpose, the Random Forest (RF) algorithm was applied, and LST was calculated in Google Earth Engine (GEE) using Landsat imageries for the years 1990, 2000, 2010, and 2020. For the RF classifier algorithm, 3500 reference random points were generated for each year, which were then randomly divided into two datasets, i.e., a training sample consisting of 70% of the points and a validation dataset (30% of the points). After LULC classification, the results were validated for 1990, 2000, 2010, and 2020, and the accuracy was 88.3%, 89%, 90.01%, and 90.8%, respectively. The study results showed that the built-up area increased from 12.31 to 43.83 km2 while the barren land in the study region decreased from 56.51 km2 to 11.62 km2. The agricultural land was 66.66 km2 in 1990, expanded to 101.38 km2, and then decreased to 79.49 km2 in 2020. The results also revealed that most of the urban expansion in the last decade (2010-2020) took place on agricultural land. The urban thermal environment also showed a gradual increase in surface temperature as recorded by the LST results. The LST results revealed that the maximum LST was 33.4°C in 1990, which increased to 36.1°C in 2020, and similarly, the minimum LST was 25.1°C, which also increased to 26.6°C in 2020. This study provides useful results for stakehol, Validerad;2023;Nivå 2;2023-05-08 (hanlid)

The use of three-dimensional maps is more effective than two-dimensional maps in representing the Earth’s surface. However, the traditional methods used to create digital surface models are not efficient for capturing the details of Earth’s features. This is because they represent only three-dimensional objects in a single texture and do not provide a realistic representation of the real world. Additionally, there is a growing demand for up-to-date and accurate geo-information, particularly in urban areas. To address this challenge, a new technique is proposed in this study that involves integrating remote sensing, Geographic Information System, and Architecture Environment software to generate a highly-detailed three-dimensional model. The method described in this study includes several steps such as acquiring high-resolution satellite imagery, gathering ground truth data, performing radiometric and geometric corrections during image preprocessing, producing a 2D map of the region of interest, constructing a digital surface model by extending the building outlines, and transforming the model into multi-patch layers to create a 3D model for each object individually. The research findings indicate that the digital surface model obtained with comprehensive information is suitable for different purposes, such as environmental research, urban development and expansion planning, and shape recognition tasks., Godkänd;2023;Nivå 0;2023-06-05 (joosat);Licens fulltext: CC BY License

Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues to improve over time, and some modern satellites, such as the Copernicus Programme's Sentinel-2 remote sensing satellites, offer a spatial resolution of 10 m across many of their spectral bands. The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data. The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data, which can be used for numerous downstream activities, particularly where data timeliness, volume and velocity are important. Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry, which currently entirely relies on manually derived data that is primarily guided by scientific reduction. Furthermore, it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis. Currently, no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences. In this paper, we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation. We use gold grade data from a South African tailing storage facility (TSF) and data from both the Landsat-8 and Sentinel remote sensing satellites. We show that various machine learning algorithms can be used given the abundance of trainin, Validerad;2023;Nivå 1;2023-04-12 (hanlid);Funder: Department of Scienceand Innovation (DSI) - National Research Foundation (NRF) ThuthukaGrant (121,973); SI-NRF CIMERA; Centre for Advanced Miningand Metallurgy (CAMM), Luleå University of Technology; Sibanye-Stillwater Ltd

Natural hazards, such as flooding, have been negatively impacting developed and emerging economies alike. The effects of floods are more prominent in countries of the Global South, where large parts of the population and infrastructure are insufficiently protected from natural hazards. From this scope, a lot of effort is required to mitigate these impacts by continuously providing new and more reliable tools to aid in mitigation and preparedness, during or after a flood event. Flood mapping followed by damage assessment plays an important role in all these stages. In this work we investigate a new dataset provided by DrivenData Labs based on Sentinel-1 (S1) imagery (VH, VV imagery and labels) to help map floods in the city of Beira in Mozambique. Exploiting Google Earth Engine (GEE), we deployed supervised and unsupervised machine learning (ML) methods on a dataset comprising imagery from 13 countries worldwide. We first mapped the floods country-by-country including Mozambique. This first part was helpful to understand the sensitivity of each method when applied to data from different regions and with different polarizations. We then trained the supervised model globally (in all 13 countries) and used it to predict floods in Beira. To assess the accuracy of the experiments we used the intersection over the union (IoU) metric, results of which we compared with the benchmark IoU achieved by the winner in the DrivenData competition for flood mapping in 2021. The implementation of unsupervised and supervised ML using VH and VV+VH produced satisfactory results, and showed to be better than using VV imagery; in Cambodia and Bolivia with VH polarization yielded IoUs values ranging from 0.819 to 0.856 which is above the benchmark (0.8094). The predictions in Beira using VH imagery yielded IoU of 0.568, which is a reasonable outcome. The proposed approach is a reliable alternative for flood mapping, especially in Mozambique due to its low cost and time effectiveness as even, QC 20230824

The development of urbanization and the transformation of green lands into impermeable land increase temperature and create urban heat islands (UHIs). Our observations with remote sensing instruments of Landsat platforms show considerable changes in land use types in Beijing city with the shrinking of green lands, expansion of built envi-ronments, and a slight increase in the temperature during the recent four decades. Using remote sensing instruments of Landsat platforms and registered data from two meteorological stations in Beijing, this study finds the relationship between land surface temperature (LST) and the increasing conversion of cultivated lands into built-up areas. This article presents innovative research that shows the mutual correlation well and recommends revisions in the land use policies for better weather. The geographically weighted regression model (GWR) with a Gaussian weighting kernel function analyzes the impact of various urban land use types on the LST and the increase UHIs. In Beijing city, green lands show fewer standard deviations (SD) in the average temperatures equal to 0.109, while the industrial spaces exhibit a high SD equal to 0.212. The outcomes of this paper contribute to finding optimal land use policies everywhere in the world with the increasing urbanization through simulating its model for a more comfortable life., QC 20230918

Previous studies have shown that Synthetic Aperture Radar (SAR) is able to detect burned areas, serving as a key data source for monitoring active wildfires in situations where optical sensors are hindered by dense smoke or cloud cover. Radar remote sensing provides rich and useful data on historical burned areas, which are critical for large-scale wildfire burned area mapping. This study aims to unveil the potential inherent in Sentinel-1 C-Band SAR data and to investigate the impact of reference masks on wildfire burned area mapping. SAR images frequently exhibit disruptive speckle noise, which manifests as isolated pixels or small regions, thereby hindering the seamless identification of burned areas through SAR data. To mitigate noise and enhance the connectivity of SAR-based burned area delineation, we propose a novel approach: a Total-Variation (TV) regularized U-Net model, tailored to learn from noisy pseudo masks derived from Sentinel-1 C-Band SAR backscattering data. To validate its efficacy, we assembled a dataset encompassing 16 geographically diverse wildfire events, incorporating SAR-based kMap, a SAR-based binary label (designated as SARREF), and an optical-based binary label (referred to as OptREF). The kMap, serving as a relative indicator of SAR backscattering change, is defined as the divergence between post-fire SAR backscattering and the temporal average, normalized by the temporal standard deviation of pre-fire SAR time series. Utilizing SAR-based kMap as input and SARREF or OptREF as labels, we systematically explored the impact of TV regularization on SAR-based burned area mapping. Our experimental findings highlight several key insights: 1) Vanilla U-Net considerably elevates the F1 score from 0.61 to 0.66 when compared to threshold-based approaches; 2) The TV-regularized U-Net exhibits a notable capacity for enhancing both accuracy and the connectivity of SAR-based burned areas. When trained with noisy SARREF, it achieves a substantial F1 sc, QC 20230830

To better understand current trends of urban population growth in Sub-Saharan Africa, high-quality spatiotemporal population estimates are necessary. While the joint use of remote sensing and deep learning has achieved promising results for population distribution estimation, most of the current work focuses on fine-scale spatial predictions derived from single date census, thereby neglecting temporal analyses. In this work, we focus on evaluating how deep learning change detection techniques can unravel temporal population dynamics at short intervals. Since Post-Classification Comparison (PCC) methods for change detection are known to propagate the error of the individual maps, we propose an end-to-end population growth mapping method. Specifically, a ResNet encoder, pretrained on a population mapping task with Sentinel-2 MSI data, was incorporated into a Siamese network. The Siamese network was trained at the census level to accurately predict population change. The effectiveness of the proposed method is demonstrated in Kigali, Rwanda, for the time period 2016-2020, using bi-temporal Sentinel-2 data. Compared to PCC, the Siamese network greatly reduced errors in population change predictions at the census level. These results show promise for future remote sensing-based population growth mapping endeavors. Code is available on GitHub., Part of ISBN 9781665493734QC 20230822

Accurate urban maps provide essential information to support sustainable urban development. Recent urban mapping methods use multi-modal deep neural networks to fuse Synthetic Aperture Radar (SAR) and optical data. However, multi-modal networks may rely on just one modality due to the greedy nature of learning. In turn, the imbalanced utilization of modalities can negatively affect the generalization ability of a network. In this paper, we investigate the utilization of SAR and optical data for urban mapping. To that end, a dual-branch network architecture using intermediate fusion modules to share information between the uni-modal branches is utilized. A cutoff mechanism in the fusion modules enables the stopping of information flow between the branches, which is used to estimate the network's dependence on SAR and optical data. While our experiments on the SEN12 Global Urban Mapping dataset show that good performance can be achieved with conventional SAR-optical data fusion (F1 score = 0.682±0.014), we also observed a clear under-utilization of optical data. Therefore, future work is required to investigate whether a more balanced utilization of SAR and optical data can lead to performance improvements., Part of ISBN 978-1-6654-9373-4QC 20230823

In recent years, climate change and human activities have caused increas- ing numbers of wildfires. Earth observation data with various spatial and temporal resolutions have shown great potential in detecting and monitoring wildfires. Advanced Baseline Imager (ABI) onboarding NOAA’s geostation- ary weather satellites Geostationary Operational Environmental Satellites R Series (GOES-R) can acquire images every 15 minutes at 2km spatial resolu- tion and has been used for early fire detection. Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) onboarding sun-synchronous satellites offer twice daily revisit and are widely used in active fire detection. VIIRS Active Fire product (VNP14IMG) has 375 m spatial resolution and MODIS Active Fire product (MCD14DL) has 1 km spatial resolution. While these products are very useful, the existing solutions have flaws, including many false alarms due to cloud cover or build- ings with roofs in high-temperature. Also, the multi-criteria threshold-based method does not leverage rich temporal information of each pixel at different timestamps and rich spatial information between neighbouring pixels. There- fore, advanced processing algorithms are needed to provide reliable detection of active fires. In this thesis, the main objective is to develop deep learning-based meth- ods for improved active fire detection, utilizing multi-sensor earth observation images. The high temporal resolution of the above satellites makes temporal information more valuable than spatial resolution. Therefore, sequential deep learning models like Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM), and Transformer are promising candidates for utilizing temporal in- formation encoded in the variation of the thermal band values. In this thesis, a GRU-based early fire detection method is proposed using GOES-R ABI time-series which shows earlier detection time of wildfires than VIIRS active fire prod, Under de senaste åren har klimatförändringar och mänsklig verksamhet orsakat allt fler skogsbränder. Jordobservationsdata med olika rumsliga och tidsmässiga upplösningar har visat sig ha stor potential när det gäller att upp- täcka och övervaka skogsbränder. Advanced Baseline Imager (ABI) på NO- AA:s geostationära vädersatelliter Geostationary Operational Environmental Satellites R Series (GOES-R) kan ta bilder var 15:e minut med en rumslig upplösning på 2 km och har använts för tidig upptäckt av bränder. Moderate Resolution Imaging Spectroradiometer (MODIS) och Visible Infrared Imaging Radiometer Suite (VIIRS) på solsynkrona satelliter har två gånger per dag och används ofta för att upptäcka aktiva bränder. VIIRS Active Fire product (VNP14IMG) har en geografisk upplösning på 375 m och MODIS Active Fire product (MCD14DL) har en geografisk upplösning på 1 km. Även om dessa produkter är mycket användbara har de befintliga lösningarna brister, bl.a. många falska larm på grund av molntäcke eller byggnader med tak med hög temperatur. Den multikriteriebaserade tröskelmetoden utnyttjar inte heller den rika tidsmässiga informationen för varje pixel vid olika tidpunkter och den rika rumsliga informationen mellan närliggande pixlar. Därför behövs avancerade bearbetningsalgoritmer för att tillförlitligt kunna upptäcka aktiva bränder. Huvudsyftet med den här avhandlingen är att utveckla metoder baserade på djupinlärning för förbättrad aktiv branddetektering med hjälp av jordob- servationsbilder med flera sensorer. Den höga tidsmässiga upplösningen hos ovannämnda satelliter gör att den tidsmässiga informationen är mer värdefull än den spatiala upplösningen. Därför är sekventiella djupinlärningsmodeller som Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM) och Transformer lovande kandidater för att utnyttja den tidsmässiga informatio- nen som är kodad i variationen av värmebandsvärdena. I den h, QC 20230526

Nowadays Earth observation satellites provide forest fire authorities and resource managers with spatial and comprehensive information for fire stabilization and recovery. Burn severity mapping is typically performed by classifying bi-temporal indices (e.g., dNBR, and RdNBR) using thresholds derived from parametric models incorporating field-based measurements. Analysts are currently expending considerable manual effort using prior knowledge and visual inspection to determine burn severity thresholds. In this study, we aim to employ highly automated approaches to provide spatially explicit damage level estimates. We first reorganize a large-scale Landsat-based bi-temporal burn severity assessment dataset (Landsat-BSA) by visual data cleaning based on annotated MTBS data (approximately 1000 major fire events in the United States). Then we apply state-of-the-art deep learning (DL) based methods to map burn severity based on the Landsat-BSA dataset. Experimental results emphasize that multi-class semantic segmentation algorithms can approximate the threshold-based techniques used extensively for burn severity classification. UNet-like models outperform other region-based CNN and Transformer-based models and achieve accurate pixel-wise classification results. Combined with the online hard example mining algorithm to reduce class imbalance issue, Attention UNet achieves the highest mIoU (0.78) and the highest Kappa coefficient close to 0.90. The bi-temporal inputs with ancillary spectral indices work much better than the uni-temporal multispectral inputs. The restructured dataset will be publicly available and create opportunities for further advances in remote sensing and wildfire communities., QC 20230515

Despite the popularity and success in burned area detection and assessment, multispectral satellite images are often affected by poor sunlight-illumination conditions, particularly at high latitudes. Given that Synthetic Aperture Radar (SAR) can effectively penetrate clouds and collect images in all-weather conditions during day and night, the complementary use of optical and SAR data can be helpful for remote-sensing measurements and assessments of burned sites. Nevertheless, the widely used burn-sensitive spectral indices hardly help analyze SAR data due to the inherent difference between optical and SAR sensors in physical imaging mechanisms. In this study, we aim to leverage multi-source data for burned area mapping and burn severity assessment by translating SAR images into optical images using ResNet-based Pix2Pix model. Experiments were performed on 8669 pairs of bitemporal Sentinel-1 SAR and Sentinel-2 optical patches over 304 large wildfire events in Canada with a wide range of land covers. The translated optical images from SAR data occupied similar spectral properties to real optical observations and the corresponding generated spectral indices (i.e., delta Normalized Burn Ratio (dNBR), relative dNBR, and Relativized Burn Ratio) also showed high agreement with real optical indices. In terms of burned area detection using the generated indices, their medium values of the area under the receiver operating characteristics curve (AUC) were over 85%, achieving promising performance that outperformed the SAR-based index. Burn severity maps derived from multi-source data achieved a relatively high Kappa coefficient of 0.77. Results showed the feasibility and effectiveness of GAN-based SAR-to-optical translation for wildfire impact assessment, paving the way for the combined use of optical and SAR data., QC 20230412

Floods are one of the most frequent natural disasters worldwide. Although the vulnerability varies from region to region, all countries are susceptible to flooding. Mozambique was hit by several cyclones in the last few decades, and in 2019, after cyclones Idai and Kenneth, the country became the first one in southern Africa to be hit by two cyclones in the same raining season. Aiming to provide the local authorities with tools to yield better responses before and after any disaster event, and to mitigate the impact and support in decision making for sustainable development, it is fundamental to continue investigating reliable methods for disaster management. In this paper, we propose a fully automated method for flood mapping in near real-time utilizing multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data acquired in the Beira municipality and Macomia district. The procedure exploits the processing capability of the Google Earth Engine (GEE) platform. We map flooded areas by finding the differences of images acquired before and after the flooding and then use Otsu's thresholding method to automatically extract the flooded area from the difference image. To validate and compute the accuracy of the proposed technique, we compare our results with the Copernicus Emergency Management Service (Copernicus EMS) data available in the study areas. Furthermore, we investigated the use of a Sentinel-2 multi-spectral instrument (MSI) to produce a land cover (LC) map of the study area and estimate the percentage of flooded areas in each LC class. The results show that the combination of Sentinel-1 SAR and Sentinel-2 MSI data is reliable for near real-time flood mapping and damage assessment. We automatically mapped flooded areas with an overall accuracy of about 87-88% and kappa of 0.73-0.75 by directly comparing our prediction and Copernicus EMS maps. The LC classification is validated by randomly collecting over 600 points for each LC, and the overall accuracy is 90-9, QC 20230329

Rapid urbanization is taking place in Low-and middle-income countries (LMICs). Often there is not sufficient data monitoring the quick urban change. This study explores the use of machine learning classification within remote sensing to foster sustainable urban practices in a secondary city in an LMIC. The aim is to extract spatially detailed land cover data and investigate its temporal evolution from 2018 to 2021. Furthermore, targeted interviews with residents were conducted to gain an in-situ understanding of the land cover changes. The research reveals a trend of increased impervious surface in Sekondi-Takoradi, especially around the urban outskirts. Some patterns of densification can also be identified, predominantly in urban areas with a mix of impervious surfaces and vegetation. These findings reveal similar land cover change patterns as previous remote sensing studies, a decrease in vegetation, and an increase in impervious surfaces. The used method can be applied at a larger scale to monitor the urbanization of secondary cities in LMICs, a field that often is neglected. These insights can contribute to achieving the UN's 11th Sustainable Development Sustainable Cities and Communities.

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.

BiG

The Arctic cryosphere is experiencing a higher rate of warming compared to the rest of the world due to Arctic amplification. As glacier elevation change provide reliable evidence of climate change it is routinely measured by satellite altimeters. Satellite altimetry, while a valuable tool for monitoring elevation change over time, is subject to inherent uncertainties caused by, among other factors, the small scale surface roughness of the target surfaces. Previous studies have identified surface roughness as a key source of uncertainty when measuring sea ice freeboard and studies suggest the surface roughness strongly influences the Synthetic Aperture Radar (SAR) signatures of sea ice. Similar studies over snow- and glacier surfaces, are rare. In this context, we attempt to conduct a small scale calibration and validation (cal/val) campaign over glacier surfaces, using the ideal location and infrastructure of the University Centre in Svalbard. We demonstrate the process, from planning through field data collection and data analysis. By doing so, we identify good as well as bad practices. Using high resolution in-situ LiDAR data, collected under two ICESat-2 (IS2) overpasses in Svalbard we generated Digital Elevation Models (DEM) and calculated surface roughness estimates across glacier- and snow surfaces. The surface roughness was quantified by calculating the Root Mean Square (RMS) of deviations from the overall topography of the surfaces. The DEMs were used for direct comparison with the satellite elevation retrievals and the observed elevation differences were tested for correlation with surface roughness at different length scales. We then investigated the effect of surface roughness on the photon cloud of the lower level ATL03 ICESat-2 data products, by quantifying the precision in the data. We found little to no correlation between RMS roughness and the observed elevation differences between in-situ and satellite data sets, possibly explained by errors in geo

Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment.

This paper presents an approach to automatic surface defect detection by a deep learning-based object detection method, particularly in challenging scenarios where defects are rare, i.e., with limited training data. We base our approach on an object detection model YOLOv8, preceded by a few steps: 1) filtering out irrelevant information, 2) enhancing the visibility of defects, namely brightness contrast, and 3) increasing the diversity of the training data through data augmentation. We evaluated the method in an industrial case study of crown wheel surface inspection in detecting Unclean Gear as well as Deburring defects, resulting in promising performances. With the combination of the three preprocessing steps, we improved the detection accuracy by 22.2% and 37.5% respectively while detecting those two defects. We believe that the proposed approach is also adaptable to various applications of surface defect detection in other industrial environments as the employed techniques, such as image segmentation, are available off the shelf., CC BY-NC-ND 4.0 DEED© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.Correspondence Address: X. Zhu; Scania CV AB (publ), Södertälje, SE-151 87, Sweden; email: xiaomeng.zhu@scania.comThis work is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

Pairs of fully polarimetric ALOS-2 PALSAR-2 L-band SAR images were used to model biomass on backscatter change over seven growth seasons in a hemi-boreal forest. The biomass change was related to backscatter change via consecutive field surveys of 263 field plots with a 10 m radius. To correct for differences in backscatter not related to biomass abundance, a HV-VV polarization ratio based correction, previously used on airborne L-band data, was applied to the data. The uncertainty of obtained predictions (lowest model mean RMSE 65.1 t/ha, lowest model mean bias 7.1 t/ha) was almost identical whether model fitting and prediction used data from the same scene pair, or different scene pairs. This could possibly attest to the feasibility of the backscatter correction for PALSAR-2 data, but no large backscatter offsets were observed for uncorrected data, and significant variance in predictions, due to the inherent noise in the data and the comparatively small area of evaluation plots, inhibit the analysis.

Synthetic aperture radar (SAR) data are widely used for remote sensing applications, such as change detection and environmental monitoring. This paper presents a recent measurement campaign for SAR images using the LORA system and investigates the applicability of the collected data for change detection. The region of interest in this study is a busy commercial harbour area in the south of Sweden. During the measurements, there were significant changes on the ground in the parking lot as trucks were disembarking a ship. The obtained SAR images were first filtered to have similar regions of interest in the Fourier domain to increase the coherence magnitude. Then, a constant false alarm rate (CFAR) algorithm was employed to detect changes with respect to trucks. In addition, optical aerial images were collected during this measurement campaign and were utilized to adjust the CFAR detection threshold. As a result, all the changed and unchanged regions corresponding to the selected targets were detected successfully. Moreover, a pattern of trucks’ utilization of the harbour’s parking lot during this peak time was obtained. The results demonstrate the applicability of the data from the ongoing measurement campaign and the possibility of further algorithm development for target detection and classification.

Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively., CC-BY 4.0The authors are grateful to the Research Council of Shahid Chamran University of Ahvaz for financial support (SCU.EG1400.26151).

Human civilization has an increasingly powerful influence on the earthsystem. Affected by climate change and land-use change, floods are occurringacross the globe and are expected to increase in the coming years. Currentsituations urge more focus on efficient monitoring of floods and detecting impactedareas. Earth observations are an invaluable source for monitoring theEarth’s surface at a large scale. In particular, the Sentinel-1 Synthetic ApertureRadar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missionsoffer high-resolution data with frequent global revisits that are widely usedfor flood detection.Current solutions such as Copernicus Emergency Management Services(CEMS), MODIS (Moderate Resolution Imaging Spectroradiometer) globalflood product, and many others use data from Sentinel and multiple othersatellites to detect floods. Although existing solutions are helpful, they alsohave several limitations. For instance, solutions like MODIS global floodproduct detect floods solely on optical images causing poor or no detection incloudy areas. In addition, these solutions are threshold-based and often requirecriteria-based adjustments. Furthermore, these solutions do not leveragerich spatial information between neighboring pixels and don’t use temporalfeatures of time series data. Therefore, advanced processing algorithms areneeded to provide a reliable method for flood detection.This thesis presents three Deep Learning (DL) models for flood detection.The first two models are supervised segmentation models proposed todetect floods on uni-temporal Sentinel-1 SAR data. The study sites containfloods from Bolivia, Ghana, India, Mekong, Nigeria, Pakistan, Paraguay,Somalia, Spain, Sri Lanka and USA. The third model is an unsupervised spatiotemporalchange detection (CD) model that detects floods on time series ofSentinel-1 SAR data. The study sites contain floods from Slovakia, Somalia,Spain, Bolivia, Mekong, Bosnia, Australia, Scotland and Germany.The two supervise, Den mänskliga civilisationen har ett allt starkare inflytande på jordsystemet. Påverkad av klimatförändringar och förändringar i markanvändningen sker översvämningar över hela världen och förväntas öka under de kommande åren. Nuvarande situationer kräver mer fokus på effektiv övervakning av översvämningar och upptäckt av drabbade områden. Jordobservationer är en ovärderlig källa för att övervaka jordens yta i stor skala. Särskilt Sentinel- 1 Synthetic Aperture Radar (SAR) och Sentinel-2 MultiSpectral Instrument (MSI)-uppdrag erbjuder högupplösta data med frekventa globala återbesök som används ofta för att detektera översvämningar. Aktuella lösningar som Copernicus Emergency Management Services (CEMS), MODIS (Moderate Resolution Imaging Spectroradiometer) globala översvämningsprodukter och många andra använder data från Sentinel och flera andra satelliter för att upptäcka översvämningar. Även om befintliga lösningar är användbara, har de också flera begränsningar. Till exempel upptäcker lösningar som MODIS globala översvämningsprodukt översvämningar enbart på optiska bilder som orsakar dålig eller ingen detektering i molniga områden. Dessutom är dessa lösningar tröskelbaserade och kräver ofta kriteriebaserade justeringar. Dessutom utnyttjar dessa lösningar inte rik rumslig information mellan angränsande pixlar och använder inte tidsseriedata. Därför behövs avancerade bearbetningsalgoritmer för att tillhandahålla en tillförlitlig metod för översvämningsdetektering. Denna avhandling presenterar tre modeller för djupinlärning (DL) för översvämningsdetektering. De två första modellerna är övervakade segmenteringsmodeller som föreslagits för att upptäcka översvämningar på uni-temporala Sentinel-1 SAR-data. Studieplatserna innehåller översvämningar från Bolivia, Ghana, Indien, Mekong, Nigeria, Pakistan, Paraguay, Somalia, Spanien, Sri Lanka och USA. Den tredje modellen är en oövervakad modell för upptäckt av spatiotemporal förändring (CD) som detekterar översvämningar på, QC 20231030

Besides providing electron density profiles (EDP), GNSS Radio Occultation (GNSS-RO) measurements allow monitoring the frequency and the areas where ionospheric scintillations occur. In this work, RO measurements composing an experimental data set are processed with the back propagation (BP) method to estimate the location of sporadic E-clouds and equatorial plasma bubbles (EPB). The data set includes non-conventional measurements tracked up to 600 km (generally around 80 km), covering F-region heights, shortly before MetOp-A was decommissioned. Results indicate the combination of extended occultations and the BP method is promising for monitoring the occurrence and characterizing ionospheric irregularities in the F-region and the E-region. © 2023 International Union of Radio Science., Radio Occultation Accuracy for Climate, Meteorology, and Space Weather - Part 2

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.

Environmental monitoring of mining regions using satellite imagery is crucial for sustainable exploitation andnpreventing geohazards. Movements due to the failure of the roof in underground coal mining, by migrating upwards and outwards from the seam being mined, could eventually appear as ground deformation. To investigate the matter further, the surface deformation that occurred over the Pabdana mining area was monitored in three time periods, between October 2, 2014, and July 27, 2019. Persistent scatterer interferometry (PSI) was used based on 150 ascending and descending Sentinel-1A images. The maximum mining subsidence rate during the studied periods was about 30 to 35 mm/yr. The PSI analysis shows that the subsidence rate varied both temporally and spatially during the three studied periods. The time series and the displacement rate for various cross-sections highlight a clear quantitative relationship between coal extraction progress and subsidence, which proceeded southward throughout the three study periods. So, considering coal mining subsidence as a geohazard, land developments and structures over the mining area may be safeguarded. The approach used in this investigation can be implemented in other similar coal mining zones.

Using forests as burial and ceremonial places is a long-standing cultural practice in Mozambique. However, this information is still not translated into land-cover and land-use maps. Thus the locations of these forests and their descriptions remain unknown. To address this gap in the knowledge, this paper presents the results of mapping 52 local heritage sites in Inhambane, and analysing land-cover changes of two locally protected forest patches. Results from spatiotemporal change analysis show that these patches experienced fewer disturbances in comparison to other areas of vegetation.

Drained wetlands can constitute a large source of greenhouse gas emissions, but the drainage networks in these wetlands are largely unmapped, and better maps are needed to aid in forest production and to better understand the climate consequences. We develop a method for detecting ditches in high resolution digital elevation models derived from LiDAR scans. Thresholding methods using digital terrain indices can be used to detect ditches. However, a single threshold generally does not capture the variability in the landscape, and generates many false positives and negatives. We hypothesise that, by combining the digital terrain indices using supervised learning, we can improve ditch detection at a landscape-scale. In addition to digital terrain indices, additional features are generated by transforming the data to include neighbouring cells for better ditch predictions. A Random Forests classifier is used to locate the ditches, and its probability output is processed to remove noise, and binarised to produce the final ditch prediction. The confidence interval for the Cohen's Kappa index ranges [0.655, 0.781] between the evaluation plots with a confidence level of 95%. The study demonstrates that combining information from a suite of digital terrain indices using machine learning provides an effective technique for automatic ditch detection at a landscape-scale, aiding in both practical forest management and in combatting climate change. © 2022 The Authors, open access

Space-geodetic techniques such as Global Navigation Satellite Systems (GNSS) and Syntenic Aperture Radar interferometry (InSAR) are powerful tools to measure and monitor ground surface motion. InSAR has widely been used for the detection and quantification of slow mass movements over the past three decades mainly at the local and regional scales. The high performance and millimeter-level measurement accuracy of radar satellite to provide a dense deformation map at different spatial and temporal resolutions are the key factors to think of using SAR data and InSAR technique as an efficient tool for geohazards motoring system at the nationwide scale.Sweden has recently joined to the countries having InSAR Ground Motion Service (GMS) at a nationwide scale. The InSAR service of Sweden, which will soon be freely available for users, provides the displacement time-series of measurement points for the entire country. The Swedish GMS project was started last year and is an ongoing collaboration between the Geological Survey of Norway (NGU) and several Swedish organizations (led by the Swedish National Space Agency (SNSA)). The InSAR-based GMS of Sweden has been generated by NGU using Sentinel-1 data (2015–2020) and the Persistent Scatterer Interferometry (PSI) technique. The web-based GMS of Sweden consists of ~1,5 billion time-series measurement points obtained from both descending and ascending satellite orbital modes.Currently, the Swedish GMS is under evaluation and validation phase and the given plan has been designed to assess the quality or validate the GMS products. We plan to conduct the data validation through two main phases: 1) a cross-comparison between InSAR measurement points and ancillary data such as GNSS, Corner Reflectors (CR), Electronic Corner Reflectors (ECR) and leveling data, and 2) assessment of tropospheric and ionospheric effects on InSAR measurement points. Specifically, we will evaluate different approaches and data for the InSAR tropospheric cor