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The Arctic region is warming at over twice the mean rate of the Northern Hemisphere and nearly four times faster than the globe since 1979. The local rate of warming is even higher in the European archipelago of Svalbard. This warming is transforming the terrestrial snow cover, which modulates surface energy exchanges with the atmosphere, accounts for most of the runoff in Arctic catchments and is also a transient reservoir of atmospherically deposited compounds, including pollutants. Improved observations, understanding and modelling of changes in Arctic snow cover are needed to anticipate the effects these changes will have on the Arctic climate, atmosphere, terrestrial ecosystems and socioeconomic factors. Svalbard has been an international hub of polar research for many decades and benefits from a well-developed science infrastructure. Here, we present an agenda for the future of snow research in Svalbard, jointly developed by a multidisciplinary community of experts. We review recent trends in snow research, identify key knowledge gaps, prioritize future research efforts and recommend supportive actions to advance our knowledge of present and future snow conditions pertaining to glacier mass balance, permafrost, surface hydrology, terrestrial ecology, the cycling and fate of atmospheric contaminants, and remote sensing of snow cover. This perspective piece addresses issues relevant to the circumpolar North and could be used as a template for other national or international Arctic research plans.

Cloud formations often obscure optical satellite-based monitoring of the Earth’s surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance for a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which are often difficult to come by in EO contexts. This is especially true when it comes to cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is common practice. To alleviate the COT data scarcity problem, in this work, we propose a novel synthetic dataset for COT estimation, which we subsequently leverage for obtaining reliable and versatile cloud masks on real data. In our dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. In particular, by thresholding COT estimates from our ML models, we show on two satellite image datasets (one that is publicly available, and one which we have collected and annotated) that reliable cloud masks can be obtained. The synthetic data, the newly collected real dataset, code and models have been made publicly available., Validerad;2024;Nivå 2;2024-04-09 (sofila);Full text license: CC BY

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regressi, Validerad;2024;Nivå 2;2024-04-02 (signyg);Funder: King Saud University (RSP2024R351);Full text license: CC BY

Rain-on-snow (ROS) events are becoming an increasingly common feature of the wintertime climate Svalbard in the High Arctic due to a warming climate. Changes in the frequency, intensity, and spatial distribution of wintertime ROS events in Svalbard are important to understand and quantify due their wide-ranging impacts on the physical environment as well as on human activity. Due to the sparse nature of ground observations across Svalbard, tools for mapping and long-term monitoring of ROS events over large spatial areas are reliant on remote sensing, snow models and atmospheric reanalyses. However, different methods of identifying and measuring ROS events can often present different interpretations of ROS climatology. This study compares a recently published Synthetic Aperture Radar (SAR) based ROS dataset for Svalbard to ROS derived from two snow models and a reanalysis dataset for 2004-2020. Although the number of ROS events differs across the datasets, all datasets exhibit both similarities and differences in the geographical distribution of ROS across the largest island, Spitsbergen. Southern and western coastal areas experience ROS most frequently during the wintertime, with the early winter (November-December) experiencing overall most events compared to the spring (March-April). Moreover, we find that different temperature thresholds are required to obtain the best spatial agreement of ROS events in the model and reanalysis datasets with ground observations. The reanalysis dataset evaluated against ground observations was superior to the other datasets in terms of accuracy due to the assimilation of ground observations into the dataset. The SAR dataset consistently scored lowest in terms of its overall accuracy due to many more false detections, an issue which is most likely explained by the persistence of moisture in the snowpack following the end of a ROS event. Our study not only highlights some spatial differences in ROS frequency and trends but also how

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-even, QC 20240222

Deep learning and remote sensing techniques have significantly advanced water surface monitoring; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DEEPAQUA, a deep learning model inspired by knowledge distillation (a.k.a. teacher–student model) to generate labeled data automatically and eliminate the need for manual annotations during the training phase. We utilize the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images. To train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. DEEPAQUA represents a significant advancement in computer vision techniques for water detection by effectively training semantic segmentation models without any manually annotated data. Experimental results show that DEEPAQUA outperforms other unsupervised methods by improving accuracy by 3%, Intersection Over Union by 11%, and F1-score by 6%. This approach offers a practical solution for monitoring wetland water extent changes without the need of ground truth data, making it highly adaptable and scalable for wetland monitoring., QC 20240108

The Arctic region is warming at over twice the mean rate of the Northern Hemisphere and nearly four times faster than the globe since 1979. The local rate of warming is even higher in the European archipelago of Svalbard. This warming is transforming the terrestrial snow cover, which modulates surface energy exchanges with the atmosphere, accounts for most of the runoff in Arctic catchments and is also a transient reservoir of atmospherically deposited compounds, including pollutants. Improved observations, understanding and modelling of changes in Arctic snow cover are needed to anticipate the effects these changes will have on the Arctic climate, atmosphere, terrestrial ecosystems and socioeconomic factors. Svalbard has been an international hub of polar research for many decades and benefits from a well-developed science infrastructure. Here, we present an agenda for the future of snow research in Svalbard, jointly developed by a multidisciplinary community of experts. We review recent trends in snow research, identify key knowledge gaps, prioritize future research efforts and recommend supportive actions to advance our knowledge of present and future snow conditions pertaining to glacier mass balance, permafrost, surface hydrology, terrestrial ecology, the cycling and fate of atmospheric contaminants, and remote sensing of snow cover. This perspective piece addresses issues relevant to the circumpolar North and could be used as a template for other national or international Arctic research plans.

Rain-on-snow (ROS) events are becoming an increasingly common feature of the wintertime climate Svalbard in the High Arctic due to a warming climate. Changes in the frequency, intensity, and spatial distribution of wintertime ROS events in Svalbard are important to understand and quantify due their wide-ranging impacts on the physical environment as well as on human activity. Due to the sparse nature of ground observations across Svalbard, tools for mapping and long-term monitoring of ROS events over large spatial areas are reliant on remote sensing, snow models and atmospheric reanalyses. However, different methods of identifying and measuring ROS events can often present different interpretations of ROS climatology. This study compares a recently published Synthetic Aperture Radar (SAR) based ROS dataset for Svalbard to ROS derived from two snow models and a reanalysis dataset for 2004-2020. Although the number of ROS events differs across the datasets, all datasets exhibit both similarities and differences in the geographical distribution of ROS across the largest island, Spitsbergen. Southern and western coastal areas experience ROS most frequently during the wintertime, with the early winter (November-December) experiencing overall most events compared to the spring (March-April). Moreover, we find that different temperature thresholds are required to obtain the best spatial agreement of ROS events in the model and reanalysis datasets with ground observations. The reanalysis dataset evaluated against ground observations was superior to the other datasets in terms of accuracy due to the assimilation of ground observations into the dataset. The SAR dataset consistently scored lowest in terms of its overall accuracy due to many more false detections, an issue which is most likely explained by the persistence of moisture in the snowpack following the end of a ROS event. Our study not only highlights some spatial differences in ROS frequency and trends but also how

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

Grain size assessments are necessary for understanding the various geomorphological, hydrological and ecological processes that occur within rivers. Recent research has shown that the application of Structure-from-Motion (SfM) photogrammetry to imagery from uncrewed aerial vehicles (UAVs) shows promise for rapidly characterising grain sizes along rivers in comparison to traditional field-based methods. Here, we evaluated the applicability of different methods for estimating grain sizes in gravel bars along a study reach in the Olentangy River in Columbus, Ohio. We collected imagery of these gravel bars with a UAV and processed those images with SfM photogrammetry software to produce three-dimensional point clouds and orthomosaics. Our evaluation compared statistical models calibrated on topographic roughness, which was computed from the point clouds, and to those based on image texture, which was computed from the orthomosaics. Our results showed that statistical models calibrated on image texture were more accurate than those based on topographic roughness. This might be because of site-specific patterns of grain size, shape and imbrication. Such patterns would have complicated the detection of topographic signatures associated with individual grains. Our work illustrates that UAV-SfM approaches show potential to be used as an accessible method for characterising surface grain sizes along rivers at higher spatial and temporal resolutions than those provided by traditional methods.

Deep learning and remote sensing techniques have significantly advanced water surface monitoring; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DeepAqua, a deep learning model inspired by knowledge distillation (a.k.a. teacher–student model) to generate labeled data automatically and eliminate the need for manual annotations during the training phase. We utilize the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images. To train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. DeepAqua represents a significant advancement in computer vision techniques for water detection by effectively training semantic segmentation models without any manually annotated data. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 3%, Intersection Over Union by 11%, and F1-score by 6%. This approach offers a practical solution for monitoring wetland water extent changes without the need of ground truth data, making it highly adaptable and scalable for wetland monitoring.

The Arctic region is warming at over twice the mean rate of the Northern Hemisphere and nearly four times faster than the globe since 1979. The local rate of warming is even higher in the European archipelago of Svalbard. This warming is transforming the terrestrial snow cover, which modulates surface energy exchanges with the atmosphere, accounts for most of the runoff in Arctic catchments and is also a transient reservoir of atmospherically deposited compounds, including pollutants. Improved observations, understanding and modelling of changes in Arctic snow cover are needed to anticipate the effects these changes will have on the Arctic climate, atmosphere, terrestrial ecosystems and socioeconomic factors. Svalbard has been an international hub of polar research for many decades and benefits from a well-developed science infrastructure. Here, we present an agenda for the future of snow research in Svalbard, jointly developed by a multidisciplinary community of experts. We review recent trends in snow research, identify key knowledge gaps, prioritize future research efforts and recommend supportive actions to advance our knowledge of present and future snow conditions pertaining to glacier mass balance, permafrost, surface hydrology, terrestrial ecology, the cycling and fate of atmospheric contaminants, and remote sensing of snow cover. This perspective piece addresses issues relevant to the circumpolar North and could be used as a template for other national or international Arctic research plans.

Cloud formations often obscure optical satellite-based monitoring of the Earth’s surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance for a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which are often difficult to come by in EO contexts. This is especially true when it comes to cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is common practice. To alleviate the COT data scarcity problem, in this work, we propose a novel synthetic dataset for COT estimation, which we subsequently leverage for obtaining reliable and versatile cloud masks on real data. In our dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. In particular, by thresholding COT estimates from our ML models, we show on two satellite image datasets (one that is publicly available, and one which we have collected and annotated) that reliable cloud masks can be obtained. The synthetic data, the newly collected real dataset, code and models have been made publicly available., Validerad;2024;Nivå 2;2024-04-09 (sofila);Full text license: CC BY

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regressi, Validerad;2024;Nivå 2;2024-04-02 (signyg);Funder: King Saud University (RSP2024R351);Full text license: CC BY

Rain-on-snow (ROS) events are becoming an increasingly common feature of the wintertime climate Svalbard in the High Arctic due to a warming climate. Changes in the frequency, intensity, and spatial distribution of wintertime ROS events in Svalbard are important to understand and quantify due their wide-ranging impacts on the physical environment as well as on human activity. Due to the sparse nature of ground observations across Svalbard, tools for mapping and long-term monitoring of ROS events over large spatial areas are reliant on remote sensing, snow models and atmospheric reanalyses. However, different methods of identifying and measuring ROS events can often present different interpretations of ROS climatology. This study compares a recently published Synthetic Aperture Radar (SAR) based ROS dataset for Svalbard to ROS derived from two snow models and a reanalysis dataset for 2004-2020. Although the number of ROS events differs across the datasets, all datasets exhibit both similarities and differences in the geographical distribution of ROS across the largest island, Spitsbergen. Southern and western coastal areas experience ROS most frequently during the wintertime, with the early winter (November-December) experiencing overall most events compared to the spring (March-April). Moreover, we find that different temperature thresholds are required to obtain the best spatial agreement of ROS events in the model and reanalysis datasets with ground observations. The reanalysis dataset evaluated against ground observations was superior to the other datasets in terms of accuracy due to the assimilation of ground observations into the dataset. The SAR dataset consistently scored lowest in terms of its overall accuracy due to many more false detections, an issue which is most likely explained by the persistence of moisture in the snowpack following the end of a ROS event. Our study not only highlights some spatial differences in ROS frequency and trends but also how

The Arctic ice-covered waters are becoming more vulnerable to oil spills due to climate-driven sea ice loss, which has increased vessel traffic and natural resource extraction. In preparation for future incidents, this thesis presents the research undertaken in the area of Arctic crude oil spill response, with the ultimate goal of accurately detecting and characterizing such spills in sea ice-covered waters using C-band radar remote sensing. The research focuses on three interconnected objectives: observation, discrimination, and modeling of surface-based C-band scatterometer data collected during experiments involving spilled crude oil in newly formed sea ice (NI) at the University of Manitoba’s Sea-ice Environmental Research Facility. The observational study seeks to establish a definitive relationship between radar signatures and the physical properties of oil-contaminated NI. The results show that multipolarization radar signatures exhibit distinct responses when oil is encapsulated within the ice, up until the oil migrates onto the ice surface. For instance, when oil is encapsulated within the ice, a 13-dB local maximum in cross-polarization was observed with a coincidental 9-dB drop in co-polarization backscatters. The discrimination study uses radar polarimetric parameters (such as entropy, mean-alpha, copolarization correlation coefficient, conformity coefficient, and more) to accurately differentiate between uncontaminated and oil-contaminated NI. The findings reveal that a threshold classification plane of 0.3 entropy and 18° mean-alpha effectively distinguishes oil-contaminated ice. To minimize oil spill false alarms, the copolarization correlation coefficient and conformity coefficient emerge as the most reliable parameters for detecting spilled oil events in NI-covered waters. The modeling study applies the small perturbation method and particle swarm optimization in an electromagnetic inversion strategy to estimate the oil thickness on NI surface throu

This remote sensing project, carried out at CommSensLab, has focused on developing a deep learning algorithm using variational autoencoders with data from ESA's SENTINEL-2 mission. The models trained with and without the MSSIM metric in the loss function have been compared, showing that the presence of MSSIM significantly improves the quality of the reconstructions and interpolations, highlighting its sensitivity to fine details and key structures. The variation of parameters such as beta, epochs and latent dimensions have influenced the results. Time constraints and technical challenges, such as network problems, have increased the time to run the code each time a modification was added to the code. The crucial role of the project managers in the successful development of the project is highlighted. The algorithm shows potential applications in the environmental field, with recommendations for future research, including the exploration of alternative metrics and hyperparameter optimization., Este proyecto dentro del ámbito de la teledetección, llevado a cabo en CommSensLab, se ha centrado en desarrollar un algoritmo de aprendizaje profundo utilizando autoencoders variacionales con datos de la misión SENTINEL-2 de la ESA. Se han comparado modelos entrenados con y sin la métrica MSSIM en la función de pérdida, evidenciando que la presencia de MSSIM mejora significativamente la calidad de las reconstrucciones e interpolaciones, destacando su sensibilidad a detalles finos y estructuras clave. La variación de parámetros como beta, epochs y dimensiones latentes han influido en los resultados. Limitaciones temporales y desafíos técnicos, como problemas de red, han aumentado el tiempo para ejecutar el código cada vez que se añadía alguna modificación a este. Se resalta el papel crucial de los directores del proyecto para que este se desarrollase satisfactoriamente. El algoritmo muestra aplicaciones potenciales en el ámbito medioambiental, con recomendaciones para futuras investigaciones, incluyendo la exploración de métricas alternativas y la optimización de hiperparámetros., Aquest projecte dins de l'àmbit de la teledetecció, dut a terme a CommSensLab, s'ha centrat a desenvolupar un algorisme d'aprenentatge profund utilitzant autoencoders variacionals amb dades de la missió SENTINEL-2 de l'ESA. S'han comparat models entrenats amb i sense la mètrica MSSIM en la funció de pèrdua, evidenciant que la presència de MSSIM millora significativament la qualitat de les reconstruccions i interpolacions, destacant-ne la sensibilitat a detalls fins i estructures clau. La variació de paràmetres com a beta, epochs i dimensions latents han influït en els resultats. Limitacions temporals i desafiaments tècnics, com ara problemes de xarxa, han augmentat el temps per executar el codi cada vegada que s'hi afegia alguna modificació. Es ressalta el paper crucial dels directors del projecte perquè aquest es desenvolupés satisfactòriament. L'algorisme mostra aplicacions potencials en l'àmbit mediambiental, amb recomanacions per a investigacions futures, incloent-hi l'exploració de mètriques alternatives i l'optimització d'hiperparàmetres.

The main objective at this project is to create a software that makes the Comparation/Validation exercise between two LiDAR (Light Detection and Ranging) systems. Those systems, although they are equivalent conceptually, they apply different technology. The first system was developed by CommSensLab group from UPC (participants in the creation of EARLINET); on the other hand, SigmaSpace, another business under Godard Space Flight Center specifications (belonging to NASA), that manage the MPLnet network following the Micro-Pulse LiDAR concept. Both have the same purpose; they measure the atmosphere aerosols. The comparation between the MPLnet LiDAR system and the one that will be used at the EarthCARE mission will be realized in a later phase, outside the scopes of this project. The software pretends to intercompare the Earlinet lidar from UPC with a MPLnet system. To achieve this objective, it has been used MATLAB tool, since we can find some functions that facilitate the work when we are reading the type of file is used to save all the information obtained from the lidar systems. Lidar systems use the format NetCDF to save all the data. There have been created some functions to make the main code cleaner as possible. Also, it has been used GUIs (Graphic User Interface) that are the windows that the user can see when is using the software. It has been achieved an automatic download of all the necessary files that the user needs to start the comparation between the systems. When the files are downloaded, plots can be generated where the user can see the comparation between the systems in specific parameters that are interesting to compare. Those parameters are backscatter, extinction, volume depolarization, particle depolarization and range corrected signal. Also, it has been implemented the option to create a Word report with all the plots selected by the user and each one of the plots with a caption that describes the necessary information. The figures used at the r

The Arctic region is warming at over twice the mean rate of the Northern Hemisphere and nearly four times faster than the globe since 1979. The local rate of warming is even higher in the European archipelago of Svalbard. This warming is transforming the terrestrial snow cover, which modulates surface energy exchanges with the atmosphere, accounts for most of the runoff in Arctic catchments and is also a transient reservoir of atmospherically deposited compounds, including pollutants. Improved observations, understanding and modelling of changes in Arctic snow cover are needed to anticipate the effects these changes will have on the Arctic climate, atmosphere, terrestrial ecosystems and socioeconomic factors. Svalbard has been an international hub of polar research for many decades and benefits from a well-developed science infrastructure. Here, we present an agenda for the future of snow research in Svalbard, jointly developed by a multidisciplinary community of experts. We review recent trends in snow research, identify key knowledge gaps, prioritize future research efforts and recommend supportive actions to advance our knowledge of present and future snow conditions pertaining to glacier mass balance, permafrost, surface hydrology, terrestrial ecology, the cycling and fate of atmospheric contaminants, and remote sensing of snow cover. This perspective piece addresses issues relevant to the circumpolar North and could be used as a template for other national or international Arctic research plans.

Rain-on-snow (ROS) events are becoming an increasingly common feature of the wintertime climate Svalbard in the High Arctic due to a warming climate. Changes in the frequency, intensity, and spatial distribution of wintertime ROS events in Svalbard are important to understand and quantify due their wide-ranging impacts on the physical environment as well as on human activity. Due to the sparse nature of ground observations across Svalbard, tools for mapping and long-term monitoring of ROS events over large spatial areas are reliant on remote sensing, snow models and atmospheric reanalyses. However, different methods of identifying and measuring ROS events can often present different interpretations of ROS climatology. This study compares a recently published Synthetic Aperture Radar (SAR) based ROS dataset for Svalbard to ROS derived from two snow models and a reanalysis dataset for 2004-2020. Although the number of ROS events differs across the datasets, all datasets exhibit both similarities and differences in the geographical distribution of ROS across the largest island, Spitsbergen. Southern and western coastal areas experience ROS most frequently during the wintertime, with the early winter (November-December) experiencing overall most events compared to the spring (March-April). Moreover, we find that different temperature thresholds are required to obtain the best spatial agreement of ROS events in the model and reanalysis datasets with ground observations. The reanalysis dataset evaluated against ground observations was superior to the other datasets in terms of accuracy due to the assimilation of ground observations into the dataset. The SAR dataset consistently scored lowest in terms of its overall accuracy due to many more false detections, an issue which is most likely explained by the persistence of moisture in the snowpack following the end of a ROS event. Our study not only highlights some spatial differences in ROS frequency and trends but also how

A Cryptocurrency known as Bitcoin (BTC) is intended to function as money and a mode of payment independent of any one individual, organisation, or body. This eliminates the requirement for financial transactions to involve a reliable third party (like a bank or mint). It can be acquired on a number of exchanges and is given to blockchain miners as compensation for validating transactions. A cryptocurrency is a digital medium of exchange similar to the US dollar that controls the creation of new monetary units and verifies the transfer of funds using cryptographic techniques and protocol. Bitcoin and Cryptocurrency provide a comprehensive introduction to the revolutionary yet often misunderstood new technologies of digital currency. Whether you are a student, software developer, tech entrepreneur, or researcher in computer science, this authoritative and self-contained book tells you everything you need to know about the new global money for the Internet age. Following chapters are included in this book: 1. Bitcoin2. Bitcoin Digital Currency: Advantages & Disadvantages3. Decrypting Crypto Tax4. Cryptocurrency, Bitcoin, And the Future- A Conceptual Study5. Decrypting Dangers: Exploring the Nexus Between Cryptocurrency and Financial Crime6. Bitcoin: Economics, Technology, and Governance7. Online Payment Opportunities: PayPal and Bitcoin8. Power, Property, and Freedom: Bitcoin Discourse9. Cryptocurrency and Banking: Challenges, Considerations, and Disruptions - A Study 10. Cryptocurrency and its impact on the Banking Industry11. Advancing Technologies in Commerce for Sustainable Practices12. Cryptocurrency and Financial Markets

From deepest of heart containing the warm pleasuring well wishes we are feeling very lucky and honoured to present you the thoroughly revised, willingly prepared and studied with high efforts, the first edition of.We hope the book will become helpful to all the readers of this book who have taken it as a source of knowledge what they seek for. The book is written with immense hard work; dedication and desperation. We have tried to put all the information available to me on these topics for the readers and tried to make it as easy as possible for the easy and correct understanding of the topics by readers. The book is written with dedicated practices of restless work with determination and passion for the writing of a book helpful on this subject. The book is containing the information mostly for the students but we believe that it can also be helpful for everyone.The book is consisting 7 Units al together in it. The unit 1 is focusing on the topic History of Development of Microbiology. The unit 2 is containing the about Diversity of Microorganisms. Unit 3 is filled with the Information about the Different types of Microscopy. The details on the Different types of sterilization technique is described in unit 4. Microbes in Human Health (Medical Microbiology and Immunology) & Environmental Microbiology is widely discussed in unit 5. The unit 6 contains about Industrial Microbiology or Microbes used in Industry. The Unit 7 is discussed about the Food and Dairy Microbiology.We are thankful to the publishers for the speedy and quality production. We shall welcome the constructive suggestion, if any, from the reader.

Landslides are mass movements of rock or soil down a slope, which may cause economic loss, damage to natural resources and frequent fatalities. To support risk management, landslide dating methods can provide useful knowledge about the date of the landslide and the frequency of occurrences, and thus potential triggers. Remote sensing techniques provide opportunities for landslide dating and are especially valuable in remote areas. However, the use of optical remote sensing is frequently hampered by cloud cover, decreasing the success rate and accuracy of dating. Here, we propose a landslide dating framework that combines the advantages of optical and SAR remote sensing satellites, because optical monitoring provides spectral changes on the ground and microwave observations provide information on surface changes due to loss of coherence. Our method combines Sentinel-1 and -2 satellite data, and is designed for cases wherein the landslide causes vegetation decrease and terrain deformation resulting in changing Normalized Difference Vegetation Index (NDVI) and SAR backscatter values. This landslide dating framework was tested and evaluated against 60 published landslides across the world. We show that the mean accuracy of landslide dating reaches 23 days when using combined Sentinel-1 and -2 imagery, which is a pronounced improvement compared to using only optical Sentinel-2 images resulting in an accuracy of 51 days. This study highlights that a combination of optical and SAR remote sensing monitoring is a promising technique for dating landslides, especially in remote areas where monitoring equipment is limited or which are frequently covered by clouds. Our method contributes to identifying failure mechanism by providing reliable date ranges of landslide occurrence, assessing landslide hazard and constructing landslide early warning systems.

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003–2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.

The main objective of this work is to describe the structure and conservation status of the forest of Prosopis flexuosa and Prosopis chilensis around Huaco and in the adjacent basin of the Bermejo River (department of Jáchal, San Juan). Currently, this genus has changed its name to Neltuma. To this end, baseline information for the conservation and sustainable management of these little-studied native forests was generated from an inventory that took into account dasonomic parameters such as conservation status and tree form. This information is linked to a forest typology based on satellite image classifications. The results are expressed in a proposed classification of four native forest types for this locality and its surroundings. This classification includes, in addition to the characteristics of the area covered by native forest, the identification of the different uses and disturbances that have impacted it in recent decades. Finally, different management and land-use planning proposals for these forests in northern San Juan are preliminarily discussed., Este trabajo tiene como objetivo principal describir la estructura y estado de conservación de los algarrobales de Prosopis flexuosa y Prosopis chilensis en torno a la localidad de Huaco y en la depresión adyacente, del Río Bermejo (en el departamento de Jáchal, San Juan). Actualmente, este género ha cambiado su nombre por el de Neltuma. Para ello se generó información de base para la conservación y el manejo sustentable de estos bosques nativos, muy poco estudiados, a partir de un inventario que tuvo en cuenta parámetros dasonómicos, como el estado de conservación y el tamaño y forma de los árboles. Dicha información se relaciona con una tipología de bosques, realizada a partir de clasificaciones de imágenes satelitales. Los resultados se expresan en una propuesta de cuatro tipos de bosques nativos para esta localidad y alrededores. Esta clasificación incluye, además de características de la superficie cubierta por bosque nativo, la identificación de los diferentes usos y disturbios por los que ha estado impactado en las últimas décadas. Por último, se discuten, de forma preliminar, diferentes propuestas de manejo y de ordenamiento territorial para estos bosques del norte sanjuanino.

Purpose: The objective of this study was to measure the fire risk of forests and to compare areas with high sensitivity to forest fires. Theoretical Framework: Many authors have demonstrated the fragility of Algerian forests in the face of fires (Belhadj, 2003; Souidi et al., 2010 and 2017). In this research paper, the researchers focused on identifying the factors responsible for the risk of forest fires in Algeria using a state-of-the-art tool, the Geographic Information System (GIS). The researchers explained a scientific process for exploring new indicators using GIS, and showed how to develop a forest fire risk map. Design/Methodology/Approach: The elaboration of the fire risk map requires the application of a model set up, and tested on forest areas in the Mediterranean region. The applied model involves the three main factors of forest fire risk assessment: topomorphology, fuel, and human occupation. Findings: According to the results obtained, our study area is dominated by a very high risk index. In addition, more than half of the surface area of our study zone is located in topomorphological conditions that are highly conducive to the multiplication of fires and can increase the risk of forest fires breaking out. This study will help us to better control the risk of fires and to manage fire-fighting operations in the field. Research, Practical & Social Implications: Mapping the risk of forest fires using GIS will help land managers and planners to identify sensitive areas for better preservation and intervention in the event of fire. Originality/Value: The results show that the number of forest fires is increasing over time and that managing them is becoming increasingly difficult. The use of a GIS for all stakeholder and managers will make it possible to combat forest fires.

Hyperspectral pansharpening is crucial for the improvement of the usability of images in various applications. However, it remains underexplored due to a scarcity of data. The primary goal of pansharpening is to enhance the spatial resolution of hyperspectral images by reconstructing missing spectral information without compromising consistency with the original data. This paper addresses the data gap by presenting a new hyperspectral dataset specifically designed for pansharpening and the evaluation of several deep learning strategies using this dataset. The new dataset has two crucial features that make it invaluable for deep learning hyperspectral pansharpening research. (1) It presents the highest cardinality of images in the state of the art, making it the first statistically relevant dataset for hyperspectral pansharpening evaluation, and (2) it includes a wide variety of scenes, ensuring robust generalization capabilities for various approaches. The data, collected by the ASI PRISMA satellite, cover about 262,200 km2 and their heterogeneity is ensured by a random sampling of the Earth’s surface. The analysis of the deep learning methods consists in the adaptation of these approaches to the PRISMA hyperspectral data and the quantitative and qualitative evaluation of their performance in this new scenario. The investigation included two settings: Reduced Resolution (RR) to evaluate the techniques in a controlled environment and Full Resolution (FR) for a real-world evaluation. In addition, for the sake of completeness, we have also included machine-learning-free approaches in both scenarios. Our comprehensive analysis reveals that data-driven neural network methods significantly outperform traditional approaches, demonstrating a superior adaptability and performance in hyperspectral pansharpening under both RR and FR protocols.

La tesis doctoral presenta un avance innovador en el análisis de entornos urbanos, profundizando en elementos fundamentales que regulan sus cambios y procesos. Se centra en los desafíos del análisis urbano de zonas extensas y complejas y del estudio a medida en escalas locales, con la implementación de conjuntos de datos de múltiples fuentes (datos abiertos y teledetección) para identificar áreas de interés para su intervención de forma remota. Este enfoque integra características biofísicas existentes del entorno construido, la vegetación (prestando atención a la escala espacio-temporal y su impacto en servicios ecosistémicos), el albedo de banda ancha, y la temperatura superficial del suelo (LST). El desarrollo de la tesis se estructura en cuatro secciones, que comienzan con una revisión exhaustiva de los métodos de evaluación urbana en remoto, seguida por la caracterización detallada de la vegetación. Posteriormente, se propone un marco de clasificación de las características biofísicas, se diagnostica el entorno urbano según el comportamiento de las características existentes y, finalmente, se definen patrones morfológicos. Las secciones concluyen con el diseño de un método para caracterizar áreas de interés y su aplicación en los procesos de toma de decisiones. El diseño del método se fundamenta en la extracción de características y cálculo de variables biofísicas a distancia, fusionando datos del Catastro (una base de datos de edificaciones en España), imágenes satelitales de alta y muy alta resolución, y datos infrarrojos térmicos (TIR). Para validar el método, se lleva a cabo en un caso de estudio que cumple con criterios establecidos previamente en la metodología, ubicado en el barrio de Canillas, Madrid, España. Los desarrollados en la tesis priorizan la integración de componentes biofísicos urbanos en su estado actual, con detalles intrincados y escalas espacio-temporales esenciales para la planificación urbana. Los resultados abordan los desafíos en los, The PhD thesis represents an innovative advance in the analysis of urban environments, delving into fundamental elements that drive their changes and processes. It focuses on the challenges of urban analysis of extensive and complex areas and tailored studies at local scales, using multisouce datasets (open data and remote sensing) to remotly identify areas of interest for intervention. This approach integrates existing biophysical characteristics of the built environment, vegetation (with attention to the spatio-temporal scale and its impact on ecosystem services), broadband albedo, and land surface temperature (LST). The thesis is structured in four sections, starting with an exhaustive review of remote urban assessment methods, followed by a detailed characterisation of vegetation. Subsequently, a classifi cation framework for biophysical features is proposed, the urban context in diagnosed according to the behaviour of the existing features and, fi nally, morphological patterns are defi ned. The sections conclude with the design of a method to characterise areas of interest to be applied in decision-making processes. The design of the method relies on the retrieval of the characteristics and calculation of biophysical variables by fusing data from the Cadastre (a database of buildings in Spain), high and very-high-resolution satellite images, and thermal infrared (TIR) data. To validate the method, it is conducted in a case study that meets the criteria previously established in the methodology, located in the neighbourhood of Canillas, Madrid, Spain. The processes carried out in the thesis prioritise the integration of urban biophysical components in their current state, with intricate details and spatiotemporal scales essential for urban planning. The results address challenges in current urban analyses and have been disseminated through papers published in scientifi c journals, books, congresses and international conferences proceedings, recognised for their

This dissertation is a three-part interconnected geographic study of fine scale soil moisture using monitoring, modeling, and remote sensing in coastal northern California. Although the focus is on hydrology and soil moisture, soil moisture naturally lends itself to be a unifying theme in physical geography connecting hydrology, agriculture, ecology, climate, wildfire, remote sensing, and humans through management actions. The objective of the three chapters is to advance the characterization and process understanding of soil-plant-water dynamics spatially and temporally and to provide high resolution soil moisture data that can be used by decision makers to improve situational awareness and planning through streamflow forecasting, landslide detection, drought warning and fire danger.In Chapter 1, monitoring data was collected in the context of a paired watershed study and soil and tree moisture probes were used to examine soil water-tree-ecosystem dynamics in Sonoma County, California. The results of this study showed: 1) there were noticeable effects on soil moisture and evapotranspiration from mechanical tree thinning and prescribed burns compared to an untreated watershed, 2) soil moisture decreased at the surface and increased in the root zone post-treatment, relative to an untreated watershed, 3) evapotranspiration decreased by 90 mm in the post treatment year for the treated watershed, 4) soil and tree moisture in situ sensors can be used as a surrogate for discrete and time consuming Live Fuel Moisture (LFM) measurements, and 5) soil moisture thresholds can be used to help identify wildfire danger and drought conditions. Improving real-time wildfire danger and drought conditions using existing or low-cost in situ sensors can improve situational awareness and planning. In Chapter 2, a novel L-band Unoccupied Aerial System (UAS) was used to map surface (0-10 cm depth) soil moisture at a high resolution in a complex, natural landscape. L-band UAS was used to ma

The primary goal of the research presented in this dissertation is to assess the prospects of utilizing modern plant breeding methodology to address the lack of biomass yield improvement in alfalfa. Biomass yield improvement in alfalfa has remained stagnant for the last ~30 years and cultivated areas are in steady decline. Significant advancements have been made in genomics and phenomics in recent years with applications in plant breeding. Chapter 1 reviews relevant literature including the role alfalfa plays in western agriculture, history, biology, breeding, and an introduction to genomic selection and high throughput phenotyping. Chapter 2 seeks to empirically test genomic selection for forage dry matter yield in alfalfa. Genomic selection has proven successful for increasing the rate of genetic gain in other crops, and models have been developed for a variety of traits in alfalfa. However, no practical testing has occurred to date. Chapter 3 builds on the findings from the previous chapter by addressing some limitations in the predictive model. Namely, building a model based on family bulks and half-sib family performance, rather than on an individual plant basis, to better reflect the commercial environment alfalfa is grown in. The results from chapter 2 and 3 provide encouraging prospects with prediction accuracies around 0.3. However, one of the greatest limitations for a successful genomic selection breeding program is the size of the training population, particularly in crops with limited resources such as alfalfa. Chapter 4 explores the potential to greatly increase the size of breeding trials without a comparable increase in costs. We utilized drone based multispectral imagery to estimate biomass of alfalfa and a range of forage grasses across a range of plot types typically used in forage breeding trials. Remote sensing and high throughput phenotyping massively reduce the labor component of data collection while simultaneously providing accurate estimate

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research d

This thesis considers three inverse problems originating in mathematical imaging, remote sensing and simulation. First, it addresses a challenging image processing task: recovering images corrupted by multiplicative noise. Motivated by the success of multiscale hierarchical decomposition methods (MHDM) in image processing, a variety of both classical and new multiplicative noise removing models are adapted to the MHDM form. On the basis of previous work, tight and refined extensions of the multiplicative MHDM process are proposed. Existence and uniqueness of solutions for the proposed models is discussed, in addition to convergence properties. Moreover, the work introduces a discrepancy principle stopping criterion which prevents recovering excess noise in the multiscale reconstruction. The validity of all the proposed models is qualitatively and quantitatively evaluated through comprehensive numerical experiments and comparisons across several images degraded by multiplicative noise. By construction, these multiplicative multiscale hierarchical decomposition methods have the added benefit of recovering many scales of an image, which can provide features of interest beyond image denoising. Second, this work considers an applied mathematical imaging problem in remote sensing. Accurate estimation of atmospheric wind velocity plays an important role in weather forecasting, flight safety assessment and cyclone tracking. Atmospheric data captured by infrared and microwave satellite instruments provide global coverage for weather analysis. Extracting wind velocity fields from such data has traditionally been done through feature tracking, correlation/matching or optical flow means from computer vision. However, these recover either sparse velocity estimates, oversmooth details or are designed for quasi-rigid body motions which over-penalize vorticity and divergence within the often turbulent weather systems. This thesis proposes a texture based optical flow procedure tail

In today's world, data is fundamental to the growth of technology. However, it is very common that vast amounts of clean, well annotated data are unavailable and so there is a desire for methods that can function with small amounts of labeled data. This includes methods that utilize hand-crafted features and can be tuned manually for a dataset without having a variety of samples, as well as machine learning methods that can succeed in a low label rate regime by utilizing mutual information. This thesis discusses work involving hand-crafted features and variational methods for image detection, segmentation, and classification of shape-coded medical testing particles. Specifically, the Canny edge detector, Hough Transforms, and snake active contours are used for the object detection and segmentation problem, which offers a strong alternative to deep learning methods and has theoretical guarantees. Furthermore, this thesis explores neural networks, graph-based learning, and active learning methods that are robust to low label environments with applications in remote sensing and hyperspectral data. These methods offer a natural fusion of deep learning methods with transductive, variational label propagation. Altogether, these thesis offers a survey of image processing as a field and showcases a wide range of ideas that are all applicable for different problems.

Graph-based machine learning is a powerful framework for analyzing and understanding complex data structures in various domains. This thesis introduces novel graph-based methods in multiple image analysis tasks, including classification, segmentation, and unmixing, as well as their application in enhancing large language models. The key contributions include: (1) the development of new core-set selection and batch active learning methods that significantly improve the efficiency of graph-based active learning while maintaining its effectiveness; (2) the integration of graph learning, active learning, and advanced feature embedding methods to construct pipelines for SAR image classification and multi- or hyperspectral image segmentation, outperforming neural network-based classifiers or segmenters in semi-supervised learning tasks with limited training data; (3) the incorporation of graph-based regularization into the optimization problem of hyperspectral unmixing, enabling the utilization of a small amount of labeled pixels to greatly improve the performance compared to blind unmixing; and (4) the extension of graph Laplacian-based methods to automatically construct knowledge graphs in combination with large language models, enhancing their information retrieval and response generation capabilities.The proposed methods showcase the effectiveness and versatility of graph-based approaches in addressing challenges such as limited labeled data, computational efficiency, and knowledge representation. The thesis demonstrates the potential of graph-based methods in pushing the boundaries of image and document analysis and their applicability in a wide range of machine learning problems.

Accurate fire severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in the Kalahari Desert of Southern Botswana. The frequent wildfires in this arid savanna region predominantly cause topkill, where vegetation experiences above-ground combustion, but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush fire severity and post-burn coppicing/regeneration events of woody vegetation. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 sq. km plot within the Modisa Wildlife Reserve, extensively burnt by the high-intensity Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.5% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Logistic regression analysis revealed a >50% probability of woody vegetation regrowth in high severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in the Kalahari.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to oth

With increased interest in forest restoration comes an urgent need to provide accurate, scalable, and cost‐effective monitoring tools. The ubiquity of smartphones has led to a surge in monitoring apps. We reviewed and assessed monitoring apps found through web searches and conversations with practitioners. We identified 42 apps that (1) automatically monitor indicators or (2) facilitate data entry. We selected the five most promising from the first category, based on their relevance, availability, stability, and user support. We compared them to traditional field techniques in a well‐studied restoration project in Costa Rica. We received further feedback from 15 collaborator organizations that evaluated these in their corresponding field restoration sites. Diameter measurements correlated well with traditional tape‐based measurements (R2 = 0.86–0.89). Canopy openness and ground cover showed weaker correlations to densiometer and quadrat cover measurements (R2 = 0.42–0.51). Apps did not improve labor efficiency but do preclude the purchase of specialized field equipment. The apps reviewed here need further development and validation to support monitoring adequately, especially in the tropics. Estimates of development and maintenance costs, as well as statistics on user uptake, are required for cost‐effective development. We recommend a coordinated effort to develop dedicated restoration monitoring apps that can speed up and standardize the collection of indicators and provide evidence on restoration outcomes alongside a centralized repository of this information.

Quantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.

The intensifying global opioid crisis, majorly attributed to fentanyl (FT) and its analogs, has necessitated the development of rapid and ultrasensitive remote/on-site FT sensing modalities. However, current approaches for tracking FT exposure through wastewater-based epidemiology (WBE) are unadaptable, time-consuming, and require trained professionals. Toward developing an extended in situ wastewater opioid monitoring system, we have developed a screen-printed electrochemical FT sensor and integrated it with a customized submersible remote sensing probe. The sensor composition and design have been optimized to address the challenges for extended in situ FT monitoring. Specifically, ZIF-8 metal-organic framework (MOF)-derived mesoporous carbon (MPC) nanoparticles (NPs) are incorporated in the screen-printed carbon electrode (SPCE) transducer to improve FT accumulation and its electrocatalytic oxidation. A rapid (10 s) and sensitive square wave voltammetric (SWV) FT detection down to 9.9 µgL-1 is thus achieved in aqueous buffer solution. A protective mixed-matrix membrane (MMM) has been optimized as the anti-fouling sensor coating to mitigate electrode passivation by FT oxidation products and enable long-term, intermittent FT monitoring. The unique MMM, comprising an insulating polyvinyl chloride (PVC) matrix and carboxyl-functionalized multi-walled carbon nanotubes (CNT-COOH) as semiconductive fillers, yielded highly stable FT sensor operation (> 95% normalized response) up to 10 h in domestic wastewater, and up to 4 h in untreated river water. This sensing platform enables wireless data acquisition on a smartphone via Bluetooth. Such effective remote operation of submersible opioid sensing probes could enable stricter surveillance of community water systems toward timely alerts, countermeasures, and legal enforcement.

Changing climate and the increase in agricultural demand has increased water scarcity and highlighted the importance of how irrigation is managed. Precision and deficit irrigation are two management strategies that have been used to save water. Precision irrigation can simply be described as applying the right amount of water to the right place, at the right time. Deficit irrigation can simply be described as providing the plant with less water than it could use. In both cases, evapotranspiration (ET) is an important tool for management decisions as it provides the “right amount” information for precision irrigation and can be used as an indicator of plant stress in deficit irrigation. Maps of ET are an even stronger tool as they can indicate spatial patterns throughout the entire field, answering the “right place” for precision irrigation and indicating potential troubled areas for deficit irrigation. With the advancement of remote sensing technologies, and specifically drones, maps of ET have become more available to growers and other managers. In this dissertation, I used remotely sensed ET for three different applications related to precision and deficit irrigation. The High-Resolution Mapping of Evapotranspiration (HRMET) model was used to create maps of ET for this dissertation. Chapter 2 evaluates the impact of capture elevation on drone-based ET estimates in a commercial Wisconsin potato field. Missions were conducted at an elevation of 30, 60, and 90 m. While there was a noticeable increase in resolution with the lower elevation missions, there were no observable differences in ET between the different elevations. This indicated that for Wisconsin potatoes it would be preferable to go with the higher capture elevation as it has smaller storage and data processing requirements while producing similar results. Additionally, I conducted a Monte Carlo based uncertainty analysis on the inputs HRMET model in Chapter 2. The spatial inputs for HRMET were the bigges

Coastal upwelling has been extensively studied as it plays a critical role in the connectivity between offshore waters and coastal ecosystems, which has impacts on water quality, fisheries, and aquaculture production. Significant efforts have been devoted to the quantification of the intensity, duration, and variability of this phenomenon by means of coastal upwelling indexes (CUIs), derived from wind, sea level pressure, or sea surface temperature data. Although valuable, first-order descriptors, such classical indexes have been reported to present some limitations. As one of the major shortcomings is the omission of the direct influence of ocean circulation, this work introduces a novel CUI, generated from remotely sensed hourly surface current observations provided by a high-frequency radar (HFR). The consistency of the proposed index (CUI-HFR) is assessed in two different oceanographic areas during two distinct time periods: in the north-western Iberian (NWI) peninsula for 2021 and in the Bay of Biscay (BOB) for 2014. To this aim, CUI-HFR is compared against a traditional CUI based on hourly wind observations (CUI-WIND) provided by two buoys. Results revealed that HFR-derived circulation maps can be effectively used for the characterization of recurring upwelling and downwelling episodes and the noticeable agreement between CUI-HFR and CUI-WIND., Peer Reviewed

Hyperspectral imaging is a key tool in numerous remote sensing applications. Images with tens or even hundreds of spectral bands contain a wealth of information to retrieve multiple geophysical parameters, to perform target detection, or land classification with unbeatable high accuracies. In addition, hyperspectral sensors are becoming smaller, and today they even fit in CubeSats. However, the amount of data they generate is so large that satellite communication systems have severe limitations to download it, especially in SmallSats. It is therefore becoming urgent to develop efficient automated algorithms that can be executed in the limited capabilities of the onboard computers of these satellites, so as to reduce the amount of data to be stored and downloaded, while keeping as much information as possible for a given scene. In this work a band selection algorithm has been designed to deal with this problem. The proposed algorithm consists of the sequential selection of the spectral bands ranked using the amount of information provided by each band, and also the correlation of these bands with the previously selected ones. The algorithm performance is assessed by means of a suite of classification tests with hyperspectral datasets from different sensors. Results show comparable or even better performance than other existing Band Selection algorithms, while outperforming in terms of computational complexity, which makes it more suitable for SmallSats with limited computing resources., This work was supported by the project GENESIS: GNSS Environmental and Societal Missions, grant PID2021-126436OB-C21, and by a FPU fellowship from the Spanish Ministry of Education. Part of this work has also been possible thanks to the Italian Space Agency (ASI) that granted access to its PRISMA database (http://prisma.asi.it/)., Peer Reviewed, Postprint (published version)

La flora marina, en especial las praderas marinas y praderas de algas, posee un rol esencial en la sostenibilidad de los ecosistemas costeros. Por esto, existe una elevada necesidad en desarrollar herramientas que permitan monitorizar de forma precisa la flora marina. La teledetección por satélite es una tecnología eficiente y económica para poder elaborar mapas de hábitats bentónicos o de tipos de fondos, permitiendo caracterizar los fondos a lo largo del tiempo. Para la generación de estos mapas, en la actualidad típicamente se implementan técnicas basadas en píxel, como modelos Machine Learning convencionales, y otras técnicas basadas en objetos, como el OBIA. No obstante, el avance de las técnicas Deep Learning permite entrenar modelos que aprovechen mejor las características de los fondos marinos para la generación de mapas de elevada precisión. En este trabajo se expone el uso del modelo U-Net, basado en Redes Neuronales Convoluciones (CNN), para la generación de mapas de hábitats bentónicos a partir de imágenes satelitales multiespectrales. La zona de estudio es El Río, estrecho entre las islas de La Graciosa y Lanzarote, islas Canarias, donde existe una elevada biodiversidad con la presencia de multitud de especies como praderas de Cymodocea nodosa, Caulerpa prolifera y algas rojas filamentosas, entre otras especies., Marine flora, especially seagrass and seaweed meadows, plays an essential role in the sustainability of marine ecosystems. Therefore, there is a great need to develop tools to accurately monitor the marine flora. Satellite remote sensing is an efficient and economical technology for mapping benthic habitats or bottom types, allowing the characterization of the seabed over time. For the generation of these maps, techniques pixel-based techniques, such as conventional Machine Learning models, and object-based techniques, such as OBIA, are currently typically implemented. However, the advancement of Deep Learning techniques allows training models that take better advantage of seabed features for the generation of high accurate maps. In this paper we present the use of the U-Net model, based on Convolutional Neural Networks (CNN), for the generation of benthic habitat maps based on multispectral satellite imagery. The study area is El Río, a strait between the islands of La Graciosa and Lanzarote, Canary Islands, where there is a high biodiversity with the presence of a multitude of species such as Cymodocea nodosa meadows, Caulerpa prolifera and red filamentous algae, among other species., Esta investigación ha sido parcialmente subvencionada por el Ministerio para la Transición Ecológica y el Reto Demográfico mediante el proyecto TARA (Ref. SPIP2022-02897)., Peer Reviewed, Postprint (published version)

Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands. However, the computational complexity of traditional methods makes the algorithms difficult to be deployed on board satellites. This is especially true for Small Satellites with limited computational and power resources. Moreover, existing band selection techniques often require the hypercube to be processed at least at Level-1B product, i.e., the bands need to be finely aligned before selecting them, demanding more computational resources for the on-board computer. This study presents a novel neural network-based approach for on-board band selection using data with coarse band-to-band aligned. This methodology not only simplifies the pre-processing requirements, but also opens new possibilities for efficient hyperspectral imaging from space on-board Small Satellites, such as classification, change and target detection., This project was part of the project "GENESIS: GNSS Environmental and Societal Missions – Subproject UPC", Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”, and by a FPU fellowship from the Spanish Ministry of Education. Part of this work has also been possible thanks to the Italian Space Agency (ASI) that granted access to its PRISMA database (http://prisma.asi.it/)., Peer Reviewed, Postprint (published version)

La eutrofización es una gran amenaza para las aguas costeras, lo que ha llevado a la adopción en todo el mundo de políticas destinadas a protegerlas y restaurarlas. Muchas de estas leyes se basan en el marco de fuerzas motrices-presión-estado-impacto-respuesta y siguen el enfoque ecosistémico. Para aplicarlos, se necesita evaluar la presión de la eutrofización y su impacto, así como proporcionar información útil para los gestores. Por un lado, la presión de eutrofización en tierra se basa en los usos del suelo, en su cobertura y en las influencias fluviales (que pueden estimarse a partir de la salinidad en aguas costeras) y en aguas costeras en la concentración de nutrientes. Y por el otro, el impacto de eutrofización en aguas costeras se basa en la concentración de clorofila-a. A partir de esta información puede establecerse la relación presión impacto y rellenar la tabla Presión-Impacto-Gestión (PIG). Esta última, ayuda en el diseño de planes de gestión para zonas costeras, al identificar dónde se deben implementar acciones de monitoreo y de mitigación. Hasta ahora esta aproximación se ha realizado en base a datos in situ, pero la teledetección permite conocer esta información a partir de imágenes satelitales de microondas, infra rojos (incluyendo las térmicas) y visibles. Para comprobar si el enfoque PIG tiene éxito basándose en datos satelitales, se aplicará a dos casos de estudio: (i) en la costa noreste española (Costa Catalana), donde esta metodología ya ha tenido éxito basándose en datos in situ, y (ii) en la costa Atlántica de Honduras (Departamentos de Atlántida y Colón), donde no existe información sobre el proceso de eutrofización. Si tiene éxito, el abordaje PIG basado en datos satelitales será una piedra angular esencial para la gestión de las aguas costeras en todo el mundo., Postprint (published version)

Adverse weather conditions present a primary challenge for ground-based LiDAR imaging systems in outdoor applications. The use of polarization has been proposed as an effective filtering mechanism. However, the number of potential situations is large, complex and difficult to parameterize with accuracy. In such conditions, advanced simulation methods enable the testing of different experimental configurations and the determination of the best possible setup. With this purpose, a Monte Carlo algorithm is presented for modeling polarized pulsed LiDAR signals in turbid media. This algorithm is designed for immediate applicability, incorporating realistic media characterization and accounting for the attributes of existing prototypes. It allows testing various experimental configurations, managing optical obstacles, adjusting polarization arrangements, and different geometries of particles within the medium. The developed algorithm accurately characterizes backscattering signals, revealing their dependence on medium properties. A relationship between visibility and backscattering energy is identified, offering insights for sensor optimization. Polarization analysis highlights the efficacy of circular polarization in mitigating scattering effects and establishes a connection with the polarimetric characteristics of imaged targets. The algorithm's application to irregular particles reveals also an unexpected behavior of polarized light, challenging established strategies. These diverse use cases exemplify the algorithm's capability to model real-world circumstances, emphasizing its significance in predicting cutting-edge situations when designing optical systems for complex and demanding outdoor scenarios., Agència de Gestió d’Ajuts Universitaris i de Recerca (2021FI_B2 00077); Ministerio de Ciencia e Innovación (PID2020-119484RB-I00)., Peer Reviewed, Postprint (published version)

The TFG focuses on the research and development of spatial localization and sensing techniques assisted by Intelligent Reflective Surfaces (IRS) within the context of 6G communication networks. Given the increase in operating frequencies in 6G networks, which include millimeter-wave bands, the project addresses the challenges and opportunities of high-frequency communication. The use of passive IRS is considered a solution to enhance system coverage and multiplexing capacity, and its potential for localization and sensing applications is also explored. The project aims to model and simulate a complete system consisting of a base station, an IRS, and multiple transmitting objects. It involves the development of strategies to control the reflection elements of the IRS and the design of estimators to determine the position and power of transmitting objects based on reflected signals., El TFG se centra en la investigación y desarrollo de técnicas de localización espacial y detección asistida por Superficies Reflectantes Inteligentes (IRS) en el contexto de las redes de comunicación 6G. Dado el aumento en las frecuencias de operación en las redes 6G, que incluyen bandas milimétricas, el proyecto aborda los desafíos y oportunidades de la comunicación a alta frecuencia. El uso de IRS pasivas se considera una solución para mejorar la cobertura y la capacidad de multiplexación del sistema, y también se explora su potencial para aplicaciones de localización y detección. El proyecto tiene como objetivo modelar y simular un sistema completo que consta de una estación base, un IRS y múltiples objetos emisores, desarrollar estrategias para controlar los elementos de reflexión del IRS, y diseñar estimadores para determinar la posición y la potencia de los objetos emisores en función de las señales reflejadas., El TFG es centra en la investigació i desenvolupament de tècniques de localització espacial i detecció assistida per Superfícies Reflectores Intel·ligents (IRS) en el context de les xarxes de comunicació 6G. Donat l'augment de les freqüències d'operació a les xarxes 6G, que inclouen bandes mil·limètriques, el projecte aborda els reptes i oportunitats de la comunicació a alta freqüència. L'ús d'IRS passives es considera una solució per millorar la cobertura i la capacitat de multiplexació del sistema, i també s'explora el seu potencial per a aplicacions de localització i detecció. El projecte té com a objectiu modelar i simular un sistema complet que consta d'una estació base, un IRS i múltiples objectes emissors, desenvolupar estratègies per controlar els elements de reflexió de l'IRS, i dissenyar estimadors per determinar la posició i la potència dels objectes emissors en funció de les senyals reflectides.

The current lack of continuous and temporal monitoring of Chlorophyll- a in certain regions is evident. There are some platforms that collected this data in the past, without current update. For this reason, a model based on a Random Forest classification was trained using the limnological classification based on chlorophyll- a concentration from SeaWiFS data and extrapolated to Landsat-7. The model achieved an accuracy of 62.29% with a moderate kappa coefficient. This approach allowed the development of a well-performing model capable of efficiently monitoring and updating chlorophyll-a data in a region lacking in-situ sensors for this variable., Peer Reviewed