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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mona, Lucia; Amiridis, Vassilis; Cuevas, Emilio; Gkikas, Antonis; +22 Authors

    Authors acknowledge dr Sangboom Ryoo of Korean Meteorological Administration in Seoul for his help and support.The research leading to these results has received funding from the COST ActionCA16202, supported by COST Association (European Cooperation in Science and Technology), from DustClim Project as part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462) and by the European Union’s Horizon 2020 research program for societal challenges – smart, green and integrated transport under grant agreement no.723986 (project EUNADICS-AV – European Natural Disaster Coordination and Information System for Aviation). L. Mona acknowledges the ACTRIS-IMP (Implementation project), funded by the European Union’s Horizon 2020 research and innovation programme (Grant no 871115) and the contribution of the ACTRIS-ITALIA JRU (CNR prot. n. 0067310/2017). S. Basart acknowledges CAMS-84 and CAMS2-82 (part of the Copernicus Atmospheric Monitoring Services, CAMS) and the Spanish Jose Castillejo mobility programme (CAS18/0033). V. Amiridis acknowledges support from the European Research Council (grant no. 725698, D-TECT). A. Gkikas acknowledges support by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (project acronym: ATLANTAS, project number: 544). S. Kazadzis acknowledges the ACTRIS-CH (Aerosol, Clouds and Trace Gases Research Infrastructure–- Swiss contribution) funded by the State Secretariat for Education, Research, and Innovation, Switzerland. Work of P. Dagsson-Waldhauserova was partly funded by the Czech Science Foundation (HLD-CHANGE project, no. 20-06168Y). C. Pérez García-Pando acknowledges the long-term support from the AXA Research Fund, as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness, and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051).B. Weinzierl acknowledges funding under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 640458, A‐LIFE). Mineral dust produced by wind erosion of arid and semi-arid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socio-economic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust forecasts tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities including in-situ, ground-based and satellite remote sensing observations, in Northern Africa, the Middle East and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders "Article signat per 26 autors/es: Lucia Mona, Vassilis Amiridis, Emilio Cuevas, Antonis Gkikas, Serena Trippetta, Sophie Vandenbussche, Angela Benedetti, Pavla Dagsson-Waldhauserova, Paola Formenti, Alexander Haefele, Stelios Kazadzis, Peter Knippertz, Benoit Laurent, Fabio Madonna, Slobodan Nickovic, Nikolaos Papagiannopoulos, Gelsomina Pappalardo, Carlos Pérez García-Pando, Thomas Popp, Sergio Rodríguez, Andrea Sealy, Nobuo Sugimoto, Enric Terradellas, Ana Vukovic Vimic, Bernadette Weinzierl, and Sara Basart " Peer Reviewed

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    CNR ExploRA
    Article . 2023
    Data sources: CNR ExploRA
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: José Telo da Gama; António López-Piñeiro; Luís Loures; José Rato Nunes;

    The sustainable management of Mediterranean agricultural soils, characterized by salinization and low organic matter content, requires a thorough understanding of their temporal and spatial evolution. The focal point of this investigation encompasses an area of 6769 ha within the Portuguese Mediterranean basin, from which as many as 686 topsoil specimens were acquired during the periods 2001/2002 and 2011/2012 for the purpose of scrutinizing soil organic matter (SOM) content, pH measured in water, and electrical conductivity (EC). The methodology employed both classical and geostatistical techniques, and the terrestrial samples were classified in accordance with the irrigation mechanisms in use (namely, drip and sprinkler systems), subsequently juxtaposed with their counterparts in rainfed systems. Predictive maps were generated using the Ordinary Kriging algorithm for spatial interpolation. The findings demonstrate that irrigated Fluvisols displayed lower SOM content compared to rainfed soils, with sprinkler-irrigated soils experiencing a 16.1% decrease and drip-irrigated soils showing a more pronounced 26.6% decrease. Moreover, drip-irrigated soils contained 12.5% less SOM compared to sprinkler-irrigated soils. The pH levels stabilized at around 6.6 in both rainfed and irrigated soils, with no significant differences observed between the irrigation methods. Furthermore, irrigated Fluvisols exhibited higher EC values compared to rainfed soils, with both sprinkler and drip-irrigated soils showing values that were 35.2% higher. These results underscore the impact of irrigation practices on soil properties, including elevated EC values due to increased soil salt accumulation. The study highlights the necessity of considering specific irrigation systems and associated practices to ensure sustainable soil health and productivity. Adopting management approaches that account for these factors is crucial for preserving optimal soil conditions in Mediterranean agricultural systems. info:eu-repo/semantics/publishedVersion

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    Other literature type . Article . 2023 . Peer-reviewed
    License: CC BY
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    Article . 2023
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      Other literature type . Article . 2023 . Peer-reviewed
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      Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Alessia D’Agata; Daniele Ponza; Florin Adrian Stroiu; Ioannis Vardopoulos; +6 Authors

    Land imperviousness reflects settlement growth and urban sprawl. Grounded on a comparative approach, a set of multidimensional statistical techniques were adopted here to quantify the evolution of land imperviousness from Copernicus High-Resolution Layers (HRLs) in a representative case study of Southern Europe (Athens, Greece). A two-way data matrix reporting the percent share of the surface land exposed to different sealing levels (101 classes ranging continuously from 0% to 100%) in the total municipal area was computed for two years (2006 and 2018) individually for 115 municipalities in metropolitan Athens. This matrix represented the information base needed to derive place-specific urban footprints and a comprehensive (global) profile of land imperviousness. Results of a Detrended Correspondence Analysis (DCA) delineated a metropolitan structure still organized along the density gradient, moving from dense settlements in central locations with dominant land classes sealed for more than 90% of their surface area to completely pervious land (0%) typical of rural locations. While the density gradient became less steep between 2006 and 2018, it continued to aliment a socioeconomic polarization in urban and rural districts with distinctive profiles of land imperviousness. Intermediate locations had more mixed imperviousness profiles as a result of urban sprawl. Differential profiles reflect place-specific urban footprints with distinctive land take rates.

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    Land
    Article . 2023
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    Authors: Helena Maria Fernandez; Fernando Granja-Martins; Olga Dziuba; David A. B. Pereira; +1 Authors

    Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographic–ecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. On 29 June 2019, an extreme precipitation event occurred in the city of Tulun in the Irkutsk oblast, Russian Federation, which caused flooding due to the increase in the water level of the Iya River that passes through the city, leaving many infrastructures destroyed and thousands of people affected. This study aims to determine the flooded areas in the city of Tulun based on two change detection methods: Radiometric Rotation Controlled by No-change Axis (RCNA) and Ratioing, using Sentinel 2 images obtained before the event (19 June 2019) and during the flood peak (29 June 2019). The results obtained by the two methodologies were compared through cross-classification, and a 98% similarity was found in the classification of the areas. The study was validated based on photointerpretation of Google Earth images. The methodology presented proved to be useful for the automatic precession of flooded areas in a straightforward, but rigorous, manner. This allows stakeholders to efficiently manage areas that are buffeted by flooding episodes. info:eu-repo/semantics/publishedVersion LA/P/0069/2020

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    Other literature type . Article . 2023 . Peer-reviewed
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      Other literature type . Article . 2023 . Peer-reviewed
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      Article . 2023
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    Authors: Kimmo Lylykangas; Rebecca Cachia; Damiano Cerrone; Kaie Kriiska; +4 Authors

    The quantification of greenhouse gas (GHG) emissions is increasingly important in spatial planning for regions, cities, and areas. The combination of territorial and consumption-based accounting (CBA) approaches can currently be considered best practice for calculating GHG emissions at sub-national levels, in terms of informing local decision-making about the different climate impacts of spatial planning policies, both within the boundaries of a given region and for the inhabitants of that region. This study introduces four European case studies that were conducted using the two quantification approaches to assess the climate impacts of locally relevant planning policies. The case studies represent different scales of spatial planning, different European planning systems, and different situations in terms of data availability. Territorial results are not suitable for inter-regional comparison, but rather for internal monitoring, while CBA allows for comparison and provides a comprehensive picture of the global carbon footprint of residents, however, with indications that are more difficult to link to spatial planning decisions. Assessing impacts, and in particular interpreting results, requires both methodological understanding and knowledge of the local context. The results of the case studies show that setting climate targets and monitoring the success of climate action through a single net emissions figure can give false indications. The study shows that the two approaches to quantifying GHG emissions provide complementary perspectives on GHG emissions at the sub-national level and thus provide a more thorough understanding of the GHG emission patterns associated with spatial planning policies. The identification of the regional differences in GHG emission sources and mitigation potentials are the main functions of sub-national GHG inventories and the impact assessment for spatial planning. Harmonization of the data collection for sub-national GHG inventories and the transparency of underlying assumptions would greatly support the coherence of climate action and the implications to spatial planning. Funding Information: The results are based on the analyses conducted in the QGasSP project (2020–2021), TA28, funded by ESPON EGTC, Service Contract ref—EE/SO2/114/2020. Publisher Copyright: © 2023 by the authors. Peer reviewed

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    Land
    Other literature type . Article . 2023 . Peer-reviewed
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    Article . 2023 . Peer-reviewed
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    Article . 2023
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      Other literature type . Article . 2023 . Peer-reviewed
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      Article . 2023
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    Authors: Kensuke Yamaguchi; Ryo Tanaka; Masaaki Morita; Toshiya Mori; +1 Authors

    Introduction: Monitoring the volcanic activity of a potentially hazardous volcano is essential for eruption warning and hazard mitigation. The SO2 flux from the volcano is one of the most important measures to understand its activity. Mount Tokachi, in Japan, is an active volcano that experienced three magmatic explosive eruptions in the 20th century (in 1926, 1962, and 1988–1989). Since 2006, geodetic observations have captured ground deformation, suggesting an inflation beneath the main crater. Moreover, since 2020 daily visual observations have detected the increase in plume heights and the occurrence of volcanic glow at the main crater. The high-time-resolution estimation of SO2 flux will help monitor the activity of Mount Tokachi and clarify the associating mechanisms. Furthermore, satellite remote sensing can estimate the vertical column density (VCD) of sulfur dioxide (SO2), enabling the daily determination of SO2 flux without the need to visit the site. Due to the improved spatial resolution, the TROPOspheric Monitoring Instrument (TROPOMI) has advanced satellite-based volcanic gas flux measurements.Methods: We have analyzed the available TROPOMI data and conducted ground-based observations to estimate the SO2 flux from Mount Tokachi at 100–2,000 tons/day.Result and Discussion: The average annual SO2 flux has been increasing since 2021, on par with the increase in plume height and volcanic glow. TROPOMI data enabled quantification of the annual SO2 flux, with a sufficient temporal resolution to monitor the volcanic activity at Mount Tokachi. However, a high flux, such as in excess of 2,000 tons/day, was observed in the winter season. The flux from the satellite data was similar to that from ground-based observations during the summer. However, a seasonal change in flux from the satellite data was observed in winter, with the flux being larger than that in summer, possibly because the flux increases during winter. Another possible reason is the influence of snow cover on satellite observations due to its high surface reflectance. We reanalyzed some TROPOMI data during the winter, in which the ground snow cover was misidentified as clouds at low altitudes. This procedure suppresses the error caused by the high surface reflectance due to winter snow coverage and improves the quality of the annual SO2 flux from satellite observations. This methodology is applicable to other high-latitude or high-altitude volcanoes during specific seasons characterized by frequent fluctuations in snow cover conditions.

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    Frontiers in Earth Science
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    Authors: Mansourmoghaddam, Mohammad; Naghipur, Negar; Rousta, Iman; Alavipanah, Seyed Kazem; +8 Authors

    Several earth science investigations depend heavily on knowing the surface energy budget and determining surface temperature. The primary factor affecting the energy balance in the surface physical processes of the planet is the land surface temperature (LST). Even in the case of small-scale green areas like local parks, plants have a significant impact on the climate of cities. The goal of this study was to estimate the construction-related impacts of the Karizland green town (green belt) on the LST of its surroundings over time, for the years 2013 (before construction began), 2015, 2020 and 2022 (after construction was completed). LST values and hot spot analyses were employed for thermal condition evaluation purposes on Landsat-8 satellite images, and normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) indices were used for examining the vegetation change. The results showed that after the establishment of the green town, the mean NDVI and FVC grew by 275% and 950%, respectively, compared to the initial period, which resulted in the addition of approximately 208.35 ha of green space to the study area. In this regard, the results showed that after these changes, compared to the first period, the mean LST decreased by 8%. In addition, the area of the class of hotspot analysis with less than 90% confidence increased by 9%. The results illustrated that almost 20% of the data in the LST range was below 55 °C in 2013, near 57 °C in 2015, and around 51 °C in 2020 and 2022. The results also showed a negative relationship between the distance from the established settlement and the values of NDVI and FVC in 2022 of 91% and 89% and in 2020 of 67% and 69%, respectively. Every year, LST has had a significant negative relationship with the NDVI and FVC of that year and a positive relationship with the LST of the following years, such that the correlation decreases in later years. In order to control LST and the temperature surrounding cities, this research strongly advises managers to develop these green towns.

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    Other literature type . Article . 2023 . Peer-reviewed
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    Authors: Samaneh Sadat Nickayin; Aubrey Jahelka; Shuwen Ye; Francesca Perrone; +1 Authors

    Self-constructed cities refer to impoverished neighborhoods that suffer from inadequate housing. Such cities are the result of individual or communal initiatives that must self-construct their neighborhoods. Most of the self-constructed cities are located near endangered bioregions on the Earth, and their continued growth will inevitably aggravate the human impact on our planet. Dwellers in these areas tackle threats such as poverty, environmental degradation, and disaster vulnerability. The lack of knowledge in planning self-constructed cities causes conditions of injustice. However, appropriate management of the existing natural capital of the surrounding areas of such cities can address the mentioned challenges and the conditions for justice. This paper aims to evaluate the role of Nature-Based Solutions (NBS) in planning procedures, to create conditions of fairness and equity in self-constructed cities. The paper focused on the linkage between the application of NBS and four principles of justice: distributive justice, restorative justice, spatial justice, and interactional justice. Considering data from a review of the literature, scrutiny of past planning and policy measures and a field study with interviews with stakeholders, the empirical result of our study delineates important strategic implications of Nature-Based Solutions for sustainable planning in the case study of San José de Chamanga, Ecuador. It was demonstrated how small-scale, natural-based interventions, instead of broader actions based on a pure engineering perspective, are economically and ecologically profitable, with a positive impact on local communities’ well-being and social cohesion.

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    Other literature type . Article . 2023 . Peer-reviewed
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    Article . 2023
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      Other literature type . Article . 2023 . Peer-reviewed
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      Article . 2023
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    Authors: Pálsson, Burkni;

    Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing) með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning). Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Ein aðferð með að nýta sér fjölbeitingarlærdóm (e. multitask learning) og hin með að nota sjálfkóðara útfærðan með földunartaugnaneti (e. convolutional neural network). Hvortveggja bætir samkvæmni og hæfni fjölrásagreiningarinnar. Ennfremur inniheldur ritgerðin yfirgripsmikið yfirlit yfir þær sjálfkóðaraaðferðir sem hafa verið birtar ásamt greinargóðri umræðu um mismunandi gerðir sjálfkóðara og útfærslur á þeim. í lok ritgerðarinnar er svo að finna gagnrýninn samanburð á 11 mismunandi aðferðum byggðum á sjálfkóðurum. Brottnáms (e. ablation) tilraunir eru gerðar til að svara spurningunni hvers vegna sjálfkóðarar eru svo árangursríkir í fjölrásagreiningu og stuttlega rætt um hvað framtíðin ber í skauti sér varðandi aðgreiningu fjölrásamynda með sjálfkóðurum. Megin framlag ritgerðarinnar er eftirfarandi: - Ný sjálfkóðaraaðferð, MTLAEU, sem nýtir á beinan hátt rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Aðferðin notar fjölbeitingarlærdóm til að aðgreina grennd af rófum í einu. - Ný aðferð, CNNAEU, sem notar 2D földunartaugnanet fyrir bæði kóðara og afkóðara og er fyrsta birta aðferðin til að gera það. Aðferðin er þjálfuð á myndbútum (e.patches) og því er rúmfræðileg bygging myndarinnar sem greina á varðveitt í gegnum aðferðina. - Yfirgripsmikil og ítarlegt fræðilegt yfirlit yfir birtar sjálfkóðaraaðferðir fyrir fjölrásagreiningu. Gefinn er inngangur að sjálfkóðurum og elstu tegundir sjálfkóðara eru kynntar. Gefið er greinargott yfirlit yfir helstu birtar aðferðir fyrir fjölrásagreiningu sem byggja á sjálfkóðurum og gerður er gangrýninn samburður á 11 mismunandi sjálfkóðaraaðferðum. The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve the performance. One by using multitask learning to simultaneously unmix a neighbourhood of pixels while the other by using a convolutional neural network autoencoder. This increases the consistency and robustness of the methods. In addition, a review of the various autoencoder methods in the literature is given along with a detailed discussion of different types of autoencoders. The thesis concludes by a critical comparison of eleven different autoencoder based methods. Ablation experiments are performed to answer the question of why autoencoders are so effective in blind hyperspectral unmixing, and an opinion is given on what the future in autoencoder unmixing holds. The Icelandic Research Fund under Grants 174075-05 and 207233-051

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    Authors: Sedona, Rocco;

    Advances in remote sensing (RS) missions in recent decades have greatly increased the volume of data that is continually acquired and made available to end users, who can utilize it in a variety of Earth observation (EO) applications. land cover (LC) maps play a key role in monitoring the Earth’s surface, providing scientists and policymakers with an accurate view of the evolution of the landscape and helping them address pressing questions, from efficient resource planning to resilience to climate change. Due to the use of classical machine learning (ML) and more recently of deep learning (DL) methods, the information content of RS data can be exploited to an unprecedented degree, fostering research, development, and deployment of workloads to address open challenges for EO applications, including LC classification. However, the larger size of the datasets needed to train state-of-the-art (SotA) DL models and the need to utilize them at scale increases the time to deployment, which can hinder their effective utilization. Adopting strategies for distributed deep learning (DDL) on high performance computing (HPC) systems provides the opportunity to speed up the training of the models, allowing faster development times for researchers. Since space agencies operate a variety of missions, data acquired by different sensors can be used to increase the temporal resolution at which a certain area is observed, with potential improvements in the accuracy of the ML/DL models. The thesis objectives are formulated with these premises in mind and were investigated using a combination of methodologies to exploit the dedicated resources of HPC systems, contributing to addressing new questions on the adoption of DDL methods for EO applications and to familiarize the RS community with such approaches, which can be of great value. Framför fjarkönnunar á síðustu áratugum hefur leitt til mikillar aukningar í stöðugri öflun gagna sem gerð eru aðgengileg til enda notenda sem geta hagnýtt þau í forritum fyrir skoðun jarðarinnar. Landþekjukort spila lykil hlutverk í eftirliti með yfirborði jarðar. Þau veita vísindafólki og stefnumótendum skýra sýn á þróun í landslaginu og hjálpar þeim að finna svör við aðkallandi vandamálum allt frá skilvirkri skipulagningu auðlinda að þoli jarðar gegn loflagsbreytingum. Vegna notkunnar klassískra aðferða í vélnámi og nýlegrar notkunnar djúptauganetsaðferða er hægt að nýta efni úr fjarkönnun að umfangi sme að ekki hefur sést áður. Það ýtir undir rannsóknir, þróun og dreifingu á nýjum kerfum sem að leita til þess að leysa núverandi áskoranir á sviði EO forrita eins og LC flokkunar. Aukin stærð gagnasafna sem þarf til þess að þjálfa nýjustu SotA DL módel og þörfin til þess að nota þau á stórum skala hefur hins vegar orsakast í auknum tíma þróunar sem að getur komið í veg fyrir skilvirka nýtingu þeirra. Innleiðing stefnu fyrir dreifðan djúplærdóm á ofurtölvukerfum gefur tækifæri til þess að hraða þjálfun módelanna og rannsakendum að auuka hraða í þróun og minnka tíma þangað til virði er fengið úr ferlinu. Þar sem að geimferðastofnanir framkvæma mismunandi verkefni þá er hægt að nota gögnin sem safnað er til þess að auka “temporal resolution” sem að svæði eru skoðuð með, einnig með mögulegum bætingu á nákvæmni í vélnáms og djúplærdóms módelum. Markmið ritgerðarinnar eru gerð út frá þessum forsendum og voru rannsökuð með blöndu af aðferðum til þess að nýta auðlindir í ofurtölvukerfum, leggja af mörkum að svara spurningum um nýtingu djúplærdómsaðferða fyrir EO hugbúnað og til þess að kynna fjarkönnunarsamfélaginu fyrir aðferðum líkt og þessum sem að geta skapað mikið verðmæti.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mona, Lucia; Amiridis, Vassilis; Cuevas, Emilio; Gkikas, Antonis; +22 Authors

    Authors acknowledge dr Sangboom Ryoo of Korean Meteorological Administration in Seoul for his help and support.The research leading to these results has received funding from the COST ActionCA16202, supported by COST Association (European Cooperation in Science and Technology), from DustClim Project as part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462) and by the European Union’s Horizon 2020 research program for societal challenges – smart, green and integrated transport under grant agreement no.723986 (project EUNADICS-AV – European Natural Disaster Coordination and Information System for Aviation). L. Mona acknowledges the ACTRIS-IMP (Implementation project), funded by the European Union’s Horizon 2020 research and innovation programme (Grant no 871115) and the contribution of the ACTRIS-ITALIA JRU (CNR prot. n. 0067310/2017). S. Basart acknowledges CAMS-84 and CAMS2-82 (part of the Copernicus Atmospheric Monitoring Services, CAMS) and the Spanish Jose Castillejo mobility programme (CAS18/0033). V. Amiridis acknowledges support from the European Research Council (grant no. 725698, D-TECT). A. Gkikas acknowledges support by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (project acronym: ATLANTAS, project number: 544). S. Kazadzis acknowledges the ACTRIS-CH (Aerosol, Clouds and Trace Gases Research Infrastructure–- Swiss contribution) funded by the State Secretariat for Education, Research, and Innovation, Switzerland. Work of P. Dagsson-Waldhauserova was partly funded by the Czech Science Foundation (HLD-CHANGE project, no. 20-06168Y). C. Pérez García-Pando acknowledges the long-term support from the AXA Research Fund, as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness, and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051).B. Weinzierl acknowledges funding under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 640458, A‐LIFE). Mineral dust produced by wind erosion of arid and semi-arid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socio-economic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust forecasts tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities including in-situ, ground-based and satellite remote sensing observations, in Northern Africa, the Middle East and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders "Article signat per 26 autors/es: Lucia Mona, Vassilis Amiridis, Emilio Cuevas, Antonis Gkikas, Serena Trippetta, Sophie Vandenbussche, Angela Benedetti, Pavla Dagsson-Waldhauserova, Paola Formenti, Alexander Haefele, Stelios Kazadzis, Peter Knippertz, Benoit Laurent, Fabio Madonna, Slobodan Nickovic, Nikolaos Papagiannopoulos, Gelsomina Pappalardo, Carlos Pérez García-Pando, Thomas Popp, Sergio Rodríguez, Andrea Sealy, Nobuo Sugimoto, Enric Terradellas, Ana Vukovic Vimic, Bernadette Weinzierl, and Sara Basart " Peer Reviewed

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    Article . 2023
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    Authors: José Telo da Gama; António López-Piñeiro; Luís Loures; José Rato Nunes;

    The sustainable management of Mediterranean agricultural soils, characterized by salinization and low organic matter content, requires a thorough understanding of their temporal and spatial evolution. The focal point of this investigation encompasses an area of 6769 ha within the Portuguese Mediterranean basin, from which as many as 686 topsoil specimens were acquired during the periods 2001/2002 and 2011/2012 for the purpose of scrutinizing soil organic matter (SOM) content, pH measured in water, and electrical conductivity (EC). The methodology employed both classical and geostatistical techniques, and the terrestrial samples were classified in accordance with the irrigation mechanisms in use (namely, drip and sprinkler systems), subsequently juxtaposed with their counterparts in rainfed systems. Predictive maps were generated using the Ordinary Kriging algorithm for spatial interpolation. The findings demonstrate that irrigated Fluvisols displayed lower SOM content compared to rainfed soils, with sprinkler-irrigated soils experiencing a 16.1% decrease and drip-irrigated soils showing a more pronounced 26.6% decrease. Moreover, drip-irrigated soils contained 12.5% less SOM compared to sprinkler-irrigated soils. The pH levels stabilized at around 6.6 in both rainfed and irrigated soils, with no significant differences observed between the irrigation methods. Furthermore, irrigated Fluvisols exhibited higher EC values compared to rainfed soils, with both sprinkler and drip-irrigated soils showing values that were 35.2% higher. These results underscore the impact of irrigation practices on soil properties, including elevated EC values due to increased soil salt accumulation. The study highlights the necessity of considering specific irrigation systems and associated practices to ensure sustainable soil health and productivity. Adopting management approaches that account for these factors is crucial for preserving optimal soil conditions in Mediterranean agricultural systems. info:eu-repo/semantics/publishedVersion

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    Other literature type . Article . 2023 . Peer-reviewed
    License: CC BY
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    Article . 2023
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      Other literature type . Article . 2023 . Peer-reviewed
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    Authors: Alessia D’Agata; Daniele Ponza; Florin Adrian Stroiu; Ioannis Vardopoulos; +6 Authors

    Land imperviousness reflects settlement growth and urban sprawl. Grounded on a comparative approach, a set of multidimensional statistical techniques were adopted here to quantify the evolution of land imperviousness from Copernicus High-Resolution Layers (HRLs) in a representative case study of Southern Europe (Athens, Greece). A two-way data matrix reporting the percent share of the surface land exposed to different sealing levels (101 classes ranging continuously from 0% to 100%) in the total municipal area was computed for two years (2006 and 2018) individually for 115 municipalities in metropolitan Athens. This matrix represented the information base needed to derive place-specific urban footprints and a comprehensive (global) profile of land imperviousness. Results of a Detrended Correspondence Analysis (DCA) delineated a metropolitan structure still organized along the density gradient, moving from dense settlements in central locations with dominant land classes sealed for more than 90% of their surface area to completely pervious land (0%) typical of rural locations. While the density gradient became less steep between 2006 and 2018, it continued to aliment a socioeconomic polarization in urban and rural districts with distinctive profiles of land imperviousness. Intermediate locations had more mixed imperviousness profiles as a result of urban sprawl. Differential profiles reflect place-specific urban footprints with distinctive land take rates.

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    Article . 2023
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    Authors: Helena Maria Fernandez; Fernando Granja-Martins; Olga Dziuba; David A. B. Pereira; +1 Authors

    Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographic–ecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. On 29 June 2019, an extreme precipitation event occurred in the city of Tulun in the Irkutsk oblast, Russian Federation, which caused flooding due to the increase in the water level of the Iya River that passes through the city, leaving many infrastructures destroyed and thousands of people affected. This study aims to determine the flooded areas in the city of Tulun based on two change detection methods: Radiometric Rotation Controlled by No-change Axis (RCNA) and Ratioing, using Sentinel 2 images obtained before the event (19 June 2019) and during the flood peak (29 June 2019). The results obtained by the two methodologies were compared through cross-classification, and a 98% similarity was found in the classification of the areas. The study was validated based on photointerpretation of Google Earth images. The methodology presented proved to be useful for the automatic precession of flooded areas in a straightforward, but rigorous, manner. This allows stakeholders to efficiently manage areas that are buffeted by flooding episodes. info:eu-repo/semantics/publishedVersion LA/P/0069/2020

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    Other literature type . Article . 2023 . Peer-reviewed
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    Authors: Kimmo Lylykangas; Rebecca Cachia; Damiano Cerrone; Kaie Kriiska; +4 Authors

    The quantification of greenhouse gas (GHG) emissions is increasingly important in spatial planning for regions, cities, and areas. The combination of territorial and consumption-based accounting (CBA) approaches can currently be considered best practice for calculating GHG emissions at sub-national levels, in terms of informing local decision-making about the different climate impacts of spatial planning policies, both within the boundaries of a given region and for the inhabitants of that region. This study introduces four European case studies that were conducted using the two quantification approaches to assess the climate impacts of locally relevant planning policies. The case studies represent different scales of spatial planning, different European planning systems, and different situations in terms of data availability. Territorial results are not suitable for inter-regional comparison, but rather for internal monitoring, while CBA allows for comparison and provides a comprehensive picture of the global carbon footprint of residents, however, with indications that are more difficult to link to spatial planning decisions. Assessing impacts, and in particular interpreting results, requires both methodological understanding and knowledge of the local context. The results of the case studies show that setting climate targets and monitoring the success of climate action through a single net emissions figure can give false indications. The study shows that the two approaches to quantifying GHG emissions provide complementary perspectives on GHG emissions at the sub-national level and thus provide a more thorough understanding of the GHG emission patterns associated with spatial planning policies. The identification of the regional differences in GHG emission sources and mitigation potentials are the main functions of sub-national GHG inventories and the impact assessment for spatial planning. Harmonization of the data collection for sub-national GHG inventories and the transparency of underlying assumptions would greatly support the coherence of climate action and the implications to spatial planning. Funding Information: The results are based on the analyses conducted in the QGasSP project (2020–2021), TA28, funded by ESPON EGTC, Service Contract ref—EE/SO2/114/2020. Publisher Copyright: © 2023 by the authors. Peer reviewed

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    Other literature type . Article . 2023 . Peer-reviewed
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    Authors: Kensuke Yamaguchi; Ryo Tanaka; Masaaki Morita; Toshiya Mori; +1 Authors

    Introduction: Monitoring the volcanic activity of a potentially hazardous volcano is essential for eruption warning and hazard mitigation. The SO2 flux from the volcano is one of the most important measures to understand its activity. Mount Tokachi, in Japan, is an active volcano that experienced three magmatic explosive eruptions in the 20th century (in 1926, 1962, and 1988–1989). Since 2006, geodetic observations have captured ground deformation, suggesting an inflation beneath the main crater. Moreover, since 2020 daily visual observations have detected the increase in plume heights and the occurrence of volcanic glow at the main crater. The high-time-resolution estimation of SO2 flux will help monitor the activity of Mount Tokachi and clarify the associating mechanisms. Furthermore, satellite remote sensing can estimate the vertical column density (VCD) of sulfur dioxide (SO2), enabling the daily determination of SO2 flux without the need to visit the site. Due to the improved spatial resolution, the TROPOspheric Monitoring Instrument (TROPOMI) has advanced satellite-based volcanic gas flux measurements.Methods: We have analyzed the available TROPOMI data and conducted ground-based observations to estimate the SO2 flux from Mount Tokachi at 100–2,000 tons/day.Result and Discussion: The average annual SO2 flux has been increasing since 2021, on par with the increase in plume height and volcanic glow. TROPOMI data enabled quantification of the annual SO2 flux, with a sufficient temporal resolution to monitor the volcanic activity at Mount Tokachi. However, a high flux, such as in excess of 2,000 tons/day, was observed in the winter season. The flux from the satellite data was similar to that from ground-based observations during the summer. However, a seasonal change in flux from the satellite data was observed in winter, with the flux being larger than that in summer, possibly because the flux increases during winter. Another possible reason is the influence of snow cover on satellite observations due to its high surface reflectance. We reanalyzed some TROPOMI data during the winter, in which the ground snow cover was misidentified as clouds at low altitudes. This procedure suppresses the error caused by the high surface reflectance due to winter snow coverage and improves the quality of the annual SO2 flux from satellite observations. This methodology is applicable to other high-latitude or high-altitude volcanoes during specific seasons characterized by frequent fluctuations in snow cover conditions.

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    Frontiers in Earth Science
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    Frontiers in Earth Science
    Article . 2023 . Peer-reviewed
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    Authors: Mansourmoghaddam, Mohammad; Naghipur, Negar; Rousta, Iman; Alavipanah, Seyed Kazem; +8 Authors

    Several earth science investigations depend heavily on knowing the surface energy budget and determining surface temperature. The primary factor affecting the energy balance in the surface physical processes of the planet is the land surface temperature (LST). Even in the case of small-scale green areas like local parks, plants have a significant impact on the climate of cities. The goal of this study was to estimate the construction-related impacts of the Karizland green town (green belt) on the LST of its surroundings over time, for the years 2013 (before construction began), 2015, 2020 and 2022 (after construction was completed). LST values and hot spot analyses were employed for thermal condition evaluation purposes on Landsat-8 satellite images, and normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) indices were used for examining the vegetation change. The results showed that after the establishment of the green town, the mean NDVI and FVC grew by 275% and 950%, respectively, compared to the initial period, which resulted in the addition of approximately 208.35 ha of green space to the study area. In this regard, the results showed that after these changes, compared to the first period, the mean LST decreased by 8%. In addition, the area of the class of hotspot analysis with less than 90% confidence increased by 9%. The results illustrated that almost 20% of the data in the LST range was below 55 °C in 2013, near 57 °C in 2015, and around 51 °C in 2020 and 2022. The results also showed a negative relationship between the distance from the established settlement and the values of NDVI and FVC in 2022 of 91% and 89% and in 2020 of 67% and 69%, respectively. Every year, LST has had a significant negative relationship with the NDVI and FVC of that year and a positive relationship with the LST of the following years, such that the correlation decreases in later years. In order to control LST and the temperature surrounding cities, this research strongly advises managers to develop these green towns.

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    Other literature type . Article . 2023 . Peer-reviewed
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    Article . 2023
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      Other literature type . Article . 2023 . Peer-reviewed
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    Authors: Samaneh Sadat Nickayin; Aubrey Jahelka; Shuwen Ye; Francesca Perrone; +1 Authors

    Self-constructed cities refer to impoverished neighborhoods that suffer from inadequate housing. Such cities are the result of individual or communal initiatives that must self-construct their neighborhoods. Most of the self-constructed cities are located near endangered bioregions on the Earth, and their continued growth will inevitably aggravate the human impact on our planet. Dwellers in these areas tackle threats such as poverty, environmental degradation, and disaster vulnerability. The lack of knowledge in planning self-constructed cities causes conditions of injustice. However, appropriate management of the existing natural capital of the surrounding areas of such cities can address the mentioned challenges and the conditions for justice. This paper aims to evaluate the role of Nature-Based Solutions (NBS) in planning procedures, to create conditions of fairness and equity in self-constructed cities. The paper focused on the linkage between the application of NBS and four principles of justice: distributive justice, restorative justice, spatial justice, and interactional justice. Considering data from a review of the literature, scrutiny of past planning and policy measures and a field study with interviews with stakeholders, the empirical result of our study delineates important strategic implications of Nature-Based Solutions for sustainable planning in the case study of San José de Chamanga, Ecuador. It was demonstrated how small-scale, natural-based interventions, instead of broader actions based on a pure engineering perspective, are economically and ecologically profitable, with a positive impact on local communities’ well-being and social cohesion.

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    Other literature type . Article . 2023 . Peer-reviewed
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      Other literature type . Article . 2023 . Peer-reviewed
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    Authors: Pálsson, Burkni;

    Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing) með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning). Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Ein aðferð með að nýta sér fjölbeitingarlærdóm (e. multitask learning) og hin með að nota sjálfkóðara útfærðan með földunartaugnaneti (e. convolutional neural network). Hvortveggja bætir samkvæmni og hæfni fjölrásagreiningarinnar. Ennfremur inniheldur ritgerðin yfirgripsmikið yfirlit yfir þær sjálfkóðaraaðferðir sem hafa verið birtar ásamt greinargóðri umræðu um mismunandi gerðir sjálfkóðara og útfærslur á þeim. í lok ritgerðarinnar er svo að finna gagnrýninn samanburð á 11 mismunandi aðferðum byggðum á sjálfkóðurum. Brottnáms (e. ablation) tilraunir eru gerðar til að svara spurningunni hvers vegna sjálfkóðarar eru svo árangursríkir í fjölrásagreiningu og stuttlega rætt um hvað framtíðin ber í skauti sér varðandi aðgreiningu fjölrásamynda með sjálfkóðurum. Megin framlag ritgerðarinnar er eftirfarandi: - Ný sjálfkóðaraaðferð, MTLAEU, sem nýtir á beinan hátt rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Aðferðin notar fjölbeitingarlærdóm til að aðgreina grennd af rófum í einu. - Ný aðferð, CNNAEU, sem notar 2D földunartaugnanet fyrir bæði kóðara og afkóðara og er fyrsta birta aðferðin til að gera það. Aðferðin er þjálfuð á myndbútum (e.patches) og því er rúmfræðileg bygging myndarinnar sem greina á varðveitt í gegnum aðferðina. - Yfirgripsmikil og ítarlegt fræðilegt yfirlit yfir birtar sjálfkóðaraaðferðir fyrir fjölrásagreiningu. Gefinn er inngangur að sjálfkóðurum og elstu tegundir sjálfkóðara eru kynntar. Gefið er greinargott yfirlit yfir helstu birtar aðferðir fyrir fjölrásagreiningu sem byggja á sjálfkóðurum og gerður er gangrýninn samburður á 11 mismunandi sjálfkóðaraaðferðum. The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve the performance. One by using multitask learning to simultaneously unmix a neighbourhood of pixels while the other by using a convolutional neural network autoencoder. This increases the consistency and robustness of the methods. In addition, a review of the various autoencoder methods in the literature is given along with a detailed discussion of different types of autoencoders. The thesis concludes by a critical comparison of eleven different autoencoder based methods. Ablation experiments are performed to answer the question of why autoencoders are so effective in blind hyperspectral unmixing, and an opinion is given on what the future in autoencoder unmixing holds. The Icelandic Research Fund under Grants 174075-05 and 207233-051

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    Doctoral thesis . 2023
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    Thesis . 2023
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      Doctoral thesis . 2023
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      Thesis . 2023
      Data sources: Datacite
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    Authors: Sedona, Rocco;

    Advances in remote sensing (RS) missions in recent decades have greatly increased the volume of data that is continually acquired and made available to end users, who can utilize it in a variety of Earth observation (EO) applications. land cover (LC) maps play a key role in monitoring the Earth’s surface, providing scientists and policymakers with an accurate view of the evolution of the landscape and helping them address pressing questions, from efficient resource planning to resilience to climate change. Due to the use of classical machine learning (ML) and more recently of deep learning (DL) methods, the information content of RS data can be exploited to an unprecedented degree, fostering research, development, and deployment of workloads to address open challenges for EO applications, including LC classification. However, the larger size of the datasets needed to train state-of-the-art (SotA) DL models and the need to utilize them at scale increases the time to deployment, which can hinder their effective utilization. Adopting strategies for distributed deep learning (DDL) on high performance computing (HPC) systems provides the opportunity to speed up the training of the models, allowing faster development times for researchers. Since space agencies operate a variety of missions, data acquired by different sensors can be used to increase the temporal resolution at which a certain area is observed, with potential improvements in the accuracy of the ML/DL models. The thesis objectives are formulated with these premises in mind and were investigated using a combination of methodologies to exploit the dedicated resources of HPC systems, contributing to addressing new questions on the adoption of DDL methods for EO applications and to familiarize the RS community with such approaches, which can be of great value. Framför fjarkönnunar á síðustu áratugum hefur leitt til mikillar aukningar í stöðugri öflun gagna sem gerð eru aðgengileg til enda notenda sem geta hagnýtt þau í forritum fyrir skoðun jarðarinnar. Landþekjukort spila lykil hlutverk í eftirliti með yfirborði jarðar. Þau veita vísindafólki og stefnumótendum skýra sýn á þróun í landslaginu og hjálpar þeim að finna svör við aðkallandi vandamálum allt frá skilvirkri skipulagningu auðlinda að þoli jarðar gegn loflagsbreytingum. Vegna notkunnar klassískra aðferða í vélnámi og nýlegrar notkunnar djúptauganetsaðferða er hægt að nýta efni úr fjarkönnun að umfangi sme að ekki hefur sést áður. Það ýtir undir rannsóknir, þróun og dreifingu á nýjum kerfum sem að leita til þess að leysa núverandi áskoranir á sviði EO forrita eins og LC flokkunar. Aukin stærð gagnasafna sem þarf til þess að þjálfa nýjustu SotA DL módel og þörfin til þess að nota þau á stórum skala hefur hins vegar orsakast í auknum tíma þróunar sem að getur komið í veg fyrir skilvirka nýtingu þeirra. Innleiðing stefnu fyrir dreifðan djúplærdóm á ofurtölvukerfum gefur tækifæri til þess að hraða þjálfun módelanna og rannsakendum að auuka hraða í þróun og minnka tíma þangað til virði er fengið úr ferlinu. Þar sem að geimferðastofnanir framkvæma mismunandi verkefni þá er hægt að nota gögnin sem safnað er til þess að auka “temporal resolution” sem að svæði eru skoðuð með, einnig með mögulegum bætingu á nákvæmni í vélnáms og djúplærdóms módelum. Markmið ritgerðarinnar eru gerð út frá þessum forsendum og voru rannsökuð með blöndu af aðferðum til þess að nýta auðlindir í ofurtölvukerfum, leggja af mörkum að svara spurningum um nýtingu djúplærdómsaðferða fyrir EO hugbúnað og til þess að kynna fjarkönnunarsamfélaginu fyrir aðferðum líkt og þessum sem að geta skapað mikið verðmæti.

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      Doctoral thesis . 2023
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