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Climate change influences the vulnerability of urban populations worldwide. To improve their adaptive capacity, the implementation of nature-based solutions (NBS) in urban areas has been identified as an appropriate action, giving urban planning and development an important role towards climate change adaptation/mitigation and risk management and resilience. However, the importance of extensively applying NBS is still underestimated, especially regarding its potential to induce significantly positive environmental and socioeconomic impacts across cities. Concerning environmental impacts, monitoring and evaluation is an important step of NBS management, where earth observation (EO) can contribute. EO is known for providing valuable disaggregated data to assess the modifications caused by NBS implementation in terms of land cover, whereas the potential of EO to uncover the role of NBS in urban metabolism modifications (e.g., energy, water, and carbon fluxes and balances) still remains underexplored. This study reviews the EO potential in the monitoring and evaluation of NBS implementation in cities, indicating that satellite observations combined with data from complementary sources may provide an evidence-based approach in terms of NBS adaptive management. EO-based tools can be applied to assess NBS’ impacts on urban energy, water, and carbon balances, further improving our understanding of urban systems dynamics and supporting sustainable urbanization.
Nektarios Chrysoulakis; Giorgos Somarakis; Stavros Stagakis; Zina Mitraka; Man-Sing Wong; Hung-Chak Ho. Monitoring and Evaluating Nature-Based Solutions Implementation in Urban Areas by Means of Earth Observation. Remote Sensing 2021, 13, 1503 .
AMA StyleNektarios Chrysoulakis, Giorgos Somarakis, Stavros Stagakis, Zina Mitraka, Man-Sing Wong, Hung-Chak Ho. Monitoring and Evaluating Nature-Based Solutions Implementation in Urban Areas by Means of Earth Observation. Remote Sensing. 2021; 13 (8):1503.
Chicago/Turabian StyleNektarios Chrysoulakis; Giorgos Somarakis; Stavros Stagakis; Zina Mitraka; Man-Sing Wong; Hung-Chak Ho. 2021. "Monitoring and Evaluating Nature-Based Solutions Implementation in Urban Areas by Means of Earth Observation." Remote Sensing 13, no. 8: 1503.
Recent advances in deep learning techniques for object detection and the availability of high-resolution images facilitate the analysis of both temporal and spatial vegetation patterns in remote areas. High-resolution satellite imagery has been used successfully to detect trees in small areas with homogeneous rather than heterogeneous forests, in which single tree species have a strong contrast compared to their neighbors and landscape. However, no research to date has detected trees at the treeline in the remote and complex heterogeneous landscape of Greece using deep learning methods. We integrated high-resolution aerial images, climate data, and topographical characteristics to study the treeline dynamic over 70 years in the Samaria National Park on the Mediterranean island of Crete, Greece. We combined mapping techniques with deep learning approaches to detect and analyze spatio-temporal dynamics in treeline position and tree density. We use visual image interpretation to detect single trees on high-resolution aerial imagery from 1945, 2008, and 2015. Using the RGB aerial images from 2008 and 2015 we test a Convolution Neural Networks (CNN)-object detection approach (SSD) and a CNN-based segmentation technique (U-Net). Based on the mapping and deep learning approach, we have not detected a shift in treeline elevation over the last 70 years, despite warming, although tree density has increased. However, we show that CNN approach accurately detects and maps tree position and density at the treeline. We also reveal that the treeline elevation on Crete varies with topography. Treeline elevation decreases from the southern to the northern study sites. We explain these differences between study sites by the long-term interaction between topographical characteristics and meteorological factors. The study highlights the feasibility of using deep learning and high-resolution imagery as a promising technique for monitoring forests in remote areas.
Mirela Beloiu; Dimitris Poursanidis; Samuel Hoffmann; Nektarios Chrysoulakis; Carl Beierkuhnlein. Using high‐resolution aerial imagery and deep learning to detect tree spatio-temporal dynamics at the treeline. 2021, 1 .
AMA StyleMirela Beloiu, Dimitris Poursanidis, Samuel Hoffmann, Nektarios Chrysoulakis, Carl Beierkuhnlein. Using high‐resolution aerial imagery and deep learning to detect tree spatio-temporal dynamics at the treeline. . 2021; ():1.
Chicago/Turabian StyleMirela Beloiu; Dimitris Poursanidis; Samuel Hoffmann; Nektarios Chrysoulakis; Carl Beierkuhnlein. 2021. "Using high‐resolution aerial imagery and deep learning to detect tree spatio-temporal dynamics at the treeline." , no. : 1.
The rate at which global climate change is happening is arguably the most pressing environmental challenge of the century and it affects our cities. Temperature is one of the most important parameters in climate monitoring and Earth Observation (EO) systems and the advances in remote sensing science increase the opportunities for monitoring the surface temperature from space. The EO4UTEMP project examines the exploitation of EO data for monitoring the urban surface temperature (UST). Large variations in surface temperatures can be observed within a couple of hours, particularly when referring to urban surfaces. The geometric, radiative, thermal, and aerodynamic properties of the urban surface are unique and exert particularly strong control on the surface temperature. EO satellites provide excellent means for mapping the land surface temperature, but the particular properties of the urban surface and the unique urban geometry in combination with the trade-off between temporal and spatial resolution of the current satellite missions impose the development of new sophisticated surface temperature retrieval methods particularly designed for urban areas. EO4TEMP develops a novel UST algorithm exploiting multi-temporal, multi-sensor, multi-resolution EO data, to be validated with in-situ measurements in urban sites and to be applied to Sentinel-3 and Sentinel-2 data. Therefore, EO4UTEMP will provide an advanced methodology for deriving frequent UST estimations at local scale (100 m), capable of resolving the diurnal variation of UST and contribute to the study of the urban energy balance.
Zina Mitraka; Nektarios Chrysoulakis. Large Scale Exploitation of Satellite Data for the Assessment of Urban Surface Temperatures. 2021, 1 .
AMA StyleZina Mitraka, Nektarios Chrysoulakis. Large Scale Exploitation of Satellite Data for the Assessment of Urban Surface Temperatures. . 2021; ():1.
Chicago/Turabian StyleZina Mitraka; Nektarios Chrysoulakis. 2021. "Large Scale Exploitation of Satellite Data for the Assessment of Urban Surface Temperatures." , no. : 1.
Urban areas around the globe are growing rapidly and as a consequence the anthropogenic effects on the environment are ever-increasing. Understanding the dynamics, procedures and mechanics behind urban greenhouse gas emissions is a challenge for the scientific community. This study investigates the variability of urban CO2 emissions in the city centre of Heraklion, a typical Mediterranean city in Greece, during a four-year period with gradual changes in the traffic regulations and changes in traffic patterns due to the recent restriction measures imposed to limit the spread of the COVID-19 pandemic. The CO2 flux (Fc) was measured using the Eddy Covariance (EC) method with a single tower-based system, permanently installed in the centre of the city. Fc was calculated at a 30-min time step and the time-series were quality-controlled and gap-filled using a moving look-up table (mLUT) technique. Fc time series were then aggregated to monthly and yearly emissions totals. Annual flux source area was estimated with the Flux Footprint Prediction (FFP) model, parameterized using measured atmospheric parameters and urban morphological parameters extracted from a Digital Surface Model. The source area was characterized by complex urban morphology and land use types. Specifically, at North of the tower a commercial zone is located, where significantly higher Fc patterns were detected, compared to South, where a residential area dominates. A gradual reduction to CO2 emissions has been observed since 2016, due to urban planning interventions related to pedestalization of extended areas in the city centre and traffic regulation. During the COVID-19 lockdown period in the Spring of 2020, the diurnal Fc patterns and the monthly aggregated Fc showed significant reductions in the order of 70 % compared to the previous years. Fc values returned to the previous years’ levels with the end of the lock-down in the summer 2020, as it was expected. Finally, during the second lock-down, started in Greece in November 2020, the CO2 emissions were higher compared to the first lock-down, reflecting a higher level of mobility in Heraklion centre.
Konstantinos Politakos; Stavros Stagakis; Nektarios Chrysoulakis. Carbon dioxide emissions variability monitoring, based on four years of Eddy Covariance measurements in a typical Mediterranean city. 2021, 1 .
AMA StyleKonstantinos Politakos, Stavros Stagakis, Nektarios Chrysoulakis. Carbon dioxide emissions variability monitoring, based on four years of Eddy Covariance measurements in a typical Mediterranean city. . 2021; ():1.
Chicago/Turabian StyleKonstantinos Politakos; Stavros Stagakis; Nektarios Chrysoulakis. 2021. "Carbon dioxide emissions variability monitoring, based on four years of Eddy Covariance measurements in a typical Mediterranean city." , no. : 1.
The characterization of the Earth’s surface cover based on predefined classes is among the fundamental activities in the domain of satellite image analysis image since the early 70s. It was the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites that start to continuously acquired images of the Earth's land surface, providing uninterrupted data to help land managers and policymakers make informed decisions about natural resources and the environment. However, in 2020, the collected data even if are of continuous flow in terms volume of terrabytes per day from various optical and radar systems, are limited in terms of spectral resolution since almost all sensors are limited to a maximum of 25 spectral channels in the visible, near-and-shortwave-and-thermal infrared spectrum. The need of denser spectral information has been highlighted in early 80s and the first satellite-based hyperspectral sensor, AVIRIS, start to provide data allowing the extraction information on material composition and precise surface cover information. Since then few attempt appear but more are undergoing for launching. In 2019, the Italian Space Agency launch the PRISMA hyperspectral satellite which collect spectral data in the 400-2500nm spectrum; in total 250 spectral channels with a spectral width of ~ 12nm, at 30m pixel size. Here we present first results of the use of Level 2D PRISMA hyperspectral data in mapping the surface characteristics of the urban and periurban area of Heraklion city along with the coastal zone of the urban front aiming at the simultaneous creation of a land-and-coastal cover map along with the extraction of coastal bathymetry information using artificial intelligence approaches within open access platforms. The use of hyperspectral information allow the separation of urban surfaces based on material signatures, while the availability of dense spectral information in the blue-green spectrum allow the more accurate retrieval of coastal seascape characteristics. It is envisaged that hyperspectral missions soon to be the normal in Earth Observation, allowing the accurate creation of geospatial information for further use in several applications.
Dimitris Poursanidis; Nektarios Chrysoulakis. PRISMA Hyperspectral – First insights in the performance in urban surface cover and coastal seascape analysis. 2021, 1 .
AMA StyleDimitris Poursanidis, Nektarios Chrysoulakis. PRISMA Hyperspectral – First insights in the performance in urban surface cover and coastal seascape analysis. . 2021; ():1.
Chicago/Turabian StyleDimitris Poursanidis; Nektarios Chrysoulakis. 2021. "PRISMA Hyperspectral – First insights in the performance in urban surface cover and coastal seascape analysis." , no. : 1.
Resilience has become an important necessity for cities, particularly in the face of climate change. Mitigation and adaptation actions that enhance the resilience of cities need to be based on a sound understanding and quantification of the drivers of urban transformation and settlement structures, human and urban vulnerability, and of local and global climate change. Copernicus, as the means for the establishment of a European capacity for Earth Observation (EO), is based on continuously evolving Core Services. A major challenge for the EO community is the innovative exploitation of the Copernicus products in dealing with urban sustainability towards increasing urban resilience. Due to the multidimensional nature of urban resilience, to meet this challenge, information from more than one Copernicus Core Services, namely the Land Monitoring Service (CLMS), the Atmosphere Monitoring Service (CAMS), the Climate Change Service (C3S) and the Emergency Management Service (EMS), is needed. Furthermore, to address urban resilience, the urban planning community needs spatially disaggregated environmental information at local (neighbourhood) scale. Such information, for all parameters needed, is not yet directly available from the Copernicus Core Services mentioned above, while several elements - data and products - from contemporary satellite missions consist valuable tools for retrieving urban environmental parameters at local scale. The H2020-Space project CURE (Copernicus for Urban Resilience in Europe) is a joint effort of 10 partners from 9 countries that synergistically exploits the above Copernicus Core Services to develop an umbrella cross-cutting application for urban resilience, consisting of individual cross-cutting applications for climate change adaptation/mitigation, energy and economy, as well as healthy cities and social environments, at several European cities. These cross-cutting applications cope with the required scale and granularity by also integrating or exploiting third-party data, in-situ observations and modelling. CURE uses DIAS (Data and Information Access Services) to develop a system capable of supporting operational applications and downstream services across Europe. The CURE system hosts the developed cross-cutting applications, enabling its incorporation into operational services in the future. CURE is expected to increase the value of Copernicus Core Services for future emerging applications in the domain of urban resilience, exploiting also the improved data quality, coverage and revisit times of the future satellite missions. Thus, CURE will lead to more efficient routine urban planning activities with obvious socioeconomic impact, as well as to more efficient resilience planning activities related to climate change mitigation and adaptation, resulting in improved thermal comfort and air quality, as well as in enhanced energy efficiency. Specific CURE outcomes could be integrated into the operational Copernicus service portfolio. The added value and benefit expected to emerge from CURE is related to transformed urban governance and quality of life, because it is expected to provide improved and integrated information to city administrators, hence effectively supporting strategies for resilience planning at local and city scales, towards the implementation of the Sustainable Development Goals and the New Urban Agenda for Europe.
Nektarios Chrysoulakis; Zina Mitraka; Mattia Marconcini; David Ludlow; Zaheer A. Khan; Brigitte Holt Andersen; Tomas Soukup; Mario Dohr; Alessandro Gandini; Juergen Kropp; Dirk Lauwaet; Christian Feigenwinter. Copernicus for Urban Resilience in Europe: the CURE Project (Conference Presentation). Remote Sensing Technologies and Applications in Urban Environments V 2020, 11535, 1153503 .
AMA StyleNektarios Chrysoulakis, Zina Mitraka, Mattia Marconcini, David Ludlow, Zaheer A. Khan, Brigitte Holt Andersen, Tomas Soukup, Mario Dohr, Alessandro Gandini, Juergen Kropp, Dirk Lauwaet, Christian Feigenwinter. Copernicus for Urban Resilience in Europe: the CURE Project (Conference Presentation). Remote Sensing Technologies and Applications in Urban Environments V. 2020; 11535 ():1153503.
Chicago/Turabian StyleNektarios Chrysoulakis; Zina Mitraka; Mattia Marconcini; David Ludlow; Zaheer A. Khan; Brigitte Holt Andersen; Tomas Soukup; Mario Dohr; Alessandro Gandini; Juergen Kropp; Dirk Lauwaet; Christian Feigenwinter. 2020. "Copernicus for Urban Resilience in Europe: the CURE Project (Conference Presentation)." Remote Sensing Technologies and Applications in Urban Environments V 11535, no. : 1153503.
The accurate land cover mapping of the Earth's surface using Earth observation data is one of the most studied, but yet the most challenging tasks of remote sensing field, particularly when it comes to urban areas. The large spectral variability of man-made structures, as well as the mixed pixel phenomenon, imposes the use of computational demanding techniques, which are not always effective for real case applications. Support vector machines (SVMs) are supervised learning models with associated learning algorithms, which are mainly used for classification and regression analysis. Specifically, a support vector classifier (SVC) constructs a hyperplane or a set of hyperplanes in a high-dimensional space, which separates the training data into different classes. These are then used to classify a whole image, or series of images. The current standard SVM algorithm for classification used by the most popular mapping software (e.g., ENVI, EnMAP) is the C-SVC. The parameterization of a C-SVC strongly affects the final classification result. Yet, there is no rule of thumb to choose the optimal parameters when classifying satellite imagery. Optimal parameterization totally depends on the training data, and to determine it for a specific case, a time-consuming trial-and-error process is inevitable. In this work, advancements for the C-SVC algorithm are proposed to enhance its performance when used to classify remote sensing data, eliminating the need for a part of manual parametrization, while ensuring increasing its performance.
Giannis Lantzanakis; Zina Mitraka; Nektarios Chrysoulakis. X-SVM: An Extension of C-SVM Algorithm for Classification of High-Resolution Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 3805 -3815.
AMA StyleGiannis Lantzanakis, Zina Mitraka, Nektarios Chrysoulakis. X-SVM: An Extension of C-SVM Algorithm for Classification of High-Resolution Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (5):3805-3815.
Chicago/Turabian StyleGiannis Lantzanakis; Zina Mitraka; Nektarios Chrysoulakis. 2020. "X-SVM: An Extension of C-SVM Algorithm for Classification of High-Resolution Satellite Imagery." IEEE Transactions on Geoscience and Remote Sensing 59, no. 5: 3805-3815.
Resilience has become an important necessity for cities, particularly in the face of climate change. Mitigation and adaptation actions that enhance the resilience of cities need to be based on a sound understanding and quantification of the drivers of urban transformation and settlement structures, human and urban vulnerability, and of local and global climate change. Copernicus, as the means for the establishment of a European capacity for Earth Observation (EO), is based on continuously evolving Core Services. A major challenge for the EO community is the innovative exploitation of the Copernicus products in dealing with urban sustainability towards increasing urban resilience. Due to the multidimensional nature of urban resilience, to meet this challenge, information from more than one Copernicus Core Services, namely the Land Monitoring Service (CLMS), the Atmosphere Monitoring Service (CAMS), the Climate Change Service (C3S) and the Emergency Management Service (EMS), is needed. Furthermore, to address urban resilience, the urban planning community needs spatially disaggregated environmental information at local (neighbourhood) scale. Such information, for all parameters needed, is not yet directly available from the Copernicus Core Services mentioned above, while several elements - data and products - from contemporary satellite missions consist valuable tools for retrieving urban environmental parameters at local scale. The H2020-Space project CURE (Copernicus for Urban Resilience in Europe) is a joint effort of 10 partners from 9 countries that synergistically exploits the above Copernicus Core Services to develop an umbrella cross-cutting application for urban resilience, consisting of individual cross-cutting applications for climate change adaptation/mitigation, energy and economy, as well as healthy cities and social environments, at several European cities. These cross-cutting applications cope with the required scale and granularity by also integrating or exploiting third-party data, in-situ observations and modelling. CURE uses DIAS (Data and Information Access Services) to develop a system capable of supporting operational applications and downstream services across Europe. The CURE system hosts the developed cross-cutting applications, enabling its incorporation into operational services in the future. CURE is expected to increase the value of Copernicus Core Services for future emerging applications in the domain of urban resilience, exploiting also the improved data quality, coverage and revisit times of the future satellite missions. Thus, CURE will lead to more efficient routine urban planning activities with obvious socioeconomic impact, as well as to more efficient resilience planning activities related to climate change mitigation and adaptation, resulting in improved thermal comfort and air quality, as well as in enhanced energy efficiency. Specific CURE outcomes could be integrated into the operational Copernicus service portfolio. The added value and benefit expected to emerge from CURE is related to transformed urban governance and quality of life, because it is expected to provide improved and integrated information to city administrators, hence effectively supporting strategies for resilience planning at local and city scales, towards the implementation of the Sustainable Development Goals and the New Urban Agenda for Europe.
Nektarios Chrysoulakis; Zina Mitraka; Mattia Marconcini; David Ludlow; Zaheer Khan; Brigitte Holt Andersen; Tomas Soukup; Mario Dohr; Alessandra Gandini; Jürgen Kropp; Dirk Lauwaet; Christian Feigenwinter. Copernicus for Urban Resilience in Europe: the CURE Project. 2020, 1 .
AMA StyleNektarios Chrysoulakis, Zina Mitraka, Mattia Marconcini, David Ludlow, Zaheer Khan, Brigitte Holt Andersen, Tomas Soukup, Mario Dohr, Alessandra Gandini, Jürgen Kropp, Dirk Lauwaet, Christian Feigenwinter. Copernicus for Urban Resilience in Europe: the CURE Project. . 2020; ():1.
Chicago/Turabian StyleNektarios Chrysoulakis; Zina Mitraka; Mattia Marconcini; David Ludlow; Zaheer Khan; Brigitte Holt Andersen; Tomas Soukup; Mario Dohr; Alessandra Gandini; Jürgen Kropp; Dirk Lauwaet; Christian Feigenwinter. 2020. "Copernicus for Urban Resilience in Europe: the CURE Project." , no. : 1.
Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST–NDVI relations over Europe, using long-term (2000–2017) MODIS images. Overlaying the LST–NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March–August). During the spring season (March–May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June–August), most of the biomes—except the northern ones—turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST–NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes.
Arnon Karnieli; Noa Ohana-Levi; Micha Silver; Tarin Paz-Kagan; Natalya Panov; Dani Varghese; Nektarios Chrysoulakis; Antonello Provenzale. Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe. Remote Sensing 2019, 11, 2406 .
AMA StyleArnon Karnieli, Noa Ohana-Levi, Micha Silver, Tarin Paz-Kagan, Natalya Panov, Dani Varghese, Nektarios Chrysoulakis, Antonello Provenzale. Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe. Remote Sensing. 2019; 11 (20):2406.
Chicago/Turabian StyleArnon Karnieli; Noa Ohana-Levi; Micha Silver; Tarin Paz-Kagan; Natalya Panov; Dani Varghese; Nektarios Chrysoulakis; Antonello Provenzale. 2019. "Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe." Remote Sensing 11, no. 20: 2406.
The global coastal seascape offers a multitude of ecosystem functions and services to the natural and human-induced ecosystems. However, the current anthropogenic global warming above pre-industrial levels is inducing the degradation of seascape health with adverse impacts on biodiversity, economy, and societies. Bathymetric knowledge empowers our scientific, financial, and ecological understanding of the associated benefits, processes, and pressures to the coastal seascape. Here we leverage two commercial high-resolution multispectral satellite images of the Pleiades and two multibeam survey datasets to measure bathymetry in two zones (0–10 m and 10–30 m) in the tropical Anguilla and British Virgin Islands, northeast Caribbean. A methodological framework featuring a combination of an empirical linear transformation, cloud masking, sun-glint correction, and pseudo-invariant features allows spatially independent calibration and test of our satellite-derived bathymetry approach. The best R2 and RMSE for training and validation vary between 0.44–0.56 and 1.39–1.76 m, respectively, while minimum vertical errors are less than 1 m in the depth ranges of 7.8–10 and 11.6–18.4 m for the two explored zones. Given available field data, the present methodology could provide simple, time-efficient, and accurate spatio-temporal satellite-derived bathymetry intelligence in scientific and commercial tasks i.e., navigation, coastal habitat mapping and resource management, and reducing natural hazards.
Samuel Pike; Dimosthenis Traganos; Dimitris Poursanidis; Jamie Williams; Katie Medcalf; Peter Reinartz; Nektarios Chrysoulakis. Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea. Remote Sensing 2019, 11, 1830 .
AMA StyleSamuel Pike, Dimosthenis Traganos, Dimitris Poursanidis, Jamie Williams, Katie Medcalf, Peter Reinartz, Nektarios Chrysoulakis. Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea. Remote Sensing. 2019; 11 (15):1830.
Chicago/Turabian StyleSamuel Pike; Dimosthenis Traganos; Dimitris Poursanidis; Jamie Williams; Katie Medcalf; Peter Reinartz; Nektarios Chrysoulakis. 2019. "Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea." Remote Sensing 11, no. 15: 1830.
High spatial and temporal resolution satellite remote sensing estimates are the silver bullet for monitoring of coastal marine areas globally. From 2000, when the first commercial satellite platforms appeared, offering high spatial resolution data, the mapping of coastal habitats and the extraction of bathymetric information have been possible at local scales. Since then, several platforms have offered such data, although not at high temporal resolution, making the selection of suitable images challenging, especially in areas with high cloud coverage. PlanetScope CubeSats appear to cover this gap by providing their relevant imagery. The current study is the first that examines the suitability of them for the calculation of the Satellite-derived Bathymetry. The availability of daily data allows the selection of the most qualitatively suitable images within the desired timeframe. The application of an empirical method of spaceborne bathymetry estimation provides promising results, with depth errors that fit to the requirements of the International Hydrographic Organization at the Category Zone of Confidence for the inclusion of these data in navigation maps. While this is a pilot study in a small area, more studies in areas with diverse water types are required for solid conclusions on the requirements and limitations of such approaches in coastal bathymetry estimations.
Dimitris Poursanidis; Dimosthenis Traganos; Nektarios Chrysoulakis; Peter Reinartz. Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry. Remote Sensing 2019, 11, 1299 .
AMA StyleDimitris Poursanidis, Dimosthenis Traganos, Nektarios Chrysoulakis, Peter Reinartz. Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry. Remote Sensing. 2019; 11 (11):1299.
Chicago/Turabian StyleDimitris Poursanidis; Dimosthenis Traganos; Nektarios Chrysoulakis; Peter Reinartz. 2019. "Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry." Remote Sensing 11, no. 11: 1299.
Coastal habitats provide a plethora of ecosystem services, yet they undergo continuous pressure and degradation due to the human-induced climate change. Conservation and management imply continuous monitoring and mapping of their spatial distribution at first. The present study explores the capabilities of the Copernicus Sentinel-2 mission and the contribution of its coastal aerosol band 1 (443 nm) for the mapping of the dominant Mediterranean coastal marine habitats and the bathymetry in three survey sites in the East Mediterranean. The selected sites have shallow to deep habitats and a high variability of oceanographic and seabed morphological conditions. The major findings of our study demonstrate the advantages of the downscaled Sentinel-2 coastal aerosol band 1 for both optically shallow habitat and satellite-derived bathymetry mapping due to its great water penetration. The use of Sentinel-2 band 1 allows detection of Posidonia oceanica seagrass beds down to 32.2 m of depth. Sentinel-2 constellation with its 10-m spatial resolution at most of the spectral bands, 5-day revisit frequency and open data policy can be an important tool to provide crucial missing information on the spatial distribution of coastal habitats and on their bathymetry distribution, especially in data-poor and/or remote areas with large gaps in a retrospective, rapid and non-intrusive manner. As such, it becomes a crucial ally for the conservation and management of coastal habitats globally.
Dimitris Poursanidis; Dimosthenis Traganos; Peter Reinartz; Nektarios Chrysoulakis. On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band. International Journal of Applied Earth Observation and Geoinformation 2019, 80, 58 -70.
AMA StyleDimitris Poursanidis, Dimosthenis Traganos, Peter Reinartz, Nektarios Chrysoulakis. On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band. International Journal of Applied Earth Observation and Geoinformation. 2019; 80 ():58-70.
Chicago/Turabian StyleDimitris Poursanidis; Dimosthenis Traganos; Peter Reinartz; Nektarios Chrysoulakis. 2019. "On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band." International Journal of Applied Earth Observation and Geoinformation 80, no. : 58-70.
Climate change and other drivers are affecting ecosystems around the globe. In order to enable a better understanding of ecosystem functioning and to develop mitigation and adaptation strategies in response to environmental change, a broad range of information, including in-situ observations of both biotic and abiotic parameters, needs to be considered. Access to sufficient and well documented in-situ data from long term observations is therefore one of the key requirements for modelling and assessing the effects of global change on ecosystems. Usually, such data is generated by multiple providers; often not openly available and with improper documentation. In this regard, metadata plays an important role in aiding the findability, accessibility and reusability of data as well as enabling reproducibility of the results leading to management decisions. This metadata needs to include information on the observation location and the research context. For this purpose we developed the Dynamic Ecological Information Management System – Site and Dataset Registry (DEIMS-SDR), a research and monitoring site registry (https://www.deims.org/) that not only makes it possible to describe in-situ observation or experimentation sites, generating persistent, unique and resolvable identifiers for each site, but also to document associated data linked to each site. This article describes the system architecture and illustrates the linkage of contextual information to observational data. The aim of DEIMS-SDR is to be a globally comprehensive site catalogue describing a wide range of sites, providing a wealth of information, including each site's location, ecosystems, facilities, measured parameters and research themes and enabling that standardised information to be openly available.
Christoph Wohner; Johannes Peterseil; Dimitris Poursanidis; Tomáš Kliment; Mike Wilson; Michael Mirtl; Nektarios Chrysoulakis. DEIMS-SDR – A web portal to document research sites and their associated data. Ecological Informatics 2019, 51, 15 -24.
AMA StyleChristoph Wohner, Johannes Peterseil, Dimitris Poursanidis, Tomáš Kliment, Mike Wilson, Michael Mirtl, Nektarios Chrysoulakis. DEIMS-SDR – A web portal to document research sites and their associated data. Ecological Informatics. 2019; 51 ():15-24.
Chicago/Turabian StyleChristoph Wohner; Johannes Peterseil; Dimitris Poursanidis; Tomáš Kliment; Mike Wilson; Michael Mirtl; Nektarios Chrysoulakis. 2019. "DEIMS-SDR – A web portal to document research sites and their associated data." Ecological Informatics 51, no. : 15-24.
Cities are now becoming the focus for CO2 emission reduction efforts worldwide and there is a growing need for establishing emission inventories, developing methodologies for improved CO2 monitoring and understanding of the multiple source, sink and storage processes inside the complex urban environment. This study combines Eddy Covariance (EC) measurements of CO2 flux (FC) during one year period over the city centre of Heraklion with analytical morphological and land cover data to achieve a thorough investigation of the spatiotemporal variability and the controlling factors of FC in the urban setting. The detailed characterization of the urban land cover and 3D structure was performed using high resolution Earth Observation data. The urban morphological data was used to parameterize a turbulent flux source area model and the land cover data used to analyse the contribution of the source area components to the measured FC. Heraklion FC does not present any specific seasonal trend according to the meteorological or vegetation changes throughout the year. Heraklion centre behaves as a net emitter diurnally and throughout the year. The diurnal variability presents a standard pattern in weekdays with a major peak in midday and a secondary in afternoon, clearly following the traffic rush-hour peaks. Space heating emissions during winter remain low, hardly affecting the seasonal and diurnal FC patterns. The main CO2 sources are the major roads and the intersections, where the heavy traffic is located. The source area analysis revealed that traffic contributes 69 % to the estimated annual CO2 emissions. Space heating contributes only 11.6 %, while human metabolism is estimated to contribute 19.4 %. Vegetation cover of the source area is very low and was assumed to have minor effect to the annual FC budget. The estimated total annual emissions of Heraklion case study reach 19.4 kg CO2 m-2 y-1.
Stavros Stagakis; Nektarios Chrysoulakis; Nektarios Spyridakis; Christian Feigenwinter; Roland Vogt. Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece. Atmospheric Environment 2019, 201, 278 -292.
AMA StyleStavros Stagakis, Nektarios Chrysoulakis, Nektarios Spyridakis, Christian Feigenwinter, Roland Vogt. Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece. Atmospheric Environment. 2019; 201 ():278-292.
Chicago/Turabian StyleStavros Stagakis; Nektarios Chrysoulakis; Nektarios Spyridakis; Christian Feigenwinter; Roland Vogt. 2019. "Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece." Atmospheric Environment 201, no. : 278-292.
Surface albedo is one of the essential climate variables as it influences the radiation budget and the energy balance. Because it is used in a variety of scientific fields, from local to global scale, spatially and temporally disaggregated albedo data are required, which can be derived from satellites. Satellite observations have led to directional-hemispherical (black-sky) and bi-hemispherical (white-sky) albedo products, but time series of high spatial resolution true (blue-sky) albedo estimations at global level are not available. Here, we exploit the capabilities of Google Earth Engine (GEE) for big data analysis to derive global snow-free land surface albedo estimations and trends at a 500-m scale, using satellite observations from 2000 to 2015. Our study reveals negative albedo trends mainly in Mediterranean, India, south-western Africa and Eastern Australia, whereas positive trends mainly in Ukraine, South Russia and Eastern Kazakhstan, Eastern Asia, Brazil, Central and Eastern Africa and Central Australia. The bulk of these trends can be attributed to rainfall, changes in agricultural practices and snow cover duration. Our study also confirms that at local scale, albedo changes are consistent with land cover/use changes that are driven by anthropogenic activities.
Nektarios Chrysoulakis; Zina Mitraka; Noel Gorelick. Exploiting satellite observations for global surface albedo trends monitoring. Theoretical and Applied Climatology 2018, 137, 1171 -1179.
AMA StyleNektarios Chrysoulakis, Zina Mitraka, Noel Gorelick. Exploiting satellite observations for global surface albedo trends monitoring. Theoretical and Applied Climatology. 2018; 137 (1-2):1171-1179.
Chicago/Turabian StyleNektarios Chrysoulakis; Zina Mitraka; Noel Gorelick. 2018. "Exploiting satellite observations for global surface albedo trends monitoring." Theoretical and Applied Climatology 137, no. 1-2: 1171-1179.
Air quality monitoring across Europe is mainly based on in situ ground stations, which are too sparse to accurately assess the exposure effects of air pollution for the entire continent. The demand for precise predictive models that estimate gridded geophysical parameters of ambient air at high spatial resolution has rapidly grown. Here, we investigate the potential of satellite-derived products to improve particulate matter (PM) estimates. Bayesian geostatistical models addressing confounding between the spatial distribution of pollutants and remotely sensed predictors were developed to estimate yearly averages of both, fine (PM2.5) and coarse (PM10) surface PM concentrations, at 1 km2 spatial resolution over 46 European countries. Model outcomes were compared to geostatistical, geographically weighted and land-use regression formulations. Rigorous model selection identified the Earth observation data which contribute most to pollutants' estimation. Geostatistical models outperformed the predictive ability of the frequently employed land-use regression. The resulting estimates of PM10 and PM2.5, which represent the main air quality indicators for the urban Sustainable Development Goal, indicate that in 2016, 66.2% of the European population was breathing air above the WHO air quality guidelines thresholds. Our estimates are readily available to policy makers and scientists assessing the effects of long-term exposure to pollution on human and ecosystem health.
Anton Beloconi; Nektarios Chrysoulakis; Alexei Lyapustin; Jürg Utzinger; Penelope Vounatsou. Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products. Environment International 2018, 121, 57 -70.
AMA StyleAnton Beloconi, Nektarios Chrysoulakis, Alexei Lyapustin, Jürg Utzinger, Penelope Vounatsou. Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products. Environment International. 2018; 121 ():57-70.
Chicago/Turabian StyleAnton Beloconi; Nektarios Chrysoulakis; Alexei Lyapustin; Jürg Utzinger; Penelope Vounatsou. 2018. "Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products." Environment International 121, no. : 57-70.
Seagrasses are traversing the epoch of intense anthropogenic impacts that significantly decrease their coverage and invaluable ecosystem services, necessitating accurate and adaptable, global-scale mapping and monitoring solutions. Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop a methodological workflow for large-scale, high spatiotemporal mapping and monitoring of seagrass habitats. The present workflow can be easily tuned to space, time and data input; here, we show its potential, mapping 2510.1 km2 of P. oceanica seagrasses in an area of 40,951 km2 between 0 and 40 m of depth in the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution. The overall accuracy of P. oceanica seagrass habitats features an overall accuracy of 72% following validation by an independent field data set to reduce bias. We envision that the introduced flexible, time- and cost-efficient cloud-based chain will provide the crucial seasonal to interannual baseline mapping and monitoring of seagrass ecosystems in global scale, resolving gain and loss trends and assisting coastal conservation, management planning, and ultimately climate change mitigation.
Dimosthenis Traganos; Bharat Aggarwal; Dimitris Poursanidis; Konstantinos Topouzelis; Nektarios Chrysoulakis; Peter Reinartz. Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas. Remote Sensing 2018, 10, 1227 .
AMA StyleDimosthenis Traganos, Bharat Aggarwal, Dimitris Poursanidis, Konstantinos Topouzelis, Nektarios Chrysoulakis, Peter Reinartz. Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas. Remote Sensing. 2018; 10 (8):1227.
Chicago/Turabian StyleDimosthenis Traganos; Bharat Aggarwal; Dimitris Poursanidis; Konstantinos Topouzelis; Nektarios Chrysoulakis; Peter Reinartz. 2018. "Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas." Remote Sensing 10, no. 8: 1227.
One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. Satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget fluxes, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (−64.1, +69.3 W m−2 for ±2 K perturbation). They also underestimate anthropogenic heat fluxes. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling.
Nektarios Chrysoulakis; Sue Grimmond; Christian Feigenwinter; Fredrik Lindberg; Jean Philippe Gastellu-Etchegorry; Mattia Marconcini; Zina Mitraka; Stavros Stagakis; Ben Crawford; Frans Olofson; Lucas Landier; William Morrison; Eberhard Parlow. Urban energy exchanges monitoring from space. Scientific Reports 2018, 8, 1 -8.
AMA StyleNektarios Chrysoulakis, Sue Grimmond, Christian Feigenwinter, Fredrik Lindberg, Jean Philippe Gastellu-Etchegorry, Mattia Marconcini, Zina Mitraka, Stavros Stagakis, Ben Crawford, Frans Olofson, Lucas Landier, William Morrison, Eberhard Parlow. Urban energy exchanges monitoring from space. Scientific Reports. 2018; 8 (1):1-8.
Chicago/Turabian StyleNektarios Chrysoulakis; Sue Grimmond; Christian Feigenwinter; Fredrik Lindberg; Jean Philippe Gastellu-Etchegorry; Mattia Marconcini; Zina Mitraka; Stavros Stagakis; Ben Crawford; Frans Olofson; Lucas Landier; William Morrison; Eberhard Parlow. 2018. "Urban energy exchanges monitoring from space." Scientific Reports 8, no. 1: 1-8.
Seagrass meadows are one of the most important coastal habitats across the globe. These are mainly constituted by the marine plants of the genus Posidonia and Thalassia. In the Mediterranean Sea, Posidonia oceanica is the dominant endemic plant that affects physical, biogeochemical, and biological processes. The decline in the spatial distribution has been attributed to excessive anthropic pressures and other large-scale environmental changes. The monitoring of the spatial distribution requires an update and accurate seagrass meadows delineation, i.e. meadow edge marking with a replicable method. The present study aims to present an approach to support the coastal marine habitat mapping, under the scheme of the Natura 2000 network using very high resolution Earth observation data and to prove that satellite images can be used for the mapping of the deep limits of the seagrass meadows. Pixel-based classification and object-oriented image analysis have been implemented for the image classification. Pixel-based Support Vector Machines and object-based Nearest Neighbor classifiers provided the best results with an overall accuracy of more than 90%, while deep limits have been successfully identified and separated from the deep waters.
Dimitris Poursanidis; Konstantinos Topouzelis; Nektarios Chrysoulakis. Mapping coastal marine habitats and delineating the deep limits of the Neptune’s seagrass meadows using very high resolution Earth observation data. International Journal of Remote Sensing 2018, 39, 8670 -8687.
AMA StyleDimitris Poursanidis, Konstantinos Topouzelis, Nektarios Chrysoulakis. Mapping coastal marine habitats and delineating the deep limits of the Neptune’s seagrass meadows using very high resolution Earth observation data. International Journal of Remote Sensing. 2018; 39 (23):8670-8687.
Chicago/Turabian StyleDimitris Poursanidis; Konstantinos Topouzelis; Nektarios Chrysoulakis. 2018. "Mapping coastal marine habitats and delineating the deep limits of the Neptune’s seagrass meadows using very high resolution Earth observation data." International Journal of Remote Sensing 39, no. 23: 8670-8687.
Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic‐related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi‐sensor/multi‐platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate.
Damiano Pasetto; Salvador Arenas‐Castro; Javier Bustamante; Renato Casagrandi; Nektarios Chrysoulakis; Anna F. Cord; Andreas Dittrich; Cristina Domingo-Marimon; Ghada El Serafy; Arnon Karnieli; Georgios A. Kordelas; Ioannis Manakos; Lorenzo Mari; Antonio Monteiro; Elisa Palazzi; Dimitris Poursanidis; Andrea Rinaldo; Silvia Terzago; Alexander Ziemba; Guy Ziv. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends. Methods in Ecology and Evolution 2018, 9, 1810 -1821.
AMA StyleDamiano Pasetto, Salvador Arenas‐Castro, Javier Bustamante, Renato Casagrandi, Nektarios Chrysoulakis, Anna F. Cord, Andreas Dittrich, Cristina Domingo-Marimon, Ghada El Serafy, Arnon Karnieli, Georgios A. Kordelas, Ioannis Manakos, Lorenzo Mari, Antonio Monteiro, Elisa Palazzi, Dimitris Poursanidis, Andrea Rinaldo, Silvia Terzago, Alexander Ziemba, Guy Ziv. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends. Methods in Ecology and Evolution. 2018; 9 (8):1810-1821.
Chicago/Turabian StyleDamiano Pasetto; Salvador Arenas‐Castro; Javier Bustamante; Renato Casagrandi; Nektarios Chrysoulakis; Anna F. Cord; Andreas Dittrich; Cristina Domingo-Marimon; Ghada El Serafy; Arnon Karnieli; Georgios A. Kordelas; Ioannis Manakos; Lorenzo Mari; Antonio Monteiro; Elisa Palazzi; Dimitris Poursanidis; Andrea Rinaldo; Silvia Terzago; Alexander Ziemba; Guy Ziv. 2018. "Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends." Methods in Ecology and Evolution 9, no. 8: 1810-1821.