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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.
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.
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.
The successful implementation of the European Commission’s Common Agricultural Policy (CAP) and the insurance coverage in case of a natural disaster requires precise and regular mapping of crop types and detailed delineation of the disasters’ effects by frequent and accurate controls. Free and open access policy to Copernicus Sentinel data offers a big volume of data to the users on a consistent and complete basis. Today, the Sentinels are involved in an increasing number of agriculture applications, but their effective exploitation is still being investigated and the development of efficient tools, aligned to the user’s needs, is yet to be realised. To this end, the DiAS (Disaster and Agriculture Sentinel Applications) project proposes methods for decision support in agriculture using Sentinel data for crop type mapping, as well as mapping of the extend of fire and flood effects in agricultural areas. The DiAS Decision Support System (DSS) is designed in consultation with potential users in participatory approach and aims to provide a prototype tool, which provides assistance to the responsible paying agencies and insurance organizations to make decisions on farmers’ subsidies and compensations. The DiAS DSS prototype and its functionalities are presented in this paper and its use is demonstrated through example applications for two test sites in Greece. The DiAS DSS demonstrates the necessity for the development of similar tools, as this emerges from the user’s requirements, and wishes to stimulate and inspire further research and development.
Zina Mitraka; Sofia Siachalou; Georgia Doxani; Petros Patias. Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications. Sustainability 2020, 12, 1233 .
AMA StyleZina Mitraka, Sofia Siachalou, Georgia Doxani, Petros Patias. Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications. Sustainability. 2020; 12 (3):1233.
Chicago/Turabian StyleZina Mitraka; Sofia Siachalou; Georgia Doxani; Petros Patias. 2020. "Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications." Sustainability 12, no. 3: 1233.
Climate change and increase of extreme weather events, besides the numerous consequences, affect significantly and put in risk the agriculture sectors. Natural disasters, such as floods and wildfires, are responsible for a great loss in agriculture production. National governments together with international bodies make an important effort to cooperate towards the response and resilience when a disaster occurs. In this frame the European Earth Observation Programme - Copernicus provides a series of observation data, in-situ measurements and services related, amongst others, to different types of disasters. Concerning the availability of this big volume of observation data, the aim of DiAS (Disaster and Agriculture Sentinel Applications) project is to revise the existing knowledge on remote sensing methods for mapping the extent of natural and/or man-made disaster over agricultural areas and propose improvements. The developed methodology will be implemented in a Decision Support System (DSS), which will be freely available and easy-to-use by non-experts. In this paper, the developed methodology focuses on mapping floods over agricultural areas. Sentinel-1 and Sentinel-2 imagery are used as input information for the comparison analysis before and after the event. The reference for results’ evaluation is the corresponding information delivered by Copernicus Emergency Management Service (EMS). Although, the evaluation results are in good agreement when they could be used, a reference of higher accuracy is needed in order to estimate accurately the quality of the output products.
G. Doxani; S. Siachalou; Z. Mitraka; P. Patias. DECISION MAKING ON DISASTER MANAGEMENT IN AGRICULTURE WITH SENTINEL APPLICATIONS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-3/W8, 121 -126.
AMA StyleG. Doxani, S. Siachalou, Z. Mitraka, P. Patias. DECISION MAKING ON DISASTER MANAGEMENT IN AGRICULTURE WITH SENTINEL APPLICATIONS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-3/W8 ():121-126.
Chicago/Turabian StyleG. Doxani; S. Siachalou; Z. Mitraka; P. Patias. 2019. "DECISION MAKING ON DISASTER MANAGEMENT IN AGRICULTURE WITH SENTINEL APPLICATIONS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8, no. : 121-126.
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.
Besides new economical, managerial and social challenges associated with growing cities, the modifications caused in the energy budget of the urban surface intensifies the existing urban heat island (UHI). UHI can vary temporally and spatially according to meteorological conditions, landscape and urban typologies. Urban cover and form, as well as anthropogenic activities, pose an important effect on the city’s thermal behaviour that influence UHI and therefore the quality of life of the citizens. In this study, we focus on quantifying the air temperature spatiotemporal patterns across the urban and peri-urban area of Heraklion, Greece at a grid of 100 m x 100 m cells. We use point air temperature observations from the Wireless Sensors Network of Heraklion and interpolate spatially by means of sophisticated geostatistical modelling parameterized with satellite derived predictors. Regression kriging interpolation technique is implemented over the study area, using different predictors to minimize the uncertainty in air temperature estimation. We deal for multicollinearity between predictors and spatio-temporal correlations between measurements. A maximum magnitude of UHI ~ 4 oC has been observed between 04:00-05:00 (UTC+3). Cross-validations indicate a mean MAE ~0.86 oC in the estimated air temperature maps.
Nikolaos Nikoloudakis; Stavros Stagakis; Giorgos Kochilakis; Zina Mitraka; Nektarios Spyridakis; Yiannis Kamarianakis. Estimation of urban air temperature spatial patterns based on sensors network observations and satellite derived predictors. Remote Sensing Technologies and Applications in Urban Environments III 2018, 10793, 1079309 .
AMA StyleNikolaos Nikoloudakis, Stavros Stagakis, Giorgos Kochilakis, Zina Mitraka, Nektarios Spyridakis, Yiannis Kamarianakis. Estimation of urban air temperature spatial patterns based on sensors network observations and satellite derived predictors. Remote Sensing Technologies and Applications in Urban Environments III. 2018; 10793 ():1079309.
Chicago/Turabian StyleNikolaos Nikoloudakis; Stavros Stagakis; Giorgos Kochilakis; Zina Mitraka; Nektarios Spyridakis; Yiannis Kamarianakis. 2018. "Estimation of urban air temperature spatial patterns based on sensors network observations and satellite derived predictors." Remote Sensing Technologies and Applications in Urban Environments III 10793, no. : 1079309.
Zina Mitraka; Nektarios Chrysoulakis. Earth Observation for Urban Climate Monitoring: Surface Cover and Land Surface Temperature. Multi-purposeful Application of Geospatial Data 2018, 1 .
AMA StyleZina Mitraka, Nektarios Chrysoulakis. Earth Observation for Urban Climate Monitoring: Surface Cover and Land Surface Temperature. Multi-purposeful Application of Geospatial Data. 2018; ():1.
Chicago/Turabian StyleZina Mitraka; Nektarios Chrysoulakis. 2018. "Earth Observation for Urban Climate Monitoring: Surface Cover and Land Surface Temperature." Multi-purposeful Application of Geospatial Data , no. : 1.
This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm’s implementation to any area of interest. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated “on-the-fly” and are available on-line via a web application.
David Parastatidis; Zina Mitraka; Nektrarios Chrysoulakis; Michael Abrams. Online Global Land Surface Temperature Estimation from Landsat. Remote Sensing 2017, 9, 1208 .
AMA StyleDavid Parastatidis, Zina Mitraka, Nektrarios Chrysoulakis, Michael Abrams. Online Global Land Surface Temperature Estimation from Landsat. Remote Sensing. 2017; 9 (12):1208.
Chicago/Turabian StyleDavid Parastatidis; Zina Mitraka; Nektrarios Chrysoulakis; Michael Abrams. 2017. "Online Global Land Surface Temperature Estimation from Landsat." Remote Sensing 9, no. 12: 1208.
To better understand the life-essential cycles and processes of our planet and to further develop remote sensing (RS) technology, there is an increasing need for models that simulate the radiative budget (RB) and RS acquisitions of urban and natural landscapes using physical approaches and considering the three-dimensional (3-D) architecture of Earth surfaces. Discrete anisotropic radiative transfer (DART) is one of the most comprehensive physically based 3-D models of Earth-atmosphere radiative transfer, covering the spectral domain from ultraviolet to thermal infrared wavelengths. It simulates the optical 3-D RB and optical signals of proximal, aerial, and satellite imaging spectrometers and laser scanners, for any urban and/or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. In this paper, we briefly introduce DART theory and present recent advances in simulated sensors (LiDAR and cameras with finite field of view) and modeling mechanisms (atmosphere, specular reflectance with polarization and chlorophyll fluorescence). A case study demonstrating a novel application of DART to investigate urban landscapes is also presented.
Jean Philippe Gastellu-Etchegorry; Nicolas Lauret; Tiangang Yin; Lucas Landier; Abdelaziz Kallel; Zbyněk Malenovský; Ahmad Al Bitar; Josselin Aval; Sahar Benhmida; Jianbo Qi; Ghania Medjdoub; Jordan Guilleux; Eric Chavanon; Bruce Cook; Douglas Morton; Nektarios Chrysoulakis; Zina Mitraka. DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2017, 10, 2640 -2649.
AMA StyleJean Philippe Gastellu-Etchegorry, Nicolas Lauret, Tiangang Yin, Lucas Landier, Abdelaziz Kallel, Zbyněk Malenovský, Ahmad Al Bitar, Josselin Aval, Sahar Benhmida, Jianbo Qi, Ghania Medjdoub, Jordan Guilleux, Eric Chavanon, Bruce Cook, Douglas Morton, Nektarios Chrysoulakis, Zina Mitraka. DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017; 10 (6):2640-2649.
Chicago/Turabian StyleJean Philippe Gastellu-Etchegorry; Nicolas Lauret; Tiangang Yin; Lucas Landier; Abdelaziz Kallel; Zbyněk Malenovský; Ahmad Al Bitar; Josselin Aval; Sahar Benhmida; Jianbo Qi; Ghania Medjdoub; Jordan Guilleux; Eric Chavanon; Bruce Cook; Douglas Morton; Nektarios Chrysoulakis; Zina Mitraka. 2017. "DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 6: 2640-2649.
Nektarios Chrysoulakis; Mattia Marconcini; Jean-Philippe Gastellu-Etchegorry; C.S.B. Grimmond; Christian Feigenwinter; Fredrik Lindberg; Fabio Del Frate; Judith Klostermann; Zina Mitraka; Thomas Esch; Lucas Landier; Andy Gabey; Eberhard Parlow; Frans Olofson. Anthropogenic heat flux estimation from space: results of the first phase of the URBANFLUXES project. Remote Sensing Technologies and Applications in Urban Environments II 2016, 100080C -100080C-9.
AMA StyleNektarios Chrysoulakis, Mattia Marconcini, Jean-Philippe Gastellu-Etchegorry, C.S.B. Grimmond, Christian Feigenwinter, Fredrik Lindberg, Fabio Del Frate, Judith Klostermann, Zina Mitraka, Thomas Esch, Lucas Landier, Andy Gabey, Eberhard Parlow, Frans Olofson. Anthropogenic heat flux estimation from space: results of the first phase of the URBANFLUXES project. Remote Sensing Technologies and Applications in Urban Environments II. 2016; ():100080C-100080C-9.
Chicago/Turabian StyleNektarios Chrysoulakis; Mattia Marconcini; Jean-Philippe Gastellu-Etchegorry; C.S.B. Grimmond; Christian Feigenwinter; Fredrik Lindberg; Fabio Del Frate; Judith Klostermann; Zina Mitraka; Thomas Esch; Lucas Landier; Andy Gabey; Eberhard Parlow; Frans Olofson. 2016. "Anthropogenic heat flux estimation from space: results of the first phase of the URBANFLUXES project." Remote Sensing Technologies and Applications in Urban Environments II , no. : 100080C-100080C-9.
Lucas Landier; Nicolas Lauret; Tiangang Yin; JeanPhilippe Gastellu-Etchegorry Ahmad Al Bitar; Christian Feigenwinter; Eberhard Parlow; Zina Mitraka; Nektarios Chrysoulakis. Remote Sensing Studies of Urban Canopies: 3D Radiative Transfer Modeling. Sustainable Urbanization 2016, 1 .
AMA StyleLucas Landier, Nicolas Lauret, Tiangang Yin, JeanPhilippe Gastellu-Etchegorry Ahmad Al Bitar, Christian Feigenwinter, Eberhard Parlow, Zina Mitraka, Nektarios Chrysoulakis. Remote Sensing Studies of Urban Canopies: 3D Radiative Transfer Modeling. Sustainable Urbanization. 2016; ():1.
Chicago/Turabian StyleLucas Landier; Nicolas Lauret; Tiangang Yin; JeanPhilippe Gastellu-Etchegorry Ahmad Al Bitar; Christian Feigenwinter; Eberhard Parlow; Zina Mitraka; Nektarios Chrysoulakis. 2016. "Remote Sensing Studies of Urban Canopies: 3D Radiative Transfer Modeling." Sustainable Urbanization , no. : 1.
Giannis Lantzanakis; Zina Mitraka; Nektarios Chrysoulakis. Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery. Fourth International Conference on Remote Sensing and Geoinformation of the Environment 2016, 96880A -96880A-6.
AMA StyleGiannis Lantzanakis, Zina Mitraka, Nektarios Chrysoulakis. Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery. Fourth International Conference on Remote Sensing and Geoinformation of the Environment. 2016; ():96880A-96880A-6.
Chicago/Turabian StyleGiannis Lantzanakis; Zina Mitraka; Nektarios Chrysoulakis. 2016. "Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery." Fourth International Conference on Remote Sensing and Geoinformation of the Environment , no. : 96880A-96880A-6.
Detailed, frequent, and accurate land surface temperature (LST) estimates from satellites may support various applications related to the urban climate. When satellite-retrieved LST is used in modeling, the level of uncertainty is important to account for. In this letter, an uncertainty estimation scheme based on Monte Carlo simulations is proposed for local-scale LST products derived from image fusion. The downscaling algorithm combines frequent low-resolution thermal measurements with surface cover information from high spatial resolution imagery. The uncertainty is estimated for all the intermediate products, allowing the analysis of individual uncertainties and their contribution to the final LST product. Uncertainties of less than 2 K was found for most part of the test area. The uncertainty estimation method, although demanding in terms of computations, can be useful for the uncertainty analysis of other satellite products.
Zina Mitraka; Georgia Doxani; Fabio Del Frate; Nektarios Chrysoulakis. Uncertainty Estimation of Local-Scale Land Surface Temperature Products Over Urban Areas Using Monte Carlo Simulations. IEEE Geoscience and Remote Sensing Letters 2016, 13, 917 -921.
AMA StyleZina Mitraka, Georgia Doxani, Fabio Del Frate, Nektarios Chrysoulakis. Uncertainty Estimation of Local-Scale Land Surface Temperature Products Over Urban Areas Using Monte Carlo Simulations. IEEE Geoscience and Remote Sensing Letters. 2016; 13 (7):917-921.
Chicago/Turabian StyleZina Mitraka; Georgia Doxani; Fabio Del Frate; Nektarios Chrysoulakis. 2016. "Uncertainty Estimation of Local-Scale Land Surface Temperature Products Over Urban Areas Using Monte Carlo Simulations." IEEE Geoscience and Remote Sensing Letters 13, no. 7: 917-921.
The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit frequency (five days with the pair of satellites in operation) will play a fundamental role in recording land cover types and monitoring land cover changes at regular intervals. Nevertheless, cloud coverage usually hinders the time series availability and consequently the continuous land surface monitoring. In an attempt to alleviate this limitation, the synergistic use of instruments with different features is investigated, aiming at the future synergy of the S-2 MultiSpectral Instrument (MSI) and Sentinel-3 (S-3) Ocean and Land Colour Instrument (OLCI). To that end, an unmixing model is proposed with the intention of integrating the benefits of the two Sentinel missions, when both in orbit, in one composite image. The main goal is to fill the data gaps in the S-2 record, based on the more frequent information of the S-3 time series. The proposed fusion model has been applied on MODIS (MOD09GA L2G) and SPOT4 (Take 5) data and the experimental results have demonstrated that the approach has high potential. However, the different acquisition characteristics of the sensors, i.e. illumination and viewing geometry, should be taken into consideration and bidirectional effects correction has to be performed in order to reduce noise in the reflectance time series.
Georgia Doxani; Zina Mitraka; Ferran Gascon; Philippe Goryl; Bojan R. Bojkov. A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data. Remote Sensing 2015, 7, 14000 -14018.
AMA StyleGeorgia Doxani, Zina Mitraka, Ferran Gascon, Philippe Goryl, Bojan R. Bojkov. A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data. Remote Sensing. 2015; 7 (10):14000-14018.
Chicago/Turabian StyleGeorgia Doxani; Zina Mitraka; Ferran Gascon; Philippe Goryl; Bojan R. Bojkov. 2015. "A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data." Remote Sensing 7, no. 10: 14000-14018.
The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities.
Zina Mitraka; Nektarios Chrysoulakis; Georgia Doxani; Fabio Del Frate; Michael Berger. Urban Surface Temperature Time Series Estimation at the Local Scale by Spatial-Spectral Unmixing of Satellite Observations. Remote Sensing 2015, 7, 4139 -4156.
AMA StyleZina Mitraka, Nektarios Chrysoulakis, Georgia Doxani, Fabio Del Frate, Michael Berger. Urban Surface Temperature Time Series Estimation at the Local Scale by Spatial-Spectral Unmixing of Satellite Observations. Remote Sensing. 2015; 7 (4):4139-4156.
Chicago/Turabian StyleZina Mitraka; Nektarios Chrysoulakis; Georgia Doxani; Fabio Del Frate; Michael Berger. 2015. "Urban Surface Temperature Time Series Estimation at the Local Scale by Spatial-Spectral Unmixing of Satellite Observations." Remote Sensing 7, no. 4: 4139-4156.
Dimitris Poursanidis; Nektarios Chrysoulakis; Zina Mitraka. Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping. International Journal of Applied Earth Observation and Geoinformation 2015, 35, 259 -269.
AMA StyleDimitris Poursanidis, Nektarios Chrysoulakis, Zina Mitraka. Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping. International Journal of Applied Earth Observation and Geoinformation. 2015; 35 ():259-269.
Chicago/Turabian StyleDimitris Poursanidis; Nektarios Chrysoulakis; Zina Mitraka. 2015. "Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping." International Journal of Applied Earth Observation and Geoinformation 35, no. : 259-269.
Deciding upon optimum planning actions in terms of sustainable urban planning involves the consideration of multiple environmental and socio-economic criteria. The transformation of natural landscapes to urban areas affects energy and material fluxes. An important aspect of the urban environment is the urban metabolism, and changes in such metabolism need to be considered for sustainable planning decisions. A spatial Decision Support System (DSS) prototyped within the European FP7-funded project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism), enables accounting for the urban metabolism of planning actions, by exploiting the current knowledge and technology of biophysical sciences. The main aim of the BRIDGE project was to bridge the knowledge and communication gap between urban planners and environmental scientists and to illustrate the advantages of considering detailed environmental information in urban planning processes. The developed DSS prototype integrates biophysical observations and simulation techniques with socio-economic aspects in five European cities, selected as case studies for the pilot application of the tool. This paper describes the design and implementation of the BRIDGE DSS prototype, illustrates some examples of use, and highlights the need for further research and development in the field.
Zina Mitraka; Emmanouil Diamantakis; Nektarios Chrysoulakis; Eduardo Anselmo Castro; Roberto San Jose; Ainhoa Gonzalez; Ivan Blecic. Incorporating Bio-Physical Sciences into a Decision Support Tool for Sustainable Urban Planning. Sustainability 2014, 6, 7982 -8006.
AMA StyleZina Mitraka, Emmanouil Diamantakis, Nektarios Chrysoulakis, Eduardo Anselmo Castro, Roberto San Jose, Ainhoa Gonzalez, Ivan Blecic. Incorporating Bio-Physical Sciences into a Decision Support Tool for Sustainable Urban Planning. Sustainability. 2014; 6 (11):7982-8006.
Chicago/Turabian StyleZina Mitraka; Emmanouil Diamantakis; Nektarios Chrysoulakis; Eduardo Anselmo Castro; Roberto San Jose; Ainhoa Gonzalez; Ivan Blecic. 2014. "Incorporating Bio-Physical Sciences into a Decision Support Tool for Sustainable Urban Planning." Sustainability 6, no. 11: 7982-8006.
Urban metabolism considers a city as a system with flows of energy and material between it and the environment. Recent advances in bio-physical sciences provide methods and models to estimate local scale energy, water, carbon and pollutant fluxes. However, good communication is required to provide this new knowledge and its implications to endusers (such as urban planners, architects and engineers). The FP7 project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism) aimed to address this gap by illustrating the advantages of considering these issues in urban planning. The BRIDGE Decision Support System (DSS) aids the evaluation of the sustainability of urban planning interventions. The Multi Criteria Analysis approach adopted provides a method to cope with the complexity of urban metabolism. In consultation with targeted end-users, objectives were defined in relation to the interactions between the environmental elements (fluxes of energy, water, carbon and pollutants) and socioeconomic components (investment costs, housing, employment, etc.) of urban sustainability. The tool was tested in five case study cities: Helsinki, Athens, London, Florence and Gliwice; and sub-models were evaluated using flux data selected. This overview of the BRIDGE project covers the methods and tools used to measure and model the physical flows, the selected set of sustainability indicators, the methodological framework for evaluating urban planning alternatives and the resulting DSS prototype.
Nektarios Chrysoulakis; Myriam Lopes; Roberto San José; Christine Susan Betham Grimmond; Mike B. Jones; Vincenzo Magliulo; Judith E.M. Klostermann; Afroditi Synnefa; Zina Mitraka; Eduardo A. Castro; Ainhoa González; Roland Vogt; Timo Vesala; Donatella Spano; Gregoire Pigeon; Peter Freer-Smith; Tomasz Staszewski; Nick Hodges; Gerald Mills; Constantinos Cartalis. Sustainable urban metabolism as a link between bio-physical sciences and urban planning: The BRIDGE project. Landscape and Urban Planning 2013, 112, 100 -117.
AMA StyleNektarios Chrysoulakis, Myriam Lopes, Roberto San José, Christine Susan Betham Grimmond, Mike B. Jones, Vincenzo Magliulo, Judith E.M. Klostermann, Afroditi Synnefa, Zina Mitraka, Eduardo A. Castro, Ainhoa González, Roland Vogt, Timo Vesala, Donatella Spano, Gregoire Pigeon, Peter Freer-Smith, Tomasz Staszewski, Nick Hodges, Gerald Mills, Constantinos Cartalis. Sustainable urban metabolism as a link between bio-physical sciences and urban planning: The BRIDGE project. Landscape and Urban Planning. 2013; 112 ():100-117.
Chicago/Turabian StyleNektarios Chrysoulakis; Myriam Lopes; Roberto San José; Christine Susan Betham Grimmond; Mike B. Jones; Vincenzo Magliulo; Judith E.M. Klostermann; Afroditi Synnefa; Zina Mitraka; Eduardo A. Castro; Ainhoa González; Roland Vogt; Timo Vesala; Donatella Spano; Gregoire Pigeon; Peter Freer-Smith; Tomasz Staszewski; Nick Hodges; Gerald Mills; Constantinos Cartalis. 2013. "Sustainable urban metabolism as a link between bio-physical sciences and urban planning: The BRIDGE project." Landscape and Urban Planning 112, no. : 100-117.
Information about the spatial distribution of urban surface emissivity is essential for surface temperature estimation which is an important component of urban microclimate and it is critical in many applications, like turbulent sensible and latent heat fluxes estimation, energy budget, urban canopy modeling, bio-climatic studies and urban planning. The proposed method presents an improvement in emissivity estimation as compared with existing methods, such as the look-up table approach, wherein emissivity and other biophysical parameters are assigned to grid cells based on land cover types. The basic premise of this method is a sub-pixel classification of urban surface into vegetation, impervious and soil, based on spectral mixture analysis. The proposed approach was applied to Landsat-7 ETM + observations over the area of Athens, Greece. Spatial distributions of surface emissivity, as well as land surface temperature in the spectral region of 10.4–12.5 μm were derived. ASTER (Advanced Spectral Reflection and Emission Radiometer) emissivity and surface temperature products were used for evaluation.
Z. Mitraka; N. Chrysoulakis. Satellite Based Estimation of Urban Surface Emissivity with the Use of Sub-Pixel Classification Techniques. Springer Atmospheric Sciences 2012, 231 -237.
AMA StyleZ. Mitraka, N. Chrysoulakis. Satellite Based Estimation of Urban Surface Emissivity with the Use of Sub-Pixel Classification Techniques. Springer Atmospheric Sciences. 2012; ():231-237.
Chicago/Turabian StyleZ. Mitraka; N. Chrysoulakis. 2012. "Satellite Based Estimation of Urban Surface Emissivity with the Use of Sub-Pixel Classification Techniques." Springer Atmospheric Sciences , no. : 231-237.