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Remote sensing and machine learning researcher. He holds BSc and PhD degrees in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece. Since 2014, he has been a member of the Laboratory of Forest Management and Remote Sensing of AUTh, where he has been involved as affiliated researcher at various EU-funded and national projects. Since 2016, he has also been a member of the rice unit of the Institute of Plant Breeding and Genetic Resources of the Hellenic Agricultural Organization – “DEMETER”. His research focuses on remote sensing and GIS applications in environmental monitoring, with emphasis on forest fire management and land cover/land use mapping.
Safety in touristic destinations is of utmost importance since tourists’ preferences change frequently in response to emerging threats. Natural hazards are a significant risk and, as such, they need to be considered in the effort for safe tourism. Services and systems monitoring and predicting extreme natural phenomena and disasters in sites of special tourist and cultural interest can lead to more effective risk management and incident response. This paper presents Xenios, a system under development in Greece that provides early warning and risk communication services via web-based and mobile phone applications. We present the user requirements analysis contacted, which led to the design of a modular system architecture through a formal Business Process Model procedure. Currently, early warning systems for wildfire, floods, and extreme weather events are offered, based on a fusion of information from satellite imagery, meteorological forecasts, and risk estimation models. Moreover, visitors’ dispersion monitoring via unmanned aerial vehicles (UAVs) and Wi-Fi connection signals is also offered, along with emergency response planning and ticketing system’s interfacing. The system is built around a modular architecture that permits the easy integration of new subsystems or other danger forecasting modules, depending on the site’s actual needs and limitations. Xenios also provides a mobile app for site visitors, which establishes a communication link for sending alarms, but also serves them with useful tourist information, so that they are encouraged to download and use the app. Finally, the opportunities for supporting a viable business model are also discussed. The results of this study could prove useful in designing other natural risk management systems for sites of cultural and natural interest.
Chrysostomos Psaroudakis; Gavriil Xanthopoulos; Dimitris Stavrakoudis; Antonios Barnias; Vassiliki Varela; Ilias Gkotsis; Anna Karvouniari; Spyridon Agorgianitis; Ioannis Chasiotis; Diamando Vlachogiannis; Athanasios Sfetsos; Konstantinos Kaoukis; Aikaterini Christopoulou; Petros Antakis; Ioannis Gitas. Development of an Early Warning and Incident Response System for the Protection of Visitors from Natural Hazards in Important Outdoor Sites in Greece. Sustainability 2021, 13, 5143 .
AMA StyleChrysostomos Psaroudakis, Gavriil Xanthopoulos, Dimitris Stavrakoudis, Antonios Barnias, Vassiliki Varela, Ilias Gkotsis, Anna Karvouniari, Spyridon Agorgianitis, Ioannis Chasiotis, Diamando Vlachogiannis, Athanasios Sfetsos, Konstantinos Kaoukis, Aikaterini Christopoulou, Petros Antakis, Ioannis Gitas. Development of an Early Warning and Incident Response System for the Protection of Visitors from Natural Hazards in Important Outdoor Sites in Greece. Sustainability. 2021; 13 (9):5143.
Chicago/Turabian StyleChrysostomos Psaroudakis; Gavriil Xanthopoulos; Dimitris Stavrakoudis; Antonios Barnias; Vassiliki Varela; Ilias Gkotsis; Anna Karvouniari; Spyridon Agorgianitis; Ioannis Chasiotis; Diamando Vlachogiannis; Athanasios Sfetsos; Konstantinos Kaoukis; Aikaterini Christopoulou; Petros Antakis; Ioannis Gitas. 2021. "Development of an Early Warning and Incident Response System for the Protection of Visitors from Natural Hazards in Important Outdoor Sites in Greece." Sustainability 13, no. 9: 5143.
Over the past 2 decades, several global burned area products have been produced and released to the public. However, the accuracy assessment of such products largely depends on the availability of reliable reference data that currently do not exist on a global scale or whose production require a high level of dedication of project resources. The important lack of reference data for the validation of burned area products is addressed in this paper. We provide the Burned Area Reference Database (BARD), the first publicly available database created by compiling existing reference BA (burned area) datasets from different international projects. BARD contains a total of 2661 reference files derived from Landsat and Sentinel-2 imagery. All those files have been checked for internal quality and are freely provided by the authors. To ensure database consistency, all files were transformed to a common format and were properly documented by following metadata standards. The goal of generating this database was to give BA algorithm developers and product testers reference information that would help them to develop or validate new BA products. BARD is freely available at https://doi.org/10.21950/BBQQU7 (Franquesa et al., 2020).
Magí Franquesa; Melanie K. Vanderhoof; Dimitris Stavrakoudis; Ioannis Z. Gitas; Ekhi Roteta; Marc Padilla; Emilio Chuvieco. Development of a standard database of reference sites for validating global burned area products. Earth System Science Data 2020, 12, 3229 -3246.
AMA StyleMagí Franquesa, Melanie K. Vanderhoof, Dimitris Stavrakoudis, Ioannis Z. Gitas, Ekhi Roteta, Marc Padilla, Emilio Chuvieco. Development of a standard database of reference sites for validating global burned area products. Earth System Science Data. 2020; 12 (4):3229-3246.
Chicago/Turabian StyleMagí Franquesa; Melanie K. Vanderhoof; Dimitris Stavrakoudis; Ioannis Z. Gitas; Ekhi Roteta; Marc Padilla; Emilio Chuvieco. 2020. "Development of a standard database of reference sites for validating global burned area products." Earth System Science Data 12, no. 4: 3229-3246.
Surface fuel load (SFL) constitutes one of the most significant fuel components and is used as an input variable in most fire behavior prediction systems. The aim of the present study was to investigate the potential of discrete-return multispectral Light Detection and Ranging (LiDAR) data to reliably predict SFL in a coniferous forest characterized by dense overstory and complex terrain. In particular, a linear regression analysis workflow was employed with the separate and combined use of LiDAR-derived structural and pulse intensity information for the load estimation of the total surface fuels and individual surface fuel types. Following a leave-one-out cross-validation (LOOCV) approach, the models developed from the different sets of predictor variables were compared in terms of their estimation accuracy. LOOCV indicated that the predictive models produced by the combined use of structural and intensity metrics significantly outperformed the models constructed with the individual sets of metrics, exhibiting an explained variance (R2) between 0.59 and 0.71 (relative Root Mean Square Error (RMSE) 19.3–37.6%). Overall, the results of this research showcase that both structural and intensity variables provided by multispectral LiDAR data are significant for surface fuel load estimation and can successfully contribute to effective pre-fire management, including fire risk assessment and behavior prediction in case of a fire event.
Alexandra Stefanidou; Ioannis Z. Gitas; Lauri Korhonen; Nikos Georgopoulos; Dimitris Stavrakoudis. Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest. Remote Sensing 2020, 12, 3333 .
AMA StyleAlexandra Stefanidou, Ioannis Z. Gitas, Lauri Korhonen, Nikos Georgopoulos, Dimitris Stavrakoudis. Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest. Remote Sensing. 2020; 12 (20):3333.
Chicago/Turabian StyleAlexandra Stefanidou; Ioannis Z. Gitas; Lauri Korhonen; Nikos Georgopoulos; Dimitris Stavrakoudis. 2020. "Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest." Remote Sensing 12, no. 20: 3333.
The authors would like to correct certain errors inadvertently made in Section 3
Alexandra Stefanidou; Ioannis Z. Gitas; Lauri Korhonen; Dimitris Stavrakoudis; Nikos Georgopoulos. Erratum: Stefanidou, A., et al. LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest. Remote Sensing 2020, 12, 1565. Remote Sensing 2020, 12, 3116 .
AMA StyleAlexandra Stefanidou, Ioannis Z. Gitas, Lauri Korhonen, Dimitris Stavrakoudis, Nikos Georgopoulos. Erratum: Stefanidou, A., et al. LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest. Remote Sensing 2020, 12, 1565. Remote Sensing. 2020; 12 (19):3116.
Chicago/Turabian StyleAlexandra Stefanidou; Ioannis Z. Gitas; Lauri Korhonen; Dimitris Stavrakoudis; Nikos Georgopoulos. 2020. "Erratum: Stefanidou, A., et al. LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest. Remote Sensing 2020, 12, 1565." Remote Sensing 12, no. 19: 3116.
Accurate canopy base height (CBH) information is essential for forest and fire managers since it constitutes a key indicator of seedling growth, wood quality and forest health as well as a necessary input in fire behavior prediction systems such as FARSITE, FlamMap and BEHAVE. The present study focused on the potential of airborne LiDAR data analysis to estimate plot-level CBH in a dense uneven-aged structured forest on complex terrain. A comparative study of two widely employed methods was performed, namely the voxel-based approach and regression analysis, which revealed a clear outperformance of the latter. More specifically, the voxel-based CBH estimates were found to lack correlation with the reference data ( R 2 = 0.15 , r R M S E = 42.36 % ) while most CBH values were overestimated resulting in an r b i a s of − 17.52 % . On the contrary, cross-validation of the developed regression model showcased an R 2 , r R M S E and r b i a s of 0 . 61 , 18.19 % and − 0.09 % respectively. Overall analysis of the results proved the voxel-based approach incapable of accurately estimating plot-level CBH due to vegetation and topographic heterogeneity of the forest environment, which however didn’t affect the regression analysis performance.
Alexandra Stefanidou; Ioannis Gitas; Lauri Korhonen; Dimitris Stavrakoudis; Nikos Georgopoulos. LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest. Remote Sensing 2020, 12, 1565 .
AMA StyleAlexandra Stefanidou, Ioannis Gitas, Lauri Korhonen, Dimitris Stavrakoudis, Nikos Georgopoulos. LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest. Remote Sensing. 2020; 12 (10):1565.
Chicago/Turabian StyleAlexandra Stefanidou; Ioannis Gitas; Lauri Korhonen; Dimitris Stavrakoudis; Nikos Georgopoulos. 2020. "LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest." Remote Sensing 12, no. 10: 1565.
Over the past two decades, several global burned area products have been produced and released to the public. However, the accuracy assessment of such products largely depends on the availability of reliable reference data that currently does not exist on a global scale or whose production requires high level dedication of project resources. The important lack of reference data for the validation of burned area products is addressed in this paper. We provide the first Burned Area Reference Database (BARD) that was created by compiling existing reference burned area datasets from different international projects. The Database contains a total of 2769 reference burned area files derived from Landsat or Sentinel-2 imagery. All reference files have been checked for internal quality and are freely provided by the authors. To ensure database consistency, all files were transformed to a common format and were properly documented by following metadata standards. This should help future users of this database to read and convert the files to their own preferred formats or projections. The database is freely available at: https://doi.org/10.21950/BBQQU7 (Franquesa et al., 2020).
Magí Franquesa; Melanie K. Vanderhoof; Renata Libonati; Julia A. Rodrigues; Alberto W. Setzer; Dimitris Stavrakoudis; Ioannis Z. Gitas; Ekhi Roteta; Marc Padilla; Emilio Chuvieco. Development of a standard database of reference sites for validating global burned area products. 2020, 2020, 1 -20.
AMA StyleMagí Franquesa, Melanie K. Vanderhoof, Renata Libonati, Julia A. Rodrigues, Alberto W. Setzer, Dimitris Stavrakoudis, Ioannis Z. Gitas, Ekhi Roteta, Marc Padilla, Emilio Chuvieco. Development of a standard database of reference sites for validating global burned area products. . 2020; 2020 ():1-20.
Chicago/Turabian StyleMagí Franquesa; Melanie K. Vanderhoof; Renata Libonati; Julia A. Rodrigues; Alberto W. Setzer; Dimitris Stavrakoudis; Ioannis Z. Gitas; Ekhi Roteta; Marc Padilla; Emilio Chuvieco. 2020. "Development of a standard database of reference sites for validating global burned area products." 2020, no. : 1-20.
Wildfires constitute a significant environmental pressure in Europe, particularly in the Mediterranean countries. The prediction of fire danger is essential for sustainable forest fire management since it provides critical information for designing effective prevention measures and for facilitating response planning to potential fire events. This study presents a new midterm fire danger index (MFDI) using satellite and auxiliary geographic data. The proposed methodology is based on estimations of a dry fuel connectivity measure calculated from the Moderate Imaging Spectrometer (MODIS) time-series data, which are combined with biophysical and topological variables to obtain accurate fire ignition danger predictions for the following eight days. The index’s accuracy was assessed using historical fire data from four large wildfires in Greece. The results showcase that the index predicted high fire danger (≥3 on a scale within [ 1 , 4 ] ) within the identified fire ignition areas, proving its strong potential for deriving reliable estimations of fire danger, despite the fact that no meteorological measurements or forecasts are used for its calculation.
Alexandra Stefanidou; Ioannis Z. Gitas; Dimitris Stavrakoudis; Georgios Eftychidis. Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers. Remote Sensing 2019, 11, 2786 .
AMA StyleAlexandra Stefanidou, Ioannis Z. Gitas, Dimitris Stavrakoudis, Georgios Eftychidis. Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers. Remote Sensing. 2019; 11 (23):2786.
Chicago/Turabian StyleAlexandra Stefanidou; Ioannis Z. Gitas; Dimitris Stavrakoudis; Georgios Eftychidis. 2019. "Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers." Remote Sensing 11, no. 23: 2786.
Rice is the major staple crop worldwide, whereas fertilization practices include mainly the application of synthetic fertilizers. A novel compost was developed using 74% of rice industrial by-products (rice bran and husks) and tested in rice cultivation in Greece’s main rice producing area. Field experimentation was conducted in two consecutive growing seasons (2017 and 2018) and comprised six fertilization treatments, including four compost rates (C1: 80, C2: 160, C3: 320 kg ha−1 of nitrogen all in split application, C4: 160 kg ha−1 of nitrogen in single application), a conventional treatment, as well as an untreated control. A total of 21 morpho-physiological and quality traits were evaluated during the experimentation. The results indicated that rice plants in all compost treatments had greater height (8%–64%) and biomass (32%–113%) compared to the untreated control. In most cases, chlorophyll content index (CCI) and quantum yield (QY) were similar or higher in C3 compared to the conventional treatment. C2 and C3 exhibited similar or greater yields, 7.5–8.7 Mg ha−1 in 2017 and 6.3–6.9 Mg ha−1 in 2018, whereas the conventional treatment resulted in 7.3 Mg ha−1 and 6.8 Mg ha−1 in the two years, respectively. No differences were observed in most quality traits that affect the rice commodity. The current study reveals that in sustainable farming systems based on circular economy, such as organic ones, the application of the proposed compost at the rate of 6 Mg ha−1 can be considered sufficient for the rice crop nutrient requirements.
Kalliopi Kadoglidou; Argyris Kalaitzidis; Dimitrios Stavrakoudis; Aggeliki Mygdalia; Dimitrios Katsantonis. A Novel Compost for Rice Cultivation Developed by Rice Industrial By-Products to Serve Circular Economy. Agronomy 2019, 9, 553 .
AMA StyleKalliopi Kadoglidou, Argyris Kalaitzidis, Dimitrios Stavrakoudis, Aggeliki Mygdalia, Dimitrios Katsantonis. A Novel Compost for Rice Cultivation Developed by Rice Industrial By-Products to Serve Circular Economy. Agronomy. 2019; 9 (9):553.
Chicago/Turabian StyleKalliopi Kadoglidou; Argyris Kalaitzidis; Dimitrios Stavrakoudis; Aggeliki Mygdalia; Dimitrios Katsantonis. 2019. "A Novel Compost for Rice Cultivation Developed by Rice Industrial By-Products to Serve Circular Economy." Agronomy 9, no. 9: 553.
The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.
Dimitris Stavrakoudis; Dimitrios Katsantonis; Kalliopi Kadoglidou; Argyris Kalaitzidis; Ioannis Z. Gitas. Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery. Remote Sensing 2019, 11, 545 .
AMA StyleDimitris Stavrakoudis, Dimitrios Katsantonis, Kalliopi Kadoglidou, Argyris Kalaitzidis, Ioannis Z. Gitas. Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery. Remote Sensing. 2019; 11 (5):545.
Chicago/Turabian StyleDimitris Stavrakoudis; Dimitrios Katsantonis; Kalliopi Kadoglidou; Argyris Kalaitzidis; Ioannis Z. Gitas. 2019. "Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery." Remote Sensing 11, no. 5: 545.
To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project.
Valentina Pagani; Tommaso Guarneri; Lorenzo Busetto; Luigi Ranghetti; Mirco Boschetti; Ermes Movedi; Manuel Campos-Taberner; Francisco Javier Garcia-Haro; Dimitrios Katsantonis; Dimitris Stavrakoudis; Elisabetta Ricciardelli; Filomena Romano; Francesco Holecz; Francesco Collivignarelli; Carlos Granell; Sven Casteleyn; Roberto Confalonieri. A high-resolution, integrated system for rice yield forecasting at district level. Agricultural Systems 2019, 168, 181 -190.
AMA StyleValentina Pagani, Tommaso Guarneri, Lorenzo Busetto, Luigi Ranghetti, Mirco Boschetti, Ermes Movedi, Manuel Campos-Taberner, Francisco Javier Garcia-Haro, Dimitrios Katsantonis, Dimitris Stavrakoudis, Elisabetta Ricciardelli, Filomena Romano, Francesco Holecz, Francesco Collivignarelli, Carlos Granell, Sven Casteleyn, Roberto Confalonieri. A high-resolution, integrated system for rice yield forecasting at district level. Agricultural Systems. 2019; 168 ():181-190.
Chicago/Turabian StyleValentina Pagani; Tommaso Guarneri; Lorenzo Busetto; Luigi Ranghetti; Mirco Boschetti; Ermes Movedi; Manuel Campos-Taberner; Francisco Javier Garcia-Haro; Dimitrios Katsantonis; Dimitris Stavrakoudis; Elisabetta Ricciardelli; Filomena Romano; Francesco Holecz; Francesco Collivignarelli; Carlos Granell; Sven Casteleyn; Roberto Confalonieri. 2019. "A high-resolution, integrated system for rice yield forecasting at district level." Agricultural Systems 168, no. : 181-190.
Nitrogen fertilization plays a key role in rice productivity and environmental impact of rice-based cropping systems, as well as on farmers’ income, representing one of the main cost items of rice farming. Average nitrogen use efficiency in rice paddies is often very low (about 30%), leading to groundwater contamination, greenhouse gases emission, and economic losses for farmers. The resulting pressure on many actors in the rice production chain has generated a need for operational tools and techniques able to increase nitrogen use efficiency. We present an operational workflow for producing nitrogen nutritional index (NNI) maps at sub-field scale based on the combined use of high-resolution satellite images and ground-based estimates of Leaf Area Index (LAI) and plant nitrogen concentration (PNC, %) data collected using smart apps. The workflow was tested in northern Italy. The analysis reveals that vegetation indices are satisfactorily correlated with LAI (r2 > 0.77, p < 0.01) and PNC (r2 > 0.55, p < 0.01); whereas most patterns of NNI maps are coherent with the available information on soil texture and performed agro-practices. Key features of the proposed approach are (i) the time- and cost-effectiveness for producing NNI maps even in operational contexts and (ii) the full exploitation of smart scouting techniques to drive field data acquisitions using smartphones as sensors. The use of operational, free-of-charge products from Sentinel-2 for real-time field monitoring to potentially support variable rate fertilizations is also discussed.
Francesco Nutini; Roberto Confalonieri; Alberto Crema; Ermes Movedi; Livia Paleari; Dimitris Stavrakoudis; Mirco Boschetti. An operational workflow to assess rice nutritional status based on satellite imagery and smartphone apps. Computers and Electronics in Agriculture 2018, 154, 80 -92.
AMA StyleFrancesco Nutini, Roberto Confalonieri, Alberto Crema, Ermes Movedi, Livia Paleari, Dimitris Stavrakoudis, Mirco Boschetti. An operational workflow to assess rice nutritional status based on satellite imagery and smartphone apps. Computers and Electronics in Agriculture. 2018; 154 ():80-92.
Chicago/Turabian StyleFrancesco Nutini; Roberto Confalonieri; Alberto Crema; Ermes Movedi; Livia Paleari; Dimitris Stavrakoudis; Mirco Boschetti. 2018. "An operational workflow to assess rice nutritional status based on satellite imagery and smartphone apps." Computers and Electronics in Agriculture 154, no. : 80-92.
Liountmila Stepanidou; Dionysis Grigoriadis; Thomas Katagis; Dimitris Stavrakoudis; Ioannis Gitas; Eleni Dragozi; Alexandra Stefanidou; Maria Tompoulidou. Mid-term fire danger index based on satellite imagery and ancillary geographic data. Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017) 2017, 32 .
AMA StyleLiountmila Stepanidou, Dionysis Grigoriadis, Thomas Katagis, Dimitris Stavrakoudis, Ioannis Gitas, Eleni Dragozi, Alexandra Stefanidou, Maria Tompoulidou. Mid-term fire danger index based on satellite imagery and ancillary geographic data. Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017). 2017; ():32.
Chicago/Turabian StyleLiountmila Stepanidou; Dionysis Grigoriadis; Thomas Katagis; Dimitris Stavrakoudis; Ioannis Gitas; Eleni Dragozi; Alexandra Stefanidou; Maria Tompoulidou. 2017. "Mid-term fire danger index based on satellite imagery and ancillary geographic data." Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017) , no. : 32.
Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.
A. Stefanidou; E. Dragozi; Dimitris Stavrakoudis; Ioannis Gitas. Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery. Geocarto International 2017, 33, 1064 -1083.
AMA StyleA. Stefanidou, E. Dragozi, Dimitris Stavrakoudis, Ioannis Gitas. Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery. Geocarto International. 2017; 33 (10):1064-1083.
Chicago/Turabian StyleA. Stefanidou; E. Dragozi; Dimitris Stavrakoudis; Ioannis Gitas. 2017. "Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery." Geocarto International 33, no. 10: 1064-1083.
The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.
Lorenzo Busetto; Sven Casteleyn; Carlos Granell; Monica Pepe; Massimo Barbieri; Manuel Campos-Taberner; Raffaele Casa; Francesco Collivignarelli; Roberto Confalonieri; Alberto Crema; Francisco Javier Garcia-Haro; Luca Gatti; Ioannis Gitas; Alberto Gonzalez-Perez; Goncal Grau-Muedra; Tommaso Guarneri; Francesco Holecz; Dimitrios Katsantonis; Chara Minakou; Ignacio Miralles; Ermes Movedi; Francesco Nutini; Valentina Pagani; Angelo Palombo; Francesco Di Paola; Simone Pascucci; Stefano Pignatti; Anna Rampini; Luigi Ranghetti; Elisabetta Ricciardelli; Filomena Romano; Dimitris G. Stavrakoudis; Daniela Stroppiana; Mariassunta Viggiano; Mirco Boschetti. Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2017, 10, 5423 -5441.
AMA StyleLorenzo Busetto, Sven Casteleyn, Carlos Granell, Monica Pepe, Massimo Barbieri, Manuel Campos-Taberner, Raffaele Casa, Francesco Collivignarelli, Roberto Confalonieri, Alberto Crema, Francisco Javier Garcia-Haro, Luca Gatti, Ioannis Gitas, Alberto Gonzalez-Perez, Goncal Grau-Muedra, Tommaso Guarneri, Francesco Holecz, Dimitrios Katsantonis, Chara Minakou, Ignacio Miralles, Ermes Movedi, Francesco Nutini, Valentina Pagani, Angelo Palombo, Francesco Di Paola, Simone Pascucci, Stefano Pignatti, Anna Rampini, Luigi Ranghetti, Elisabetta Ricciardelli, Filomena Romano, Dimitris G. Stavrakoudis, Daniela Stroppiana, Mariassunta Viggiano, Mirco Boschetti. Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017; 10 (12):5423-5441.
Chicago/Turabian StyleLorenzo Busetto; Sven Casteleyn; Carlos Granell; Monica Pepe; Massimo Barbieri; Manuel Campos-Taberner; Raffaele Casa; Francesco Collivignarelli; Roberto Confalonieri; Alberto Crema; Francisco Javier Garcia-Haro; Luca Gatti; Ioannis Gitas; Alberto Gonzalez-Perez; Goncal Grau-Muedra; Tommaso Guarneri; Francesco Holecz; Dimitrios Katsantonis; Chara Minakou; Ignacio Miralles; Ermes Movedi; Francesco Nutini; Valentina Pagani; Angelo Palombo; Francesco Di Paola; Simone Pascucci; Stefano Pignatti; Anna Rampini; Luigi Ranghetti; Elisabetta Ricciardelli; Filomena Romano; Dimitris G. Stavrakoudis; Daniela Stroppiana; Mariassunta Viggiano; Mirco Boschetti. 2017. "Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 12: 5423-5441.
This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R2 > 0.93) and good accuracies (RMSE < 0.83, rRMSEm < 23.6% and rRMSEr < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.
Manuel Campos-Taberner; Francisco Javier García-Haro; Gustau Camps-Valls; Gonçal Grau-Muedra; Francesco Nutini; Lorenzo Busetto; Dimitrios Katsantonis; Dimitris Stavrakoudis; Chara Minakou; Luca Gatti; Massimo Barbieri; Francesco Holecz; Daniela Stroppiana; Mirco Boschetti. Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index. Remote Sensing 2017, 9, 248 .
AMA StyleManuel Campos-Taberner, Francisco Javier García-Haro, Gustau Camps-Valls, Gonçal Grau-Muedra, Francesco Nutini, Lorenzo Busetto, Dimitrios Katsantonis, Dimitris Stavrakoudis, Chara Minakou, Luca Gatti, Massimo Barbieri, Francesco Holecz, Daniela Stroppiana, Mirco Boschetti. Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index. Remote Sensing. 2017; 9 (3):248.
Chicago/Turabian StyleManuel Campos-Taberner; Francisco Javier García-Haro; Gustau Camps-Valls; Gonçal Grau-Muedra; Francesco Nutini; Lorenzo Busetto; Dimitrios Katsantonis; Dimitris Stavrakoudis; Chara Minakou; Luca Gatti; Massimo Barbieri; Francesco Holecz; Daniela Stroppiana; Mirco Boschetti. 2017. "Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index." Remote Sensing 9, no. 3: 248.
M. Tompoulidou; A. Stefanidou; D. Grigoriadis; E. Dragozi; D. Stavrakoudis; I.Z. Gitas; N. Kerle; M. Gerke; S. Lefevre. National fuel type mapping methodology using geographic object based image analysis and landsat 8 oli imagery. GEOBIA 2016 : Solutions and Synergies 2016, 1 .
AMA StyleM. Tompoulidou, A. Stefanidou, D. Grigoriadis, E. Dragozi, D. Stavrakoudis, I.Z. Gitas, N. Kerle, M. Gerke, S. Lefevre. National fuel type mapping methodology using geographic object based image analysis and landsat 8 oli imagery. GEOBIA 2016 : Solutions and Synergies. 2016; ():1.
Chicago/Turabian StyleM. Tompoulidou; A. Stefanidou; D. Grigoriadis; E. Dragozi; D. Stavrakoudis; I.Z. Gitas; N. Kerle; M. Gerke; S. Lefevre. 2016. "National fuel type mapping methodology using geographic object based image analysis and landsat 8 oli imagery." GEOBIA 2016 : Solutions and Synergies , no. : 1.
Maria Tompoulidou; Alexandra Stefanidou; Dionysios Grigoriadis; Eleni Dragozi; Dimitris Stavrakoudis; Ioannis Z. Gitas. The Greek National Observatory of Forest Fires (NOFFi). Fourth International Conference on Remote Sensing and Geoinformation of the Environment 2016, 96880N -96880N-9.
AMA StyleMaria Tompoulidou, Alexandra Stefanidou, Dionysios Grigoriadis, Eleni Dragozi, Dimitris Stavrakoudis, Ioannis Z. Gitas. The Greek National Observatory of Forest Fires (NOFFi). Fourth International Conference on Remote Sensing and Geoinformation of the Environment. 2016; ():96880N-96880N-9.
Chicago/Turabian StyleMaria Tompoulidou; Alexandra Stefanidou; Dionysios Grigoriadis; Eleni Dragozi; Dimitris Stavrakoudis; Ioannis Z. Gitas. 2016. "The Greek National Observatory of Forest Fires (NOFFi)." Fourth International Conference on Remote Sensing and Geoinformation of the Environment , no. : 96880N-96880N-9.
Monitoring post-fire vegetation response using remotely-sensed images is a top priority for post-fire management. This study investigated the potential of very-high-resolution (VHR) GeoEye images on detecting the field-measured burn severity of a forest fire that occurred in Evros (Greece) during summer 2011. To do so, we analysed the role of topographic conditions and burn severity, as measured in the field immediately after the fire (2011) and one year after (2012) using the Composite Burn Index (CBI) for explaining the post-fire vegetation response, which is measured using VHR satellite imagery. To determine this relationship, we applied redundancy analysis (RDA), which allowed us to identify which satellite variables among VHR spectral bands and Normalized Difference Vegetation Index (NDVI) can better express the post-fire vegetation response. Results demonstrated that in the first year after the fire event, variations in the post-fire vegetation dynamics can be properly detected using the GeoEye VHR data. Furthermore, results showed that remotely-sensed NDVI-based variables are able to encapsulate burn severity variability over time. Our analysis showed that, in this specific case, burn severity variations are mildly affected by the topography, while the NDVI index, as inferred from VHR data, can be successfully used to monitor the short-term post-fire dynamics of the vegetation recovery.
Eleni Dragozi; Ioannis Z. Gitas; Sofia Bajocco; Dimitris G. Stavrakoudis. Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece. Remote Sensing 2016, 8, 566 .
AMA StyleEleni Dragozi, Ioannis Z. Gitas, Sofia Bajocco, Dimitris G. Stavrakoudis. Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece. Remote Sensing. 2016; 8 (7):566.
Chicago/Turabian StyleEleni Dragozi; Ioannis Z. Gitas; Sofia Bajocco; Dimitris G. Stavrakoudis. 2016. "Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece." Remote Sensing 8, no. 7: 566.
A local search-based version of the so-called genetic sequential image segmentation (GeneSIS) algorithm is presented in this paper, for the classification of remotely sensed images. The new method combines the properties of the GeneSIS framework with the principles of the region growing segmentation algorithms. Localized GeneSIS operates on a fine-segmented image obtained after preliminary watershed transformation. Segmentation proceeds by iterative expansions emanating from object cores, i.e., connected components of marked watersheds. At each expansion trial, the process involves three successively performed operations: 1) generation of the object's neighborhood to a specified order; 2) local exploration of the neighborhood through an evolutionary algorithm to identify the best expansion to be merged; and 3) rearrangement of the object neighborhoods. We propose two priority strategies for the selection of the objects to be expanded and two different modes of operation performing either supervised or semisupervised segmentation of the image. The combination of the priority strategies and segmentation modes lead to four different implementations of localized GeneSIS. Due to the local search approach adopted here, the resulting algorithms have considerably lower execution times, while at the same time, they provide comparable classification accuracies compared to those produced by previous GeneSIS variants. Experimental analysis is conducted using a hyperspectral forest image, a multispectral agricultural image, and the Pavia Centre image over an urban area. Comparative results are also provided with existing segmentation algorithms.
Stelios K. Mylonas; Dimitris Stavrakoudis; John B. Theocharis; George C. Zalidis; Ioannis Gitas. A Local Search-Based GeneSIS algorithm for the Segmentation and Classification of Remote-Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016, 9, 1470 -1492.
AMA StyleStelios K. Mylonas, Dimitris Stavrakoudis, John B. Theocharis, George C. Zalidis, Ioannis Gitas. A Local Search-Based GeneSIS algorithm for the Segmentation and Classification of Remote-Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016; 9 (4):1470-1492.
Chicago/Turabian StyleStelios K. Mylonas; Dimitris Stavrakoudis; John B. Theocharis; George C. Zalidis; Ioannis Gitas. 2016. "A Local Search-Based GeneSIS algorithm for the Segmentation and Classification of Remote-Sensing Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 4: 1470-1492.
Dimitris Stavrakoudis; Ioannis Gitas; Christos Karydas; Polychronis Kolokoussis; Vassilia Karathanassi. Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach. Image and Signal Processing for Remote Sensing XXI 2015, 964324 -964324-12.
AMA StyleDimitris Stavrakoudis, Ioannis Gitas, Christos Karydas, Polychronis Kolokoussis, Vassilia Karathanassi. Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach. Image and Signal Processing for Remote Sensing XXI. 2015; ():964324-964324-12.
Chicago/Turabian StyleDimitris Stavrakoudis; Ioannis Gitas; Christos Karydas; Polychronis Kolokoussis; Vassilia Karathanassi. 2015. "Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach." Image and Signal Processing for Remote Sensing XXI , no. : 964324-964324-12.