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Professor and Director of the Laboratory of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, Greece and an elected fellow of the Cambridge Philosophical Society. He holds a B.Sc. degree in Forestry and Natural Environment from the Aristotle University of Thessaloniki, Greece, and M.Phil. And PhD degrees in GIS and Remote Sensing from the Department of Geography, Cambridge University, U.K. He has been involved in various national and international projects and has long experience working as a consultant in GIS/RS issues for national and international organizations, as well as for the industry.
BACKGROUND Nutritional quality in bell pepper is related to the ripening stage of the fruit at harvest and postharvest storage. Its determination requires time-consuming, tissue-destructive, analytical laboratory techniques. The objective of this study was to investigate the effect of ripening stage and of postharvest storage period on fruit nutritional quality, and whether it is feasible to develop reliable models for assessing the nutritional components in peppers using non-destructive methods. The dry matter, soluble solids, ascorbic acid, phenolics, chlorophylls, carotenoids and the total antioxidant capacity were determined in bell pepper fruits at six ripening stages, from green to full red, during storage at 10 °C for 8 days. Color, chlorophyll fluorescence, visible/near infrared (Vis/NIR) spectroscopy, red-green-blue (R-G-B) and red-green-near infrared (R-G-NIR) digital imaging were tested for assessing the nutritional quality of peppers. RESULTS The nutritional composition was mainly affected by the ripening stage of bell pepper fruits at harvest and only to a small degree by the storage period. Indeed, the more advanced ripening stage of fruit at harvest resulted in superior nutritional quality. Most of the non-destructive techniques reliably predicted the internal quality of the fruit. The genetic algorithm (GA), the variable importance in projection (VIP) scores, and the variable inflation factor (VIF) tests identified nine distinct regions and four specific wavelengths on the whole visible/NIR electromagnetic spectrum that exhibited the most significant effect in the assessment of the nutritional components. CONCLUSION It is possible to predict individual nutritional components in bell pepper fruit reliably and non-destructively, and irrespective of the ripening stage of fruits at harvest. © 2021 Society of Chemical Industry.
Dimitrios S. Kasampalis; Pavlos Tsouvaltzis; Konstantinos Ntouros; Athanasios Gertsis; Ioannis Gitas; Dimitrios Moshou; Anastasios S. Siomos. Nutritional composition changes in bell pepper as affected by the ripening stage of fruits at harvest or postharvest storage and assessed non‐destructively. Journal of the Science of Food and Agriculture 2021, 1 .
AMA StyleDimitrios S. Kasampalis, Pavlos Tsouvaltzis, Konstantinos Ntouros, Athanasios Gertsis, Ioannis Gitas, Dimitrios Moshou, Anastasios S. Siomos. Nutritional composition changes in bell pepper as affected by the ripening stage of fruits at harvest or postharvest storage and assessed non‐destructively. Journal of the Science of Food and Agriculture. 2021; ():1.
Chicago/Turabian StyleDimitrios S. Kasampalis; Pavlos Tsouvaltzis; Konstantinos Ntouros; Athanasios Gertsis; Ioannis Gitas; Dimitrios Moshou; Anastasios S. Siomos. 2021. "Nutritional composition changes in bell pepper as affected by the ripening stage of fruits at harvest or postharvest storage and assessed non‐destructively." Journal of the Science of Food and Agriculture , no. : 1.
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.
Despite the fact that wildland fires have always been an integral part of many ecosystems, their increased frequency and intensity have reinforced the need of fire managers for updated and highly accurate information associated with the spatial distribution of forest fuels. In 2015, a fuel type mapping method was developed in the framework of the “National Observatory of Forest Fires (NOFFi)” project resulting in the generation of a national fuel type map. In this study, we aimed at examining the potential of the newly available Sentinel-2 satellite images for the improvement of the NOFFi’s mapping method in terms of accuracy and update effectiveness of the national fuel type map. Results demonstrate Sentinel-2 data will likely improve the resolution and reliability of national fuel type maps, increasing mapping efficiency for operational purposes.
Alexandra Stefanidou; Ioannis Z. Gitas; Thomas Katagis. A national fuel type mapping method improvement using sentinel-2 satellite data. Geocarto International 2020, 1 -21.
AMA StyleAlexandra Stefanidou, Ioannis Z. Gitas, Thomas Katagis. A national fuel type mapping method improvement using sentinel-2 satellite data. Geocarto International. 2020; ():1-21.
Chicago/Turabian StyleAlexandra Stefanidou; Ioannis Z. Gitas; Thomas Katagis. 2020. "A national fuel type mapping method improvement using sentinel-2 satellite data." Geocarto International , no. : 1-21.
Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model.
Georgios Ovakoglou; Thomas K. Alexandridis; Jan G.P.W. Clevers; Ioannis Z. Gitas. Downscaling of MODIS leaf area index using landsat vegetation index. Geocarto International 2020, 1 -24.
AMA StyleGeorgios Ovakoglou, Thomas K. Alexandridis, Jan G.P.W. Clevers, Ioannis Z. Gitas. Downscaling of MODIS leaf area index using landsat vegetation index. Geocarto International. 2020; ():1-24.
Chicago/Turabian StyleGeorgios Ovakoglou; Thomas K. Alexandridis; Jan G.P.W. Clevers; Ioannis Z. Gitas. 2020. "Downscaling of MODIS leaf area index using landsat vegetation index." Geocarto International , no. : 1-24.
The monitoring of inland water resources in arid environments is an essential element due to their fragility. Reliable prediction of the water quality parameters helps to control and manage the water resources in arid regions. Water quality parameters were estimated using remote sensing data acquired from the beginning of 2017 until the end of 2018. The prediction of the water quality parameters was comprehended by using an adjusted autoregressive integrated moving average (ARIMA) and its extension seasonal ARIMA (S-ARIMA). Maximum Chlorophyll Index (MCI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI) were the tested water quality parameters using Sentinel-2 sensor on temporal resolution basis of the sensor. Results indicated that the implementation of the ARIMA model failed to sustain a reliable prediction longer than one-month time while S-ARIMA succeeded to maintain a robust prediction for the first 3 months with confidence level of 96%. MCI has its ARIMA at (1,2,2) and S-ARIMA at (1,2,2) (2,1,1)6, GNDVI has its ARIMA at (2,1,2) and S-ARIMA at (2,1,2) (2,2,2)6, and finally, NDTI has its ARIMA at (2,2,2) and S-ARIMA at (2,2,2) (1,1,2)6. The accuracy of S-ARIMA predictions reached 82% at 6-month prediction period. Meanwhile, there was no solid prediction model that lasted till 12 months. Each of the forecasted water quality parameters is unique in its prediction settings. S-ARIMA model is a more reliable model because the seasonality feature is inherited within the forecasted water quality parameters.
Mohamed Elhag; Ioannis Gitas; Anas Othman; Jarbou Bahrawi; Aris Psilovikos; Nassir Al-Amri. Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia. Environment, Development and Sustainability 2020, 23, 1392 -1410.
AMA StyleMohamed Elhag, Ioannis Gitas, Anas Othman, Jarbou Bahrawi, Aris Psilovikos, Nassir Al-Amri. Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia. Environment, Development and Sustainability. 2020; 23 (2):1392-1410.
Chicago/Turabian StyleMohamed Elhag; Ioannis Gitas; Anas Othman; Jarbou Bahrawi; Aris Psilovikos; Nassir Al-Amri. 2020. "Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia." Environment, Development and Sustainability 23, no. 2: 1392-1410.
High-quality soils are an important resource affecting the quality of life of human societies, as well as terrestrial ecosystems in general. Thus, soil erosion and soil loss are a serious issue that should be managed, in order to conserve both artificial and natural ecosystems. Predicting soil erosion has been a challenge for many years. Traditional field measurements are accurate, but they cannot be applied to large areas easily because of their high cost in time and resources. The last decade, satellite remote sensing and predictive models have been widely used by scientists to predict soil erosion in large areas with cost-efficient methods and techniques. One of those techniques is the Revised Universal Soil Loss Equation (RUSLE). RUSLE uses satellite imagery, as well as precipitation and soil data from other sources to predict the soil erosion per hectare in tons, in a given instant of time. Data acquisition for these data-demanding methods has always been a problem, especially for scientists working with large and diverse datasets. Newly emerged online technologies like Google Earth Engine (GEE) have given access to petabytes of data on demand, alongside high processing power to process them. In this paper we investigated seasonal spatiotemporal changes of soil erosion with the use of RUSLE implemented within GEE, for Pindos mountain range in Greece. In addition, we estimated the correlation between the seasonal components of RUSLE (precipitation and vegetation) and mean RUSLE values.
S. Papaiordanidis; I.Z. Gitas; T. Katagis. Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE) cloud-based platform. Dokuchaev Soil Bulletin 2020, 36-52 .
AMA StyleS. Papaiordanidis, I.Z. Gitas, T. Katagis. Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE) cloud-based platform. Dokuchaev Soil Bulletin. 2020; (100):36-52.
Chicago/Turabian StyleS. Papaiordanidis; I.Z. Gitas; T. Katagis. 2020. "Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE) cloud-based platform." Dokuchaev Soil Bulletin , no. 100: 36-52.
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.
Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments.
Mohamed Elhag; Ioannis Gitas; Anas Othman; Jarbou Bahrawi; Petros Gikas. Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia. Water 2019, 11, 556 .
AMA StyleMohamed Elhag, Ioannis Gitas, Anas Othman, Jarbou Bahrawi, Petros Gikas. Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia. Water. 2019; 11 (3):556.
Chicago/Turabian StyleMohamed Elhag; Ioannis Gitas; Anas Othman; Jarbou Bahrawi; Petros Gikas. 2019. "Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia." Water 11, no. 3: 556.
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.
Fire has a diverse range of impacts on Earth's physical and social systems. Accurate and up to date information on areas affected by fire is critical to better understand drivers of fire activity, as well as its relevance for biogeochemical cycles, climate, air quality, and to aid fire management. Mapping burned areas was traditionally done from field sketches. With the launch of the first Earth observation satellites, remote sensing quickly became a more practical alternative to detect burned areas, as they provide timely regional and global coverage of fire occurrence. This review paper explores the physical basis to detect burned area from satellite observations, describes the historical trends of using satellite sensors to monitor burned areas, summarizes the most recent approaches to map burned areas and evaluates the existing burned area products (both at global and regional scales). Finally, it identifies potential future opportunities to further improve burned area detection from Earth observation satellites.
Emilio Chuvieco; Florent Mouillot; Guido van der Werf; Jesús San Miguel; Mihai Tanase; Nikos Koutsias; Mariano García; Marta Yebra; Marc Padilla; Ioannis Gitas; Angelika Heil; Todd J. Hawbaker; Louis Giglio. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment 2019, 225, 45 -64.
AMA StyleEmilio Chuvieco, Florent Mouillot, Guido van der Werf, Jesús San Miguel, Mihai Tanase, Nikos Koutsias, Mariano García, Marta Yebra, Marc Padilla, Ioannis Gitas, Angelika Heil, Todd J. Hawbaker, Louis Giglio. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment. 2019; 225 ():45-64.
Chicago/Turabian StyleEmilio Chuvieco; Florent Mouillot; Guido van der Werf; Jesús San Miguel; Mihai Tanase; Nikos Koutsias; Mariano García; Marta Yebra; Marc Padilla; Ioannis Gitas; Angelika Heil; Todd J. Hawbaker; Louis Giglio. 2019. "Historical background and current developments for mapping burned area from satellite Earth observation." Remote Sensing of Environment 225, no. : 45-64.
Land cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across areas; thus, they are not being limited by national or other administrative nomenclature boundaries and their production approaches. Moreover, their increased spatial resolution, and consequently their local relevance, is of high importance for users at a local scale. During the last decade, several GCLCs were developed, including the Global Historical Land-Cover Change Land-Use Conversions (GLC), the Globeland-30 (GLOB), Corine-2012 (CLC) and GMES/ Copernicus Initial Operation High Resolution Layers (GIOS). Accuracy assessment is of high importance for product credibility towards incorporation into decision chains and implementation procedures, especially at local scales. The present study builds on the collaboration of scientists participating in the Global Observations of Forest Cover—Global Observations of Land Cover Dynamics (GOFC-GOLD), South Central and Eastern European Regional Information Network (SCERIN). The main objective is to quantitatively evaluate the accuracy of commonly used GCLCs at selected representative study areas in the SCERIN geographic area, which is characterized by extreme diversity of landscapes and environmental conditions, heavily affected by anthropogenic impacts with similar major socio-economic drivers. The employed validation strategy for evaluating and comparing the different products is detailed, representative results for the selected areas from nine SCERIN countries are presented, the specific regional differences are identified and their underlying causes are discussed. In general, the four GCLCs products achieved relatively high overall accuracy rates: 74–98% for GLC (mean: 93.8%), 79–92% for GLOB (mean: 90.6%), 74–91% for CLC (mean: 89%) and 72–98% for GIOS (mean: 91.6%), for all selected areas. In most cases, the CLC product has the lower scores, while the GLC has the highest, closely followed by GIOS and GLOB. The study revealed overall high credibility and validity of the GCLCs products at local scale, a result, which shows expected benefit even for local/regional applications. Identified class dependent specificities in different landscape types can guide the local users for their reasonable usage in local studies. Valuable information is generated for advancing the goals of the international GOFC-GOLD program and aligns well with the agenda of the NASA Land-Cover/Land-Use Change Program to improve the quality and consistency of space-derived higher-level products.
Ioannis Manakos; Monika Tomaszewska; Ioannis Gkinis; Olga Brovkina; Lachezar Filchev; Levent Genc; Ioannis Z. Gitas; Andrej Halabuk; Melis Inalpulat; Anisoara Irimescu; Georgi Jelev; Konstantinos Karantzalos; Thomas Katagis; Lucie Kupková; Mykola Lavreniuk; Minučer Mesaroš; Denis Mihailescu; Mihai Nita; Tomas Rusnak; Premysl Stych; Frantisek Zemek; Jana Albrechtová; Petya Campbell. Comparison of Global and Continental Land Cover Products for Selected Study Areas in South Central and Eastern European Region. Remote Sensing 2018, 10, 1967 .
AMA StyleIoannis Manakos, Monika Tomaszewska, Ioannis Gkinis, Olga Brovkina, Lachezar Filchev, Levent Genc, Ioannis Z. Gitas, Andrej Halabuk, Melis Inalpulat, Anisoara Irimescu, Georgi Jelev, Konstantinos Karantzalos, Thomas Katagis, Lucie Kupková, Mykola Lavreniuk, Minučer Mesaroš, Denis Mihailescu, Mihai Nita, Tomas Rusnak, Premysl Stych, Frantisek Zemek, Jana Albrechtová, Petya Campbell. Comparison of Global and Continental Land Cover Products for Selected Study Areas in South Central and Eastern European Region. Remote Sensing. 2018; 10 (12):1967.
Chicago/Turabian StyleIoannis Manakos; Monika Tomaszewska; Ioannis Gkinis; Olga Brovkina; Lachezar Filchev; Levent Genc; Ioannis Z. Gitas; Andrej Halabuk; Melis Inalpulat; Anisoara Irimescu; Georgi Jelev; Konstantinos Karantzalos; Thomas Katagis; Lucie Kupková; Mykola Lavreniuk; Minučer Mesaroš; Denis Mihailescu; Mihai Nita; Tomas Rusnak; Premysl Stych; Frantisek Zemek; Jana Albrechtová; Petya Campbell. 2018. "Comparison of Global and Continental Land Cover Products for Selected Study Areas in South Central and Eastern European Region." Remote Sensing 10, no. 12: 1967.
Fire is a widespread Earth system process with important carbon and climate feedbacks. Multispectral remote sensing has enabled mapping of global spatiotemporal patterns of fire and fire effects, which has significantly improved our understanding of interactions between ecosystems, climate, humans and fire. With several upcoming spaceborne hyperspectral missions like the Environmental Mapping And Analysis Program (EnMAP), the Hyperspectral Infrared Imager (HyspIRI) and the Precursore Iperspettrale Della Missione Applicativa (PRISMA), we provide a review of the state-of-the-art and perspectives of hyperspectral remote sensing of fire. Hyperspectral remote sensing leverages information in many (often more than 100) narrow (smaller than 20 nm) spectrally contiguous bands, in contrast to multispectral remote sensing of few (up to 15) non-contiguous wider (greater than 20 nm) bands. To date, hyperspectral fire applications have primarily used airborne data in the visible to short-wave infrared region (VSWIR, 0.4 to 2.5 μm). This has resulted in detailed and accurate discrimination and quantification of fuel types and condition, fire temperatures and emissions, fire severity and vegetation recovery. Many of these applications use processing techniques that take advantage of the high spectral resolution and dimensionality such as advanced spectral mixture analysis. So far, hyperspectral VSWIR fire applications are based on a limited number of airborne acquisitions, yet techniques will approach maturity for larger scale application when spaceborne imagery becomes available. Recent innovations in airborne hyperspectral thermal (8 to 12 μm) remote sensing show potential to improve retrievals of temperature and emissions from active fires, yet these applications need more investigation over more fires to verify consistency over space and time, and overcome sensor saturation issues. Furthermore, hyperspectral information and structural data from, for example, light detection and ranging (LiDAR) sensors are highly complementary. Their combined use has demonstrated advantages for fuel mapping, yet its potential for post-fire severity and combustion retrievals remains largely unexplored.
Sander Veraverbeke; Philip Dennison; Ioannis Gitas; Glynn Hulley; Olga Kalashnikova; Thomas Katagis; Le Kuai; Ran Meng; Dar Roberts; Natasha Stavros. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment 2018, 216, 105 -121.
AMA StyleSander Veraverbeke, Philip Dennison, Ioannis Gitas, Glynn Hulley, Olga Kalashnikova, Thomas Katagis, Le Kuai, Ran Meng, Dar Roberts, Natasha Stavros. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment. 2018; 216 ():105-121.
Chicago/Turabian StyleSander Veraverbeke; Philip Dennison; Ioannis Gitas; Glynn Hulley; Olga Kalashnikova; Thomas Katagis; Le Kuai; Ran Meng; Dar Roberts; Natasha Stavros. 2018. "Hyperspectral remote sensing of fire: State-of-the-art and future perspectives." Remote Sensing of Environment 216, no. : 105-121.
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.
Irene Chrysafis; Giorgos Mallinis; Ioannis Gitas; Maria Tsakiri-Strati. Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method. Remote Sensing of Environment 2017, 199, 154 -166.
AMA StyleIrene Chrysafis, Giorgos Mallinis, Ioannis Gitas, Maria Tsakiri-Strati. Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method. Remote Sensing of Environment. 2017; 199 ():154-166.
Chicago/Turabian StyleIrene Chrysafis; Giorgos Mallinis; Ioannis Gitas; Maria Tsakiri-Strati. 2017. "Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method." Remote Sensing of Environment 199, no. : 154-166.
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.