Περιλαμβάνονται, με χρονολογική σειρά, δημοσιεύσεις στις οποίες έχουν χρησιμοποιηθεί δεδομένα από το εθνικό δίκτυο παρακολούθησης λιμνών.
Δημοσιεύσεις σε επιστημονικά περιοδικά
Perivolioti, Triantafyllia-Maria; Tompoulidou, Maria; Giourieva, Viktoria; Mouratidis, Antonios; Apostolakis, Antonios
The impact of submerged aquatic vegetation on satellite-derived bathymetry in shallow waters: an accuracy assessment Δημοσίευση σε επιστημονικό περιοδικό
In: European Journal of Remote Sensing, vol. 59, iss. 1, pp. 2667786, 2026.
Περίληψη | Σύνδεσμοι | BibTeX | Ετικέτες: lake bathymetry, Lake Trichonida, machine learning, remote sensing, Sentinel-2 imagery, vegetation bias
@article{Perivolioti31122026,
title = {The impact of submerged aquatic vegetation on satellite-derived bathymetry in shallow waters: an accuracy assessment},
author = {Triantafyllia-Maria Perivolioti and Maria Tompoulidou and Viktoria Giourieva and Antonios Mouratidis and Antonios Apostolakis},
url = {https://www.tandfonline.com/doi/full/10.1080/22797254.2026.2667786},
doi = {10.1080/22797254.2026.2667786},
year = {2026},
date = {2026-05-07},
urldate = {2026-05-07},
journal = {European Journal of Remote Sensing},
volume = {59},
issue = {1},
pages = {2667786},
abstract = {The aim of this study was to evaluate the performance of four widely used Satellite-derived Bathymetry (SDB) models (Stumpf, Lyzenga, Random Forest and XGBoost) in different scenarios of varying proportions of bare substrate and submerged aquatic vegetation in lake shallow-waters. Particularly, six scenarios were created, ranging from 0% to 100% submerged vegetation within a depth range of 0–10 m. The predictive accuracy of SDB models was assessed using an updated reference digital terrain model (DTM). The performance of the models was evaluated across multiple depth ranges and bottom types (bare substrate and submerged vegetation). Evaluation metrics included root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²). One to one scatterplots demonstrated higher model performance in bare substrate, with R² values ranging from 0.56 to 0.63 for Stumpf/Lyzenga and 0.65–0.70 for XGBoost/Random Forest in bare substrate areas. Scenario-level CV of RMSE remained acceptable (<30%), decreasing from ~0.058 (0% vegetation) to ~0.022 (100% vegetation). Random Forest and XGBoost outperformed Lyzenga and Stumpf models under mixed scenarios. This study improves the understanding of SDB training requirements in mixed lake bottom types and offers valuable insights for improving the application of SDB in lakes.},
keywords = {lake bathymetry, Lake Trichonida, machine learning, remote sensing, Sentinel-2 imagery, vegetation bias},
pubstate = {published},
tppubtype = {article}
}
The aim of this study was to evaluate the performance of four widely used Satellite-derived Bathymetry (SDB) models (Stumpf, Lyzenga, Random Forest and XGBoost) in different scenarios of varying proportions of bare substrate and submerged aquatic vegetation in lake shallow-waters. Particularly, six scenarios were created, ranging from 0% to 100% submerged vegetation within a depth range of 0–10 m. The predictive accuracy of SDB models was assessed using an updated reference digital terrain model (DTM). The performance of the models was evaluated across multiple depth ranges and bottom types (bare substrate and submerged vegetation). Evaluation metrics included root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²). One to one scatterplots demonstrated higher model performance in bare substrate, with R² values ranging from 0.56 to 0.63 for Stumpf/Lyzenga and 0.65–0.70 for XGBoost/Random Forest in bare substrate areas. Scenario-level CV of RMSE remained acceptable (<30%), decreasing from ~0.058 (0% vegetation) to ~0.022 (100% vegetation). Random Forest and XGBoost outperformed Lyzenga and Stumpf models under mixed scenarios. This study improves the understanding of SDB training requirements in mixed lake bottom types and offers valuable insights for improving the application of SDB in lakes.
Markogianni, Vassiliki; Kalivas, Dionissios P.; Petropoulos, George P.; Giovos, Rigas; Dimitriou, Elias
Comparative Analysis of Trophic Status Assessment Using Different Sensors and Atmospheric Correction Methods in Greece’s WFD Lake Network Δημοσίευση σε επιστημονικό περιοδικό
In: Remote Sensing, vol. 17, iss. 11, no. 1822, 2025, ISSN: 2072-4292.
Περίληψη | Σύνδεσμοι | BibTeX | Ετικέτες: atmospheric correction (AC), Carlson’s Trophic State Index (TSI), GEE-platform, lake water quality, Landsat, Sentinel-2 imagery, WFD
@article{rs17111822,
title = {Comparative Analysis of Trophic Status Assessment Using Different Sensors and Atmospheric Correction Methods in Greece’s WFD Lake Network},
author = {Vassiliki Markogianni and Dionissios P. Kalivas and George P. Petropoulos and Rigas Giovos and Elias Dimitriou },
doi = {10.3390/rs17111822},
issn = {2072-4292},
year = {2025},
date = {2025-05-23},
urldate = {2025-05-23},
journal = {Remote Sensing},
volume = {17},
number = {1822},
issue = {11},
abstract = {Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R2 ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time.},
keywords = {atmospheric correction (AC), Carlson’s Trophic State Index (TSI), GEE-platform, lake water quality, Landsat, Sentinel-2 imagery, WFD},
pubstate = {published},
tppubtype = {article}
}
Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R2 ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time.
Perivolioti, Triantafyllia-Maria; Zachopoulos, Konstantinos; Zioga, Marianthi; Tompoulidou, Maria; Katsavouni, Sotiria; Kemitzoglou, Dimitra; Terzopoulos, Dimitrios; Mouratidis, Antonios; Tsiaoussi, Vasiliki
Monitoring the Impact of Floods on Water Quality Using Optical Remote Sensing Imagery: The Case of Lake Karla (Greece) Δημοσίευση σε επιστημονικό περιοδικό
In: Water, vol. 16, no. 23, 2024, ISSN: 2073-4441.
Περίληψη | Σύνδεσμοι | BibTeX | Ετικέτες: investigative monitoring, Sentinel-2 imagery, Storm Daniel, Thessaly Plain flood, Water Quality
@article{w16233502,
title = {Monitoring the Impact of Floods on Water Quality Using Optical Remote Sensing Imagery: The Case of Lake Karla (Greece)},
author = {Triantafyllia-Maria Perivolioti and Konstantinos Zachopoulos and Marianthi Zioga and Maria Tompoulidou and Sotiria Katsavouni and Dimitra Kemitzoglou and Dimitrios Terzopoulos and Antonios Mouratidis and Vasiliki Tsiaoussi},
editor = {Juan Miguel Soria},
doi = {10.3390/w16233502},
issn = {2073-4441},
year = {2024},
date = {2024-12-05},
urldate = {2024-12-05},
journal = {Water},
volume = {16},
number = {23},
abstract = {This study investigates the performance of published bio-optical remote sensing indices/algorithms for monitoring water quality changes in Lake Karla, Greece, caused by Storm Daniel after the September 2023 flooding event. Commonly applied indices were utilised to estimate chlorophyll-a (Chl-a) and total suspended solids (TSS) using Sentinel-2 high-resolution optical imagery. In situ measurements were undertaken and water samples were collected during the pre-flooding period, post-flooding, and one-year post-flood, providing a basis for validating the remote sensing models. Monitoring results showed that most physicochemical parameters changed considerably. Chl-a and TSS were estimated by testing five and seven indices, respectively. Regarding the Chl-a estimation, the NDCI and 2-BDA indices outperformed other models, having high correlations with in situ Chl-a measurements and effectively following the in situ Chl-a temporal trends. Among the TSS indices, NDWI and TUR-IND demonstrated better performances, effectively capturing the variations in suspended solids. Overall, this study highlights the potential of Sentinel-2 imagery in assessing water quality changes, particularly in response to flooding events. It is an exploratory approach to assess the feasibility of utilising optical satellite data for evaluating the environmental impacts of natural disasters on lake water quality and supports decision-making in environmental management. Additionally, it identifies potential challenges and considerations that must be addressed to ensure effective application.},
keywords = {investigative monitoring, Sentinel-2 imagery, Storm Daniel, Thessaly Plain flood, Water Quality},
pubstate = {published},
tppubtype = {article}
}
This study investigates the performance of published bio-optical remote sensing indices/algorithms for monitoring water quality changes in Lake Karla, Greece, caused by Storm Daniel after the September 2023 flooding event. Commonly applied indices were utilised to estimate chlorophyll-a (Chl-a) and total suspended solids (TSS) using Sentinel-2 high-resolution optical imagery. In situ measurements were undertaken and water samples were collected during the pre-flooding period, post-flooding, and one-year post-flood, providing a basis for validating the remote sensing models. Monitoring results showed that most physicochemical parameters changed considerably. Chl-a and TSS were estimated by testing five and seven indices, respectively. Regarding the Chl-a estimation, the NDCI and 2-BDA indices outperformed other models, having high correlations with in situ Chl-a measurements and effectively following the in situ Chl-a temporal trends. Among the TSS indices, NDWI and TUR-IND demonstrated better performances, effectively capturing the variations in suspended solids. Overall, this study highlights the potential of Sentinel-2 imagery in assessing water quality changes, particularly in response to flooding events. It is an exploratory approach to assess the feasibility of utilising optical satellite data for evaluating the environmental impacts of natural disasters on lake water quality and supports decision-making in environmental management. Additionally, it identifies potential challenges and considerations that must be addressed to ensure effective application.
Tompoulidou, Maria; Karadimou, Elpida; Apostolakis, Antonis; Tsiaoussi, Vasiliki
A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring Δημοσίευση σε επιστημονικό περιοδικό
In: Remote Sensing, vol. 16, no. 5, 2024, ISSN: 2072-4292.
Περίληψη | Σύνδεσμοι | BibTeX | Ετικέτες: aquatic vegetation, General Earth and Planetary Sciences, GEOBIA, lake monitoring, Mediterranean lakes, remote sensing, Sentinel-2 imagery, WFD
@article{Tompoulidou2024,
title = {A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring},
author = {Maria Tompoulidou and Elpida Karadimou and Antonis Apostolakis and Vasiliki Tsiaoussi},
doi = {10.3390/rs16050916},
issn = {2072-4292},
year = {2024},
date = {2024-03-05},
urldate = {2024-03-05},
journal = {Remote Sensing},
volume = {16},
number = {5},
publisher = {MDPI AG},
abstract = {Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring.},
keywords = {aquatic vegetation, General Earth and Planetary Sciences, GEOBIA, lake monitoring, Mediterranean lakes, remote sensing, Sentinel-2 imagery, WFD},
pubstate = {published},
tppubtype = {article}
}
Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring.