Accurate monitoring of the Danube Delta dynamics using Copernicus data

Dumitru, Corneliu Octavian; Dax, Gabriel; Schwarz, Gottfried; Cazacu, Constantin; Adamescu, Mihai; Datcu, Mihai

doi:10.1117/12.2532314

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…One study (Niculescu et al 2017) mapped the wetland habitats from this region and assessed the anthropogenic effects on local ecosystems, by combining Sentinel 1 and Sentinel 2 data. The potential of Sentinel 2 data for discriminating between coastal and river water types at the Danube Delta-Black Sea confluence was successfully employed (Dumitru et al 2019). Based on Sentinel 1 and Sentinel 2 time data series data fusion, an automatic algorithm was implemented to overcome the issue of cloud presence (Oteman et al 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, some major issues that the traditional monitoring programs are faced with comprise the high costs associated with field work, time consuming and the difficulties of physical access to the sampling sites. Moreover, another crucial aspect that the monitoring sampling programs of water quality are faced with is the reliability of the results per se (Dumitru et al 2019). Such parameters often characterize the water quality at the local level, hence faced with the issue of spatial representativeness, especially for extensive and diverse types of aquatic ecosystems (Niculescu et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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How accurate is the remote sensing based estimate of water physico-chemical parameters in the Danube Delta (Romania)?

Necula¹,

Tușa²,

Sidoroff³

et al. 2022

AFR

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The current paper estimated the physico-chemical properties of water in the Danube Delta (Romania), based on Sentinel 2 remote sensing data. Eleven sites from the Danube Delta were sampled in spring and autumn for three years (2018-2020) and 21 water physico-chemical parameters were measured in laboratory. Several families of machine learning algorithms, translated into hundreds of models with different parameterizations for each machine learning algorithm, based on remote sensing data input from Sentinel 2 spectral bands, were employed to find the best models that predicted the values measured in laboratory. This was a novel approach, reflected in the types of selected models that minimised the values of performance metrics for the tested parameters. For alkalinity, calcium, chloride, carbon dioxide, hardness, potassium, sodium, ammonium, dissolved oxygen, sulphates, and suspended matter the results were promising, with an overall percentage bias of the estimates of +/- 10% from the observed values. For copper, magnesium, nitrites, nitrates, turbidity and zinc the estimates were fairly accurate, with percentage biases in the interval +/- 10% - 20%, whereas for detergents, led, and phosphates the percentage bias was higher than 20%. Overall, the results of the current study showed fairly good estimates between remote sensing based estimates and laboratory measured values for most water physico-chemical parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How accurate is the remote sensing based estimate of water physico-chemical parameters in the Danube Delta (Romania)?

Necula¹,

Tușa²,

Sidoroff³

et al. 2022

AFR

View full text Add to dashboard Cite

show abstract

“…The Danube Delta is the second largest river delta in Europe and one of the best-preserved deltas on the continent (Dumitru, et al, 2019).…”

Section: River Deltas and Coastlinesmentioning

confidence: 99%

“…Sentinel-1 and Sentinel-2. Due to the different revisit periods of each satellite and the partly visible cloud coverage of the area by Sentinel-2, the closest acquisitions of both satellites were May 18 th , 2016 for Sentinel-1, and April 28 th , 2016 for Sentinel-2 (Dumitru, et al, 2019).…”

Section: River Deltas and Coastlinesmentioning

confidence: 99%

Machine Learning Techniques for Knowledge Extraction From Satellite Images: Application to Specific Area Types

Dumitru

Schwarz

Datcu

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. When we want to extract knowledge form satellite images, several well-known image classification and analysis techniques can be concatenated or combined to gain a more detailed target understanding. In our case, we concentrated on specific extended target areas such as polar ice-covered surfaces, forests shrouded by fire plumes, flooded areas, and shorelines. These image types can be described by characteristic features and statistical relationships. Here, we demonstrate that both multispectral (optical) as well as SAR (Synthetic Aperture Radar) images can be used for knowledge extraction. The free availability of image data provided by the European Sentinel-1 and Sentinel-2 satellites allowed us to conduct a series of experiments that verified our classification approaches. This could already be verified in our recent work by quantitative quality tests.

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AL4SLEO: An Active Learning Solution for the Semantic Labelling of Earth Observation Satellite Images—Part 1

Dumitru,

Schwarz,

Datcu

2023

Springer Tracts in Nature-Inspired Computing

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Accurate monitoring of the Danube Delta dynamics using Copernicus data

Cited by 5 publications

References 11 publications

How accurate is the remote sensing based estimate of water physico-chemical parameters in the Danube Delta (Romania)?

How accurate is the remote sensing based estimate of water physico-chemical parameters in the Danube Delta (Romania)?

Machine Learning Techniques for Knowledge Extraction From Satellite Images: Application to Specific Area Types

AL4SLEO: An Active Learning Solution for the Semantic Labelling of Earth Observation Satellite Images—Part 1

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