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Wyszukujesz frazę "Sentinel images" wg kryterium: Temat


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Tytuł:
Wykrywanie wody na zdjęciach optycznych Sentinel-2 na podstawie wskaźników wodnych
The detection of water on Sentinel-2 imagery based on water indices
Autorzy:
Robak, A.
Gadawska, A.
Milczarek, M.
Lewiński, S.
Powiązania:
https://bibliotekanauki.pl/articles/132357.pdf
Data publikacji:
2016
Wydawca:
Polskie Towarzystwo Geograficzne
Tematy:
Sentinel-2
obrazowanie optyczne
wskaźniki wodne
detekcja wody
korekcja atmosferyczna
optical satellite images
water indices
water detection
atmospheric correction
Opis:
Copernicus Programme managed by the European Commission and implemented in partnership with i.a. the European Space Agency (ESA) provides free access to satellite data from Sentinel mission including Sentinel-2 high resolution optical satellite data. The aim of the research was to recognize opportunities of water detection on Sentinel-2 imagery. Satellite data was analyzed before and after atmospheric correction. A number of tests were carried out using indices selected from the literature. Based on the gained experience, a new index for water detection has been proposed, Sentinel Water Mask (SWM), specially adapted for Sentinel-2 images. Its construction is based on the highest difference between spectral values of water surface and other land cover forms. SWM provides quick and effective detection of water which is especially important in flood assessment for crisis management. Research was performed on unprocessed images of Sentinel-2 Level-1C and images after atmospheric correction (Level-2A). Water was detected with the use of threshold values determined by the visual interpretation method. The accuracy of the obtained water masks was assessed on the basis of validation points. The performed analysis allowed to indicate indices, which enable estimation of areas covered by water on Sentinel-2 images with high classification accuracy, this is: AWEInsh (Automated Water Extraction Index), MNDWI (Modified Normalized Difference Water Index), NDWIMcFeeters (Normalized Difference Water Index). Their application allowed for achievement of overall accuracy of water detection oscillating around 95% and high Kappa coefficient. The usage of the proposed SWM index leads to slightly better results (more than 96%). The sensitivity to the selection of threshold values of analyzed indices was assessed and then the optimal threshold ranges were determined. The optimal threshold value for NDWIMcFeeters should be included in the value range (0.1, 0.2), for MNDWI (0.2, 0.3) and for SWM (1.4, 1.6). The unambiguous threshold range for AWEInsh index was impossible to indicate due to the large range of values.
Źródło:
Teledetekcja Środowiska; 2016, 55; 59-72
1644-6380
Pojawia się w:
Teledetekcja Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Monitoring of Water Bodies and Non-vegetated Areas in Selenica ‑ Albania with Sar and Optical Images
Autorzy:
Belba, Pietro
Kucaj, Spartak
Thanas, Jorgaq
Powiązania:
https://bibliotekanauki.pl/articles/2105519.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
SAR image
optical image
collocated image
water bodies
NDVI
Sentinel images
Opis:
The availability of Sentinel satellites for providing open data with optical and SAR imagery leads to better opportunities related to Earth surface mapping and monitoring. Recently, optical fusion with radar data has shown improvement in classification quality and the accuracy of information acquired. In this setting, the main objective of this research is to monitor the environmental impact of an open-pit mine on water, vegetation, and non-vegetation areas by exploring the single and combined use of Sentinel-1 and Sentinel-2 data. The data utilized in this paper were collected from the European Space Agency Copernicus program. After selecting the Selenica region, we explored the products in the Sentinel Application Platform. According to our data, Sentinel-2 misses the small water ponds but successfully identifies the river and open-pit areas. It mistakenly identifies urban structures and cloud areas as non-vegetated and does not identify non-vegetated areas which correspond to mining operation areas. Sentinel-1 identifies very small water ponds and delivers additional information in the cloudy areas, but misses a part of the river. Alongside the strong contribution in identifying the vegetation, it also roughly identifies the non-vegetation areas of mining operations.
Źródło:
Geomatics and Environmental Engineering; 2022, 16, 3; 5--25
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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