Small Water Bodies Identification by means of Remote Sensing

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Small Water Bodies Identification by means of Remote Sensing

Popis výsledku v původním jazyce

Remotely sensed data are frequently used to identify water bodies. In comparison with UAV data, they are limited by resolution and availability. Paper evaluates suitability of Landsat 8, Sentinel 2 and UAV data in the case of identification of shorelines of smaller water bodies. For the study, surrounding of Pardubice city (the Czech Republic) is an area of interest. Three data sources are used: Landsat 8, Sentinel 2 and UAV data. Several algorithms are used for spectral enhancement and data classification: Iso Cluster, Maximum Likelihood, Class Probability, Principal Components, and NDWI. Manual classification is used as a reference method. No post-classification method is used to preserve shapes of small water bodies. Error matrix is used for evaluation of the classification quality. Multi-criteria evaluation shows that Sentinel 2 data classified by means of Iso Cluster provides the best results. NDWI is very close to the best results. Next, we demonstrate that UAV can provide data with a higher spatial resolution on demand for reasonable costs so they are more suitable for small water bodies. Heterogeneity of the data and treetops overlapping the shoreline led to the manual classification based on the results of Iso Cluster classification.

Název v anglickém jazyce

Small Water Bodies Identification by means of Remote Sensing

Popis výsledku anglicky

Remotely sensed data are frequently used to identify water bodies. In comparison with UAV data, they are limited by resolution and availability. Paper evaluates suitability of Landsat 8, Sentinel 2 and UAV data in the case of identification of shorelines of smaller water bodies. For the study, surrounding of Pardubice city (the Czech Republic) is an area of interest. Three data sources are used: Landsat 8, Sentinel 2 and UAV data. Several algorithms are used for spectral enhancement and data classification: Iso Cluster, Maximum Likelihood, Class Probability, Principal Components, and NDWI. Manual classification is used as a reference method. No post-classification method is used to preserve shapes of small water bodies. Error matrix is used for evaluation of the classification quality. Multi-criteria evaluation shows that Sentinel 2 data classified by means of Iso Cluster provides the best results. NDWI is very close to the best results. Next, we demonstrate that UAV can provide data with a higher spatial resolution on demand for reasonable costs so they are more suitable for small water bodies. Heterogeneity of the data and treetops overlapping the shoreline led to the manual classification based on the results of Iso Cluster classification.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

7th International Conference on Cartography and GIS : proceedings vol. 1, 2

ISBN

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ISSN

1314-0604

e-ISSN

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Počet stran výsledku

8

Strana od-do

718-726

Název nakladatele

Bulgarian Cartographic Association

Místo vydání

Sofie

Místo konání akce

Sozopol

Datum konání akce

18. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000526176700079