Remotely sensed NDVI as an indicator of drought stress on the vegetation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43210%2F13%3A00213248" target="_blank" >RIV/62156489:43210/13:00213248 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.cbks.cz/SbornikSkalice2013/pdf/Semer%C3%A1dov%C3%A1.pdf" target="_blank" >http://www.cbks.cz/SbornikSkalice2013/pdf/Semer%C3%A1dov%C3%A1.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Remotely sensed NDVI as an indicator of drought stress on the vegetation

Popis výsledku v původním jazyce

The ability of remotely sensed Normalized Difference Vegetation Index (NDVI) in form of seasonal greenness (SG) was tested to explain water balance and drought occurrence through various vegetation covers. For this purpose data from 6 districts (Olomouc,Přerov, Znojmo, Břeclav, Žďár nad Sázavou and Havlíčkův Brod) within the Czech Republic were analysed. Namely , data mining technique was used, which was originally developed for machine learning community, but is increasingly used for a variety of applications. Relative form of Palmer Drought Severity Index (rPDSI) was used as dependent variable to indicate drought occurrence. Standardized Precipitation Index (SPI),Percent of Average SG (PASG), Start of Season Anomaly (SOSA) and district identification were used as independent variables. MODIS (Moderate Resolution Imaging Spectroradiometer) observations from Terra satellite were used as a source of NDVI and Cubist software v 2.07 (RuleQuest Research, Australia) for data mining. The co

Název v anglickém jazyce

Remotely sensed NDVI as an indicator of drought stress on the vegetation

Popis výsledku anglicky

The ability of remotely sensed Normalized Difference Vegetation Index (NDVI) in form of seasonal greenness (SG) was tested to explain water balance and drought occurrence through various vegetation covers. For this purpose data from 6 districts (Olomouc,Přerov, Znojmo, Břeclav, Žďár nad Sázavou and Havlíčkův Brod) within the Czech Republic were analysed. Namely , data mining technique was used, which was originally developed for machine learning community, but is increasingly used for a variety of applications. Relative form of Palmer Drought Severity Index (rPDSI) was used as dependent variable to indicate drought occurrence. Standardized Precipitation Index (SPI),Percent of Average SG (PASG), Start of Season Anomaly (SOSA) and district identification were used as independent variables. MODIS (Moderate Resolution Imaging Spectroradiometer) observations from Terra satellite were used as a source of NDVI and Cubist software v 2.07 (RuleQuest Research, Australia) for data mining. The co

Druh

D - Stať ve sborníku

CEP obor

DG - Vědy o atmosféře, meteorologie

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Environmental Changes And Adaptation Strategies

ISBN

978-80-552-1066-7

ISSN

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e-ISSN

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Počet stran výsledku

3

Strana od-do

1-3

Název nakladatele

Slovak University of Agriculture in Nitra

Místo vydání

Nitra

Místo konání akce

Skalica

Datum konání akce

1. 1. 2013

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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