Using GIS and maximum likelihood classification to model forest altitudinal zones

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F13%3A00200297" target="_blank" >RIV/62156489:43410/13:00200297 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Using GIS and maximum likelihood classification to model forest altitudinal zones

Popis výsledku v původním jazyce

There are 10 forest altitudinal zones for forest ecosystem on the territory of the Czech republic are described by phytocoenological studies using bioindicator species of plants. This classification is influenced by many abiotic factors. Using factors describing the site requirements of bioindicator species, allow estimating forest altitudinal zones by comprehensive modeling. As the potentially relevant factors the average temperature, precipitation, solar radiation, topographic exposure, aspect, slope,curvature, stream distance, soil and geology were identified. Their spatial distribution was mapped using spatial analysis techniques and Python regression code. Resulting rasters were subjected to discriminant analyzes to identify the significant abiotic factors. Results helped to merge the factors in a comprehensive analytical model based on the maximum likelihood classification and classification function of discriminant analysis, were match of result models and input typological dat

Název v anglickém jazyce

Using GIS and maximum likelihood classification to model forest altitudinal zones

Popis výsledku anglicky

There are 10 forest altitudinal zones for forest ecosystem on the territory of the Czech republic are described by phytocoenological studies using bioindicator species of plants. This classification is influenced by many abiotic factors. Using factors describing the site requirements of bioindicator species, allow estimating forest altitudinal zones by comprehensive modeling. As the potentially relevant factors the average temperature, precipitation, solar radiation, topographic exposure, aspect, slope,curvature, stream distance, soil and geology were identified. Their spatial distribution was mapped using spatial analysis techniques and Python regression code. Resulting rasters were subjected to discriminant analyzes to identify the significant abiotic factors. Results helped to merge the factors in a comprehensive analytical model based on the maximum likelihood classification and classification function of discriminant analysis, were match of result models and input typological dat

Druh

D - Stať ve sborníku

CEP obor

GK - Lesnictví

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Implementation of DSS Tools into Forestry Practice

ISBN

978-80-228-2510-8

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

59-70

Název nakladatele

Technical univerzity in Zvolen

Místo vydání

Zvolen

Místo konání akce

Zvolen

Datum konání akce

1. 1. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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