Utilization of Geographically Weighted Regression (GWR)iIn Forestry Modeling
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F15%3A43909067" target="_blank" >RIV/62156489:43410/15:43909067 - isvavai.cz</a>
Výsledek na webu
—
DOI - Digital Object Identifier
—
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Utilization of Geographically Weighted Regression (GWR)iIn Forestry Modeling
Popis výsledku v původním jazyce
In forestry, spatial heterogeneity is one of the major drivers of biological diversity, Spatial heterogeneity results from the spatial interactions between a number of biotic and abiotic factors and the differential responses of organisms to these factors. The underlying idea of GWR is that parameters may be estimated anywhere in the study area given a dependent variable and a set of independent variables. In this study the GWR regression behaviour was analysed on the case of modelling of Petterson height-diameter function in large forest area.Monospecific stands of Pinus halepensis Mill. were studied in mountain ranges located in South-East Spain. The GWR model was calculated using R programming language and GIS software, and the validation was made through residual analysis and regression diagnostic tools. Results of GWR model based on plot average data give bad results in all the analysed points (average deviation from local model was about 1.5m, with confidence interval about 1-2m)
Název v anglickém jazyce
Utilization of Geographically Weighted Regression (GWR)iIn Forestry Modeling
Popis výsledku anglicky
In forestry, spatial heterogeneity is one of the major drivers of biological diversity, Spatial heterogeneity results from the spatial interactions between a number of biotic and abiotic factors and the differential responses of organisms to these factors. The underlying idea of GWR is that parameters may be estimated anywhere in the study area given a dependent variable and a set of independent variables. In this study the GWR regression behaviour was analysed on the case of modelling of Petterson height-diameter function in large forest area.Monospecific stands of Pinus halepensis Mill. were studied in mountain ranges located in South-East Spain. The GWR model was calculated using R programming language and GIS software, and the validation was made through residual analysis and regression diagnostic tools. Results of GWR model based on plot average data give bad results in all the analysed points (average deviation from local model was about 1.5m, with confidence interval about 1-2m)
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
GK - Lesnictví
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2015
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů