Utilization of Geographically Weighted Regression (GWR)iIn Forestry Modeling

Result code in 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>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Utilization of Geographically Weighted Regression (GWR)iIn Forestry Modeling
Original language description
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)
Czech name
—
Czech description
—

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)