Integrated models Norway spruce dominated stands Mid Sweden

user_name Erik Valinger
user_organisation Swedish University of Agricultural Sciences
Faculty of Forestry
Department of Silviculture
S-901 83 Umea
Sweden
date 21th May 1997
model_name Integrated model
description Describes probability of snow and wind damage at sites using tree, stand,
and site characteristics from the thickest sample tree at each sample plot,
and measured stand and site data from the same plot.
software Developed in SAS 6.10 but Excel 7.0 will do
operating_system PC Windows 95
isumm There are six input variables in model.
The risk of damage is modeled using the characteristics height,
volume ha-1, longitude, latitude, altitude, and crown length.
Valid only for Norway spruce in mid Sweden.
Ispecfile Similar to that for earlier Umea models
iexpspecfile n/a
ivar1 Height (h)
ifile1 ?
idescr1 m
isensitiv1 Sensitive
isensdescr1 Logistic procedure
ivar2 Volume ha-1 (vha)
ifile2 ?
idescr2 0.1 m3 ha-1
isensitiv2 Sensitive
isensdescr2 Logistic procedure
ivar3 Longitude (long)
ifile3 ?
idescr3 degrees
isensitiv3 Sensitive
isensdescr3 Logistic procedure
ivar4 Latitude (lat)
ifile4 ?
idescr4 degrees
isensitiv4 Sensitive
isensdescr4 Logistic procedure
ivar5 Altitude (alt)
ifile5 m
isensitiv5 sensitive>
isensdescr5 Logistic procedure
ivar6 Crown length (crl)
ifile6 ?
idescr6 m
isensitiv6 Sensitive
csumm height, h -0.1693
volume ha-1, vha 0.00411
longitude, long 0.2319
latitude, lat -1.3365
altitude, alt 0.00957
crown length, crl 0.1494
Model miprob=1-(1/(1+exp(71.8221-(0.1693*h)+(0.00411*vha)
+(0.2319*long)-(1.3365*lat)+(0.00957*alt)+(0.1494*crl))))
(then multiply by 100 to get the result in percent)
cspecfile None
cexpspec mgran
ospecfile ?
oexpspecfile See above
ovar1 miprob
ofile1 ?
odescr1<?td> Site probability of snow and wind damage
stat_meth Logistic procedure for probability of damage from snow and wind
at a site using tree, stand, and site characteristics when dominating species
is Norway spruce
Chi-square for covariates
869.469 with 6 DF (p=0.0001)
Pr > Chi-square
Intercept 0.0001
h 0.0001
vha 0.0001
long 0.0001
lat 0.0001
alt 0.0001
crl 0.0001
mech_meth n/a
log_meth Logistic model
Model miprob=1-(1/(1+exp(71.8221-(0.1693*h)+(0.00411*vha)+(0.2319*long)
-(1.3365*lat)+(0.00957*alt)+(0.1494*crl))))
(then the result is multiplied by 100 to get it in percent)
scope Model developed from a sample of data from mid Sweden (579 sample plots)
6.1% of the plots had one or more damaged sample trees, damage=1.
Model have been proven to predict damage well by cross-validation
other ?
copyright Swedish University of Agricultural Sciences
Faculty of Forestry
Department of Silviculture
contact Associate Professor Erik Valinger