I found this link to resolve: '3 GB Switch Windows Vista'

 

http://usa.autodesk.com/adsk/servlet/ps/item?siteID=123112 <http://usa.autodesk.com/adsk/servlet/ps/item?siteID=123112&id=9583842&linkID=9242018> &id=9583842&linkID=9242018

 

 

Da: Adam Terando [mailto:ajt180@psu.edu] 
Inviato: martedì 5 agosto 2008 13.24
A: Alessandro
Oggetto: Re: R: [R-sig-Geo] R: LIDAR Problem in R (THANKS for HELP)

 

If an extra GB of memory will solve the problem, you might try doing a google search on '3 GB Switch Windows Vista' and see if it's possible to do such a thing (it probably is possible). 

Adam

On Tue, Aug 5, 2008 04:17 PM, "Alessandro" <alessandro.montaghi@unifi.it> wrote:



Thanks

 

But No this is the problem, I don’t ditch  VISTA (the nightmare) in this moment

 

 

 

Da: Adam Terando [mailto:ajt180@psu.edu] 
Inviato: martedì 5 agosto 2008 13.00
A: Alessandro
Cc: 'Matt Oliver'; r-sig-geo@stat.math.ethz.ch
Oggetto: Re: [R-sig-Geo] R: LIDAR Problem in R (THANKS for HELP)

 

If  you don't have access to a 64-bit system that works with R and/or more than 4 gb ram you might try using a 3 gb switch in your boot.ini file on windows. This is because windows will often conveniently limit you to 2 gb memory because of its reserved overhead. 

http://www.microsoft.com/whdc/system/platform/server/PAE/PAEmem.mspx

I don't know if that switch works on Vista though since they want you to just upgrade to 64-bit Vista and buy more RAM. 

On my XP Pro machine this switch has worked very well with Matlab and R in allowing me more access to physcial ram. Maybe you have a copy of XP Pro laying around and you can ditch Vista?

Adam


On Tue, Aug 5, 2008 03:40 PM, "Alessandro" <alessandro.montaghi@unifi.it> wrote:

 

 

 
My notebook is:
 

 

 
 
 

 

 
Hp Pavilion dv6700 notebook PC
 

 

 
Intel() Core �2 Duo CPU T9300 �2.50gHz 2,50GHz
 

 

 
RAM: 4.00 GB
 

 

 
OS: 32bit
 

 

 
Windows (TERRIBLE!!!!!!!!!) VISTA
 

 

 
 
 

 

 
 
 

 

 
Da: matt.j.oliver@gmail.com [mailto:matt.j.oliver@gmail.com] Per conto
di
 
Matt Oliver
 
Inviato: marted� 5 agosto 2008 12.02
 
A: Alessandro
 
Cc: r-help@r-project.org; r-sig-geo@stat.math.ethz.ch
 
Oggetto: Re: [R-sig-Geo] LIDAR Problem in R (THANKS for HELP)
 

 

 
 
 

 

 
you can try
 

 

 
memory.limit(size=4000)
 

 

 
only if you have 4GB of memory on the system
 

 

 
This is not guaranteed to solve your problem though
 

 

 

 

 
With big datasets like lidar, you are much better off getting access to
a
 
64bit system with a ton of RAM (>64GB).
 

 

 
Cheers
 

 

 
Matt
 

 

 

 

 

 

 
On Tue, Aug 5, 2008 at 1:47 PM, Alessandro
<alessandro.montaghi@unifi.it>
 
wrote:
 

 

 
 
 

 

 
Hi All,
 

 

 
 
 

 

 
I am a PhD student in forestry science and I am working with LiDAR data
set
 
(huge data set). I am a brand-new in R and geostatistic (SORRY,
 
my
 
background it's in forestry) but I wish improve my skill for
improve
 
myself.
 
I wish to develop a methodology to processing a large data-set of
points
 
(typical in LiDAR) but there is a problem with memory. I had created
a
 
subsample data-base but the semivariogram is periodic shape and I am not
to
 
able to try a fit the model. This is a maximum of two weeks of work
(day
 
bay
 
day) SORRY. Is there a geostatistical user I am very happy to listen
his
 
suggests. Data format is X, Y and Z (height to create the DEM) in
txt
 
format
 

 

 
I have this questions:
 

 

 
 
 

 

 
1.       After the random selection (10000
points) and
 
fit.semivariogram
 
model is it possible to use all LiDAR points? Because the new LiDAR power
is
 
to use huge number of points (X;Y; Z value) to create a very high
 
resolution
 
map of DEM and VEGETATION. The problem is the memory, but we can use
a
 
cluster-linux network to improve the capacity of R
 

 

 
 
 

 

 
2.       Is it possible to improve the memory
capacity?
 

 

 
 
 

 

 
3.       The data has a trend and the qqplot
shows a Gaussian trend. Is it
 
possible to normalize the data (i.e. with log)?
 

 

 
 
 

 

 
4.       When I use this R code "subground.uk
=
 
krige(log(Z)~X+Y, subground,
 
new.grid, v.fit, nmax=40)" to appear an Error massage: Error in
 
eval(expr,
 
envir, enclos) : oggetto "X" non trovato
 

 

 
 
 

 

 
I send you a report and attach the image to explain better. 
 

 

 
 
 

 

 
all procedure is write in R-software and to improve in gstat . The number
of
 
points of GROUND data-set (4x2 km) is 5,459,916.00. The random sub-
 
set from
 
original data-set is 10000 (R code is:  > samplerows
 
<-sample(1:nrow(testground),size=10000,replace=FALSE)
 
> subground
 
<-testground[samplerows,])
 

 

 
 
 

 

 
1.       Original data-set Histogram: show two
populations;
 

 

 
2.       original data-set density plot: show
again two populations of data;
 

 

 
3.        Original data-set Boxplot: show
there aren't outlayers un the
 
data-set (the classification with terrascan is good and therefore
there
 
isn't a problem with original data)
 

 

 
4.        ordinary kriging: show a trend in
the space (hypothesis: the
 
points are very close in the space)
 

 

 
5.       de-trend dataset with:  v <-
variogram (log(Z)~X+Y,
 
subground,
 
cutoff=1800, width=100)
 

 

 
6.       map of semi-variogram: show an
anisotropy in the space (0� is
 
Nord=
 
135� major radius 45� minus radius)
 

 

 
7.       semi-variogram with anisotropy (0�, 45�, 90�, 135�), shows
a
 
 best
 
shape is from 135�
 

 

 
8. semi-variogram fit with Gaussian Model. R code is (see the fig):
 

 

 
> v = variogram(Z~X+Y, subground, cutoff=1800, width=200,
 
alpha=c(135)
 

 

 
> v.fit = fit.variogram(v, vgm(psill = 1, model="Gau",
 
range=1800, nugget=
 
0, anis=c(135, 0.5)
 

 

 
 
 

 

 
R code:
 

 

 
 
 

 

 
testground2 <-
 
read.table(file="c:/work_LIDAR_USA/R_kriging_new_set/ground_26841492694149_x
 
yz.txt", header=T)
 

 

 
class (testground2)
 

 

 
coordinates (testground2)=~X+Y # this makes testground a
 
SpatialPointsDataFrame
 

 

 
class (testground2)
 

 

 
str(as.data.frame(testground)
 

 

 
 
 

 

 
hist(testground$Z,nclass=20) #this makes a histogram
 

 

 
plot(density(testground$Z) #this makes a plot density
 

 

 
boxplot(testground$Z)#this makes a boxplot
 

 

 
 
 

 

 
samplerows<-sample(1:nrow(testground),size=10000,replace=FALSE) #select n.
 
points from all data-base
 

 

 
subground <-testground[samplerows,]
 

 

 
hist(subground$Z,nclass=20) #this makes a histogram
 

 

 
plot(density(subground$Z) #this makes a plot density
 

 

 
boxplot(subground$Z)#this makes a boxplot
 

 

 
spplot(subground["Z"], col.regions=bpy.colors(), at = seq(850,1170,10)
 

 

 
 
 

 

 
library(maptools)
 

 

 
library(gstat)
 

 

 
plot(variogram(Z~1, subground) #Ordinary Kriging (without detrend)
 

 

 
# if there is a trend we must use a detrend fuction Z~X+Y
 

 

 
x11(); plot(variogram(log(Z)~X+Y, subground, cutoff=1800, width=80)
 
#Universal Kriging (with detrend)
 

 

 
x11(); plot(variogram(log(Z)~X+Y, subground, cutoff=1800, width=80, map=T)
 

 

 
x11(); plot(variogram(log(Z)~X+Y, subground, cutoff=1800, width=80,
 
alpha=c(0, 45, 90, 135)
 

 

 
v = variogram(log(Z)~X+Y, subground, cutoff=1800, width=80, alpha=c(135,
 
45)
 

 

 
v.fit = fit.variogram(v, vgm(psill = 1, model="Gau", range=1800, nugget= 0,
 
anis=c(135, 0.5)
 

 

 
plot(v, v.fit, plot.nu=F, pch="+")
 

 

 
# create the new grid
 

 

 
new.grid <- spsample(subground, type="regular", cellsize=c(1,1)
 

 

 
gridded(new.grid) <- TRUE
 

 

 
fullgrid(new.grid) <- TRUE
 

 

 
new.grid@grid
 

 

 
#using Universal Kriging
 

 

 
subground.uk = krige(log(Z)~X+Y, subground, new.grid, v.fit, nmax=40)
 
#ERROR
 

 

 
 
 

 

 
 
 

 

 
 
 

 

 
 
 

 

 

 

 
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