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<div class=Section1>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><span lang=EN-US style='color:#1F497D'>Hi All,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>I am<span style='color:#1F497D'> a PhD
student in forestry science and I am working with LiDAR data set (huge data
set)</span>. <span style='color:#1F497D'>I am</span> a brand-new in R and
geostatistic (SORRY<span style='color:#1F497D'>, my background it’s in forestry</span>)
but I wish improve my skill for improve myself. <span style='color:#1F497D'> 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<o:p></o:p></span></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#1F497D'>I have this
questions:<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l1 level1 lfo2'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>1.<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>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<o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:18.0pt'><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l1 level1 lfo2'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>2.<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US style='color:#1F497D'>Is it
possible to improve the memory capacity?</span><span lang=EN-US><o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l1 level1 lfo2'><![if !supportLists]><span
lang=EN-US style='color:#1F497D'><span style='mso-list:Ignore'>3.<span
style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span
lang=EN-US style='color:#1F497D'>The data has a trend and the qqplot shows a
Gaussian trend. Is it possible to normalize the data (i.e. with log)?<o:p></o:p></span></p>
<p class=MsoListParagraph style='margin-left:0cm'><span lang=EN-US
style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l1 level1 lfo2'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>4.<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>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<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>I send you a report and attach the image to
explain better. <o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>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,])<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l0 level1 lfo4'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>1.<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>Original data-set Histogram:
show two populations;<o:p></o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l0 level1 lfo4'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>2.<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>original data-set density plot:
show again two populations of data;<o:p></o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l0 level1 lfo4'><![if !supportLists]><span
lang=EN-US style='color:#1F497D'><span style='mso-list:Ignore'>3.<span
style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span
lang=EN-US> 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)<span style='color:#1F497D'><o:p></o:p></span></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l0 level1 lfo4'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>4.<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US> ordinary kriging: show a
trend in the space (hypothesis: the points are very close in the space)<o:p></o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l0 level1 lfo4'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>5.<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>de-trend dataset with: v
<- variogram (log(Z)~X+Y, subground, cutoff=1800, width=100))<o:p></o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l0 level1 lfo4'><![if !supportLists]><span
lang=EN-US><span style='mso-list:Ignore'>6.<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>map of semi-variogram: show an
anisotropy in the space (0° is Nord= 135° major radius 45° minus radius)<o:p></o:p></span></p>
<p class=MsoListParagraph style='text-indent:-18.0pt;mso-list:l0 level1 lfo4'><![if !supportLists]><span
lang=EN-US style='color:#1F497D'><span style='mso-list:Ignore'>7.<span
style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span
lang=EN-US>semi-variogram with anisotropy (0°, 45°, 90°, 135°)<span
style='color:#1F497D'>, shows a</span> <span style='color:#1F497D'>b</span>est
shape is from 135°<span style='color:#1F497D'><o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:18.0pt'><span lang=EN-US
style='color:#1F497D'>8. </span><span lang=EN-US>semi-variogram fit with
Gaussian Model. R code is<span style='color:#1F497D'> (see the fig)</span>: <span
style='color:#1F497D'><o:p></o:p></span></span></p>
<p class=MsoNormal style='margin-left:88.8pt;text-indent:17.4pt'><span
lang=EN-US>> v = variogram(Z~X+Y, subground, cutoff=1800, width=200,
alpha=c(135))<o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:70.8pt;text-indent:35.4pt'><span
lang=EN-US>> v.fit = fit.variogram(v, vgm(psill = 1, model="Gau",
range=1800, nugget= 0, anis=c(135, 0.5)))<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>R code:<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>testground2 <- read.table(file="c:/work_LIDAR_USA/R_kriging_new_set/ground_26841492694149_xyz.txt",
header=T)<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>class (testground2)<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>coordinates (testground2)=~X+Y # this makes
testground a SpatialPointsDataFrame<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>class (testground2)<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>str(as.data.frame(testground))<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>hist(testground$Z,nclass=20) #this makes a
histogram<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>plot(density(testground$Z)) #this makes a
plot density<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>boxplot(testground$Z)#this makes a boxplot<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>samplerows<-sample(1:nrow(testground),size=10000,replace=FALSE)
#select n. points from all data-base<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>subground <-testground[samplerows,]<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>hist(subground$Z,nclass=20) #this makes a
histogram<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>plot(density(subground$Z)) #this makes a
plot density<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>boxplot(subground$Z)#this makes a boxplot<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>spplot(subground["Z"],
col.regions=bpy.colors(), at = seq(850,1170,10))<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US>library(maptools)<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>library(gstat)<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>plot(variogram(Z~1, subground)) #Ordinary
Kriging (without detrend)<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US># if there is a trend we must use a detrend
fuction Z~X+Y<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>x11(); plot(variogram(log(Z)~X+Y,
subground, cutoff=1800, width=80)) #Universal Kriging (with detrend)<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>x11(); plot(variogram(log(Z)~X+Y,
subground, cutoff=1800, width=80, map=T))<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>x11(); plot(variogram(log(Z)~X+Y,
subground, cutoff=1800, width=80, alpha=c(0, 45, 90, 135)))<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>v = variogram(log(Z)~X+Y, subground,
cutoff=1800, width=80, alpha=c(135, 45))<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>v.fit = fit.variogram(v, vgm(psill = 1,
model="Gau", range=1800, nugget= 0, anis=c(135, 0.5)))<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>plot(v, v.fit, plot.nu=F,
pch="+")<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US># create the new grid<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>new.grid <- spsample(subground,
type="regular", cellsize=c(1,1))<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>gridded(new.grid) <- TRUE<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>fullgrid(new.grid) <- TRUE<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>new.grid@grid<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>#using Universal Kriging<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>subground.uk = krige(log(Z)~X+Y, subground,
new.grid, v.fit, nmax=40) #ERROR<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='color:#1F497D'><o:p> </o:p></span></p>
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