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_xyz.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
[EMAIL PROTECTED]
#using Universal Kriging
subground.uk = krige(log(Z)~X+Y, subground, new.grid, v.fit, nmax=40)
#ERROR
