[R] LIDAR Problem in R (THANKS for HELP)

[Alessandro]

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

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

3.        Fig 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.       Fig 4  - plot random data-set in the space

5.        Fig 5 – ordinary kriging: show a trend in the space (hypothesis:
the points are very close in the space)

6.       Fig 6 –de-trend dataset with:  v <- variogram (log(Z)~X+Y,
subground, cutoff=1800, width=100))

7.       Fig 7 – map of semi-variogram: show an anisotropy in the space (0°
is Nord= 135° major radius 45° minus radius)

8.       Fig 8- semi-variogram with anisotropy (0°, 45°, 90°, 135°).  Best
shape is from 135°

9.       Fig 9-  semi-variogram fit with Gaussian Model. R code is: 

> 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 a grid

#using Universal Kriging

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