Dear Grass'ers
I have written a bash script to extract the maximum LiDAR height value within a
predefined sample site (Vector with 314 sample locations each 10m squared). The
script is below. Essentially I use v.extract to select one of my 314 sample
sites (Flight_sample2). This temporary vector is then used to select all LiDAR
points falling within its boundaries using v.select. Following this I use
v.univar to calculate summary statistics of the points selected which is then
written to temp_file. sed and awk are then used to extract the maximum height
value which is then used to update (v.db.update) the original flight sample
file. The script does exactly what it is suppose to do and I am chuffed with
the result, however, it does take an enormous amount of time. For my largest
area, over 200 samples it took almost two whole days (running non-stop) to
complete the task. I may need to calculate additional summary statistics at a
later date and would like to speed up the operation.
Can anyone suggest a faster way to do this? I am a 'noob' when it comes to bash
scripts and programming in general so any additional help would be greatly
appreciated. Additionally, I am using the dbf driver.
Many thanks
Wesley
**********************************************************************************************************************************************************************
#!/bin/bash
x=61
a=0
while [ $x -le 284 ]
do
v.extract [EMAIL PROTECTED] output=sam_$x type=area layer=1 new=-1
where=SAM=$x --overwrite
v.select [EMAIL PROTECTED] atype=point alayer=1 binput=sam_$x
btype=area blayer=1 output=b_sam_$x operator=overlap --overwrite
v.univar map=b_sam_$x type=point column=dbl_5 layer=1 > temp_file
a=`sed -n '5p' temp_file | awk '{print $2}'`
echo "$x"
echo "Maximum Lidar point height for this sample location sam_$x is: $a"
v.db.update map=Flight_sample2 layer=1 column=mx_LR value=$a
where=SAM=$x
g.remove vect=b_sam_$x,sam_$x
echo "Sample $x from area One has now been processed"
x=$(( $x + 1 ))
done
**********************************************************************************************************************************************************************
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