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NAME

r.hants - Approximates a periodic time series and creates approximated output.

KEYWORDS

raster, series, filtering

SYNOPSIS

r.hants
r.hants --help
r.hants [-lhzi] [input=name[,name,...]] [file=name] [suffix=string] [amplitude=string] [phase=string] nf=integer [fet=float] [dod=integer] [range=lo,hi] [time_steps=float[,float,...]] [base_period=integer] [delta=float] [--help] [--verbose] [--quiet] [--ui]

Flags:

-l
Reject low outliers
-h
Reject high outliers
-z
Don't keep files open
-i
Do not extrapolate, only interpolate
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog

Parameters:

input=name[,name,...]
Name of input raster map(s)
file=name
Input file with raster map names, one per line
suffix=string
Suffix for output maps
The suffix will be appended to input map names
Default: _hants
amplitude=string
Prefix for output amplitude maps
phase=string
Prefix for output phase maps
nf=integer [required]
Number of frequencies
fet=float
Fit error tolerance
dod=integer
Degree of over-determination
Default: 0
range=lo,hi
Ignore values outside this range
time_steps=float[,float,...]
Time steps of the input maps
base_period=integer
Length of the base period
delta=float
Threshold for high amplitudes
Delta should be between 0 and 1
Default: 0

Table of contents

DESCRIPTION

r.hants performs a harmonic analysis of time series in order to estimate missing values and identify outliers. For each input map, an output map with the suffix suffix (default: _hants) is created.

The option nf, number of frequencies, should be carefully chosen. Different numbers of frequencies should be tested first on a small test region before running the module on the full region. As a rule of thumb, the number of frequencies should be at least estimated periodicity + 3, e.g. for NDVI with an annual cycle (one peak per year), the number of frequencies should be at least 4 when analysing one year. If two peaks are assumed per year, the number of frequencies should be at least 5 when analysing one year.

The number of frequencies should not be too large, either. With a large number of frequencies, outliers can no longer be identified because the fit is "too good", i.e. outliers can be represented by the estimates of the curve. Moreover, the number of frequencies should be smaller than n input maps / 2 if missing values should be reconstructed.

NOTES

The optional amplitude and phase output maps contain the amplitude and phase for each frequency. The amplitude maps can be used to identify the dominant frequency with r.series method=max_raster. The baseline frequeny (base period) has the suffix .0, its first harmonic has the suffix .1, its second harmonic has the suffix .2, etc. The value of the output of r.series method=max_raster is identical to the number of the suffix. With the amplitude output maps for NDVI input, this can be used to determine the number of peaks in vegetation growth within the base period, where 0 (zero) means that the dominant frequency is the base period, i.e. one peak per base period.

HANTS operates in time, i.e. it looks at the time series of each cell. To fit a harmonic curve, it requires that the time series of each cell has a minimum amount of valid data. The number of valid observations must always be greater than or equal to the number of parameters that describe the harmonic curve (2 x nf - 1). The user can decide to use more observations than this minimum required. The option dod (degree of over-determination) is the minimum number of "extra" valid observations that should be considered to fit the curve. This parameter is optional, but it is recommended to be set.

In general, HANTS discards some information trying to represent the input time series with a limited number of sine/cosine functions. Therefore, most of the times, 1) it does not provide an exact match with the input data and, 2) it produces a smoothed output. With more frequencies, it is possible to get a better match with the input data, but also potential overshoots. The latter can be alleviated by setting dod > 0 at the cost of further smoothing in the output.

The range parameter can be set to low,high thresholds: values outside of this range are treated as NULL. The low,high thresholds are floating point, so use -inf or inf for a single threshold (e.g., range=0,inf to ignore negative values, or range=-inf,-200.4 to ignore values above -200.4).

The length of the base_period is by default the number of input maps. If the user wants a base period of one year and the input or file options (note that they are mutually exclusive) provides a list of maps covering one year, then there is no need to set the base period. Besides, if the input maps are equidistant in time, e.g. every 8 days, there is no need to set time_steps. However, if the interval is not constant (i.e. masp are not equidistant), the user needs to assign time steps. These must always increase (i.e. each time step must be larger than the previous one) and the total number of time steps must be equal to the number of input maps.

Optionally, low and/or high outliers can be removed by means of the -l and -h flags, respectively. In this case, the parameter fet (fit error tolerance) must be provided. The value of fet is relative to the value range of the variable being considered. For further details on the usage of the option fet, see Roerink et al. (2000).

The maximum number of raster maps that can be processed is given by the per-user limit of the operating system. For example, the soft limits for users are typically 1024. The soft limit can be changed with e.g. ulimit -n 4096 (UNIX-based operating systems) but it cannot be higher than the hard limit. If it is too low, you can as superuser add an entry in

/etc/security/limits.conf
# <domain>      <type>  <item>         <value>
your_username  hard    nofile          4096
This would raise the hard limit to 4096 files. Also have a look at the overall limit of the operating system
cat /proc/sys/fs/file-max
which on modern Linux systems is several 100,000 files.

Use the file option to analyze large amount of raster maps without hitting open files limit and the size limit of command line arguments. The computation is slower than with the input option method. For every single row in the output map(s) all input maps are opened and closed. The amount of RAM will rise linearly with the number of specified input maps. The input and file options are mutually exclusive. The option input is a comma separated list of raster map names and the option file is a text file with a new line separated list of raster map names. Note that the order of maps in one option or the other is very important.

EXAMPLES

Average temperature data example

This small example is based on a climatic dataset for North Carolina which was from publicly available data (monthly temperature averages and monthly precipitation sums from 2000 to 2012, downloadable as GRASS GIS 7 location):
# set computational region to one of the maps
g.region raster=2004_03_tempmean -p
Visualize the time series as animation:
# note: color table is different from standard "celsius"
g.gui.animation rast=`g.list type=raster pattern="*tempmean" sep=comma`

Since HANTS is CPU intensive, we test for now at lower resolution:

g.region -p res=5000

# HANTS: Harmonic analysis of the 156 input maps...
# just wildly guessing the parameters for a test run:

# generate and check list of input maps (the order matters!)
g.list type=raster pattern="20??_??_tempmean" output=tempmean.csv

r.hants file=tempmean.csv nf=6 dod=5 delta=0.1 base_period=12

# assign reasonable color tables for temperature
for map in `g.list type=raster pattern="*tempmean_hants"` ; do
    r.colors $map color=celsius
done

# assign degree Celsius color table
r.colors 2000_06_tempmean_hants color=celsius

# verify with one of the 156 results (still at reduced resolution):
r.mapcalc "2000_06_tempmean_diff = 2000_06_tempmean - 2000_06_tempmean_hants"

r.colors 2000_06_tempmean_diff color=differences
d.mon wx0
d.rast 2000_06_tempmean_hants
d.rast 2000_06_tempmean_diff


r.univar 2000_06_tempmean_diff -g
n=5066
null_cells=5040
cells=10106
min=-0.0899336115228095
max=0.359362050140941
range=0.449295661663751
mean=0.188579838052468
...

# see HANTS time series as animation
g.gui.animation rast=`g.list type=raster pattern="*tempmean_hants" sep=comma`


# Check HANTS behaviour in a given point 
east=740830
north=168832

for map in `g.list rast pat="20??_??_tempmean"` ; do   
  r.what map=$map coordinates=$east,$north >> time_series_orig.csv
done

for map in `g.list rast pat="*tempmean_hants"` ; do 
  r.what map=$map coordinates=$east,$north >> time_series_hants.csv 
done

# merge files:
echo "east|north|temp_orig|temp_hants" > time_series_final.csv

paste -d'|' time_series_orig.csv time_series_hants.csv | \
      cut -d'|' -f1,2,4,8 >> time_series_final.csv

# Resulting CSV file: 'time_series_final.csv'

Using t.* modules to check for basic statistics

The temporal framework (t.* modules) can be used to assess basic statistics:
# create spatio temporal data set with hants output maps
t.create type=strds temporaltype=absolute  output=tempmean_hants \
  title="Mean Temperature HANTS" description="Mean Temperature reconstructed with HANTS"

# register maps in the strds
t.register -i type=raster input=tempmean_hants \
  maps=`g.list raster pattern=*tempmean_hants sep=,` start="2000-01-01" \
  increment="1 months"

# getting general info of the strds (including max and min of the whole series)
t.info type=strds input=tempmean_hants

# getting statistics for each map in the series
t.rast.univar -h tempmean_hants > stats_hants.txt

SEE ALSO

r.series r.series.lwr

REFERENCES

Roerink, G. J., Menenti, M. and Verhoef, W., 2000. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21 (9), 1911-1917. DOI: 10.1080/014311600209814

AUTHOR

Markus Metz

Last changed: $Date: 2017-10-25 11:00:34 +0200 (Wed, 25 Oct 2017) $

SOURCE CODE

Available at: r.hants source code (history)


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© 2003-2017 GRASS Development Team, GRASS GIS 7.2.3svn Reference Manual