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.
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 user-specific 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 the latter is too low, you can as superuser add an entry in:
/etc/security/limits.conf # <domain> <type> <item> <value> your_username hard nofile 4096
Use the -z flag to analyze large amounts of raster maps without hitting open files limit and the file option to avoid hitting the size limit of command line arguments. Note that the computation using the file option is slower than with the input option. 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 former is a comma separated list of raster map names and the latter 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.GRASS GIS 7 location):
# set computational region to one of the maps g.region raster=2004_03_tempmean -p
# 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'
# 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
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
Latest change: Monday Jan 30 19:52:26 2023 in commit: cac8d9d848299297977d1315b7e90cc3f7698730
© 2003-2023 GRASS Development Team, GRASS GIS 8.2.2dev Reference Manual