++PARAM_TSA_START++ # INPUT/OUTPUT DIRECTORIES # ------------------------------------------------------------------------ # Lower Level datapool (parent directory of tiled input data) # Type: full directory path DIR_LOWER = /data/10_lcf_level_2 # Higher Level datapool (parent directory of tiled output data) # Type: full directory path DIR_HIGHER = /data/20_lcf_spectral_temporal_metrics # This is the directory where provenance files should be saved. # Type: full directory path DIR_PROVENANCE = /data/temp # MASKING # ------------------------------------------------------------------------ # Analysis Mask datapool (parent directory of tiled analysis masks) # If no analsys mask should be applied, give NULL. # Type: full directory path DIR_MASK = NULL # Basename of analysis masks (e.g. WATER-MASK.tif). # Masks need to be binary with 0 = off / 1 = on # Type: Basename of file BASE_MASK = NULL # OUTPUT OPTIONS # ------------------------------------------------------------------------ # Output format, which is either uncompressed flat binary image format aka # ENVI Standard or GeoTiff. GeoTiff images are compressed with LZW and hori- # zontal differencing; BigTiff support is enabled; the Tiff is structured # with striped blocks according to the TILE_SIZE (X) and BLOCK_SIZE (Y) speci- # fications. Metadata are written to the ENVI header or directly into the Tiff # to the FORCE domain. If the size of the metadata exceeds the Tiff's limit, # an external .aux.xml file is additionally generated. # Type: Character. Valid values: {ENVI,GTiff} OUTPUT_FORMAT = GTiff # File that contains custom GDAL output options. This is only used if # OUTPUT_FORMAT = CUSTOM. If OUTPUT_FORMAT = CUSTOM, this file is mandatory. # The file should be written in tag and value notation. The first two lines # are mandatory and specify GDAL driver and file extension, e.g. DRIVER = GTiff # and EXTENSION = tif. The driver name refers to the GDAL short driver names. # Lines 3ff can hold a variable number of GDAL options (up to 32 are allowed). # Please note: with opening output options up to the user, it is now possible to # give invalid or conflicting options that result in the failure of creating files. # Type: full file path FILE_OUTPUT_OPTIONS = NULL # This parameter controls whether the output is written as multi-band image, or # if the stack will be exploded into single-band files. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_EXPLODE = FALSE # PARALLEL PROCESSING # ------------------------------------------------------------------------ # This module is using a streaming mechanism to speed up processing. There # are three processing teams (3 Threads) that simultaneously handle Input, # Processing, and Output. Example: when Block 2 is being processed, data # from Block 3 are already being input and results from Block 1 are being # output. Each team can have multiple sub-threads to speed up the work. The # number of threads to use for each team is given by following parameters. # Type: Integer. Valid range: [1,... NTHREAD_READ = 8 NTHREAD_COMPUTE = 32 NTHREAD_WRITE = 4 # PROCESSING EXTENT AND RESOLUTION # ------------------------------------------------------------------------ # Analysis extent, given in tile numbers (see tile naming) # Each existing tile falling into this square extent will be processed # A shapefile of the tiles can be generated with force-tabulate-grid # Type: Integer list. Valid range: [-999,9999] X_TILE_RANGE = 69 69 Y_TILE_RANGE = 43 43 # Allow-list of tiles. Can be used to further limit the analysis extent to # non-square extents. The allow-list is intersected with the analysis extent, # i.e. only tiles included in both the analysis extent AND the allow-list will # be processed. # Optional. If NULL, the complete analysis extent is processed # Type: full file path FILE_TILE = NULL # This parameter can be used to override the default blocksize of the input # images (as specified in the datacube-definition.prj file). This can be # necessary if the default blocksize is too large for your system and you # cannot fit all necessary data into RAM. Note that processing of larger # blocksizes is more efficient. The tilesize must be dividable by the blocksize # without remainder. Set to 0, to use the default blocksize # Type: Double. Valid range: 0 or [RESOLUTION,TILE_SIZE] BLOCK_SIZE = 0 # Analysis resolution. The tile (and block) size must be dividable by this # resolution without remainder, e.g. 30m resolution with 100km tiles is not possible # Type: Double. Valid range: ]0,BLOCK_SIZE] RESOLUTION = 10 # How to reduce spatial resolution for cases when the image resolution is higher # than the analysis resolution. If FALSE, the resolution is degraded using Nearest # Neighbor resampling (fast). If TRUE, an approx. Point Spread Function (Gaussian # lowpass with FWHM = analysis resolution) is used to approximate the acquisition # of data at lower spatial resolution # Type: Logical. Valid values: {TRUE,FALSE} REDUCE_PSF = FALSE # If you have spatially enhanced some Level 2 ARD using the FORCE Level 2 ImproPhe # module, this switch specifies whether the data are used at original (FALSE) or # enhanced spatial resolution (TRUE). If there are no improphe'd products, this # switch doesn't have any effect # Type: Logical. Valid values: {TRUE,FALSE} USE_L2_IMPROPHE = TRUE # SENSOR ALLOW-LIST # ------------------------------------------------------------------------ # Sensors to be used in the analysis. Multi-sensor analyses are restricted # to the overlapping bands. Following sensors are available: LND04 (6-band # Landsat 4 TM), LND05 (6-band Landsat 5 TM), LND07 (6-band Landsat 7 ETM+), # LND08/09 (6-band Landsat 8-9 OLI), SEN2A (10-band Sentinel-2A), SEN2B (10-band # Sentinel-2B), sen2a (4-band Sentinel-2A), sen2b (4-band Sentinel-2B), # S1AIA (2-band VV-VH Sentinel-1A IW ascending), S1BIA (2-band VV-VH Senti- # nel-1B IW ascending), S1AID (2-band VV-VH Sentinel-1A IW descending), S1BID # (2-band VV-VH Sentinel-1B IW descending), MOD01 (7-band Terra MODIS), MOD02. # (7-band Aqua MODIS). # The resulting outputs are named according to their band designation, i.e. # LNDLG ((6-band Landsat legacy bands), SEN2L (10-band Sentinel-2 land surface # bands), SEN2H (4-band Sentinel-2 high-res bands), R-G-B (3-band visual) or # VVVHP (VV/VH polarized), MODIS (7-band MODIS). # BAP Composites with such a band designation can be input again (e.g. # SENSORS = LNDLG). # Type: Character list. Valid values: {LND04,LND05,LND07,LND08,LND09,SEN2A, # SEN2B,sen2a,sen2b,S1AIA,S1BIA,S1AID,S1BID,MOD01,MOD02,LNDLG,SEN2L,SEN2H,R-G-B,VVVHP,MODIS} SENSORS = SEN2A SEN2B # Perform a spectral adjustment to Sentinel-2? # This method can only be used with following sensors: SEN2A, SEN2B, LND04, LND05, LND07, # LND08, LND09, MOD01, MOD02. # A material-specific spectral harmonization will be performed, which will convert the # spectral response of any of these sensors to Sentinel-2A. Non-existent bands will be # predicted, too. # Type: Logical. Valid values: {TRUE,FALSE} SPECTRAL_ADJUST = FALSE # QAI SCREENING # ------------------------------------------------------------------------ # This list controls, which QAI flags are masked out before doing the analysis. # Type: Character list. Valid values: {NODATA,CLOUD_OPAQUE,CLOUD_BUFFER, # CLOUD_CIRRUS,CLOUD_SHADOW,SNOW,WATER,AOD_FILL,AOD_HIGH,AOD_INT,SUBZERO, # SATURATION,SUN_LOW,ILLUMIN_NONE,ILLUMIN_POOR,ILLUMIN_LOW,SLOPED,WVP_NONE} SCREEN_QAI = NODATA CLOUD_OPAQUE CLOUD_BUFFER CLOUD_CIRRUS CLOUD_SHADOW SNOW SUBZERO SATURATION # Threshold for removing outliers. Triplets of observations are used to determine # the overall noise in the time series by computinglinearly interpolating between # the bracketing observations. The RMSE of the residual between the middle value # and the interpolation is the overall noise. Any observations, which have a # residual larger than a multiple of the noise are iteratively filtered out # (ABOVE_NOISE). Lower/Higher values filter more aggressively/conservatively. # Likewise, any masked out observation (as determined by the SCREEN_QAI filter) # can be restored if its residual is lower than a multiple of the noise # (BELOW_NOISE). Higher/Lower values will restore observations more aggres- # sively/conservative. Give 0 to both parameters to disable the filtering. # Type: Float. Valid range: [0,... ABOVE_NOISE = 4 BELOW_NOISE = 1 # PROCESSING TIMEFRAME # ------------------------------------------------------------------------ # Time extent for the analysis. All data between these dates will be used in # the analysis. # Type: Date list. Format: YYYY-MM-DD DATE_RANGE = 2018-03-01 2018-10-31 # DOY range for filtering the time extent. Day-of-Years that are outside of # the given interval will be ignored. Example: DATE_RANGE = 2010-01-01 # 2019-12-31, DOY_RANGE = 91 273 will use all April-Sepember observations from # 2010-2019. If you want to extend this window over years give DOY min > # DOY max. Example: DATE_RANGE = 2010-01-01 2019-12-31, DOY_RANGE = 274 90 # will use all October-March observations from 2010-2019. # Type: Integer list. Valid values: [1,365] DOY_RANGE = 1 365 # SPECTRAL INDEX # ------------------------------------------------------------------------ # Perform the time series analysis using the specified band or index. # Multiple indices can be processed ar once to avoid multiple reads of the # same file. Only necessary bands will be input. You will be alerted if the # index cannot be computed based on the requested SENSORS. The index SMA is # a linear spectral mixture analysis and is dependent on the parameters # specified in the SPECTRAL MIXTURE ANALYSIS section below. # Type: Character list. Valid values: {BLUE,GREEN,RED,NIR,SWIR1,SWIR2,RE1, # RE2,RE3,BNIR,NDVI,EVI,NBR,NDTI,ARVI,SAVI,SARVI,TC-BRIGHT,TC-GREEN,TC-WET, # TC-DI,NDBI,NDWI,MNDWI,NDMI,NDSI,SMA,kNDVI,NDRE1,NDRE2,CIre,NDVIre1,NDVIre2, # NDVIre3,NDVIre1n,NDVIre2n,NDVIre3n,MSRre,MSRren} INDEX = BLUE GREEN RED RE1 RE2 RE3 BNIR NIR SWIR1 SWIR2 # Standardize the TSS time series with pixel mean and/or standard deviation? # Type: Logical. Valid values: {NONE,NORMALIZE,CENTER} STANDARDIZE_TSS = NONE # Output the quality-screened Time Series Stack? This is a layer stack of # index values for each date. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_TSS = FALSE # SPECTRAL MIXTURE ANALYSIS # ------------------------------------------------------------------------ # This block only applies if INDEX includes SMA # ------------------------------------------------------------------------ # Endmember file holding the endmembers according to the SENSORS band subset # Type: full file path FILE_ENDMEM = NULL # Sum-to-One constrained unmixing? # Type: Logical. Valid values: {TRUE,FALSE} SMA_SUM_TO_ONE = TRUE # Non-negativity constrained unmixing? # Type: Logical. Valid values: {TRUE,FALSE} SMA_NON_NEG = TRUE # Apply shade normalization? If TRUE, the last endmember FILE_ENDMEM needs # to be the shade spectrum # Type: Logical. Valid values: {TRUE,FALSE} SMA_SHD_NORM = TRUE # Endmember to be used for the analysis. This number refers to the column, # in which the desired endmember is stored (FILE_ENDMEM). # Type: Integer. Valid range: [1,NUMBER_OF_ENDMEMBERS] SMA_ENDMEMBER = 1 # Output the SMA model Error? This is a layer stack of model RMSE for # each date. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_RMS = FALSE # INTERPOLATION PARAMETERS # ------------------------------------------------------------------------ # Interpolation method. You can choose between no, linear, moving average # or Radial Basis Function Interpolation. # Type: Character. Valid values: {NONE,LINEAR,MOVING,RBF} INTERPOLATE = RBF # Max temporal distance for the moving average filter in days. For each # interpolation date, MOVING_MAX days before and after are considered. # Type: Integer. Valid range: [1,365] MOVING_MAX = 16 # Sigma (width of the Gaussian bell) for the RBF filter in days. For each # interpolation date, a Gaussian kernel is used to smooth the observations. # The smoothing effect is stronger with larger kernels and the chance of # having nodata values is lower. Smaller kernels will follow the time series # more closely but the chance of having nodata values is larger. Multiple # kernels can be combined to take advantage of both small and large kernel # sizes. The kernels are weighted according to the data density within each # kernel. # Type: Integer list. Valid range: [1,365] RBF_SIGMA = 8 16 32 # Cutoff density for the RBF filter. The Gaussian kernels have infinite width, # which is computationally slow, and doesn't make much sense as observations # that are way too distant (in terms of time) are considered. Thus, the # tails of the kernel are cut off. This parameter specifies, which percen- # tage of the area under the Gaussian should be used. # Type: Float. Valid range: ]0,1] RBF_CUTOFF = 0.95 # Definition of how many modes per season are used for harmonic interpolation, # i.e. uni-modal (1), bi-modal s(2), or tri-modal (3). # Type: Integer. Valid range: {1,2,3} HARMONIC_MODES = 3 # Subset of the time period to which the harmonic should be fitted. # For example, if the analysis timeframe is DATE_RANGE = 2015-01-01 2022-06-20, # all data from 2015-2022 will be considered. If HARMONIC_FIT_RANGE = 2015-01-01 2017-12-31, # the harmonic will only be fitted to the first 3 years of data. # Type: Date list. Format: YYYY-MM-DD HARMONIC_FIT_RANGE = 2015-01-01 2017-12-31 # Output of the near-real time product? # The product will contain the residual between the extrapolated harmonic and the actual data # following the defined end of the harmonic fit range. # This option requires harmonic interpolation (INTERPOLATE) and a forecast period (HARMONIC_FIT_RANGE). # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_NRT = FALSE # This parameter gives the interpolation step in days. # Type: Integer. Valid range: [1,... INT_DAY = 16 # Standardize the TSI time series with pixel mean and/or standard deviation? # Type: Logical. Valid values: {NONE,NORMALIZE,CENTER} STANDARDIZE_TSI = NONE # Output the Time Series Interpolation? This is a layer stack of index # values for each interpolated date. Note that interpolation will be per- # formed even if OUTPUT_TSI = FALSE - unless you specify INTERPOLATE = NONE. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_TSI = FALSE # PYTHON UDF PARAMETERS # ------------------------------------------------------------------------ # This file specifies the file holding user-defined python code. You can skip this # by setting FILE_PYTHON = NULL, but this requires OUTPUT_PYP = FALSE. # Two functions are required to communicate with FORCE: # 0) The global space can be used to import modules etc. # 1) An initialization function that defines the number and names of output bands: # ``def forcepy_init(dates, sensors, bandnames):`` # 2) A function that implements the user-defined functionality, see ``PYTHON_TYPE`` # Type: full file path FILE_PYTHON = NULL # Type of user-defined function. # 1) ``PIXEL`` expects a pixel-function that receives the time series of a single pixel # as 4D-nd.array [nDates, nBands, nrows, ncols]. A multi-processing pool is spawned to # parallely execute this function with ``NTHREAD_COMPUTE`` workers. # ``def forcepy_pixel(inarray, outarray, dates, sensors, bandnames, nodata, nproc):`` # 2) ``BLOCK`` expects a pixel-function that receives the time series of a complete # processing unit as 4D-nd.array [nDates, nBands, nrows, ncols]. No parallelization is # done on FORCE's end. # ``def forcepy_block(inblock, outblock, dates, sensors, bandnames, nodata, nproc):`` # Type: Character. Valid values: {PIXEL,BLOCK} PYTHON_TYPE = PIXEL # Output the results provided by the python UDF? If TRUE, FILE_PYTHON must exist. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_PYP = FALSE # SPECTRAL TEMPORAL METRICS # ------------------------------------------------------------------------ # Output Spectral Temporal Metrics? The remaining parameters in this block # are only evaluated if TRUE # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_STM = TRUE # Which Spectral Temporal Metrics should be computed? The STM output files # will have as many bands as you specify metrics (in the same order). # Currently available statistics are the average, standard deviation, mini- # mum, maximum, range, skewness, kurtosis, any quantile from 1-99%, and # interquartile range. Note that median is Q50. # Type: Character list. Valid values: {MIN,Q01-Q99,MAX,AVG,STD,RNG,IQR,SKW,KRT,NUM} STM = Q25 Q50 Q75 # FOLDING PARAMETERS # ------------------------------------------------------------------------ # Which statistic should be used for folding the time series? This parameter # is only evaluated if one of the following outputs in this block is requested. # Currently available statistics are the average, standard deviation, mini- # mum, maximum, range, skewness, kurtosis, median, 10/25/75/90% quantiles, # and interquartile range # Type: Character. Valid values: {MIN,Q10,Q25,Q50,Q75,Q90,MAX,AVG,STD, # RNG,IQR,SKW,KRT,NUM FOLD_TYPE = AVG # Standardize the FB* time series with pixel mean and/or standard deviation? # Type: Logical. Valid values: {NONE,NORMALIZE,CENTER} STANDARDIZE_FOLD = NONE # Output the Fold-by-Year/Quarter/Month/Week/DOY time series? These are layer # stacks of folded index values for each year, quarter, month, week or DOY. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_FBY = FALSE OUTPUT_FBQ = FALSE OUTPUT_FBM = FALSE OUTPUT_FBW = FALSE OUTPUT_FBD = FALSE # Compute and output a linear trend analysis on any of the folded time series? # Note that the OUTPUT_FBX parameters don't need to be TRUE to do this. # See also the TREND PARAMETERS block below. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_TRY = FALSE OUTPUT_TRQ = FALSE OUTPUT_TRM = FALSE OUTPUT_TRW = FALSE OUTPUT_TRD = FALSE # Compute and output an extended Change, Aftereffect, Trend (CAT) analysis on # any of the folded time series? # Note that the OUTPUT_FBX parameters don't need to be TRUE to do this. # See also the TREND PARAMETERS block below. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_CAY = FALSE OUTPUT_CAQ = FALSE OUTPUT_CAM = FALSE OUTPUT_CAW = FALSE OUTPUT_CAD = FALSE # LAND SURFACE PHENOLOGY PARAMETERS - SPLITS-BASED # ------------------------------------------------------------------------ # The Land Surface Phenology (LSP) options are only available if FORCE was # compiled with SPLITS (see installation section in the FORCE user guide). # ------------------------------------------------------------------------ # For estimating LSP for one year, some data from the previous/next year # need to be considered to find the seasonal minima, which define a season. # The parameters are given in DOY, i.e. LSP_DOY_PREV_YEAR = 273, and # LSP_DOY_NEXT_YEAR = 91 will use all observations from October (Year-1) # to March (Year+1) # Type: Integer. Valid range: [1,365] LSP_DOY_PREV_YEAR = 273 LSP_DOY_NEXT_YEAR = 91 # Seasonality is of Northern-, Southern-hemispheric or of mixed type? If # mixed, the code will attempt to estimate the type on a per-pixel basis. # Type: Character. Valid values: {NORTH,SOUTH,MIXED} LSP_HEMISPHERE = NORTH # How many segments per year should be used for the spline fitting? More # segments follow the seasonality more closely, less segments smooth the # time series stronger. # Type: Integer. Valid range: [1,... LSP_N_SEGMENT = 4 # Amplitude threshold for detecing Start, and End of Season, i.e. the date, # at which xx% of the amplitude is observed # Type: Float. Valid range: ]0,1[ LSP_AMP_THRESHOLD = 0.2 # LSP won't be derived if the seasonal index values do not exceed following # value. This is useful to remove unvegetated surfaces. # Type: Integer. Valid range: [-10000,10000] LSP_MIN_VALUE = 500 # LSP won't be derived if the seasonal amplitude is below following value # This is useful to remove surfaces that do not have a seasonality. # Type: Integer. Valid range: [0,10000] LSP_MIN_AMPLITUDE = 500 # Which Phenometrics should be computed? There will be a LSP output file for # each metric (with years as bands). # Currently available are the dates of the early minimum, start of season, # rising inflection, peak of season, falling inflection, end of season, late # minimum; lengths of the total season, green season; values of the early # minimum, start of season, rising inflection, peak of season, falling # inflection, end of season, late minimum, base level, seasonal amplitude; # integrals of the total season, base level, base+total, green season; rates # of averahe rising, average falling, maximum rising, maximum falling. # Type: Character list. Valid values: {DEM,DSS,DRI,DPS,DFI,DES,DLM,LTS,LGS, # VEM,VSS,VRI,VPS,VFI,VES,VLM,VBL,VSA,IST,IBL,IBT,IGS,RAR,RAF,RMR,RMF} LSP = VSS VPS VES VSA RMR IGS # Standardize the LSP time series with pixel mean and/or standard deviation? # Type: Logical. Valid values: {NONE,NORMALIZE,CENTER} STANDARDIZE_LSP = NONE # Output the Spline fit? This is a layer stack of fitted index values for # interpolated date. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_SPL = FALSE # Output the Phenometrics? These are layer stacks per phenometric with as many # bands as years (excluding one year at the beginning/end of the time series. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_LSP = FALSE # Compute and output a linear trend analysis on the requested Phenometric time # series? Note that the OUTPUT_LSP parameters don't need to be TRUE to do this. # See also the TREND PARAMETERS block below. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_TRP = FALSE # Compute and output an extended Change, Aftereffect, Trend (CAT) analysis on # the requested Phenometric time series? # Note that the OUTPUT_LSP parameters don't need to be TRUE to do this. # See also the TREND PARAMETERS block below. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_CAP = FALSE # LAND SURFACE PHENOLOGY PARAMETERS - POLAR-BASED # ------------------------------------------------------------------------ # Threshold for detecing Start of Season in the cumulative time series. # Type: Float. Valid range: ]0,1[ POL_START_THRESHOLD = 0.2 # Threshold for detecing Mid of Season in the cumulative time series. # Type: Float. Valid range: ]0,1[ POL_MID_THRESHOLD = 0.5 # Threshold for detecing End of Season in the cumulative time series. # Type: Float. Valid range: ]0,1[ POL_END_THRESHOLD = 0.8 # Should the start of each phenological year be adapated? # If FALSE, the start is static, i.e. Date of Early Minimum and Date of Late # Minimum are the same for all years and 365 days apart. If TRUE, they differ # from year to year and a phenological year is not forced to be 365 days long. # Type: Logical. Valid values: {TRUE,FALSE} POL_ADAPTIVE = TRUE # Which Polarmetrics should be computed? There will be a POL output file for # each metric (with years as bands). # Currently available are the dates of the early minimum, late minimum, peak of season, # start of season, mid of season, end of season, early average vector, average vector, # late average vector; lengths of the total season, green season, between averge vectors; # values of the early minimum, late minimum, peak of season, start of season, mid of season, # end of season, early average vector, average vector, late average vector, base level, # green amplitude, seasonal amplitude, peak amplitude, green season mean , green season # variability, dates of start of phenological year, difference between start of phenological # year and its longterm average; integrals of the total season, base level, base+total, # green season, rising rate, falling rate; rates of average rising, average falling, maximum # rising, maximum falling. # Type: Character list. Valid values: {DEM,DLM,DPS,DSS,DMS,DES,DEV,DAV,DLV,LTS, # LGS,LGV,VEM,VLM,VPS,VSS,VMS,VES,VEV,VAV,VLV,VBL,VGA,VSA,VPA,VGM,VGV,DPY,DPV, # IST,IBL,IBT,IGS,IRR,IFR,RAR,RAF,RMR,RMF} POL = VSS VPS VES VSA RMR IGS # Standardize the POL time series with pixel mean and/or standard deviation? # Type: Logical. Valid values: {NONE,NORMALIZE,CENTER} STANDARDIZE_POL = NONE # Output the polar-transformed time series? These are layer stack of cartesian X- # and Y-coordinates for each interpolated date. This results in two files, product # IDs are PCX and PCY. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_PCT = FALSE # Output the Polarmetrics? These are layer stacks per polarmetric with as many # bands as years. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_POL = FALSE # Compute and output a linear trend analysis on the requested Polarmetric time # series? Note that the OUTPUT_POL parameters don't need to be TRUE to do this. # See also the TREND PARAMETERS block below. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_TRO = FALSE # Compute and output an extended Change, Aftereffect, Trend (CAT) analysis on # the requested Polarmetric time series? # Note that the OUTPUT_POL parameters don't need to be TRUE to do this. # See also the TREND PARAMETERS block below. # Type: Logical. Valid values: {TRUE,FALSE} OUTPUT_CAO = FALSE # TREND PARAMETERS # ------------------------------------------------------------------------ # This parameter specifies the tail-type used for significance testing of # the slope in the trend analysis. A left-, two-, or right-tailed t-test # is performed. # Type: Character. Valid values: {LEFT,TWO,RIGHT} TREND_TAIL = TWO # Confidence level for significance testing of the slope in the trend analysis # Type: Float. Valid range: [0,1] TREND_CONF = 0.95 # In the Change, Aftereffect, Trend (CAT) analysis: do you want to # put a penalty on non-permanent change for the change detection? # This can help to reduce the effect of outliers. # Type: Logical. Valid values: {TRUE,FALSE} CHANGE_PENALTY = FALSE ++PARAM_TSA_END++