GenerateTrend

Summary

Estimates the trend for each pixel along a dimension for one or more variables in a multidimensional raster.

Discussion

For more information about how this function works, see the Generate Trend raster function.

The referenced raster dataset for the raster object is temporary. To make it permanent, you can call the raster object's save method.

Syntax

GenerateTrend(raster, dimension_name, {regression_type}, {cycle_length}, {cycle_unit}, {harmonic_frequency}, {polynomial_order}, {ignore_nodata}, {rmse}, {r2}, {slope_p_value}, {seasonal_period})

Parameter	Explanation	Data Type
raster	The input multidimensional raster.	Raster
dimension_name	The name of the dimension along which a trend will be extracted for the variable or variables selected in the analysis.	String
regression_type	Specifies the type of line to be used to fit to the pixel values along a dimension. LINEAR—Variable pixel values will be fitted along a linear trend line. This is the default. HARMONIC—Variable pixel values will be fitted along a harmonic trend line. POLYNOMIAL—Variable pixel values will be fitted along a second-order polynomial trend line. MANN-KENDALL—Variable pixel values will be evaluated for monotonic trend using the Mann-Kendall trend test. SEASONAL-KENDALL—Variable pixel values will be evaluated for monotonic trend using the Seasonal-Kendall trend test. The default value is LINEAR.	String
cycle_length	The length of periodic variation to model. This argument is required when `regression_type` is set to `HARMONIC`. For example, leaf greenness often has one strong cycle of variation in a single year, so `cycle_length` is 1 year. Hourly temperature data has one strong cycle of variation throughout a single day, so the `cycle_length` is 1 day. The default value is 1. The default value is 1.	Integer
cycle_unit	Specifies the time unit to be used for the length of a harmonic cycle. DAYS—The unit for the length of the harmonic cycle is days. YEARS—The unit for the length of the harmonic cycle is years. This is the default. The default value is YEARS.	String
harmonic_frequency	The number of models to use in the trend fitting when the `regression_type` is `HARMONIC`. The default is 1, or one harmonic cycle per year. This argument is only included in the trend analysis when the dimension being analyzed is time. The default value is 1.	Integer
polynomial_order	The polynomial order number to use in the trend fitting when the `regression_type` is `POLYNOMIAL`. The default is 2, or second-order polynomial. This argument is only included in the trend analysis when the dimension being analyzed is time. The default value is 2.	Integer
ignore_nodata	Specifies whether NoData values are ignored in the analysis. `True`—The analysis will include all valid pixels along a given dimension and ignore any NoData pixels. This is the default. `False`—The analysis will result in NoData if there are any NoData values for the pixels along the given dimension. The default value is True.	Boolean
rmse	Specifies whether the root mean square error (RMSE) of the trend fit line will be calculated. `True`—The RMSE will be calculated. This is the default. `False`—The RMSE will not be calculated. The default value is True.	Boolean
r2	Specifies whether the R-squared goodness-of-fit statistic for the trend fit line will be calculated. `True`—The R-squared value will be calculated. `False`—The R-squared value will not be calculated. This is the default. The default value is False.	Boolean
slope_p_value	Specifies whether the p-value statistic for the slope coefficient of the trend line will be calculated. `True`—The p-value will be calculated. `False`—The p-value will not be calculated. This is the default. The default value is False.	Boolean
seasonal_period	Specifies the unit to use for seasonal period. This is required when the `regression_type` argument is set to `SEASONAL-KENDALL`. DAYS—The unit for the length of the seasonal period is days. This is the default. MONTHS—The unit for the length of the seasonal period is months. The default value is DAYS.	String

Return value

Data Type	Explanation
Raster	The output raster.

Code sample

GenerateTrendRaster example

This example calculates the harmonic trend fit along an NDVI time series.

# Import system modulesimport arcpy
import arcpy
from arcpy.ia import *

# Set the local variables
in_multidimensional_raster = "C:/data/ndvi_time_series.crf"
dimension_name = "StdTime"
regression_type = "HARMONIC"
cycle_length = 1
cycle_unit = "YEARS"
harmonic_frequency = 1
polynomial_order = None
ignore_nodata = True

rmse = True
r2 = False
slope_p_value = False
seasonal_period = None

# Apply GenerateTrendRaster function
trend_raster = arcpy.ia.GenerateTrend(in_multidimensional_raster,
                dimension_name, regression_type, cycle_length, cycle_unit,
                harmonic_frequency, polynomial_order, ignore_nodata, rmse,
                r2, slope_p_value, seasonal_period)

# Save the output
trend_raster.save("C:/arcpyExamples/outputs/ndvi_trend_raster.crf")