Predict Using Trend Raster (Image Analyst)

Disponible con licencia de Image Analyst.

Resumen

Calcula un ráster multidimensional previsto utilizando el ráster de tendencia de salida de la herramienta Generar ráster de tendencia.

Uso

• This tool uses the output from the Generate Trend Raster tool as the input multidimensional trend raster.

• Esta herramienta produce un dataset ráster multidimensional en formato de ráster de nube (CRF). En este momento, no se admite ningún otro formato de salida.

• De forma predeterminada, la salida del ráster multidimensional se comprimirá con el tipo de compresión LZ77. Sin embargo, se recomienda que cambie el tipo de compresión a LERC y ajuste el valor de error máximo en función de sus datos. Por ejemplo, si espera que los resultados del análisis sean precisos hasta tres decimales, use 0,001 como valor de error máximo. Lo mejor es evitar requisitos de precisión innecesarios, puesto que aumentarán el tiempo de procesamiento y el tamaño de almacenamiento.

Parámetros

Cuadro de diálogo

Etiqueta	Explicación	Tipo de datos
Input Trend Raster	The input multidimensional trend raster from the Generate Trend Raster tool.	Raster Dataset; Raster Layer; Mosaic Dataset; Mosaic Layer; Image Service; File
Variables [Dimension Info] (Description) (Opcional)	The variable or variables that will be predicted in the analysis. If no variables are specified, all variables will be used.	String
Dimension Definition (Opcional)	Specifies the method used to provide prediction dimension values. By value—The prediction will be calculated for a single dimension value or a list of dimension values defined by the Values parameter (dimension_values in Python). This is the default.For example, you want to predict yearly precipitation for the years 2050, 2100, and 2150. By interval—The prediction will be calculated for an interval of the dimension defined by a start and an end value.For example, you want to predict yearly precipitation for every year between 2050 and 2150.	String
Values (Opcional)	The dimension value or values to be used in the prediction. The format of the time, depth, and height values must match the format of the dimension values used to generate the trend raster. If the trend raster was generated for the StdTime dimension, the format would be YYYY-MM-DDTHH:MM:SS, for example 2050-01-01T00:00:00. Multiple values are separated with a semicolon. This parameter is required when the Dimension Definition parameter is set to By value.	String
Start (Opcional)	The start date, height, or depth of the dimension interval to be used in the prediction.	String
End (Opcional)	The end date, height, or depth of the dimension interval to be used in the prediction.	String
Value Interval (Opcional)	The number of steps between two dimension values to be included in the prediction. The default value is 1. For example, to predict temperature values every five years, use a value of 5.	Double
Unit (Opcional)	Specifies the unit that will be used for the interval value. This parameter only applies when the dimension of analysis is a time dimension. Hours—The prediction will be calculated for each hour in the range of time described by the Start, End, and Value Interval parameters. Days—The prediction will be calculated for each day in the range of time described by the Start, End, and Value Interval parameters. Weeks—The prediction will be calculated for each week in the range of time described by the Start, End, and Value Interval parameters. Months—The prediction will be calculated for each month in the range of time described by the Start, End, and Value Interval parameters. Years—The prediction will be calculated for each year in the range of time described by the Start, End, and Value Interval parameters.	String

Valor de retorno

Etiqueta	Explicación	Tipo de datos
Output Multidimensional Raster	El dataset ráster multidimensional de formato de ráster de nube (CRF) de salida.	Raster

Python

PredictUsingTrendRaster(in_multidimensional_raster, {variables}, {dimension_def}, {dimension_values}, {start}, {end}, {interval_value}, {interval_unit})

Nombre	Explicación	Tipo de datos
in_multidimensional_raster	The input multidimensional trend raster from the Generate Trend Raster tool.	Raster Dataset; Raster Layer; Mosaic Dataset; Mosaic Layer; Image Service; File
variables [variables,...] (Opcional)	The variable or variables that will be predicted in the analysis. If no variables are specified, all variables will be used.	String
dimension_def (Opcional)	Specifies the method used to provide prediction dimension values. BY_VALUE—The prediction will be calculated for a single dimension value or a list of dimension values defined by the Values parameter (dimension_values in Python). This is the default.For example, you want to predict yearly precipitation for the years 2050, 2100, and 2150. BY_INTERVAL—The prediction will be calculated for an interval of the dimension defined by a start and an end value.For example, you want to predict yearly precipitation for every year between 2050 and 2150.	String
dimension_values [dimension_values,...] (Opcional)	The dimension value or values to be used in the prediction. The format of the time, depth, and height values must match the format of the dimension values used to generate the trend raster. If the trend raster was generated for the StdTime dimension, the format would be YYYY-MM-DDTHH:MM:SS, for example 2050-01-01T00:00:00. Multiple values are separated with a semicolon. This parameter is required when the dimension_def parameter is set to BY_VALUE.	String
start (Opcional)	The start date, height, or depth of the dimension interval to be used in the prediction.	String
end (Opcional)	The end date, height, or depth of the dimension interval to be used in the prediction.	String
interval_value (Opcional)	The number of steps between two dimension values to be included in the prediction. The default value is 1. For example, to predict temperature values every five years, use a value of 5.	Double
interval_unit (Opcional)	Specifies the unit that will be used for the interval value. This parameter only applies when the dimension of analysis is a time dimension. HOURS—The prediction will be calculated for each hour in the range of time described by the start, end, and interval_value parameters. DAYS—The prediction will be calculated for each day in the range of time described by the start, end, and interval_value parameters. WEEKS—The prediction will be calculated for each week in the range of time described by the start, end, and interval_value parameters. MONTHS—The prediction will be calculated for each month in the range of time described by the start, end, and interval_value parameters. YEARS—The prediction will be calculated for each year in the range of time described by the start, end, and interval_value parameters.	String

Valor de retorno

Nombre	Explicación	Tipo de datos
out_multidimensional_raster	El dataset ráster multidimensional de formato de ráster de nube (CRF) de salida.	Raster

Muestra de código

PredictUsingTrendRaster example 1 (Python window)

This example generates the forecasted precipitation and temperature for January 1, 2050, and January 1, 2100.

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

# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")

# Execute 
predictOutput = PredictUsingTrendRaster("C:/Data/LinearTrendCoefficients.crf",
	"temp;precip", "BY_VALUE", "2050-01-01T00:00:00;2100-01-01T00:00:00")
	
# Save output
predictOutput.save("C:/Data/Predicted_Temp_Precip.crf")

PredictUsingTrendRaster example 2 (stand-alone script)

This example generates the forecasted NDVI values for each month in year 2025.

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

# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")

# Define input parameters
inFile = "C:/Data/HarmonicTrendCoefficients.crf"
variables = "NDVI"
dimension_definition = "BY_INTERVAL"
start = "2025-01-01T00:00:00"
end = "2025-12-31T00:00:00"
interval_value = 1
interval_unit = "MONTHS"

# Execute - predict the monthly NDVI in 2025 
predictOutput = PredictUsingTrendRaster(inFile, variables, 
	dimension_definition, '', start, end, interval_value, interval_unit)
	
# Save output
predictOutput.save("C:/data/predicted_ndvi.crf")

Entornos

Tamaño de celda, Compresión, Espacio de trabajo actual, Transformaciones geográficas, NoData, Palabra clave CONFIG de salida, Sistema de coordenadas de salida, Extensión, Factor de procesamiento en paralelo, Pirámide, Estadísticas de ráster, Método de remuestreo, Espacio de trabajo temporal, Alinear ráster, Tamaño de tesela

Casos especiales

Información de licenciamiento

• Basic: Requiere Image Analyst
• Standard: Requiere Image Analyst
• Advanced: Requiere Image Analyst