Predict Using Trend Raster (Image Analyst)

Disponible avec une licence Image Analyst.

Résumé

Calcul un raster multidimensionnel prévu en utilisant le raster de tendance en sortie à partir de l’outil Générer un raster de tendance.

Utilisation

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

• Cet outil crée un jeu de données raster multidimensionnelles au format CRF (Cloud Raster Format). Actuellement, aucun autre format en sortie n’est pris en charge.

• Par défaut, le raster multidimensionnel en sortie sera compressé selon le type de compression LZ77. Nous vous recommandons néanmoins de remplacer le type de compression par le type de compression LERC et d’ajuster la valeur d’erreur maximale en fonction de vos données. Par exemple, si vous voulez que la précision des résultats de l’analyse soit de trois décimales, utilisez la valeur d’erreur maximale 0,001. Il est préférable d’éviter les exigences de précision inutiles, car elles ne feront qu’augmenter le temps de traitement et l’espace nécessaire au stockage.

Syntaxe

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

Paramètre	Explication	Type de données
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,...] (Facultatif)	The variable or variables that will be predicted in the analysis. If no variables are specified, all variables will be used.	String
dimension_def (Facultatif)	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,...] (Facultatif)	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 (Facultatif)	The start date, height, or depth of the dimension interval to be used in the prediction.	String
end (Facultatif)	The end date, height, or depth of the dimension interval to be used in the prediction.	String
interval_value (Facultatif)	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 (Facultatif)	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

Valeur renvoyée

Nom	Explication	Type de données
out_multidimensional_raster	Jeu de données raster multidimensionnelles CRF (Cloud Raster Format) en sortie.	Raster

Exemple de code

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")

Environnements

Taille de cellule, Compression, Espace de travail courant, Transformations géographiques, NoData, Mot-clé de configuration en sortie, Système de coordonnées en sortie, Etendue, Facteur de traitement parallèle, Pyramide, Statistiques raster, Méthode de ré-échantillonnage, Espace de travail temporaire, Raster de capture, Taille de tuile

Informations de licence

• Basic: Requiert Image Analyst
• Standard: Requiert Image Analyst
• Advanced: Requiert Image Analyst