Predict Using Trend Raster (Image Analyst)

Доступно с лицензией Image Analyst.

Краткая информация

Вычисляет прогнозируемый многомерный растр, используя выходной растр тренда из инструмента Создать растр тренда.

Использование

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

• Этот инструмент создает многомерный набор растровых данных в формате Cloud Raster Format (CRF). В настоящее время другие форматы выхода не поддерживаются.

• По умолчанию выходной многомерный растр будет сжиматься с использованием типа сжатия LZ77. Однако рекомендуется изменить тип сжатия на LERC и настроить максимальное значение ошибки на основе ваших данных. Например, если вы ожидаете, что результаты анализа будут иметь точность до трех десятичных знаков, используйте 0,001 для максимального значения ошибки. Лучше избегать излишних требований к точности, поскольку они увеличивают время обработки и размер хранилища.

Параметры

Диалог

Подпись	Описание	Тип данных
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) (Дополнительный)	The variable or variables that will be predicted in the analysis. If no variables are specified, all variables will be used.	String
Dimension Definition (Дополнительный)	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 (Дополнительный)	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 (Дополнительный)	The start date, height, or depth of the dimension interval to be used in the prediction.	String
End (Дополнительный)	The end date, height, or depth of the dimension interval to be used in the prediction.	String
Value Interval (Дополнительный)	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 (Дополнительный)	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

Возвращаемое значение

Подпись	Описание	Тип данных
Output Multidimensional Raster	Выходной многомерный набор растровых данных Cloud Raster Format (CRF).	Raster

Python

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

Имя	Описание	Тип данных
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,...] (Дополнительный)	The variable or variables that will be predicted in the analysis. If no variables are specified, all variables will be used.	String
dimension_def (Дополнительный)	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,...] (Дополнительный)	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 (Дополнительный)	The start date, height, or depth of the dimension interval to be used in the prediction.	String
end (Дополнительный)	The end date, height, or depth of the dimension interval to be used in the prediction.	String
interval_value (Дополнительный)	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 (Дополнительный)	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

Возвращаемое значение

Имя	Описание	Тип данных
out_multidimensional_raster	Выходной многомерный набор растровых данных Cloud Raster Format (CRF).	Raster

Пример кода

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")

Параметры среды

Размер ячейки, Сжатие, Текущая рабочая область, Географические преобразования, NoData, Выходное ключевое слово CONFIG, Выходная система координат, Экстент, Коэффициент параллельной обработки, Пирамидные слои, Статистика растра, Метод пересчета, Временная рабочая область, Растр привязки, Размер листа

Особые случаи

Информация о лицензиях

• Basic: Обязательно Image Analyst
• Standard: Обязательно Image Analyst
• Advanced: Обязательно Image Analyst