Disponible con licencia de Image Analyst.
A time series of imagery or rasters is made up of data collected over time, usually at regular time intervals, often for the purpose of analyzing changes at the earth's surface. In ArcGIS Pro, a time series of raster data can be organized in a multidimensional raster dataset or multidimensional mosaic dataset, and tools can be used to extract information about a pixel's history over time.
The purpose of modeling a pixel's history over tens or hundreds of images is typically to find the date at which some type of change occurred.
The CCDC Analysis raster function and the LandTrendr Analysis raster function can be chained together with the Detect Change Using Change Analysis raster function to extract date of change information from a multidimensional raster.
The Analyze Changes Using LandTrendr tool or the Analyze Changes Using CCDC tool, in conjunction with the Detect Change Using Change Analysis Raster tool, can be used to identify changes in pixel values over time to indicate land use or land cover changes.
The Change Detection Wizard combines the available tools and functions to guide you through the process of extracting date of change information from a time series of imagery or rasters. The output from the wizard is a raster in which each pixel has a date value corresponding to the time of a particular type of change.
The following section provides details on each pane in the Change Detection Wizard when performing time series change detection.
Change Detection Wizard
El Asistente de detección de cambios se inicia desde el botón desplegable de detección de cambios de la pestaña Imágenes, en el grupo Análisis. El botón no está disponible si no está trabajando en una escena de mapa 2D o si no tiene la extensión de Image Analyst .
Configure
The first pane in the Change Detection Wizard is the Configure pane, where you can select the Change Detection Method option you want to use. To extract date of change information from a multidimensional raster, set the Change Detection Method to Time series change detection.
| Parameter | Description |
|---|---|
| Input Raster | The input multidimensional raster dataset to be analyzed. Supported inputs include multidimensional Cloud Raster Format (.crf) files, multidimensional mosaic datasets, or multidimensional image services. La finalidad de esta herramienta es extraer los cambios de una entidad observada, de modo que las imágenes multidimensionales de entrada ideales deben capturar una observación coherente a lo largo del tiempo y no deben incluir interferencias atmosféricas o de sensores, nubes ni sombras de nubes. La práctica recomendada es utilizar datos que se han normalizado y se pueden enmascarar con una banda QA, por ejemplo, los productos Landsat Collection 1 Surface Reflectance con una máscara de nube. If you have already generated a change analysis raster using the Analyze Changes Using LandTrendr or the Analyze Changes Using CCDC tools, you can provide the result as the input raster in the wizard, and the next pane will be skipped. |
Analyze Time Series
The Analyze Time Series pane allows you to specify the type of model to run to perform time series analysis, and to configure the model. This pane will not appear if you entered an existing change analysis raster in the Configure pane.
The parameters visible in this pane depend on the modeling option selected in the Change Analysis Method parameter:
- CCDC—The Continuous Change Detection and Classification (CCDC) algorithm will be used to evaluate changes in pixel values over time. To use this option, the input multidimensional raster must contain at least 12 slices, spanning at least 1 year. For information on the algorithm and parameters, see How Analyze Changes Using CCDC works.
- LandTrendr—The Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm will be used to evaluate changes in pixel values over time. For more information on the algorithm and parameters, see How the Analyze Changes Using LandTrendr tool works.
CCDC Change Analysis Parameters
| Parameter | Description |
|---|---|
| Bands for Detecting Change | The spectral bands to analyze for change detection. The default is to use all bands. |
| Bands for Temporal Masking | The bands to use for cloud, cloud shadow, and snow masking. Because cloud shadow and snow are very dark in the shortwave infrared (SWIR) band, and clouds and snow are very bright in the green band, it is recommended that you use the SWIR and green bands for masking. If no bands are selected, no masking will occur. |
| Chi-squared Threshold for Detect Change | The chi-square statistic change probability threshold. If an observation has a calculated change probability that is above this threshold, it is flagged as an anomaly, which is a potential change event. The default value is 0.99. |
| Minimum Consecutive Anomaly Observations | The minimum number of consecutive anomaly observations that must occur before an event is considered a change. A pixel must be flagged as an anomaly for the specified number of consecutive time slices before it is considered a true change. The default value is 6. |
| Updating Fitting Frequency (in years) | The frequency, in years, at which to update the time series model with new observations. Updating a model frequently can be computationally costly and the benefit can be minimal. For example, if there are 365 slices or clear observations per year in the multidimensional raster, and the updating frequency is for every observation, the processing will be 365 times more computationally expensive compared to updating once per year, but the accuracy may not be higher. The default value is 1. |
LandTrendr Change Analysis Parameters
| Parameter | Description |
|---|---|
| Processing Band | La banda que se utilizará para segmentar las trayectorias de valores de píxel a lo largo del tiempo. Elija la banda que capturará mejor los cambios en la entidad que desea observar. The default is the first band. |
| Snapping Date | La fecha utilizada para seleccionar una división para cada año en el dataset multidimensional de entrada. Se seleccionará la división con la fecha más cercana a la fecha de ajuste. Este parámetro es obligatorio si el dataset de entrada contiene datos subanuales. El valor predeterminado es 06-30 o 30 de junio, aproximadamente a mediados del año natural. |
| Maximum Number of Segments | Número máximo de segmentos que se ajustarán a la serie temporal para cada píxel. El valor predeterminado es 5. |
| Vertex Count Overshoot | The number of additional vertices beyond maximum number of segments + 1 that can be used to fit the model during the initial stage of identifying vertices. Later in the modeling process, the number of additional vertices will be reduced to maximum number of segments + 1. The default is 2. |
| Spike Threshold | Umbral que se va a utilizar para atenuar picos o anomalías en la trayectoria del valor de píxel. El valor debe estar entre 0 y 1, donde 1 significa que no hay atenuación. El valor predeterminado es 0,9. |
| Recovery Threshold | The recovery threshold value, in years. A feature in a landscape will often take time to recover from a nonpermanent change such as a forest fire or insect infestation. Use this parameter to control the rate of recovery recognized by the model. If a segment has a recovery rate that is faster than 1/recovery threshold, the segment is discarded and not included in the time series model. The value must range between 0 and 1. The default is 0.25. |
| Minimum Number of Observations | Número mínimo de observaciones válidas necesarias para realizar el ajuste. El número de años del dataset multidimensional de entrada debe ser igual o mayor que este valor. El valor predeterminado es 6. |
| P-Value Threshold | Umbral de valor p correspondiente a un modelo que se va a seleccionar. Una vez detectados los vértices en la etapa inicial del ajuste del modelo, la herramienta ajustará cada segmento y calculará el valor p para determinar la importancia del modelo. En la siguiente iteración, el modelo disminuirá el número de segmentos en uno y volverá a calcular el valor p. Esto continuará y, si el valor p es menor que el valor especificado en este parámetro, el modelo se seleccionará y la herramienta dejará de buscar un modelo mejor. Si no se selecciona ningún modelo, la herramienta seleccionará un modelo con un valor p menor que el lowest p-value × best model proportion value. El valor predeterminado es 0,01. |
| Best Model Proportion | El mejor valor de proporción de modelo. Durante el proceso de selección de modelo, la herramienta calculará el valor p de cada modelo y seleccionará un modelo que tenga la mayor cantidad de vértices mientras mantiene el valor p más pequeño (más significativo) basado en este valor de proporción. Un valor de 1 significa que el modelo tiene el valor p más bajo, pero es posible que no tenga un número elevado de vértices. El valor predeterminado es 1,25. |
| Prevent One Year Recovery | Especifica si se excluirán los segmentos que presentan una recuperación de un año.
|
| Recovery Has Increasing Trend | Especifica si la recuperación tiene una tendencia creciente (positivo).
The recovery from a change in landscape can occur in the positive or negative direction. For example, when a landscape experiences forest loss, a time series of vegetation index values shows a drop in index values, and the recovery shows a gradual increase in vegetation index values, or a positive recovery trend. |
| Output Other Bands | Especifica si se incluirán otras bandas en los resultados.
|
Detect Date of Change
The Detect Date of Change pane provides the parameters for you to specify the date of change information you want to extract from the model.
| Parameter | Description |
|---|---|
| Change Type | Specifies the change information to calculate for each pixel. When using the CCDC change analysis method, you can choose from the following options:
When using the LandTrendr change analysis method, the following additional options are available:
|
| Maximum Number of Changes | El número máximo de cambios por píxel que se calculará. Este número corresponde al número de bandas del ráster de salida. El valor predeterminado es 1, lo cual significa que solamente se calculará una fecha de cambio y que el ráster de salida contendrá una sola banda. This parameter is not applied when the Change Type parameter is set to Number of changes. |
| Segment Date | Especifica si la fecha se extrae al principio de un segmento de cambio o al final. This parameter is available only when using the LandTrendr change analysis method. |
| Change Direction | Specifies the direction of change to be included in the analysis.
This parameter is available only when using the LandTrendr change analysis method. |
| Filter by Year | Especifica si se debe limitar la salida durante un rango de años.
This parameter is available only when using the LandTrendr change analysis method. Use this parameter to identify changes that occurred within a specific time period, for example, if you are looking for changes that occurred in a landscape during five years of drought. If checked, you must enter the minimum and maximum years to use for filtering results. |
| Filter by Duration | Especifica si se filtrará por la duración del cambio.
This parameter is available only when using the LandTrendr change analysis method. Use this parameter to identify changes that occurred over a given range of years, for example, if you are only interested in abrupt changes over 1 or 2 years. You can calculate the duration you are interested in using the formula end year - start year +1. Gaps in the time series will be included. If checked, you must enter the minimum and maximum duration values to use for filtering results. |
| Filter by Magnitude | Especifica si se filtrará por la magnitud del cambio.
This parameter is available only when using the LandTrendr change analysis method. Use this parameter to identify changes of a given magnitude, for example, if you are only looking for large changes in the vegetation index NDVI. Magnitude is an absolute value, so the minimum and maximum values cannot be negative. To specify directional change, use the Change Direction parameter. If checked, you must enter the minimum and maximum magnitude values to use for filtering results. |
| Output Date of Change Raster | The output dataset. La salida es un ráster multibanda en el que cada banda contiene información de cambios en función del tipo de cambio seleccionado y del número máximo de cambios especificado. Por ejemplo, si el parámetro Tipo de cambio está definido como Hora del cambio más temprano y el parámetro Número máximo de cambios está definido como 2, la herramienta calcula las dos fechas más tempranas en las que se produjeron cambios durante toda la serie temporal para cada píxel. El resultado es un ráster en el que la primera banda contiene las fechas del cambio más temprano por píxel y la segunda banda contiene las fechas del segundo cambio más temprano por píxel. |
Example
The following example extracts the date of the most rapid change from a time series of yearly NDVI rasters from 2000 to 2019.
- Add the NDVI multidimensional raster dataset to your map.
- With the layer selected in the Contents pane, open the Change Detection Wizard from the Imagery tab in the Analysis group.
- In the Configure pane, set the Change Detection Method to Time Series Change and ensure the Input Raster is set to the NDVI multidimensional raster. Click Next.
- In the Analyze Time Series pane, configure the parameters to perform LandTrendr modeling.
- Set the Change Analysis Method parameter to LandTrendr.
- Set the Maximum Number of Segments parameter to 10.
- Leave all other defaults.
- Click Next.
- In the Detect Change pane, configure the parameters to extract the beginning of the most rapid, highly negative (loss of NDVI) change in the series.
- Set the Change Type to Time of Fastest Change.
- Set the Change Direction to Decreasing.
- Check the Filter by Magnitude check box.
- Set the Minimum magnitude to 0.5 and the Maximum magnitude to 2.
- For the Output Date of Change Raster, type FastestNDVILoss.crf.
- Click Run.