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When a surface raster is supplied as input, then the actual surface distance between cells is calculated. To perform cost analysis using Distance Accumulation, a cost surface is needed. If a cost surface is provided, then the result is an accumulated cost distance raster. When horizontal and vertical factors are supplied, then directionality is considered as cost is accumulated. There are four source characteristics that can be used. These characteristics, which can be either of the source or the movers from the source, are controlled by specific parameters: 1. Initial accumulation:\u202fsets the initial cost before the movement begins. 2. Maximum accumulation:\u202fspecifies how much cost a source can accumulate before reaching its limit. 3. Multiplier to apply to costs:\u202fspecifies the mode of travel or magnitude at the source. 4. Travel direction:\u202fidentifies whether the mover is starting at a source and moving to non-source locations, or, is starting at non-source locations and moving back to a source.
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For linear trend analysis, the output contains three-band rasters, where: Band 1 = Slope, Band 2 = Intercept, Band 3 = Root Mean Square Error (RMSE) or the error around the line of best fit. For polynomial trend analysis, the number of bands in the output depends on the polynomial order. Second-order polynomial fitting produces a four-band raster, where: Band 1 = Polynomial_2, Band 2 = Polynomial_1, Band 3 = Polynomial_0, and Band 4 = RMSE. Third-order polynomial fitting produces a five-band raster, where: Band 1 = Polynomial_3, Band 2 = Polynomial_2, Band 3 = Polynomial_1, Band 4 = Polynomial_0, Band 5 = RMSE. For harmonic trend analysis, the number of bands in the output depends on the harmonic frequency. When the frequency is set to 1, the output is a five-band raster, where: Band 1 = Slope, Band 2 = Intercept, Band 3 = Harmonic_sin1, Band 4 = Harmonic_cos1, Band 5 = RMSE. When the frequency is set to 2, the output is a seven-band raster, where: Band 1 = Slope, Band 2 = Intercept, Band 3 = Harmonic_sin1, Band 4 = Harmonic_cos1, Band 5 = Harmonic_sin2, Band 6 = Harmonic_cos2, Band 7 = RMSE.
The output is a multidimensional raster layer in which each slice is a multiband raster containing information about the trend line. If you are analyzing the trend for a single variable in a dataset containing a single dimension (for example, time), there will be a single slice in the output dataset. If you are analyzing a single variable for a dataset containing multiple dimensions (for example, time and depth), each slice will contain trend information for each dimension value.<\/div><\/div>",
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The amount of generalization can be controlled with the Number of Cells parameter. By default, this value is 1, which means that the selected zones will expand by the amount corresponding to one cell's size. To increase the degree of generalization, you can specify a larger value for this parameter. Conceptually, this is like running the tool as many times as the number specified, with the results of the previous run being the input into the subsequent iteration.<\/div><\/div>",
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The inputs to Euclidean Back Direction are the source that direction will be calculated to. An optional input is a raster that represents barriers within the study area. For both the source raster and the barrier raster, the background value should be NoData, while sources and barriers are represented with valid values. Valid values include zero. By default, the calculation will process to the combined extent of the sources and barriers, plus 2 rows and columns. If the analysis is only needed within a specified distance from the sources, then the Maximum Distance parameter can be used. Euclidean Back Direction supports both a Planar method and a Geodesic method in the calculation. The Planar method calculation will be performed on a projected flat plane using a 2D Cartesian coordinate system. The Geodesic method calculation will be performed on the ellipsoid, which means that regardless of input or output projection, the results do not change.
The result from the Euclidean Back Direction function can be used in conjunction with the result from the Euclidean Distance function to determine the shortest paths from locations within the study area back to the source. Both the Euclidean Back Direction result and the Euclidean Distance result are used in the Cost Path function along with destinations to generate the shortest paths.<\/div><\/div>",
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Use the Dimension Definition parameter to slice dimensions using an interval, a value, or a range of values. For example, if you have 10 years of ocean salinity data, collected monthly and at every 2 meters depth up to 500 meters, you might use the different dimension definition options for the following scenarios. Scenario 1: Extract salinity data for the month of January over the 10-year period. Choose By Values, set Dimension to StdTime, and set Values to January. Scenario 2: Slice salinity data over a depth range from 0 to 150 meters. Choose By Ranges, set Dimension to StdZ, and set Minimum Value to -150 and Maximum Value to 0. Scenario 3: Extract salinity data for the first 10 days of every January over a 10-year period. Choose By Iteration, set Dimension to StdTime, set Start of first iteration and End of first iteration to the corresponding start and end of the iteration period, set Step to 1, and set Unit to Years.<\/div>",
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"description": "The Linear Spectral Unmixing function calculates the fractional cover for individual pixels that contain multiple land cover types. It generates a multiband layer, where each band corresponds to the fractional abundance of each land cover class. For example, you can use it to perform land cover classification on a multispectral image to identify photosynthetic vegetation, bare soil, and dead or nonphotosynthetic vegetation.
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"description": "Distance Allocation calculates the which source each location in the study area is allocated to. If only the sources are provided for input, then locations are allocated using a straight-line distance to the closest source. If both sources and barriers are used as input, then Distance Allocation calculates the straight-line distance around barriers to determine which source a location is allocated to. For both the source raster and the barrier raster, the background value should be NoData, while sources and barriers are represented with valid cell values. Valid values include zero.
To perform cost analysis using Distance Allocation, a cost surface is needed. If a cost surface is provided, then the result is an allocation raster based on accumulative cost, not on straight line distance. When horizontal and vertical factors are supplied, then directionality is considered as cost is accumulated. When a surface raster supplied as input, then the actual surface distance covered when passing between cells is calculated when allocation is determined. There are four source characteristics that can be used. These characteristics, which can be either of the source or the movers from the source, are controlled by specific parameters: 1. Initial Accumulation:\u202fsets the initial cost before the movement begins. 2. Maximum Accumulation:\u202fspecifies how much cost a source can accumulate before reaching its limit. 3. Multiplier to apply to costs:\u202fspecifies the mode of travel or magnitude at the source. 4. Travel direction:\u202fidentifies whether the mover is starting at a source and moving to non-source locations, or, is starting at non-source locations and moving back to a source.
By default, the result from Distance Allocation is a single band, which is the distance allocation raster. Checking the Boolean option Generate source row and column as additional bands in output will result in a multiband raster consisting of three bands. The first band is the distance allocation band, the second band contains a row index, and the third band contains a column index. These indices identify the location of the source cell that is the least accumulated cost distance away. The source row index and source column index can be used together to perform intensity mapping. If you query any location in your study area across bands two and three, you will know the row and column of the least cost source for that location.<\/div><\/div>",
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The zones you select will shrink, or reduce in size, by having cells from surrounding zones expand into them. Conceptually, the selected Zone Values can be viewed as foreground zones, while the other values remain background zones. The cells in the foreground zones can be replaced by cells in the background zones. Thin islands inside a zone, which can be viewed as sharing boundaries with the zone, may also be replaced.
The amount of generalization can be controlled with the Number of Cells parameter. By default, this value is 1, which means that the selected zones will shrink by the amount corresponding to one cell's size. To increase the degree of generalization, you can specify a larger value for this parameter. Conceptually, this is like running the tool as many times as the number specified, with the results of the previous run being the input into the subsequent iteration.<\/div><\/div>",
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"description": "The CCDC Analysis function evaluates changes in pixel values over time using the Continuous Change Detection and Classification (CCDC) algorithm and generates a multidimensional raster containing the model results. It is only supported in conjunction with the Detect Change Using Change Analysis function in a raster function template. To produce a raster output, connect the CCDC Analysis function with the Detect Change Using Change Analysis function, save as a raster function template, and run Raster Analysis with the function template.
This function uses the Continuous Change Detection and Classification (CCDC) algorithm to evaluate changes in pixel values over time for a stack of images. In a time series of optical imagery or imagery derivatives (for example, NDVI), pixel values can fluctuate for several reasons: 1. Seasonal change -- Pixel value changes reflect changes in vegetation due to seasonal variability of temperature and precipitation. In the Northern Hemisphere, for example, we expect to see higher density of green vegetation in summer compared to winter; 2. Gradual change -- Pixel value changes reflect trends in vegetation or surface water due to climate variability or long-term land management practices. For example, bare soil may gradually increase in area due to long-term decline in precipitation; and 3. Abrupt change -- Pixel value changes reflect land cover changes that occur suddenly due to deforestation, urban development, natural disaster, and so on. The CCDC algorithm identifies all three change types with the primary purpose of identifying abrupt change. Harmonic regression and trend models are fitted to the data to estimate seasonal and gradual change, and sudden deviations from the trend models are indications of abrupt change.
The CCDC algorithm was originally designed for Landsat TM, Landsat ETM+ and Landsat OLI data Surface Reflectance or Brightness Temperature data. However, Detect Change Using Change Analysis fucntion in conjunction with this function will detect change for multiband imagery from any supported sensor, as well as single band imagery derivatives such as band indexes. For example, you can perform continuous change detection on a Normalize Difference Vegetation Index (NDVI) raster, because abrupt changes in NDVI can be indicative of deforestation.<\/div><\/div>",
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One or more of the weighted cost functions (Distance Accumulation or Distance Allocation) are generally required to be run prior to running the Optimal Path As Raster function to create the input distance accumulation and back direction rasters. These are mandatory input raster layers to the Optimal Path function. The values on the output optimal path represent the number of paths at a given location. In many cases, paths follow the same route, leaving a source and then diverging to go to different destinations. For example, a value of one indicates that there is only one optimal path at a given location, while a value of five means at that location there are five optimal paths going through that cell in the study area.
The input destination data must be a raster layer. The set of destination cells consists of all cells in the input raster that have valid values. Cells that have NoData values are not included in the set. The value zero is considered a legitimate destination. A destination raster can be created using the extraction tools. If you have destination or source features, you can convert them to raster using the Rasterize Features function. Use the distance accumulation or back direction raster as the Raster input to the rasterization function. This will ensure the feature is rasterized using the same cell size, extent, and spatial reference as the other rasters going into the Optimal Path As Raster function. The Optimal Path As Raster function can also be used to derive the path of least resistance down a digital elevation model (DEM). In this case, use the DEM for the input distance accumulation raster and the output from the Flow Direction function for the back direction Raster.<\/div><\/div>",
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"description": "The Trend To RGB function converts a trend raster to a three-band (red, green, and blue) raster. The trend raster is generated from the Generate Trend raster function or the CCDC Analysis raster function.
This function is useful for visualizing model coefficient data from the Generate Trend function or the CCDC Analysis function. Both functions estimate trends in changing pixel values, but the results of the functions are difficult to interpret directly.
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