So I installed the 64-bit Background Geoprocessing of ArcGIS and run the above code again, then it came up with another problem: I found the reason and solution from this question: Unfortunately, the arcpy (python 2.7, 32 bit) had the memory error because there were too many large rasters (I'm dealing with the global extent). Print("successfully output the evi_mean.tif with the 0.25 degree resolution!") Print("successfully output the evi_mean.tif!")Ĭellsize025 = "".format(arcpy.Describe(inf).meanCellWidth, arcpy.Describe(inf).meanCellHeight)Īrcpy.Resample_management(AvgRas, outname_res, cellsize025, "NEAREST") LowerLeft = arcpy.Point(r.extent.XMin, r.extent.YMin)Ī = Raster(inf) # convert the crs to wgs84ĪvgRas = arcpy.NumPyArrayToRaster(Average, lowerLeft, cellWidth, cellHeight, r.noDataValue) # turn into raster If array >= 0 : # verdict invalid valueĪverage = sum / count # cal the mean value Sum = numpy.zeros(shape=array.shape) # save the accumulating valueĬount = numpy.zeros(shape=array.shape) # save the counting numberĪverage = numpy.zeros(shape=array.shape) # save the mean value Rasters = glob.glob(os.path.join(inws, "*.tif"))Īrray = arcpy.RasterToNumPyArray(r) # convert to numpy I adapted it into my code: import arcpy, sys, os, glob So I tried the second one which converted each raster to array to skip NA pixel. This allows you to refine model parameters to focus on specific change events before processing the data using the Analyze Changes Using CCDC or Analyze Changes Using LandTrendr tools for an entire dataset.After having converted the original monthly MOD13C2 product (from year 2000-2020) to raster format, now I have to calculate the mean value of the 251 rasters.įirst I have tried this tutorial which simply used the algebra function that behaved badly in cooperating NA values. The Continuous Change Detection and Classification (CCDC) method or the Landsat-based detection of trends in disturbance and recovery (LandTrendr) method. The Pixel Time Series Change Explorer allows you to identify changes in a single pixel value over time using If you are comparing two rasters or two time slices from a single multidimensional raster, you can also save the output as a raster function template to be used for further processing, or you can create a polygon feature class. The wizard allows you to save your final output as a new difference raster dataset. You can provide two categorical or continuous raster datasets, or you can provide a time series of imagery in a multidimensional raster. The Change Detection Wizard provides a guided workflow for performing change detection. Each of these tools can be coupled with the Detect Change Using Change Analysis Raster tool to identify information about the time and magnitude of changes for each pixel in your time series. To perform change detection along a time series of raster imagery, you can use the Analyze Changes Using CCDC tool or the Analyze Changes Using LandTrendr tool. You can also use the Compute Change raster function to perform change detection in real time between two raster datasets. The geoprocessing tools in the Change Detection toolset allow you to perform simple change detection between raster datasets. When comparing continuous rasters, the result is the magnitude and direction of the change. When comparing thematic land-cover rasters, the result contains information about the type of change that occurred. The output from change detection is a difference raster where each pixel contains the type or magnitude of change. Change can occur because of anthropogenic activity, abrupt natural disturbances, or long-term climatological or environmental trends. It is the comparison of multiple raster datasets, typically collected for one area at different times, to determine the type, magnitude, and location of change. Change detection is one of the fundamental applications in imagery and remote sensing.
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