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sounds like your cell size and units for input/output might be different - make sure they match exactly in your environment settings, otherwise the software might be filling in the gaps with interpolated values, creating a muddy look


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In your examples you are tiling two different files. In the first xml it is "mar_apr.tif" and in the second "mar_apr_CLIP1.tif". Maybe this is the reason for the difference in the output - because your input files differs in fact as well? The old MapTiler made only color palette expansion and similar small modifications which should not affect the location ...


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You have discovered one option - adding 255 to the "Additional No Value" in Layer Properties/Transparency. Another option is to use a VRT, define 255 as the source nodata value and define 0 as the VRT no data value using gdalbuildvrt. For example: gdalbuildvrt -srcnodata 255 -vrtnodata 0 out.vrt in.tif To do this on all tiffs in a folder, use something ...


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Try the following workflow: Reclassify your rasters so that Value = 1. Calculate Cell Statistics using a "SUM" statistic. Any value in the resulting raster > 1 is an overlap area. Additionally, the value of the resulting raster indicates how many overlapping rasters there are.


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There are two different ways that Tiles can be addressed, the TMS standard specifies that tile coordinates start at the bottom left, but in practice most software is using a coordinate system with the Y axis reversed from TMS: it starts at the top left instead of the bottom right. I don't know that there is really an official name for this scheme, some ...


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I cracked this in the end but it is not idea. I removed the ST_Colorband call and now the rasters merge as they should using the colorband of the RASTER raster:- raster_geom AS ( SELECT ST_AsRaster(ST_Buffer(wkb_geometry,5), --SCHEDULE GEOMETRY rast, --reference raster ...


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To start with, make a tileindex shapefile of your images with the gdaltindex utility. Edit the shapefile by adding new columns for your metadata like to which layer each image belongs to. Add the shapefile into your project and query it with the identify tool for finding the image metadata.


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In case anybody else ever needs an answer to this question, I decided after a bit of research that a permutation based method comparing kappa values would be suitable. McNemars test would've been suitable if my classifications were independent of one another, but as they were obviously not the test wasn't. So I randomly sampled 300 of the 2000 validation ...


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Arcpy use the Numpy array format (hidden to the users) as PyGGIS that uses the Python GDAL module. provider = raster.dataProvider() # path the original file filePath = str(provider.dataSourceUri()) # open the original file from osgeo import gdal raster_or = gdal.Open(filePath) # create a numpy array numpy_array = dataSet.ReadAsArray() # shape of ...


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I would start with TileMill, some excellent open source software. This will allow you to design maps, pulling data from shape files, postgis, etc, and render then via Mapnik to various different formats. TillMill is used by the OSM project for rendering their tiles. TillMill has a form of CSS for designing maps, called CartoCSS, and this is used to ...


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It sounds like the asker was able to answer their own question, but did so through an edit to the question. The solution was to use the Zonal Statistics as Table tool from the Zonal toolset of the Spatial Analyst toolbox. A link to the ArcGIS Resource Center article is here. The option for ALL was chosen for the Statistics Type.


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This option is not included in QGIS but here is what I do: First classify using desired color ramp. Then go to Settings -> Style Manager -> Color Ramp (shows in figure below): Now you can use 'Snipping Tool' to copy the ramp that you used. For example figure below shows the 'Blues' color ramp copied using snipping tool: Now, in 'Composer Manager', use ...


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I believe what you are experiencing is more or less a copy of this question. The coordinates in the rainfall data are in longitude/latitude, but with values ranging from 0 to 360, instead of -180 to 180 (as your political boundaries are). See the GPCC spatial note here (emphasis mine): Spatial Coverage: 0.5 degree latitude x 0.5 degree ...


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To check the number of points per grid cell from a LIDAR point cloud in GRASS you can use the r.in.xyz module. This module creates a grid from the point cloud using a "method" parameter by which you choose how to aggregate the points when importing. If you choose method=n then the resulting raster will contain a count of points for each cell. By setting ...


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You might like to use Fusion. It is a free software for point cloud processing and visualization . 1- Look into the manual for a command program called Catalog. It returns descriptive statistics from the point cloud. What you want is the Catalog's switch density:area,min,max. The manual description says: Creates an image for all data files that shows ...


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Your expression should be "taharuu_grass_rst@1" >=0.003 and "taharuu_grass_rst@1"<= 0.5


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Use LDOPE-1.7 (https://lpdaac.usgs.gov/tools/ldope_tools), using "create_mask". this function takes MOD35_L2 HDF and creates a cloud mask in hdf. use MRTSwath tool for projection/re-sampling/clipping and convert new hdf to GeoTiff.


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There are a few posts on the possible source of the striping that are too long to copy here and post, for example https://geonet.esri.com/message/248734?sr=search&searchId=8194652f-cac8-4737-93a2-c5dccdeb29ff&searchIndex=5#248734 http://ned.usgs.gov/about.html http://www.ctmap.com/assets/pdfprojects/destripe.pdf Some of the issues are associated ...


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The answer seems to be that r.topidx works when called through the GRASS tools, which are available after selecting and loading the GRASS plugin. It is available, but does not work (for me) when called from the Processing Toolbox. I found this confusing, because some GRASS routines do work when called from the Processing Toolbox. To use r.topidx (and ...


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You can perform these calculation on a per cell basis using Cell Statistics (Spatial Analyst). Specify the "SUM" statistic type.


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You should be able to use the method you were describing above, but create your vector layer using the 'vector grid' tool under the vector>research tools menu. This has a checkbox to align the extents and resolution of the vector grid to a raster layer, so should give you a perfect match with your original raster, and also remember to check the option to ...


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As the question allows for other packages I'd like to propose a solution using RIOS (https://bitbucket.org/chchrsc/rios/). It is build on top of the GDAL Python bindings but provides a simpler interface, taking care of the actual raster I/O. RIOS will provide the pixel values as a NumPy array, so you could use any of the inbuit stats functions or utilise ...


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First to save/export a raster layer, you can use Raster/Conversion/Translate (covert format). You can define the file format, the CRS and null values, among other things. Selecting the raster layer and chosing save as is not the procedure (I think you may have a little of ArcMap in mind...) Nevertheles I have to agree with Curlew, are you sure that you ...


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1 square metre is 10.7639104 square feet according to Google. Therefore if the cell size is 1 each cell is 1 square metre (1 x 1), the area covered by class is: Count x 10.7639104 if the cell size is 2 metres the area is 4 sq.m. (2 x 2) and the area covered by class is: 4 x Count x 10.7639104 If the cell size is irregular then multiply the width and ...


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Does it have to be in ArcGIS or could it be another package? If you don't mind using another package there is a free viewer called TuiView (https://bitbucket.org/chchrsc/tuiview/) which will display z-profiles very similar to ENVI you might want to look at. There are some more details about TuiView in this blog post: ...


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Try open source program GridMap Reclass at SourceForge: http://sourceforge.net/projects/mapgridreclass/


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The problem you have to solve is that a vector grid is displayed in a scale-independent pixel size, while a raster image is defined by a fixed cell size. You could rasterize your vector grid, and merge it with the satellite image, but zooming out the grid will get smaller until it can not be seen anymore. The other way round, a 10km, 1-pixel-wide grid will ...


1

Yes, the extent isn't accurate. If you have spatial analyst extension then use IsNull to create a binary raster, raster to polygon (no simplify) and then dissolve with no fields to create a clipping polygon. Once you've got that then just clip as normal. If you haven't got Spatial Analyst then it's a bit more tricky.


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There are two things in that zip file: the "raw" raster data koppen_ics a layer file The layer file is just a pointer to the original data, with additional information about symbology (in this case, providing short text names for the various climate zones). Layer files are useful for saving a set of layers and symbology, but they can't be used if the ...


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Additional option is to use MapSurfer.NET (C#, VB.NET) framework for styling and rendering maps in raster or vector format. You can fully automate the process of producing maps using the built-in style editor (similar to TileMill) and its functionality, or even use SDK to built your own chain of operations you want to perform. Note, if you really need not ...


1

Regression does not require all of the variables be on a common scale in order to be compared to each other. Whether you reclass them before running the analysis, or the analysis tool you choose lets you do so within the tool is up to you and the tool choice. You would of course want to keep a copy of any original data. I might suggest reviewing the ArcGIS ...


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You don't say what scripting language, so I've assumed python. Have you considered the arcpy.mapping module? Another option is the Mapnik library.


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Note that the g.region you used does not change the resolution of your raster layer. In general, what you do with the g.region function is setting the 'working resolution' and extent. In the case of your example, you are telling grass to use the extent of the raster layer my_raster and a resolution of 0.5. Functions will use that resolution rather than the ...


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The following script uses a SearchCursor to extract the "Count" field rows. You can see that I am using the da module, which is available with ArcGIS 10.1+ as this method is much more efficient. import arcpy raster = r'C:\path\to\raster' # Create a search cursor for raster attribute with arcpy.da.SearchCursor(raster, ["OBJECTID", "COUNT"]) as cursor: ...


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A very simple way to calculate the area of a raster is: raster = <path to raster> ext = arcpy.Describe(raster).extent area = ext.width * ext.height


1

Instead of using cursors to find the raster area I would use Describe and use the raster band properties to find the area of my raster. Once I know the cell width, cell height, raster width, and raster height of my raster I can calcuate its area. However, this method does not give the units of the raster, you'll need to know them ahead of its calculation. ...


2

-1.#IND returns when an incorrect mathematical operation is done, like dividing by zero or the square root of a negative number. Maybe GRASS tool used standard deviation of NoData set as -9999. I suggest two solutions: Check your source NoData value, if it is negative, change it to positive. If you cannot change it or if it's positive, you can convert ...


1

The shapefile format does not support raster columns. There is a help page entitled Adding raster datasets as attributes in a feature class that says this explicitly and offers a workaround: A shapefile cannot contain an attribute field to hold raster datasets. In this case, it would be advantageous to use a hyperlink. If using a shapefile is a ...


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I suppose that you need the value of the raster in your column at the location of your points. first, convert your csv to a shapefile (make XY event layer). You should check that your coordinate systems are correctly set (at least, the points must fall on top of the raster). Try to avoid projecting your raster, because resampling often modifies the raster. ...


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There are a couple of ways I could think of to do it in the Symbology tab, which won't affect your original raster values: Set each of the classes to the same color. Select the five classes you want to consolidate. Right-click, and choose "Group Values." (This is described in "Combining Categories" for grouping vector data, but it also works for raster ...


1

Given your examples above, you must be using "Nearest Neighbour" option to do the resampling in the Project Raster tool. That's why some values "disappear". What else can it do, it takes the nearest value (measured by cell center position) and assigns it to the new cell in the new coord sys. A histogram of the cell values between the old & new can never ...


0

I agree with Vince. Use a mosaic dataset in ArcGIS. It does a good job of on-the-fly cropping of map marginalia by using the footprint of the actual data area. See here : What is a mosaic dataset Creating a mosaic dataset


1

How did you do the setting them all in CRS WGS84? Using Set CRS for Layerhas definitely corrupted your data. This changes the CRS, but does not reproject the coordinates to the new CRS. So delete those layers with 6digit coordinates, and add them again. Usually the CRS information is stored in the file, and should not be altered. As mkennedy noted, they can ...


2

The first error message comes from the fact that your raster is in float, so you have more that 65535 (aka 16bit) possible unique values, but this does not mean that the values is larger than 65535. You should convert your a raster to integer (e.g. with copy raster or with Int(raster) in map algebra tools) if you want to be able to build unique values, but ...


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I think you have to make the addition and subtraction rasters as raster objects as shown in the latter example of the help files Subtracting rasters


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Gdal_rasterize uses the center of the pixel. As a workaround I would either rasterize a buffer around your input polygon (with half pixel size) or apply mathematical morphology on the result (aka erosion of one pixel)


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I think you're missing something in what johns has said in his comment. When you are looking at the symbology for the raster there are two renderers to choose from - stretched and RGB composite. However, on the RGB composite settings there is also a stretch option. You need to set this to None. There are a couple of ways to export a raster. If using the ...


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You can save the original layer file then add the exported tiff to map and import the saved layer file symbology in the raster Layer Properties -> Symbology tab. If the original raster is still in the map document you can also select it from the drop-down menu.


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newarray = array * 2.0 performs the math on the entire array, not just on one element. It should instead be something like this: raster = arcpy.Raster(r"C:\test.jpg") array = arcpy.RasterToNumPyArray(raster) # modify cell array[0,0] *= 2.0 # save to a new raster newraster = NumPyArrayToRaster(array) newraster.save(r"C:\export.gdb\t") Or, if you want ...


1

Your output image will have more pixels than the sum of your input images, but this does not explain the large difference. I suggest that you look at the characteristics of your images based on gdalinfo in order to see what compression is used and check that the extents are correct. (assuming your input images have the same size, it makes 20000 * 12000 ...



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