The previous section demonstrated how to save raster data. A pragmtic solution would use gdalwarp and do this on the shell. On the one hand, this is convenient, but sometimes, you need to perform this task as a intermediate step, and creating and deleting files is tedious and error-prone. Ideally, you would have a python function that would perform the projection for you.

GDAL allows this by defining in-memory raster files. GDAL also makes available a function, gdal. ReprojectImage that exposes most of the abilities of gdalwarp. We shall combine these two tricks to carry out the reprojection. The main complication comes from the need of gdal. We still need to create the output dataset. This means that we need to define the geotransform and size of the output dataset before the projection is made.

This entails gathering information on the extent of the original dataset, projecting it to the destination projection, and calculating the number of pixels and geotransform parameters from there. This is a heavily commented function that performs just that task:.

However, we can save the in-memory raster to any format supported by GDAL very conveniently by making a copy of the dataset. This literally takes two lines of code. We expand the main part of the program to i save the result of the reprojection as a GeoTIFF file, ii read the resulting datafile and iii plot it:.

Finding things in raster files. Enter search terms or a module, class or function name. Navigation index previous GDAL notes v0. The idea here is to reproject from one system to another, as well as to change the pixel size.

The procedure is slightly long-winded, but goes like this: 1. Set up the two Spatial Reference systems. Open the original dataset, and get the geotransform 3. Calculate bounds of new geotransform by projecting the UL corners 4.Instructions for starting a terminal window are here: Getting onto our servers using NX.

You can try it out on the test DEM:. In this tutorial we will convert the format of the DEM. The reason why is because the University of Edinburgh topographic analysis software takes specific data formats. All three can be read by GIS software:.

Sea of Okhotsk daily ice concentrations and isotherms overlayed on OSCAR in 2011

To set the file format you use the -of flag, an example would be:. You might also want to clip your raster to a smaller area. An alternative is to use gdalwarp for clipping:. Since this is a gdalwarp operation, you can add all the bells and whistles to this, such as:. The main thing to note about the -te operation is that the clip will be in the coordinates of the source raster input.

You can look at the extent of the raster using gdalinfo. This is called mosaic to new raster in ArcMap. If you want to merge to an ENVI file the recomended file format you could do this:. You use the -of flag to denote the output format in this case an ENVI file. Here the -ps flag is for pixel size. Assuming this is a projected coordinate system see below in metres, you will get 30x30m pixels. For convert to this coordinate system you use gdalwarp. Details about the target coordinate system are in quotes, you want:.

Here is a map of UTM zones. For example, if you want zone 44 where the headwaters of the Ganges areyou would use:. There are several other flags that could be quite handy for a complete list see here.

It uses nearest neighbour resampling by default. So say you wanted to resample to 4 metres:. The options are:. You can see more about this above in Clip-using-gdal. Getting the tools working outside of the University of Edinburgh network. Enter search terms or a module, class or function name. Navigation index next previous Open source topographic analysis v0. You can download these from the GDAL website. Interagency Elevation Inventory.

Other countries are not so progressive about releasing data but you could rummage around the links here. For 10m data of the United States you can go to the national map viewer.

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To get ordinance survey data at 10m resolution you should use Digimap note: requires institutional subscription. Much more data can be found via search engines. All three can be read by GIS software: asc An ascii-based data format.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I would like to resample a raster from a high resolution to a low resolution with different extent in a defined grid cell.

Is there a way of using an existing raster file as input for the snapping? In the raster package, aggregate and resample seem to be adequate but I can't find how to do it. This requires of course the installation of gdal utilities. You can use projectRaster for this if you have a raster in one projection and resolution and you need output in a different particular resolution and projetion. The from argument is your high resolution raster and the to argument is your low res raster.

Make sure you choose the correct method for aggregation i. Learn more. How to resample a raster snapping to an existing grid? Ask Question. Asked 6 years, 6 months ago. Active 6 years, 6 months ago.

gdalwarp r

Viewed 7k times. Wraf Wraf 9 9 silver badges 22 22 bronze badges.

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Do you need to interpolate onto a different grid, or what? All raster files, so far as I know, define data on a uniform rectangular grid, so "using an existing raster file" would just mean "aggregating or interpolating from MxN to LxK grid. The question lacked clarity and had no example. Active Oldest Votes. Thanks but as my two files are in the same projection, the project does not work. About the aggregate, I cannot specify a file directly in order to snap to the grid, because I don't want to use a "fact" argument but a file?

Wraf you should really provide a reproducible example showing what you have done then. I don't think I expend any more effort on this guessing at your requirements. Please read how to make a great reproducible example and update your question accordingly! Indeed, not easy to find an answer when not reproducible example is given. But the dataset used isquite big and making such example is not that easy.

Wraf Check out how I made an example raster in my post from scratch or from file. Make some data up to illustrate the problem. Sign up or log in Sign up using Google.

Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog.The default is to select a polynomial order based on the number of GCPs. The tolerance is passed to adjust when a GCP will be eliminated.

Note that GCP refinement only works with polynomial interpolation. The tolerance is in pixel units if no projection is available, otherwise it is in SRS units. If width or height is set to 0, the other dimension will be guessed from the computed resolution.

Note that -ts cannot be used with -tr. The default choice, AUTO, will select the overview level whose resolution is the closest to the target resolution. Specify an integer value 0-based, i. Or specify NONE to force the base resolution to be used. Set a warp options. Multiple -wo options may be listed. Working pixel data type. The data type of pixels in the source image and destination image buffers.

Set nodata masking values for input bands different values can be supplied for each band. If more than one value is supplied all values should be quoted to keep them together as a single operating system argument. Masked values will not be used in interpolation.

Use a value of None to ignore intrinsic nodata settings on the source dataset. Set nodata values for output bands different values can be supplied for each band. New files will be initialized to this value and if possible the nodata value will be recorded in the output file. Set the amount of memory in megabytes that the warp API is allowed to use for caching.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

It only takes a minute to sign up. Also read some topics like this but still have some problems understanding the different outputs from R compared to gdal using OSGeo4W Shell in windows. As can be seen the image consists of black normal values and white pixels NA.

Using R casues to generate some grey pixels because of resampling while this is not the case in GDAL. I wonder why? EDIT: I added another picture to the previous ones green box that is the result using "ngb" method and R. Sign up to join this community. The best answers are voted up and rise to the top.

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How to Geocode Sentinel-1 with GDAL

Asked 1 year, 10 months ago. Active 1 year, 10 months ago. Viewed times. Seji Seji 47 9 9 bronze badges. Why did you ignore the answer from previous question? Where can we download the tif file? Yes, the "ngb" method fixes the issue but still the result from R and gdal are different. Here is the image link if you are interested: ufile. Fixes how if it fixes, isn't it solved? Why didn't you use 'ngb' method it in your code here then? It seems to me based on your current code the picture you showed in the question is not a result from 'ngb' method.

gdalwarp r

Please, edit your question you may include the link for the tif download also.Web maps are generally made up of many small, square images called tiles, which are placed side by side in order to create the illusion of a very large seamless image [for a good explanation, see here ]. Tiled based maps can be made up of many tiles.

Loading all those tiles would be inefficient and slow. That is where the MBtilesdeveloped by Mapbox, come in. The MBtiles specification is an efficient format for storing millions of tiles in a single SQLite database. I have written before about Tilemill, a great tool to create great looking interactive maps.

Since then more tools have come available to create MBtiles.

gdalwarp r

Arguably most important is that starting with version 1. This means that it is possible for all these software tools, in theory at least, to export raster layers to MBtiles. It works great on smaller rasters, but you may want to go out for a cup of coffee if used for larger raster layers.

Here I will however illustrate how you can use GDAL directly and gdal2mbtiles to convert your raster layer to a tiled raster in MBtile format. You furthermore need to reproject your layer to Web Mercator projection EPGS:which can be done with the gdalwarp function.

gdalwarp r

Export : Converting a raster layer to a MBtiles map takes two steps. This will only create the highest zoom level. Next, use the gdaladdo function to build overview images, i.

This method is very fast and easy enough. I need to find out where the problem lies, but in the meantime, I am using the next tool, gdal2mbtile. The gdal2mbtile application can download from Github. The tool combines the gdal2tiles part of GDAL and mbutil tools. The command line interface CLI is almost the same as that of gdal2tiles.

This will create a mbtile raster with zoom levels. It runs, for me at least, flawlessly and very fast. Tilemill is still a great tool, especially as it is one of the few tools I know that makes it easy to create interactive MBtiles i. Unfortunately, the project is not actively developed any more it seems, and I had problems installing it on Ubuntu Luckily I found a fork, Tileoven ,that I could install without problems.

Give it a try if you, like me, cannot install Tilemill. There is actually several other project on Github with the name gdal2mbtiles. If you have tried it out already, let me know. The lock might come from eg QGIS.

Thanks, but I never open the SQLite file, just running the code lines as shown in the post. Check e. You are commenting using your WordPress. You are commenting using your Google account.The program can reproject to any supported projection, and can also apply GCPs stored with the image if the image is "raw" with control information.

What if nodata is different in each image? Will increasing RAM increase the speed of gdalwarp? If the nodata tag is set in the geotiff header, gdalwarp will use it automatically, you don't need to do anything. However, -srcnodata overrides this, so if you are handling a bunch of images with different values to be ignored you need to either a pre-process them to have the same to-be-ignored value, or b set the nodata flag for each file. Use b if you need to preserve the original values for some reason, for example:.

If you are wondering why the -dstnodata is there, it's because although gdalwarp automatically honours input nodata, but before GDAL 1. Adding ram will almost certainly increase the speed. Disks are the slowest part of the process.

By default gdalwarp won't take much advantage of RAM. Using the flag "-wm " will operate on MB chunks at a time which is better than the default. To increase the io block cache size may also help. This can be done on the command like:. Beyond that it is doubtful more memory will make a substantial difference. Check CPU usage while gdalwarp is running. Otherwise they are CPU bound. A symptom of this is:. Our understanding is that 32 bit processes are frequently subject to memory fragmentation problems and so even in a process with - in theory - 2GB of heap RAM space available it is still often difficult to allocate large blocks of memory.

In this case with -wm set the main warp buffers can be quite large MB for the example and it seems it has failed to allocate the buffer. Use more modest buffers or not use the options at all or else get a 64 bit executable for gdalwarp. When blocks are read from disk, or written to disk, they are cached in a global block cache by the GDALRasterBlock class. You can think of this as primarily an IO cache, and it mostly benefits you when you might need to read or write file blocks more than once.

This could occur, for instance, in a scanline oriented input file which is processed in multiple chunks horizontally by gdalwarp. The -wm flag affects the warping algorithm. The warper will total up the memory required to hold the input and output image arrays and any auxilary masking arrays and if they are larger than the "warp memory" allowed it will subdivide the chunk into smaller chunks and try again.

If the -wm value is very small there is some extra overhead in doing many small chunks so setting it larger is better but it is a matter of diminishing returns. To get a better sense of how things are working, you might want to try running in debug mode.

For instance:.