As mentioned in the comments, you need to download the .asc grid format from the download page (see image below). After that you can use QGIS > Raster > Translate to convert the .asc grids to GeoTIFF. The data does download in individual tiles so you may also need to mosaic them or create a virtual raster or similar
According to the GDAL GeoTIFF driver page:
ZSTD is available since GDAL 2.3 when using internal libtiff and if
GDAL built against libzstd >=1.0, or if built against external libtiff with zstd support.
The DebianGIS and downstream UbuntuGIS packages are built against an external libtiff which doesn't have ZSTD support. From a mailing list discussion ...
If you are using QGIS 3.10 or later, you have better control over the GDAL tools - See the visual change log.
It means, GDAL-Contour tool can take direct input from users to which elevation (height) it should create contour lines.
Run the tool (in the Processing Toolbox > GDAL > Raster Extraction) and find Advanced parameters > Additional command-...
First of all, a file extension is a purely cosmetic thing.
You can rename a GeoTIFF file to funny_cats.mpeg and it would still be a GeoTIFF as the filename does not affect the actual bits and bytes of the file. The extension is just by convention. Some operating systems (like Windows) use it to decide which program should try loading a file but ...
Under the Save as raster window, there is a section called Create options, click the plus + button. Under Name column, write tfw, and under Value column write yes. It will export the raster as tif file format, and it will add a tfw file as well in your hard drive.
GDAL's XYZ driver is not particularly robust (e.g. "Ungridded dataset: At line 3, change of Y direction" with a perfectly gridded dataset), so here are a few alternative methods that should yield the same result.
Most of the array look-ups are done with pandas. This method is probably the fastest.
import numpy as np
import pandas as pd
As of today, there is no specific book to learn how to use the program. You can always learn with the official documintation (https://step.esa.int/main/doc/) & the official tutorials : http://step.esa.int/main/doc/tutorials/
Resampling applies when the gridding of the input file is different from the gridding in the new space (most of the time, a warped space due to reprojection). It also makes sense when the size of the grid changes. But when the size and shape of the grid remains constant there is no resampling.
If you want to smooth your DEM at a constant pixel size, you ...
When creating a new GeoTiff Gdal takes no account of how the input tiff was processed and writes your new file out as raw data.
You need to add some creation options to compress and tile it. See Paul Ramsey's compression for dummies for more details.
gdal_merge.py -co COMPRESS=deflate -co TILED=YES -separate -o myoutput.tif *.cropped.tif
I could reproduce these results with Landsat-8 panchromatic band.
When you are using cv2.imread(fl,0) it reads image as greyscale 8 bit (0-255), you should either read it as cv2.imread(fl,cv2.IMREAD_UNCHANGED) or cv2.imread(fl,-1) since your original image has 16 bit depth (0-65535)
as for the other question
What are those numbers that I see in the arrays? ...
Instead of using Photoshop, you can fix the problem directly on QGIS.
Hillshade rendering (or any other raster rendering) happens individual layer by layer, as each layer know nothing about their neighbours.
You can bundle the all 9 rasters in a single layer by creating a virtual raster. You have a tool to create the virtual raster in the processing toolbox.
Below is one approach. It masks pixels in ee.Image.pixelLonLat() that are not white enough and doesn't have enough connected pixels (to get rid of noise). These thresholds are definitely up for tweaking. It runs reduceRegion() with a toList() reducer, assembles the collected lon/lat values into a feature collection and exports it.
var edgy = ndwi.convolve(p)...
You need a projection that places the focus on New Zealand.
Set the Project CRS to EPSG:3851 (Project > Properties... > CRS)
Reproject the bathymetry tif to EPSG:3851 (Raster > Projections > Warp (Reproject)...)
Here is the result:
According to the ImageMosaic configuration documentation:
In case of custom format datetimes in filename, an additional format
element should be added after the regex, preceded by a comma, defining
the custom representation.
So in your case, the regex in your timeregex.properties file (or whatever you named it in indexer.properties file) should be ...
To rearrange the axis (i.e., move the bands channel to make it the first axis), you can use np.rollaxis.
with rasterio.open('image.tif', 'w', **meta) as dst:
# Note: assumes band info is in axis 2. Also, break up commands for clarity.
rolled_array = np.rollaxis(array, axis=2) # Roll axis 2 to 0th position
input = r"D:\ESCUELA\ hh.tif"
out = "D:\ESCUELA\ prueba.bil"
gdal.Warp(srcDSOrSrcDSTab = input,
just add a loop for all the files in your path
path = r'D:\ESCUELA\imgSat8'
out = 'D:\ESCUELA\prueba'
1) Go up in the file structure, for example this here:
2) Download the file, in the case of the link above: grids_germany_halfyear_precipitation_201819.asc.gz
3) Than unpack the downloaded gzip file (see: https://en.wikipedia.org/wiki/Gzip) - I used 7zip on Windows, ...
I've not tried this but I think the example in the gdal_calc manual page
gdal_calc.py -A input.tif --outfile=result.tif --calc="A*(A>0)" --NoDataValue=0
could be changed to
gdal_calc.py -A input.tif --outfile=result.tif --calc="A*(A==1)" --NoDataValue=0
should work to extract value 1.
The GeoTIFF format is an OGC standard that encapsulates the coordinate reference system and crs transform (what would be in a "world" file alongside a standard tiff, jpeg, or png image) in tiff tags in the image itself among other relevant information. This means no sidecar files are required and everything the consuming system needs to know about the image ...
Run a little test. Make a 10x10 random raster:
> r1 = raster(matrix(rnorm(100),10,10))
Feed to ks.test:
One-sample Kolmogorov-Smirnov test
D = 0.052428, p-value = 0.9463
alternative hypothesis: two-sided
Now normally you'd feed ks.test a numeric vector of ...
Generally, it's a lot better to use NaN.
I can think of one reason not to, however. NaN only exists with floating-point data types. So if you need to write out or read a GeoTIFF with integer values (e.g. perhaps you have some final classification with five classes, represented as integers 1-5), then you cannot use NaN and save it out with a UInt8 datatype (...
The question seems to be asked earlier here https://stackoverflow.com/questions/45167863/how-to-load-an-geotiff-image-over-google-map, https://stackoverflow.com/questions/19492967/georeference-tiff-image-into-the-google-map and https://stackoverflow.com/questions/44493354/overlaying-a-rasterfile-tiff-into-google-map here.
Google docs reveal that you can ...
There are various implementations of watersheds and hydrology delineation using the the A* algorithm. For instance in grass gis r.watershed and python pysheds.
In this instance I recommend the grass gis implementation as the method was developed on SRTM data.
edit: even more specific, the problem lies with .astype(uint16). Without this conversion, it loads fine. Also show(limg.read()) also works
The problem lies with the show(rio.open(georeferenced.tif)). The data is saved properly, and when loaded all operations you would want to perform should work fine. if you're only interested in showing the image, ...
Using GDAL utilities
As per the comments you may be able to use gdal_translate assuming you know the upper left and bottom right coordinates:
gdal_translate -of GTiff -a_srs 'EPSG:<4326>' -a_ullr <upper left x> <upper left y> <lower right x> <lower right y> <img_path> <dest_img>
The -a_srs allows you to set the ...
If you want to load a multi-band raster up, don't use raster() function, use brick() or stack() instead. raster() only opens single-band rasters, even from a multi-band raster (you can set the band you want to open).
Rotate works with multi-band rasters, so you'll be fine after opening the file:
r <- raster(nrow=18, ncol=36)
m <- matrix(1:ncell(r), ...