To use Earth Engine to extract landcover information for a polygon region, do the following steps. The example demonstrates using the ESA GlobCover 2009 landcover dataset, but other landcover datasets could be analyzed similarly.
Open up the Earth Engine Code Editor application at https://code.earthengine.google.com/. If you do not currently have access to ...
Spatial Analyst is necessary for most raster tasks in ArcGIS beyond simple display and clipping.
If you have that, then you can use Extract by Attributes to create new rasters of just one value. It would be the value from the original raster though, and you'd have to Reclassify it to 1 or 0.
You can use Reclassify directly to generate a new raster and map ...
Here is an example of using rioxarray to mask out data with a shapefile:
from shapely.geometry import mapping
MSWEP_monthly2 = xarray.open_dataarray('D:\G3P\DATA\Models\MSWEP\MSWEP_monthly.nc4')
Since you are dealing with a rasterStack type of data you need to extract values using the extract function.The outcome therefore will be a vector.
As you see in the following line, the method for the extract function is:
in the position of x you will put the raster object and in the y the locations.
Then use the as.data.frame function to ...
The simplest option would be the use of Raster Calculator with a conditional statement.
Your statement may look something like the following with 'threshold' replaced with whatever value you like. The resulting raster will give you pixels with value=1 where values are greater than threshold, and NoData where values are less than threshold.
Short answer: because with weights=FALSE, only cells whose centroid falls into a polygon are accounted. With weights=TRUE, all cells that have at least a small part (but not necessarily the centroid) are reported.
Long answer: There are two aspects to take into account, weight and small, and hence multiple cases:
weight = FALSE (the case described by @...
As described in ?extract, A cell is covered if its center is inside the polygon (but see the weights option for considering partly covered cells; and argument small for getting values for small polygons anyway).
Therefore, if you run the following code using weights = FALSE (default), only values from the 4th and 6th cell are returned. On the other hand, ...
I have encountered this error multiple times but still havent figured out why exactly it happens. The only thing that always works for me is when I dont change the default save location (default geodatabase) and output file name. Then the tool works correctly for some reason. As soon as I select my own workspace to save the output or rename the output in the ...
There is a couple of methods that can help you to clip raster by polygon/mask.
Best way is to type processing.alglist("clip") in console and test some of methods that popped out, e. g. "saga:clipgridwithpolygon".
In R you can use the extent function in the raster package to easily create a polygon representing the extent that you want to clip to.
Add libraries and example data.
coordinates(meuse) <- ~x+y
Create extent polygon and clip data using the intersect function from the raster package. You would add ...
I repeated your script line by line from Python window.
I removed 2 lines in your script and added:
outMask = sa.ExtractByMask('band2.tif', 'Clip109071.shp')
is what one expects to see.
So exporting to grid format fixed the problem.
The SearchCursor will return geometries with the SHAPE@ token which can be used as extracting features etc.:
SHAPE@ —A geometry object for the feature.
feature_class = r'C:\test.gdb\polygon'
with arcpy.da.SearchCursor(feature_class,'SHAPE@') as cursor:
for row in cursor:
#do something with row
You can of course also return ...
This looks like a problem with your polygon layer rather than the tool itself. Using the files you supplied, I encounter the same issue. There appears to be one feature, but multiple entries in the table. Running a number of different tools, the processing log often spits out Feature n has invalid geometry. Skipping...
I can't make sense of what's wrong ...
It looks like your problem is that the polygon data is multipart geometry. This means that you have multiple features (polygons) associated with single rows (attributes). Even if raster::extract works, this makes very little sense from a results standpoint. For your data to match, you need to explode your geometry into single part.
Here is an example, you ...
The spatial reference of the tif is not defined. You will need to define it before proceeding with the clip.
Size is 9600, 15120
**Coordinate System is **
Origin = (1773760.000000000000000,5897760.000000000000000)
Pixel Size = (1.000000000000000,-...
The tool you're looking for is called 'Extract by Mask' in ArcMap, but other packages have equivalents
use Raster Calculator and enter this command:
Con("raster" == green_value, 1)
if you want to keep the original value:
Con("raster" == green_value, "raster")
you can replace == with other conditionals (e.g., >, <, >=, <=, !=)
There is no need to define the workspace if you are explicitly defining the variable paths. Additionally, you are formatting the paths incorrectly--try using r'C:\path\to\your\data'.
I would recommend writing the output raster to .tif or .img format. As your script is currently configured, it is trying to output a grid format raster.
This is how I would ...
Your model appears to be correct and you are using the %value% correctly, that is the ID of what your are calling your station number. The error message says you have no spatial reference, i.e. either your DEM or Polygon dataset are missing a defined coordinate system. So you need to make sure they have that set. Look into the Define Projection tool.
It looks like, this has to do with the cell resolution of your DEM. If you zoom really right in, you will see that the size of the cells are bigger than the gap created between the DEM and the polygon outlines. Therefore, you either work with these extracts. OR if you would like a perfect area that fills the polygons you have to:
RESAMPLE your DEM into a ...
1) resample results in 50% improvement
I was able to get about 50% improvement by resampling directly from the cld raster to a new raster with the same extent/resolution as r and a nearest neighbor sampling method:
The reason your output image is 8-bit is because you explicitly ensure the image is 8-bit the step before you write it.
# Clip the image using the mask
clip = gdalnumeric.numpy.choose(mask, (clip,0)).astype(gdalnumeric.numpy.uint8)
# Save clipping as tiff
gdalnumeric.SaveArray(clip, "%s.tif" % output, format="GTiff", prototype=raster)
The raster is clipped exactly to the shapefile boundaries.
But remember, a raster is a kind of matrix. It's composed by n rows and m columns, so the extent of it is rectangular.
The black area are pixels without values, so you only have valid pixels in the extent of the shapefile.
If you want to hide null pixels, check this answer How to display NoData ...
Here's a script that should work with your data, if I got you correctly. I tested it with my own data and it creates the desired output rasters. Note that depending on the number of features in your feature class and the number of input rasters you pass to the tool, it could be running for a while. Make sure to change the lines that name your output rasters ...
You can use rasterize tool to convert the grid vector into raster. In this tool you need to choose the attribute field that represents the precipitation and choose the x and y cell sizes that matches the grid x, y spacing. The tool is located in Processing toolbox -> GDAL/OGR tools -> Analysis -> Rasterize (vector to raster) tool
Then use Zonal ...
It's easy to mask specific raster values in the Layer Styling panel.
Option one: when the raster has a wide range of values
Choose "Singleband Gray" or Singleband Pseudocolor"
Expand the Min / Max Value Settings section, choose "Cumulative count cut" and enter 90% for the min value, and 100% for the max value
Choose the Contrast Enhancement setting that ...
I havn't tested, but I think it works like that :
If your region is delimited by a shapefile, you can use the function "gbuffer" from package "rgeos", then use mask:
# 500m buffer if shapefile's crs: +proj=utm /// +units=m
buffer = gBuffer(myregion,width = 500)
# inverse=F will mask outside of buffer I think
test = mask(test.ex, buffer, ...
You can use rasterio.mask method:
with fiona.open("file.geojson", "r") as geojson:
features = [feature["geometry"] for feature in geojson]
with rasterio.open("file.tiff") as src:
out_image, out_transform = rasterio.mask.mask(src, features, crop=True)