Develop Reference

Issues resampling raster to the resolution of another raster

I am trying to take a population raster and resample+reproject it to match the shape and resolution of a precipitation raster.
Data Links:
Population Data: https://figshare.com/ndownloader/files/10257111
Precipitation Data: https://www.ncei.noaa.gov/data/nclimgrid-monthly/access/nclimgrid_prcp.nc
The Population Data is a series of rasters per decade of 5 different population models covering the continental US. If you simply select one of the rasters I can work out the rest (I have combined into a multiband raster anyways). For example if you use the pop_m4_2010 raster that would help. The resolution is 1x1km, and the projection is Albers Equal Area Conic NAD 83 ESRI:102003.
The Precipitation Data is a netcdf file covering monthly precipitation data for the continental US. The resolution is 5x5km and the projection is WGS84 EPSG:4326.
I converted the netcdf to tiff using the following code:
import xarray as xr
import rioxarray as rio
prcp_file = xr.open_dataset('nclimgrid_prcp.nc')
prp = prcp_file['prcp']
prp = prp.rio.set_spatial_dims(x_dim='lon', y_dim='lat')
prp.rio.write_crs("epsg:4326", inplace=True)
prp.rio.to_raster('prp_raster.tiff')
I also used QGIS to open the population files (add raster layer, navigate into the downloaded folder for pop_m4_2010 and select the "w001001.adf" file). When I do this in a WGS84 project QGIS automatically appears to force reprojection but I am new to this so I am unsure if it is correct.
From this point I have tried several things to resample the population raster to match the 5x5 resolution of the precipitation raster.
In QGIS Processing Toolbox GRASS r.resample
In QGIS Processing Toolbox Raster Layer Zonal Statistics
In Python, honestly I have lost track of all of the different forum posts and tutorials I have followed on GDAL.Warp, Rasterio.Warp, affine transformations, rio.reproject_match, etc. Below are a few examples of the code attempts.
Many of these appear to work (particularly the rio.reproject_match seemed simple and effective). However, none of these appear to be working as intended. When I test the accuracy of the resulting population raster by passing zonal stats of a county vector shapefile the resulting sum of population in the area is either 0, or wildly inaccurate.
What am I doing wrong?
Reproject_Match:
import rioxarray # for the extension to load
import xarray
import matplotlib.pyplot as plt
%matplotlib inline
def print_raster(raster):
print(
f"shape: {raster.rio.shape}\n"
f"resolution: {raster.rio.resolution()}\n"
f"bounds: {raster.rio.bounds()}\n"
f"sum: {raster.sum().item()}\n"
f"CRS: {raster.rio.crs}\n"
)
xds = rioxarray.open_rasterio('pop_m4_2010.tif')
xds_match = rioxarray.open_rasterio('prp_raster.tiff')
fig, axes = plt.subplots(ncols=2, figsize=(12,4))
xds.plot(ax=axes[0])
xds_match.plot(ax=axes[1])
plt.draw()
print("Original Raster:\n----------------\n")
print_raster(xds)
print("Raster to Match:\n----------------\n")
print_raster(xds_match)
xds_repr_match = xds.rio.reproject_match(xds_match)
print("Reprojected Raster:\n-------------------\n")
print_raster(xds_repr_match)
print("Raster to Match:\n----------------\n")
print_raster(xds_match)
xds_repr_match.rio.to_raster("reproj_pop.tif")
Another way with Rasterio.Warp:
import rasterio
from rasterio.warp import calculate_default_transform, reproject, Resampling
#open source raster
srcRst =rasterio.open('pop_m4_2010.tif')
print("source raster crs:")
print(srcRst.crs)
dstCrs = {'init': 'EPSG:4326'}
print("destination raster crs:")
print(dstCrs)
#calculate transform array and shape of reprojected raster
transform, width, height = calculate_default_transform(
srcRst.crs, dstCrs, srcRst.width, srcRst.height, *srcRst.bounds)
print("transform array of source raster")
print(srcRst.transform)
print("transform array of destination raster")
print(transform)
#working of the meta for the destination raster
kwargs = srcRst.meta.copy()
kwargs.update({
'crs': dstCrs,
'transform': transform,
'width': width,
'height': height
})
#open destination raster
dstRst = rasterio.open('pop_m4_2010_reproj4326.tif', 'w', **kwargs)
#reproject and save raster band data
for i in range(1, srcRst.count + 1):
reproject(
source=rasterio.band(srcRst, i),
destination=rasterio.band(dstRst, i),
#src_transform=srcRst.transform,
src_crs=srcRst.crs,
#dst_transform=transform,
dst_crs=dstCrs,
resampling=Resampling.bilinear)
print(i)
#close destination raster
dstRst.close()
And here is a second attempt with Rasterio.Warp:
import rasterio
from rasterio.warp import calculate_default_transform, reproject, Resampling
prcp = rasterio.open('prp_raster.tiff', mode = 'r')
with rasterio.open('pop_m4_2010.tif') as dataset:
# resample data to target shape
data = dataset.read(out_shape=(dataset.count,prcp.height,prcp.width), resampling=Resampling.bilinear)
# scale image transform
transform = dataset.transform * dataset.transform.scale((dataset.width / data.shape[-1]),
(dataset.height / data.shape[-2]))
# Register GDAL format drivers and configuration options with a
# context manager.
with rasterio.Env():
profile = src.profile
profile.update(
dtype=rasterio.float32,
count=1,
compress='lzw')
with rasterio.open('pop_m4_2010_resampledtoprcp.tif', 'w', **profile) as dst:
dst.write(data.astype(rasterio.float32))

This is how you can do that with R.
library(terra)
pop <- rast("USA_HistoricalPopulationDataset/pop_m5_2010")
wth <- rast("nclimgrid_prcp.nc")
wpop <- project(pop, wth, "sum")
Inspect the results.
wpop
#class : SpatRaster
#dimensions : 596, 1385, 1 (nrow, ncol, nlyr)
#resolution : 0.04166666, 0.04166667 (x, y)
#extent : -124.7083, -67, 24.5417, 49.37503 (xmin, xmax, ymin, ymax)
#coord. ref. : lon/lat WGS 84
#source(s) : memory
#name : pop_m5_2010
#min value : 0.0
#max value : 423506.7
global(pop, "sum", na.rm=TRUE)
# sum
#pop_m5_2010 306620886
global(wpop, "sum", na.rm=TRUE)
# sum
#pop_m5_2010 306620761
You can save the results to file with something like this
writeRaster(wpop, "pop.tif")
And you could do this in one step for all population data like this:
ff <- list.files(pattern="0$", "USA_HistoricalPopulationDataset", full=TRUE)
apop <- rast(ff)
wapop <- project(apop, wth, "sum")
The population numbers you are getting are probably wrong because you are using bilinear interpolation when projecting (warping). That is not appropriate for (population) count data. You could first transform it to population density, warp, and transform back. I do that below, getting a result that is similar to what you get with the more direct approach that I have shown above.
csp <- cellSize(pop)
csw <- cellSize(wth[[1]])
popdens <- pop / csp
popdens <- project(popdens, wth, "bilinear")
popcount <- popdens * csw
popcount
#class : SpatRaster
#dimensions : 596, 1385, 1 (nrow, ncol, nlyr)
#resolution : 0.04166666, 0.04166667 (x, y)
#extent : -124.7083, -67, 24.5417, 49.37503 (xmin, xmax, ymin, ymax)
#coord. ref. : lon/lat WGS 84
#source(s) : memory
#name : pop_m5_2010
#min value : 0.0
#max value : 393982.5
global(popcount, "sum", na.rm=TRUE)
# sum
#pop_m5_2010 304906042

get elevation from lat/long of geotiff data in gdal

I have a mosaic tif file (gdalinfo below) I made (with some additional info on the tiles here) and have looked extensively for a function that simply returns the elevation (the z value of this mosaic) for a given lat/long. The functions I've seen want me to input the coordinates in the coordinates of the mosaic, but I want to use lat/long, is there something about GetGeoTransform() that I'm missing to achieve this?
This example for instance here shown below:
from osgeo import gdal
import affine
import numpy as np
def retrieve_pixel_value(geo_coord, data_source):
"""Return floating-point value that corresponds to given point."""
x, y = geo_coord[0], geo_coord[1]
forward_transform = \
affine.Affine.from_gdal(*data_source.GetGeoTransform())
reverse_transform = ~forward_transform
px, py = reverse_transform * (x, y)
px, py = int(px + 0.5), int(py + 0.5)
pixel_coord = px, py
data_array = np.array(data_source.GetRasterBand(1).ReadAsArray())
return data_array[pixel_coord[0]][pixel_coord[1]]
This gives me an out of bounds error as it's likely expecting x/y coordinates (e.g. retrieve_pixel_value([153.023499,-27.468968],dataset). I've also tried the following from here:
import rasterio
dat = rasterio.open(fname)
z = dat.read()[0]
def getval(lon, lat):
idx = dat.index(lon, lat, precision=1E-6)
return dat.xy(*idx), z[idx]
Is there a simple adjustment I can make so my function can query the mosaic in lat/long coords?
Much appreciated.
Driver: GTiff/GeoTIFF
Files: mosaic.tif
Size is 25000, 29460
Coordinate System is:
PROJCRS["GDA94 / MGA zone 56",
BASEGEOGCRS["GDA94",
DATUM["Geocentric Datum of Australia 1994",
ELLIPSOID["GRS 1980",6378137,298.257222101004,
LENGTHUNIT["metre",1]],
ID["EPSG",6283]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433,
ID["EPSG",9122]]]],
CONVERSION["UTM zone 56S",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",0,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",153,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.9996,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",500000,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",10000000,
LENGTHUNIT["metre",1],
ID["EPSG",8807]],
ID["EPSG",17056]],
CS[Cartesian,2],
AXIS["easting",east,
ORDER[1],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]],
AXIS["northing",north,
ORDER[2],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]]]
Data axis to CRS axis mapping: 1,2
Origin = (491000.000000000000000,6977000.000000000000000)
Pixel Size = (1.000000000000000,-1.000000000000000)
Metadata:
AREA_OR_POINT=Area
Image Structure Metadata:
INTERLEAVE=BAND
Corner Coordinates:
Upper Left ( 491000.000, 6977000.000) (152d54'32.48"E, 27d19'48.33"S)
Lower Left ( 491000.000, 6947540.000) (152d54'31.69"E, 27d35'45.80"S)
Upper Right ( 516000.000, 6977000.000) (153d 9'42.27"E, 27d19'48.10"S)
Lower Right ( 516000.000, 6947540.000) (153d 9'43.66"E, 27d35'45.57"S)
Center ( 503500.000, 6962270.000) (153d 2' 7.52"E, 27d27'47.16"S)
Band 1 Block=25000x1 Type=Float32, ColorInterp=Gray
NoData Value=-999
Update 1 - I tried the following:
tif = r"mosaic.tif"
dataset = rio.open(tif)
d = dataset.read()[0]
def get_xy_coords(latlng):
transformer = Transformer.from_crs("epsg:4326", dataset.crs)
coords = [transformer.transform(x, y) for x,y in latlng][0]
#idx = dataset.index(coords[1], coords[0])
return coords #.xy(*idx), z[idx]
longx,laty = 153.023499,-27.468968
coords = get_elevation([(laty,longx)])
print(coords[0],coords[1])
print(dataset.width,dataset.height)
(502321.11181384244, 6961618.891167777)
25000 29460
So something is still not right. Maybe I need to subtract the coordinates from the bottom left/right of image e.g.
coords[0]-dataset.bounds.left,coords[1]-dataset.bounds.bottom
where
In [78]: dataset.bounds
Out[78]: BoundingBox(left=491000.0, bottom=6947540.0, right=516000.0, top=6977000.0)
Update 2 - Indeed, subtracting the corners of my box seems to get closer.. though I'm sure there is a much nice way just using the tif metadata to get what I want.
longx,laty = 152.94646, -27.463175
coords = get_xy_coords([(laty,longx)])
elevation = d[int(coords[1]-dataset.bounds.bottom),int(coords[0]-dataset.bounds.left)]
fig,ax = plt.subplots(figsize=(12,12))
ax.imshow(d,vmin=0,vmax=400,cmap='terrain',extent=[dataset.bounds.left,dataset.bounds.right,dataset.bounds.bottom,dataset.bounds.top])
ax.plot(coords[0],coords[1],'ko')
plt.show()

You basically have two distinct steps:
Convert lon/lat coordinates to map coordinates, this is only necessary if your input raster is not already in lon/lat. Map coordinates are the coordinates in the projection that the raster itself uses
Convert the map coordinates to pixel coordinates.
There are all kinds of tool you might use, perhaps to make things simpler (like pyproj, rasterio etc). But for such a simple case it's probably nice to start with doing it all in GDAL, that probably also enhances your understanding of what steps are needed.
Inputs
from osgeo import gdal, osr
raster_file = r'D:\somefile.tif'
lon = 153.023499
lat = -27.468968
lon/lat to map coordinates
# fetch metadata required for transformation
ds = gdal.OpenEx(raster_file)
raster_proj = ds.GetProjection()
gt = ds.GetGeoTransform()
ds = None # close file, could also keep it open till after reading
# coordinate transformation (lon/lat to map)
# define source projection
# this definition ensures the order is always lon/lat compared
# to EPSG:4326 for which it depends on the GDAL version (2 vs 3)
source_srs = osr.SpatialReference()
source_srs.ImportFromWkt(osr.GetUserInputAsWKT("urn:ogc:def:crs:OGC:1.3:CRS84"))
# define target projection based on the file
target_srs = osr.SpatialReference()
target_srs.ImportFromWkt(raster_proj)
# convert
ct = osr.CoordinateTransformation(source_srs, target_srs)
mapx, mapy, *_ = ct.TransformPoint(lon, lat)
You could verify this intermediate result by for example adding it as Point WKT in something like QGIS (using the QuickWKT plugin, making sure the viewer has the same projection as the raster).
map coordinates to pixel
# apply affine transformation to get pixel coordinates
gt_inv = gdal.InvGeoTransform(gt) # invert for map -> pixel
px, py = gdal.ApplyGeoTransform(gt_inv, mapx, mapy)
# it wil return fractional pixel coordinates, so convert to int
# before using them to read. Round to nearest with +0.5
py = int(py + 0.5)
px = int(px + 0.5)
# read pixel data
ds = gdal.OpenEx(raster_file) # open file again
elevation_value = ds.ReadAsArray(px, py, 1, 1)
ds = None
The elevation_value variable should be the value you're after. I would definitelly verify the result independently, try a few points in QGIS or the gdallocationinfo utility:
gdallocationinfo -l_srs "urn:ogc:def:crs:OGC:1.3:CRS84" filename.tif 153.023499 -27.468968
# Report:
# Location: (4228P,4840L)
# Band 1:
# Value: 1804.51879882812
If you're reading a lot of points, there will be some threshold at which it would be faster to read a large chunk and extract the values from that array, compared to reading every point individually.
edit:
For applying the same workflow on multiple points at once a few things change.
So for example having the inputs:
lats = np.array([-27.468968, -27.468968, -27.468968])
lons = np.array([153.023499, 153.023499, 153.023499])
The coordinate transformation needs to use ct.TransformPoints instead of ct.TransformPoint which also requires the coordinates to be stacked in a single array of shape [n_points, 2]:
coords = np.stack([lons.ravel(), lats.ravel()], axis=1)
mapx, mapy, *_ = np.asarray(ct.TransformPoints(coords)).T
# reshape in case of non-1D inputs
mapx = mapx.reshape(lons.shape)
mapy = mapy.reshape(lons.shape)
Converting from map to pixel coordinates changes because the GDAL method for this only takes single point. But manually doing this on the arrays would be:
px = gt_inv[0] + mapx * gt_inv[1] + mapy * gt_inv[2]
py = gt_inv[3] + mapx * gt_inv[4] + mapy * gt_inv[5]
And rounding the arrays to integer changes to:
px = (px + 0.5).astype(np.int32)
py = (py + 0.5).astype(np.int32)
If the raster (easily) fits in memory, reading all points would become:
ds = gdal.OpenEx(raster_file)
all_elevation_data = ds.ReadAsArray()
ds = None
elevation_values = all_elevation_data[py, px]
That last step could be optimized by checking highest/lowest pixel coordinates in both dimensions and only read that subset for example, but it would require normalizing the coordinates again to be valid for that subset.
The py and px arrays might also need to be clipped (eg np.clip) if the input coordinates fall outside the raster. In that case the pixel coordinates will be < 0 or >= xsize/ysize.

I keep getting blank images while trying to Georeference an image with GDAL

#!/usr/bin/env python3
import numpy as np
from osgeo import gdal
from osgeo import osr
# Load an array with shape (197, 250, 3)
# Data with dim of 3 contain (value, longitude, latitude)
data = np.load("data.npy")
# Copy the data and coordinates
array = data[:,:,0]
lon = data[:,:,1]
lat = data[:,:,2]
nLons = array.shape[1]
nLats = array.shape[0]
# Calculate the geotransform parameters
maxLon, minLon, maxLat, minLat = [lon.max(), lon.min(), lat.max(), lat.min()]
resLon = (maxLon - minLon) / nLons
resLat = (maxLat - minLat) / nLats
# Get the transform
geotransform = (minLon, resLon, 0, maxLat, 0, -resLat)
# Create the ouptut raster
output_raster = gdal.GetDriverByName('GTiff').Create('myRaster.tif', nLons, nLats, 1,
gdal.GDT_Int32)
# Set the geotransform
output_raster.SetGeoTransform(geotransform)
srs = osr.SpatialReference()
# Set to world projection 4326
srs.ImportFromEPSG(4326)
output_raster.SetProjection(srs.ExportToWkt())
output_raster.GetRasterBand(1).WriteArray(array)
output_raster.FlushCache()
The code above is meant to georeference a raster using GDAL but returns blank tiff files. I have vetted the data and variables, I, however, suspect the problem could be from geotransform variables. The documentation demands the variable to be:
top-left-x, w-e-pixel-resolution, 0,
top-left-y, 0, n-s-pixel-resolution (negative value)
I have used lats and lons not sure I'm getting which one corresponds to x and which to y. It could be something else but I'm not quite sure.

Overall your approach looks correct to me, but it's hard to tell without seeing the data you're using, but here are some points to consider:
First, there's a difference between the output file being empty, and/or being in the wrong location, georeferencing relates only to the latter.
When working interactive, you should also make sure to properly close the Dataset using output_raster = None, that will also trigger flushing for you.
You could start by testing if GDAL reads the same data that you intended to write. Using something like:
ds = gdal.Open('myRaster.tif')
data_from_disk = ds.ReadAsArray()
ds = None
np.testing.assert_array_equal(data_from_disk, array)
If those are not identical, it could be an issue with the datatype. Like writing floats close to 0 as integers, causing them to clip to 0 giving the appearance of an "empty" file.
Regarding the georeferencing, the projection you use has the coordinates in degrees. If yours are in radians your output ends up close to null-island.
Your approach also assumes that the data and lat/lon arrays are on a regular grid (having a constant resolution). That might not be the case (especially if the data comes with a 2D grid of coordinates).
Often when coordinate arrays are given, they are defined as valid for the center of the pixel. Compared to GDAL's geotransform which is defined for the (outer) edge of the pixel. So you might need to account for that by subtracting half the resolution. And this also impacts your calculation of the resolution, which in the case for the center-definition should probably use / (nLons-1) & / (nLats-1). Or alternatively verify with:
# for a regular grid
resLon = lon[0,1] - lon[0,0]
resLat = lat[1,0] - lat[0,0]
When I run your snippet with some dummy data, it gives me a correct output (ignoring the center/edge issue mentioned above).
lat, lon = np.mgrid[89:-90:-2, -179:180:2]
array = np.sqrt(lon**2 + lat**2).astype(np.int32)

Spatial resampling of netCDF file in python

I am working with Sentinel 3 SLSTR data which comes in netCDF file format. The file contains 11 bands:
S1-S6 (500 m resolution) and S7-S9 and F1 & F2 (1000 m resolution). S1-S6 contains radiance values and S7-S9 contains brightness temperature values. Right now, I want to resample my S7-S9 band to 500 m resolution to match the resolution of S1-S6 bands.
I am using xarray to read the netCDF files. There is a function xarray.Dataset.resample() but the documentation says that it resample to a new temporal resolution.
I also tried to resample using gdal but couldn't get any result.
import gdal
import xarray as xr
import matplotlib.pyplot as plt
data = xr.open_dataset('S7_BT_in.nc') # one of the files in 1000 m resolution
geo = xr.open_dataset(path+'geodetic_an.nc') # file containing the geodetic values
ds = data['S7_BT_in'] # fetching variable I need to work on
lat = geo['latitude_an'] # fetching latitude values
lon = geo['longitude_an'] # fetching longitude values
#assigning latitude and longitude values to the coordinates of ds
ds = ds.assign_coords(coords = {'Latitude': lat, 'Longitude': lon})
x = gdal.Open('ds') # Opening the netCDF file using gdal
# resampling the data to 500 m resolution
xreproj = gdal.Warp('resampled.nc', x, xRes = 500, yRes = 500)
This is the error I am getting:
SystemError: <built-in function wrapper_GDALWarpDestName> returned NULL without setting an error.
I also tried opening the file directly using gdal but still getting the same error.

The geotransform read by gdal from a TerraSAR-X Tiff is incorrect

I am using python's gdal module to analyse satellite images - RADARSAT-2 and TerraSAR-X - saved as .tif files. I need to fetch pixel values at coordinates read from a shapefile. While the code works fine for the RS2 images, I'm having trouble with the TSX images.
The geotransform read by gdal is off for the TSX products, which yields negative pixel indices for the location of shapefile features on the image. The same piece of code works fine for RS2 products.
Any idea of what's going on and how to fix it ?
Example of a correct geotransform from a RS2 product:
(-74.98992283355103, 7.186522272956171e-05, 0.0, 62.273587708987776, 0.0, -7.186522272956171e-05)
Example of the geotransforms I get for TSX products :
(506998.75, 2.5, 0.0, 6919001.25, 0.0, -2.5)
Code snippet :
import gdal
gdal.UseExceptions()
# Read image, band, geotransform
dataset = gdal.Open(paths['TSXtiff'])
band = dataset.GetRasterBand(band_index)
gt = dataset.GetGeoTransform()
# Read shapefile
shapefile = ogr.Open(paths["Shapefile"])
layer = shapefile.GetLayer()
# Add pixel_value for each feature to associated list
pixels_at_shp = []
for feature in layer :
geometry = feature.GetGeometryRef()
# Coordinates in map units
# In GDAL, mx = px*gt[1] + gt[0], my = py*gt[5] + gt[3]
mx,my = geometry.GetX(), geometry.GetY()
# Convert to pixel coordinates
px = int((mx-gt[0])/gt[1])
py = int((my-gt[3])/gt[5])
band_values = band.ReadAsArray(px,py,1,1)
pixels_at_shp.append(band_values)
shapefile = None
return pixels_at_shp

The TSX product was projected while the RS2 product was simply georeferenced. Solution : go back to lat/long degrees coordinates by reprojecting the GeoTIFF files onto WGS84.
I used the gdal command line tool to do the reprojection, like so :
for f in *.tif
do
gdalwarp "$f" "${f%%.*}_reproj.tif" -t_srs "+proj=longlat +ellps=WGS84"
done