Reading raster files with Rasterio¶
Rasterio is a highly useful module for raster processing which you can use for reading and writing several different raster formats in Python. Rasterio is based on GDAL and Python automatically registers all known GDAL drivers for reading supported formats when importing the module. Most common file formats include for example TIFF and GeoTIFF, ASCII Grid and Erdas Imagine .img -files.
Landsat 8 bands are stored as separate GeoTIFF -files in the original package. Each band contains information of surface reflectance from different ranges of the electromagnetic spectrum.
Download data¶
Download the data package from here. The package contains various TIF-files that will be explored during the tutorials.
Let’s start with inspecting one of the files we downloaded:
In [16]:
import rasterio
import os
import numpy as np
%matplotlib inline
# Data dir
data_dir = "L5_data"
fp = os.path.join(data_dir, "Helsinki_masked_p188r018_7t20020529_z34__LV-FIN.tif")
# Open the file:
raster = rasterio.open(fp)
# Check type of the variable 'raster'
type(raster)
Out[16]:
rasterio.io.DatasetReader
Okey so from here we can see that our raster variable is a
rasterio._io.RasterReader type which means that we have opened the
file for reading.
Read raster file properties¶
Let’s have a closer look at the properties of the file:
In [5]:
# Projection
raster.crs
Out[5]:
CRS({'proj': 'tmerc', 'lat_0': 0, 'lon_0': -183, 'k': 0.9996, 'x_0': 500000, 'y_0': 0, 'datum': 'WGS84', 'units': 'm', 'no_defs': True})
In [6]:
# Affine transform (how raster is scaled, rotated, skewed, and/or translated)
raster.transform
Out[6]:
Affine(28.5, 0.0, 698592.0,
0.0, -28.5, 6697870.5)
In [7]:
# Dimensions
print(raster.width)
print(raster.height)
1288
1439
In [8]:
# Number of bands
raster.count
Out[8]:
7
In [9]:
# Bounds of the file
raster.bounds
Out[9]:
BoundingBox(left=698592.0, bottom=6656859.0, right=735300.0, top=6697870.5)
In [10]:
# Driver (data format)
raster.driver
Out[10]:
'GTiff'
In [11]:
# No data values for all channels
raster.nodatavals
Out[11]:
(None, None, None, None, None, None, None)
In [12]:
# All Metadata for the whole raster dataset
raster.meta
Out[12]:
{'driver': 'GTiff',
'dtype': 'uint8',
'nodata': None,
'width': 1288,
'height': 1439,
'count': 7,
'crs': CRS({'proj': 'tmerc', 'lat_0': 0, 'lon_0': -183, 'k': 0.9996, 'x_0': 500000, 'y_0': 0, 'datum': 'WGS84', 'units': 'm', 'no_defs': True}),
'transform': Affine(28.5, 0.0, 698592.0,
0.0, -28.5, 6697870.5)}
Get raster bands¶
Different bands of a satellite images are often stacked together in one
raster dataset. In our case, all seven bands of the Landsat 8 scene are
included in our GeoTIFF and the count is hence 7.
In order to have a closer look at the values stored in the band, we will take advantage of the GDAL Band API.
In [14]:
# Read the raster band as separate variable
band1 = raster.read(1)
# Check type of the variable 'band'
print(type(band1))
# Data type of the values
print(band1.dtype)
<class 'numpy.ndarray'>
uint8
Now we have the values of the raster band stored in the variable
band1.
Data type of the band can be interpreted with the help of GDAL documentation on Pixel data types. Unsigned integer is always equal or greater than zero and signed integer can store also negative values. For example, an unsigned 16-bit integer can store 2^16 (=65,536) values ranging from 0 to 65,535.
Band statistics¶
Next, let’s have a look at the values that are stored in the band. As the values of the bands are stored as numpy arrays, it is extremely easy to calculate basic statistics by using basic numpy functions.
In [18]:
# Read all bands
array = raster.read()
# Calculate statistics for each band
stats = []
for band in array:
stats.append({
'min': band.min(),
'mean': band.mean(),
'median': np.median(band),
'max': band.max()})
# Show stats for each channel
stats
Out[18]:
[{'min': 0, 'mean': 59.63132232528628, 'median': 61.0, 'max': 255},
{'min': 0, 'mean': 43.13342814842951, 'median': 43.0, 'max': 255},
{'min': 0, 'mean': 36.29418775547201, 'median': 31.0, 'max': 255},
{'min': 0, 'mean': 35.0946303937776, 'median': 13.0, 'max': 255},
{'min': 0, 'mean': 37.63263502518571, 'median': 13.0, 'max': 255},
{'min': 0, 'mean': 105.8221477777442, 'median': 114.0, 'max': 175},
{'min': 0, 'mean': 26.28348760569581, 'median': 14.0, 'max': 255}]