- Be able to define 3 spatial attributes of a raster dataset: extent, coordinate reference system and resolution.
- Access spatial metadata of a raster dataset in Python.
On this page, you will learn about three important spatial attributes associated with raster data that in this lesson: coordinate reference systems (CRS), resolution, and spatial extent.
1. Coordinate Reference System
The Coordinate Reference System or
CRS of a spatial object tells
Python where the raster is located in geographic space. It also tells
Python what mathematical method should be used to “flatten” or project the raster in geographic space.
What Makes Spatial Data Line Up On A Map?
You will discuss Coordinate Reference systems in more detail in next weeks class. For this week, just remember that data from the same location but saved in different coordinate references systems will not line up in any GIS or other program.
Thus, it’s important when working with spatial data in a program like
Python to identify the coordinate reference system applied to the data and retain it throughout data processing and analysis.
View Raster Coordinate Reference System (CRS) in Python
You can view the
CRS string associated with your
Python object using the
# Import necessary packages import os import matplotlib.pyplot as plt import numpy as np from shapely.geometry import Polygon, mapping import rasterio as rio from rasterio.mask import mask from rasterio.plot import show # Package created for the earth analytics program import earthpy as et
/opt/conda/lib/python3.8/site-packages/rasterio/plot.py:260: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(arr.shape) is 2:
# Get data and set working directory et.data.get_data("colorado-flood") os.chdir(os.path.join(et.io.HOME, 'earth-analytics'))
# Define relative path to file lidar_dem_path = os.path.join("data", "colorado-flood", "spatial", "boulder-leehill-rd", "pre-flood", "lidar", "pre_DTM.tif") # View crs of raster imported with rasterio with rio.open(lidar_dem_path) as src: print(src.crs)
You can assign this string to a Python object, too.
# Assign crs to myCRS object myCRS = src.crs myCRS
CRS EPSG code for your
lidar_dem object is 32613. Next, you can look that EPSG code up on the spatial reference.org website to figure out what CRS it refers to and the associated units. In this case you are using UTM zone 13 North.
Digging deeper you can view the proj 4 string which tells us that the horizontal units of this project are in meters (
The CRS format, returned by python, is in a
EPSG format. This means that the projection information is represented by a single number. However on the spatialreference.org website you can also view the proj4 string which will tell you a bit more about the horizontal units that the data are in. An overview of proj4 is below.
+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
Converting EPSG to Proj4 in Python
A python package for this class called ‘earthpy’ contains a dictionary that will help you convert EPSG codes into a Proj4 string. This can be used with rasterio in order to determine the metadata for a given EPSG code. For example, if you wish to know the units of the EPSG code above, you can do the following:
# Each key of the dictionary is an EPSG code print(list(et.epsg.keys())[:10])
['29188', '26733', '24600', '32189', '4899', '29189', '26734', '7402', '26951', '29190']
# You can convert to proj4 like so: proj4 = et.epsg['32613'] print(proj4)
+proj=utm +zone=13 +datum=WGS84 +units=m +no_defs
# Finally you can convert this into a rasterio CRS like so: crs_proj4 = rio.crs.CRS.from_string(proj4) crs_proj4
You’ll focus on the first few components of the CRS in this tutorial.
+proj=utmThe projection of the dataset. Your data are in Universal Transverse Mercator (UTM).
+zone=18The UTM projection divides up the world into zones, this element tells you which zone the data is in. Harvard Forest is in Zone 18.
+datum=WGS84The datum was used to define the center point of the projection. Your raster uses the
+units=mThis is the horizontal units that the data are in. Your units are meters.
Important: You are working with lidar data which has a Z or vertical value as well. While the horizontal units often match the vertical units of a raster they don’t always! Be sure the check the metadata of your data to figure out the vertical units!
Next, you’ll learn about spatial extent of your raster data. The spatial extent of a raster or spatial object is the geographic area that the raster data covers.
The spatial extent of an
Python spatial object represents the geographic “edge” or location that is the furthest north, south, east and west. In other words,
extent represents the overall geographic coverage of the spatial object.
You can access the spatial extent using the
.bounds attribute in
BoundingBox(left=472000.0, bottom=4434000.0, right=476000.0, top=4436000.0)
A raster has horizontal (x and y) resolution. This resolution represents the area on the ground that each pixel covers. The units for your data are in meters as determined by the CRS above. In this case, your data resolution is 1 x 1. This means that each pixel represents a 1 x 1 meter area on the ground. You can view the resolution of your data using the
# What is the x and y resolution for your raster data? src.res