Overview

Teaching: 40 min
Exercises: 20 min

Questions

• How to compute raster statistics on different zones delineated by a vector data?

Objectives

• Extract zones from the vector dataset

• Convert vector data to raster

• Calculate raster statistics over zones

Introduction

Statistics on predefined zones of the raster data are commonly used for analysis and to better understand the data. These zones are often provided within a single vector dataset, identified by certain vector attributes. For example, in the previous episodes, we used the crop field polygon dataset. The fields with the same crop type can be identified as a “zone”, resulting in multiple zones in one vector dataset. One may be interested in performing statistical analysis over these crop zones.

In this episode, we will explore how to calculate zonal statistics based on the types of crops in cropped_field.shp . To do this, we will first identify zones from the vector data, then rasterize these vector zones. Finally the zonal statistics for ndvi will be calculated over the rasterized zones.

Making vector and raster data compatible

First, let’s load the NDVI.tif file saved in the previous episode to obtained our calculated raster ndvi data. We also use the squeeze() function in order to reduce our raster data ndvi dimension to 2D by removing the singular band dimension - this is necessary for use with the rasterize and zonal_stats functions:

import rioxarray
ndvi = rioxarray.open_rasterio("NDVI.tif")
ndvi_sq = ndvi.squeeze()

Let’s also read the crop fields vector data from our saved cropped_field.shp file and view the CRS information.

field = gpd.read_file('cropped_field.shp')
field.crs

<Derived Projected CRS: EPSG:28992>
Name: Amersfoort / RD New
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: Netherlands - onshore, including Waddenzee, Dutch Wadden Islands and 12-mile offshore coastal zone.
- bounds: (3.2, 50.75, 7.22, 53.7)
Coordinate Operation:
- name: RD New
- method: Oblique Stereographic
Datum: Amersfoort
- Ellipsoid: Bessel 1841
- Prime Meridian: Greenwich

In order to use the vector data as a classifier for our raster, we need to convert the vector data to the appropriate CRS. We can perform the CRS conversion from the vector CRS (EPSG:28992) to our raster ndvi CRS (EPSG:32631) and view the data with:

field_to_raster_crs = field.to_crs(ndvi.rio.crs)
field_to_raster_crs

category	gewas	gewascode	jaar	status	geometry
0	Grasland	Grasland, blijvend	265	2020	Definitief	POLYGON ((634234.009 5807461.338, 634232.049 5...
1	Grasland	Grasland, blijvend	265	2020	Definitief	POLYGON ((634514.198 5807699.177, 634504.207 5...
2	Grasland	Grasland, blijvend	265	2020	Definitief	POLYGON ((633115.463 5808493.238, 633109.078 5...
3	Grasland	Grasland, blijvend	265	2020	Definitief	POLYGON ((634803.514 5808081.449, 634809.802 5...
4	Grasland	Grasland, blijvend	265	2020	Definitief	POLYGON ((634184.289 5807370.958, 634200.036 5...
...	...	...	...	...	...	...
4867	Grasland	Grasland, blijvend	265	2020	Definitief	POLYGON ((631384.726 5809352.385, 631383.343 5...
4868	Grasland	Grasland, blijvend	265	2020	Definitief	POLYGON ((635240.367 5806904.896, 635245.819 5...
4869	Grasland	Grasland, blijvend	265	2020	Definitief	POLYGON ((636074.093 5816782.787, 636123.922 5...
4870	Grasland	Grasland, tijdelijk	266	2020	Definitief	POLYGON ((627526.751 5816828.877, 627674.251 5...
4871	Grasland	Grasland, natuurlijk. Hoofdfunctie landbouw.	331	2020	Definitief	POLYGON ((642317.485 5813024.516, 642326.058 5...
4872 rows × 6 columns

Rasterizing our vector data

Before calculating zonal statistics, we first need to rasterize our field_to_raster_crs vector geodataframe with the rasterio.features.rasterize function. With this function, we aim to produce a grid with numerical values representing the types of crop as defined by the column gewascode from field_cropped - gewascode stands for the crop codes as defined by the Netherlands Enterprise Agency (RVO) for different types of crops or gewas (Grassland, permanent; Grassland, temporary; corn fields; etc.). This grid of values thus defines the zones for the xrspatial.zonal_stats function, where each pixel in the zone grid overlaps with a corresponding pixel in our NDVI raster.

We can generate the geometry, gewascode pairs for each vector feature to be used as the first argument to rasterio.features.rasterize as:

geom = field_to_raster_crs[['geometry', 'gewascode']].values.tolist()
geom

[[<shapely.geometry.polygon.Polygon at 0x7ff88666f670>, 265],
 [<shapely.geometry.polygon.Polygon at 0x7ff86bf39280>, 265],
 [<shapely.geometry.polygon.Polygon at 0x7ff86ba1db80>, 265],
 [<shapely.geometry.polygon.Polygon at 0x7ff86ba1d730>, 265],
 [<shapely.geometry.polygon.Polygon at 0x7ff86ba1d400>, 265],
 [<shapely.geometry.polygon.Polygon at 0x7ff86ba1d130>, 265],
...
 [<shapely.geometry.polygon.Polygon at 0x7ff88685c970>, 265],
 [<shapely.geometry.polygon.Polygon at 0x7ff88685c9a0>, 265],
 [<shapely.geometry.polygon.Polygon at 0x7ff88685c9d0>, 265],
 [<shapely.geometry.polygon.Polygon at 0x7ff88685ca00>, 331],
 ...]

This generates a list of the shapely geometries from the geometry column, and the unique field ID from the gewascode column in the field_to_raster_crs geodataframe.

We can now rasterize our vector data using rasterio.features.rasterize:

from rasterio import features
field_cropped_raster = features.rasterize(geom, out_shape=ndvi_sq.shape, fill=0, transform=ndvi.rio.transform())

The argument out_shape specifies the shape of the output grid in pixel units, while transform represents the projection from pixel space to the projected coordinate space. We also need to specify the fill value for pixels that are not contained within a polygon in our shapefile, which we do with fill = 0. It’s important to pick a fill value that is not the same as any value already defined in gewascode or else we won’t distinguish between this zone and the background.

We convert the output of the rasterio.features.rasterize function, which generates a numpy array np.ndarray, to xarray.DataArray which will be used further:

import xarray as xr
field_cropped_raster_xarr = xr.DataArray(field_cropped_raster)

Calculate zonal statistics

In order to calculate the statistics for each crop zone, we call the function, xrspatial.zonal_stats. The xrspatial.zonal_stats function takes as input zones, a 2D xarray.DataArray, that defines different zones, and values, a 2D xarray.DataArray providing input values for calculating statistics.

We call the zonal_stats function with field_cropped_raster_xarr as our classifier and the 2D raster with our values of interest ndvi_sq to obtain the NDVI statistics for each crop type:

from xrspatial import zonal_stats
zonal_stats(field_cropped_raster_xarr, ndvi_sq)

	zone	mean	max	min	sum	std	var	count
0	0	0.266531	0.999579	-0.998648	38887.648438	0.409970	0.168075	145903.0
1	259	0.520282	0.885242	0.289196	449.003052	0.111205	0.012366	863.0
2	265	0.775609	0.925955	0.060755	66478.976562	0.091089	0.008297	85712.0
3	266	0.794128	0.918048	0.544686	1037.925781	0.074009	0.005477	1307.0
4	331	0.703056	0.905304	0.142226	10725.819336	0.102255	0.010456	15256.0
5	332	0.681699	0.849158	0.178113	321.080261	0.123633	0.015285	471.0
6	335	0.648063	0.865804	0.239661	313.662598	0.146582	0.021486	484.0
7	863	0.388575	0.510572	0.185987	1.165724	0.144245	0.020807	3.0

The zonal_stats function calculates the minimum, maximum, and sum for each zone along with statistical measures such as the mean, variance and standard deviation for each rasterized vector zone. In our raster data-set zone = 0, corresponding to non-crop areas, has the highest count followed by zone = 265 which corresponds to ‘Grasland, blijvend’ or ‘Grassland, permanent’. The highest mean NDVI is observed for zone = 266 for ‘Grasslands, temporary’ with the lowest mean, aside from non-crop area, going to zone = 863 representing ‘Forest without replanting obligation’. Thus, the zonal_stats function can be used to analyse and understand different sections of our raster data. The definition of the zones can be derived from vector data or from classified raster data as presented in the challenge below:

Challenge: Calculate zonal statistics for zones defined by ndvi_classified

Let’s calculate NDVI zonal statistics for the different zones as classified by ndvi_classified in the previous episode.

Load both raster data-sets and convert into 2D xarray.DataArray. Then, calculate zonal statistics for each class_bins. Inspect the output of the zonal_stats function.

Answers

1) Load and convert raster data into suitable inputs for zonal_stats:

ndvi = rioxarray.open_rasterio("NDVI.tif")
ndvi_classified = rioxarray.open_rasterio("NDVI_classified.tif")
ndvi_sq = ndvi.squeeze()
ndvi_classified_sq = ndvi_classified.squeeze()

2) Create and display the zonal statistics table.

zonal_stats(ndvi_classified_sq, ndvi_sq)

Key Points

• Zones can be extracted by attribute columns of a vector dataset

• Zones can be rasterized using rasterio.features.rasterize

• Calculate zonal statistics with xrspatial.zonal_stats over the rasterized zones.