Skip to content

Latest commit

 

History

History
82 lines (62 loc) · 2.45 KB

esri-land-cover.md

File metadata and controls

82 lines (62 loc) · 2.45 KB
Raw

Indexing ESRI Land Cover

ESRI Land Cover data is available from a public bucket, although it's not publically queryable. The data is documented here and here.

We've created a list of the available tiles here.

There is a script to index the data, which is described below.

An ODC product definition for ESRI Land Cover data is inluded in this repo. You can this to datacube using the following command, but the indexing tool will do this automatically for you:

datacube product add products/esri_land_cover.yaml

Installing the required tools

Install the scripts required by following the instructions here like this:

pip install --extra-index-url="https://packages.dea.ga.gov.au" odc_apps_dc_tools

Indexing the data

To index all 700 scenes, you can run the command below. Excluded the --add-product if you already added the product in the previous step.

esri-lc-to-dc --add-product

Plotting example

To plot the data, you can use this code to set up the same colour ramp as ESRI use:

import datacube

from matplotlib import colors as mcolours
import numpy as np

dc = datacube.Datacube()

product = "esri_land_cover"
# This is a point in Hobart
lat, lon = -42.822771, 147.234277
buf = 0.25
lons = (lon - buf, lon + buf)
lats = (lat - buf, lat + buf)

# Load the data at 10 m resolution
ds = dc.load(
    product=product,
    longitude=lons,
    latitude=lats,
    resolution=(-10, 10),
    output_crs="epsg:6933"
)

# Colour it like the ESRI colour map
cmap = mcolours.ListedColormap([
      np.array([0, 0, 0]) / 255,
      np.array([65, 155, 223]) / 255,
      np.array([57, 125, 73]) / 255,
      np.array([136, 176, 83]) / 255,
      np.array([122, 135, 198]) / 255,
      np.array([228, 150, 53]) / 255,
      np.array([223, 195, 90]) / 255,
      np.array([196 ,40, 27]) / 255,
      np.array([165, 155, 143]) / 255,
      np.array([168, 235, 255]) / 255,
      np.array([97, 97, 97]) / 255
])
bounds=range(0,12)
norm = mcolours.BoundaryNorm(np.array(bounds), cmap.N)
ds.isel(time=0).classification.plot.imshow(cmap=cmap, norm=norm, size=10)

ESRI Land Cover