Exploring Land Cover Data (Impact Observatory)

General Exploration Standard Python

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RoHub doi

Context

Purpose

Introduce manipulation and exploratory analysis of classified land use and cover data, using example data created by Impact Observatory from ESA Sentinel-2 imagery.

Dataset description

There are now many classified (categorical) land cover data products freely available that are useful for Environmental Data Science. These include:

These products are provided as 2D rasters (spatial) or 3D data cubes (spatio-temporal). The number and classification of discrete land cover classes varies between products, but at their most basic will distinguish between broad land covers such as ‘crops’, ‘forest’ and ‘built-up’. The nominal (categorical) character of the data influences the types of analysis appropriate.

This notebook uses data created by Impact Observatory. The data are a time series for 2017-2021 of annual global land use and land cover (LULC) mapped at 10m spatial resolution. The data are derived from ESA Sentinel-2 imagery with each annual map specifying individual pixels as belonging to one of 9 LULC classes. The Impact Observatory LULC model uses deep learning methods to infer a single annual LULC class for each pixel in a Sentinel-2 image. Each annual global LULC map is produced by aggregating multiple inferences for images from across a given year (requiring processing approximately 2 million images to create each annual map).

Highlights

Contributions

Notebook

  • James Millington (author), Dept of Geography, King’s College London, @jamesdamillington

  • Anne Fouilloux (reviewer), Dept of Geosciences, University of Oslo, @annefou

  • Amandine Debus (reviewer), Dept of Geography, University of Cambridge, @aedebus

Dataset originator/creator

The data are available under a Creative Commons BY-4.0 license.

Dataset reference and documentation

Code

Load libraries

#system
import os
import warnings
warnings.filterwarnings(action='ignore')

#data handling
import pystac_client
import odc.stac
from pystac.extensions.item_assets import ItemAssetsExtension

import geopandas as gpd
import rasterio as rio
import numpy as np
import pandas as pd
from shapely.geometry import Polygon
import xarray as xr
import rioxarray

#visualisation
import matplotlib.pyplot as plt
import matplotlib.colors as mplc
import holoviews as hv
import hvplot.pandas  
from holoviews import opts, dim

#data analysis
from sklearn import metrics  #for confusion matrix
from rasterstats import zonal_stats