Xavier Barton - Guides

Rational

Take a set of shapefiles, download the ESA WorldCover GeoTIFFs that the shapefile elements cover and determine the percentage of each land use type in each shape file element.

Input Files

Shapefile

.shp
.cpg
.dbf
.prj
.shx

Learn how to produce this shapefile from a CSV of sample names, latitudes and longitudes here: Site Locations to Shapefile

Scripts Used

Github Repository of Scripts

Environmental Data / Land Use - ESA WorldCover

download-from-shapefile.py
percent.py
landuse_pies.py

Steps

1. Downloading ESA LandCover tiles

python download_from_shapefile.py -s '/path/to/shapefile/output_shapes.shp'

Arguments

Path to your .shp (e.g. output_shapes.shp)

Outputs

A number of .tif files titled ESA_WorldCover_10m_2021_v200_XXXXXX_Map.tif

There can be a variation in the number of files downloaded depending on the spread of your collection sites, normally 2 or 3 if it's within the state.
This .tif file is essentially like a map with multiple layers of a particular region; some groups (shapes/polygons) will be covered in one file, and other groups will be in another.

2. Extracting Land Use within Shapefiles

python percent.py '/path/to/geotiffs_folder' 'path/to/shapefile.shp' landuse_output.csv

Don’t worry about the warnings as long as it says “Results saved to …”

Each column in the CSV represents a percentage of each land type contained within each shapefile element.

ESA Land Use Codes

Layer (Class)	Colour (RGB)	Hex	Colour	Map Code
Trees	0,100,0	#006400	Trees	10
Shrub Land	255, 187, 34	#FFBB22	Shrub Land	20
Grassland	255, 255, 76	#FFFF4C	Grassland	30
Cropland	240, 150, 255	#F096FF	Cropland	40
Built	250, 0, 0	#FA0000	Built	50
Bare / Sparse Vegetation	180, 180, 180	#B4B4B4	Bare / Sparse Vegetation	60
Snow and Ice	240, 240, 240	#F0F0F0	Snow and Ice	70
Permanent Water Bodies	0, 100, 200	#0064C8	Permanent Water Bodies	80
Herbaceous Wetland	0, 150, 160	#0096A0	Herbaceous Wetland	90
Mangroves	0, 207, 117	#00CF75	Mangroves	95
Moss and Lichen	250, 230, 160	#FAE6A0	Moss and Lichen	100

Arguments

Path to your .shp from previous steps (e.g. output_shapes.shp)
Path to a directory (folder) containing GeoTIFFs downloaded in previous step (e.g. /geotifs)

Outputs

CSV containing assigned groups and the percentage of each land use type that the shapefile polygon contains (e.g. landuse_output.csv)

3. Generating a Map of Land Use in Each Group (optional)

python landuse_pies.py '/path/to/shapefile/output_shapes.shp' '/path/to/landuse/percentages/landuse_output.csv'

Arguments

Path to your .shp from previous steps (e.g. output_shapes.shp)
Path to the CSV of land use percentages produced in previous step (e.g. landuse_output.csv)

Outputs

HTML map containing group polygons and pie charts of land use cover (landuse_pies.html)