Criterion B Details

# Default ecosystem code for template development.
# This line is replaced by build_ecosystem_pages.py for each ecosystem.
ecosystem_code = 'T1.2.1'

Import Python modules.

import os
import yaml
from pathlib import Path
from lonboard import Map
from rle_python_gee.ecosystems import Ecosystems
from rle_python_gee.eoo import make_eoo
from rle_python_gee.aoo import make_aoo_grid

Load the country config file.

project_root = os.environ.get('PIXI_PROJECT_ROOT', str(Path('..').resolve()))
config_path = Path(project_root) / 'config' / 'country_config.yaml'
with open(config_path) as f:
    config = yaml.safe_load(f)

Load & Filter Ecosystem Data

Load data for all the ecosystems.

source = config['ecosystem_source']
ecosystems = Ecosystems.from_file(
    source['data'],
    ecosystem_column=source['ecosystem_code_column'],
    ecosystem_name_column=source['ecosystem_name_column'],
    functional_group_column=source['functional_group_column']
)

Filter by the T1.2.1 and check the number of features.

ecosystem = ecosystems.filter(ecosystem_code)
has_data = ecosystem.size() > 0
print(f'{ecosystem.size() = }')
if not has_data:
    from IPython.display import Markdown, display
    display(Markdown(
        f'**No spatial data found for {ecosystem_code}.** '
        f'Criterion B calculations are skipped.'
    ))
ecosystem.size() = 84

Extent of Occurrence (EOO) (subcriterion B1)

Extent of occurrence (EOO). The EOO of an ecosystem is the area (km2) of a minimum convex polygon – the smallest polygon in which no internal angle exceeds 180° that encompasses all known current spatial occurrences of the ecosystem type.

The minimum convex polygon (also known as a convex hull) must not exclude any areas, discontinuities or disjunctions, regardless of whether the ecosystem can occur in those areas or not. Regions such as oceans (for terrestrial ecosystems), land (for coastal or marine ecosystems), or areas outside the study area (such as in a different country) must remain included within the minimum convex polygon to ensure that this standardised method is comparable across ecosystem types. In addition, these features contribute to spreading risks across the distribution of the ecosystem by making different parts of its distribution more spatially independent.

Calculate EOO

Start by calculating the convex hull of the ecosystem’s distribution.

import geopandas as gpd

if has_data:
    ecosystem_geometry = ecosystem.geometry.union_all()
    gdf_ecosystem_polygons = gpd.GeoDataFrame(geometry=[ecosystem_geometry], crs=ecosystem.geometry.crs)
    hull = ecosystem_geometry.convex_hull
    gdf_hull = gpd.GeoDataFrame(geometry=[hull], crs=ecosystem.geometry.crs)

Display the ecosystem’s distribution and the convex hull.

from lonboard import Map, PolygonLayer
from rle_python_gee.viz import smart_map

if has_data:
    eoo_hull = make_eoo(ecosystem).compute()
    display(smart_map([eoo_hull, ecosystem]))
/home/runner/work/rle-tyler-20/rle-tyler-20/.pixi/envs/default/lib/python3.11/site-packages/lonboard/_geoarrow/ops/reproject.py:116: UserWarning: Input being reprojected to EPSG:4326 CRS.
Lonboard is only able to render data in EPSG:4326 projection.
  warnings.warn(
/home/runner/work/rle-tyler-20/rle-tyler-20/.pixi/envs/default/lib/python3.11/site-packages/lonboard/_geoarrow/ops/reproject.py:116: UserWarning: Input being reprojected to EPSG:4326 CRS.
Lonboard is only able to render data in EPSG:4326 projection.
  warnings.warn(
if has_data:
    hull_ea = gdf_hull.to_crs("ESRI:54034")
    eoo = hull_ea.geometry.iloc[0].area / 1e6
    print(f'EOO is {eoo:.1f} km2')
EOO is 2011.7 km2

Then calculate the area of the convex hull polygon.

Direct calculation of EOO

EOO can also be calculated directly using …

if has_data:
    ecosystem.eoo

Verify that the area returned by calling make_eoo(ecosystem).compute().area_km2 is the same as the area of the convex hull polygon.

if has_data:
    assert ecosystem.eoo == eoo

Area of Occupancy (AOO) (subcriterion B2)

The protocol for this adjustment includes the following steps:

  1. Intersect AOO grid with the ecosystem’s distribution map.
  2. Calculate extent of the ecosystem type in each grid cell (area) and sum these areas to obtain the total ecosystem area (total area).
  3. Arrange grid cells in ascending order based on their area (smaller first). Calculate accumulated sum of area per cell (cumulative area).
  4. Calculate cumulative proportion by dividing cumulative area by total area (cumulative proportion takes values between 0 and 1)
  5. Calculate AOO by counting the number of cells with a cumulative proportion greater than 0.01 (i.e. exclude cells that in combination account for up to 1% of the total mapped extent of the ecosystem type).

AOO Calculation Details

Intersect AOO grid and ecosystem map

  1. Intersect AOO grid with the ecosystem’s distribution map
from pathlib import Path
from rle_python_gee.aoo import make_aoo_grid_cached

if has_data:
    cache_path = Path(project_root) / '.cache' / 'aoo_grid.parquet'
    aoo_grid = make_aoo_grid_cached(ecosystems, cache_path=cache_path)
    aoo_grid_filtered = aoo_grid.filter_by_ecosystem(ecosystem_code)

Visualize variations in the AOO grid.

from matplotlib.colors import LinearSegmentedColormap
from lonboard.colormap import apply_continuous_cmap
from rle_python_gee.aoo import slugify_ecosystem_name

ecosystem_column = slugify_ecosystem_name(ecosystem_code)
if has_data:
    cmap = LinearSegmentedColormap.from_list("white_red", ["white", "red"])
    values = aoo_grid_filtered.grid_cells[ecosystem_column].values
    normalized = (values - values.min()) / (values.max() - values.min())
    colors = apply_continuous_cmap(normalized, cmap)
    display(smart_map([(aoo_grid_filtered, {"get_fill_color": colors}), ecosystem]))
/home/runner/work/rle-tyler-20/rle-tyler-20/.pixi/envs/default/lib/python3.11/site-packages/lonboard/_geoarrow/ops/reproject.py:116: UserWarning: Input being reprojected to EPSG:4326 CRS.
Lonboard is only able to render data in EPSG:4326 projection.
  warnings.warn(

Calculate grid cell area and total area

  1. Calculate extent of the ecosystem type in each grid cell (area) and sum these areas to obtain the total ecosystem area (total area).
if has_data:
    keep = ['geometry', 'grid_col', 'grid_row', ecosystem_column]
    gdf = aoo_grid_filtered.grid_cells[keep]
    display(gdf)
geometry grid_col grid_row T1_2_1
0 POLYGON ((-72.76354 7.98358, -72.76354 8.07488... -811 88 0.001224
1 POLYGON ((-72.76354 8.07488, -72.76354 8.16621... -811 89 0.057771
2 POLYGON ((-72.67371 7.80102, -72.67371 7.89229... -810 86 0.047898
3 POLYGON ((-72.67371 7.89229, -72.67371 7.98358... -810 87 0.032588
4 POLYGON ((-72.67371 7.98358, -72.67371 8.07488... -810 88 0.008510
5 POLYGON ((-72.67371 8.07488, -72.67371 8.16621... -810 89 0.005267
6 POLYGON ((-72.58387 7.61854, -72.58387 7.70977... -809 84 0.024333
7 POLYGON ((-72.58387 7.80102, -72.58387 7.89229... -809 86 0.124427
8 POLYGON ((-72.58387 7.89229, -72.58387 7.98358... -809 87 0.348859
9 POLYGON ((-72.58387 7.98358, -72.58387 8.07488... -809 88 0.314078
10 POLYGON ((-72.58387 8.07488, -72.58387 8.16621... -809 89 0.057531
11 POLYGON ((-72.58387 8.16621, -72.58387 8.25756... -809 90 0.021904
12 POLYGON ((-72.49404 7.61854, -72.49404 7.70977... -808 84 0.009261
13 POLYGON ((-72.49404 7.70977, -72.49404 7.80102... -808 85 0.170308
14 POLYGON ((-72.49404 7.80102, -72.49404 7.89229... -808 86 0.259769
15 POLYGON ((-72.49404 7.89229, -72.49404 7.98358... -808 87 0.043424
16 POLYGON ((-72.49404 7.98358, -72.49404 8.07488... -808 88 0.205809
17 POLYGON ((-72.49404 8.07488, -72.49404 8.16621... -808 89 0.219096
18 POLYGON ((-72.49404 8.16621, -72.49404 8.25756... -808 90 0.046407
19 POLYGON ((-72.40421 7.61854, -72.40421 7.70977... -807 84 0.006006
20 POLYGON ((-72.40421 7.70977, -72.40421 7.80102... -807 85 0.060013
21 POLYGON ((-72.40421 7.80102, -72.40421 7.89229... -807 86 0.349297
22 POLYGON ((-72.40421 7.89229, -72.40421 7.98358... -807 87 0.133761
23 POLYGON ((-72.40421 7.98358, -72.40421 8.07488... -807 88 0.118924
24 POLYGON ((-72.31438 7.98358, -72.31438 8.07488... -806 88 0.029849

The column T1_2_1 contains the (fractional) area of the ecosystem in each grid cell.

Sum up the areas of each grid cell to get the total area.

if has_data:
    total_area = gdf[ecosystem_column].sum()
    display(total_area)
np.float64(2.6963111050247246)

Calculate cumulative area

  1. Arrange grid cells in ascending order based on their area (smaller first). Calculate accumulated sum of area per cell (cumulative area).
if has_data:
    gdf = gdf.sort_values(by=ecosystem_column)
    gdf["cumulative_area"] = gdf[ecosystem_column].cumsum()
    display(gdf)
geometry grid_col grid_row T1_2_1 cumulative_area
0 POLYGON ((-72.76354 7.98358, -72.76354 8.07488... -811 88 0.001224 0.001224
5 POLYGON ((-72.67371 8.07488, -72.67371 8.16621... -810 89 0.005267 0.006491
19 POLYGON ((-72.40421 7.61854, -72.40421 7.70977... -807 84 0.006006 0.012497
4 POLYGON ((-72.67371 7.98358, -72.67371 8.07488... -810 88 0.008510 0.021006
12 POLYGON ((-72.49404 7.61854, -72.49404 7.70977... -808 84 0.009261 0.030267
11 POLYGON ((-72.58387 8.16621, -72.58387 8.25756... -809 90 0.021904 0.052171
6 POLYGON ((-72.58387 7.61854, -72.58387 7.70977... -809 84 0.024333 0.076504
24 POLYGON ((-72.31438 7.98358, -72.31438 8.07488... -806 88 0.029849 0.106353
3 POLYGON ((-72.67371 7.89229, -72.67371 7.98358... -810 87 0.032588 0.138940
15 POLYGON ((-72.49404 7.89229, -72.49404 7.98358... -808 87 0.043424 0.182365
18 POLYGON ((-72.49404 8.16621, -72.49404 8.25756... -808 90 0.046407 0.228772
2 POLYGON ((-72.67371 7.80102, -72.67371 7.89229... -810 86 0.047898 0.276669
10 POLYGON ((-72.58387 8.07488, -72.58387 8.16621... -809 89 0.057531 0.334200
1 POLYGON ((-72.76354 8.07488, -72.76354 8.16621... -811 89 0.057771 0.391971
20 POLYGON ((-72.40421 7.70977, -72.40421 7.80102... -807 85 0.060013 0.451984
23 POLYGON ((-72.40421 7.98358, -72.40421 8.07488... -807 88 0.118924 0.570908
7 POLYGON ((-72.58387 7.80102, -72.58387 7.89229... -809 86 0.124427 0.695335
22 POLYGON ((-72.40421 7.89229, -72.40421 7.98358... -807 87 0.133761 0.829096
13 POLYGON ((-72.49404 7.70977, -72.49404 7.80102... -808 85 0.170308 0.999404
16 POLYGON ((-72.49404 7.98358, -72.49404 8.07488... -808 88 0.205809 1.205212
17 POLYGON ((-72.49404 8.07488, -72.49404 8.16621... -808 89 0.219096 1.424309
14 POLYGON ((-72.49404 7.80102, -72.49404 7.89229... -808 86 0.259769 1.684077
9 POLYGON ((-72.58387 7.98358, -72.58387 8.07488... -809 88 0.314078 1.998156
8 POLYGON ((-72.58387 7.89229, -72.58387 7.98358... -809 87 0.348859 2.347014
21 POLYGON ((-72.40421 7.80102, -72.40421 7.89229... -807 86 0.349297 2.696311

Calculate cumulative proportion

  1. Calculate cumulative proportion by dividing cumulative area by total area (cumulative proportion takes values between 0 and 1)
if has_data:
    gdf["cumulative_proportion"] = gdf["cumulative_area"] / total_area
    display(gdf)
geometry grid_col grid_row T1_2_1 cumulative_area cumulative_proportion
0 POLYGON ((-72.76354 7.98358, -72.76354 8.07488... -811 88 0.001224 0.001224 0.000454
5 POLYGON ((-72.67371 8.07488, -72.67371 8.16621... -810 89 0.005267 0.006491 0.002407
19 POLYGON ((-72.40421 7.61854, -72.40421 7.70977... -807 84 0.006006 0.012497 0.004635
4 POLYGON ((-72.67371 7.98358, -72.67371 8.07488... -810 88 0.008510 0.021006 0.007791
12 POLYGON ((-72.49404 7.61854, -72.49404 7.70977... -808 84 0.009261 0.030267 0.011225
11 POLYGON ((-72.58387 8.16621, -72.58387 8.25756... -809 90 0.021904 0.052171 0.019349
6 POLYGON ((-72.58387 7.61854, -72.58387 7.70977... -809 84 0.024333 0.076504 0.028374
24 POLYGON ((-72.31438 7.98358, -72.31438 8.07488... -806 88 0.029849 0.106353 0.039444
3 POLYGON ((-72.67371 7.89229, -72.67371 7.98358... -810 87 0.032588 0.138940 0.051530
15 POLYGON ((-72.49404 7.89229, -72.49404 7.98358... -808 87 0.043424 0.182365 0.067635
18 POLYGON ((-72.49404 8.16621, -72.49404 8.25756... -808 90 0.046407 0.228772 0.084846
2 POLYGON ((-72.67371 7.80102, -72.67371 7.89229... -810 86 0.047898 0.276669 0.102610
10 POLYGON ((-72.58387 8.07488, -72.58387 8.16621... -809 89 0.057531 0.334200 0.123947
1 POLYGON ((-72.76354 8.07488, -72.76354 8.16621... -811 89 0.057771 0.391971 0.145373
20 POLYGON ((-72.40421 7.70977, -72.40421 7.80102... -807 85 0.060013 0.451984 0.167631
23 POLYGON ((-72.40421 7.98358, -72.40421 8.07488... -807 88 0.118924 0.570908 0.211737
7 POLYGON ((-72.58387 7.80102, -72.58387 7.89229... -809 86 0.124427 0.695335 0.257884
22 POLYGON ((-72.40421 7.89229, -72.40421 7.98358... -807 87 0.133761 0.829096 0.307493
13 POLYGON ((-72.49404 7.70977, -72.49404 7.80102... -808 85 0.170308 0.999404 0.370656
16 POLYGON ((-72.49404 7.98358, -72.49404 8.07488... -808 88 0.205809 1.205212 0.446986
17 POLYGON ((-72.49404 8.07488, -72.49404 8.16621... -808 89 0.219096 1.424309 0.528243
14 POLYGON ((-72.49404 7.80102, -72.49404 7.89229... -808 86 0.259769 1.684077 0.624586
9 POLYGON ((-72.58387 7.98358, -72.58387 8.07488... -809 88 0.314078 1.998156 0.741070
8 POLYGON ((-72.58387 7.89229, -72.58387 7.98358... -809 87 0.348859 2.347014 0.870454
21 POLYGON ((-72.40421 7.80102, -72.40421 7.89229... -807 86 0.349297 2.696311 1.000000

Count AOO cells

  1. Calculate AOO by counting the number of cells with a cumulative proportion greater than 0.01 (i.e. exclude cells that in combination account for up to 1% of the total mapped extent of the ecosystem type).
if has_data:
    aoo = len(gdf[gdf["cumulative_proportion"] > 0.01])
    print(f'AOO is {aoo} cells')
AOO is 21 cells

AOO Calculation (direct call)

if has_data:
    aoo_count = ecosystem.aoo
    print(f'AOO: {aoo_count} grid cells')
AOO: 21 grid cells
if has_data:
    display(smart_map([aoo_grid_filtered, ecosystem]))
/home/runner/work/rle-tyler-20/rle-tyler-20/.pixi/envs/default/lib/python3.11/site-packages/lonboard/_geoarrow/ops/reproject.py:116: UserWarning: Input being reprojected to EPSG:4326 CRS.
Lonboard is only able to render data in EPSG:4326 projection.
  warnings.warn(

Criterion B Summary

Ecosystem Code Ecosystem Name EOO AOO
T1.2.1 Catatumbo Ocaña dry forests 2012 km² 21 cells