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extract_country_data.py
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extract_country_data.py
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#!/usr/bin/python
# vim: set fileencoding=utf-8 :
"""Extract counts of each Köppen-Geiger/slope/land cover/soil health for each country,
for use in Project Drawdown solution models."""
import argparse
import math
import os.path
import pdb
import signal
import sys
import tempfile
import osgeo.gdal
import osgeo.gdal_array
import osgeo.ogr
import numpy as np
import pandas as pd
import admin_names
import geoutil
pd.set_option("display.max_rows", 500)
pd.set_option("display.max_columns", 40)
pd.options.display.float_format = '{:.2f}'.format
osgeo.gdal.PushErrorHandler("CPLQuietErrorHandler")
np.set_printoptions(threshold=sys.maxsize)
np.seterr(all='raise')
class KGlookup:
"""Lookup table of pixel color to Köppen-Geiger class.
Mappings come from legend.txt file in ZIP archive from
https://www.nature.com/articles/sdata2018214.pdf at http://www.gloh2o.org/koppen/
"""
kg_colors = {
( 0, 0, 255): 'Af', ( 0, 120, 255): 'Am', ( 70, 170, 250): 'Aw',
(255, 0, 0): 'BWh', (255, 150, 150): 'BWk', (245, 165, 0): 'BSh',
(255, 220, 100): 'BSk',
(255, 255, 0): 'Csa', (200, 200, 0): 'Csb', (150, 150, 0): 'Csc',
(150, 255, 150): 'Cwa', (100, 200, 100): 'Cwb', ( 50, 150, 50): 'Cwc',
(200, 255, 80): 'Cfa', (100, 255, 80): 'Cfb', ( 50, 200, 0): 'Cfc',
(255, 0, 255): 'Dsa', (200, 0, 200): 'Dsb', (150, 50, 150): 'Dsc',
(150, 100, 150): 'Dsd', (170, 175, 255): 'Dwa', ( 90, 120, 220): 'Dwb',
( 75, 80, 180): 'Dwc', ( 50, 0, 135): 'Dwd', ( 0, 255, 255): 'Dfa',
( 55, 200, 255): 'Dfb', ( 0, 125, 125): 'Dfc', ( 0, 70, 95): 'Dfd',
(178, 178, 178): 'ET', (102, 102, 102): 'EF',
}
def __init__(self, mapfilename, maskdim='1km'):
self.maskdim = maskdim
self.img = osgeo.gdal.Open(mapfilename, osgeo.gdal.GA_ReadOnly)
self.band = self.img.GetRasterBand(1)
self.ctable = self.band.GetColorTable()
def km2(self, x, y, ncols, nrows, maskblock, km2block, df, admin):
block = self.band.ReadAsArray(x, y, ncols, nrows)
masked = np.ma.masked_array(block, mask=np.logical_not(maskblock))
for label in np.unique(masked):
if label is np.ma.masked:
continue
r, g, b, a = self.ctable.GetColorEntry(int(label))
color = (r, g, b)
if color == (255, 255, 255) or color == (0, 0, 0):
# blank pixel == masked off, just skip it.
continue
typ = self.kg_colors[color]
df.loc[admin, typ] += km2block[masked == label].sum()
def get_columns(self):
return self.kg_colors.values()
class ESA_LC_lookup:
"""Pixel color to Land Cover class in C3S-LC-L4-LCCS-Map-300m-P1Y-2018-v2.1.1.tif
There are legends of LCCS<->color swatch in both
http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-QuickUserGuide-LC-Maps_v2-0-7.pdf
and section 9.1 of http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf
but these were generated from Microsoft Word documents and contain embedded color profiles
for color correction, meaning that what is displayed on the screen (and to any sort of
Digital Color Meter tool) have been shifted and do not match the original colors. Do not
attempt to use the color legend in these files.
There is a table of RGB to LCCS values in
http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Legend.csv
however: the GeoTIFF file as shipped is greyscale with no color table. Each pixel is 8 bits
not because it is looked up in a color table, it is 8 bits because it has been converted to
greyscale. The authors appear to have carefully chosen the RGB values such that when
converted to greyscale, LCCS class 10 will have a grey value of 10, LCCS class 11 will have
a grey value of 11, and so on.
So we don't need a lookup table, greyscale absolute values directly equal the LCCS class."""
def __init__(self, mapfilename, maskdim='333m'):
self.maskdim = maskdim
self.img = osgeo.gdal.Open(mapfilename, osgeo.gdal.GA_ReadOnly)
self.band = self.img.GetRasterBand(1)
def km2(self, x, y, ncols, nrows, maskblock, km2block, df, admin):
block = self.band.ReadAsArray(x, y, ncols, nrows)
masked = np.ma.masked_array(block, mask=np.logical_not(maskblock)).filled(-1)
for label in np.unique(masked):
if label is np.ma.masked or label == 0 or label == 255:
continue
df.loc[admin, label] += km2block[masked == label].sum()
def get_columns(self):
"""Return list of LCCS classes present in this dataset."""
return [10, 11, 12, 20, 30, 40, 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 110, 120,
121, 122, 130, 140, 150, 151, 152, 153, 160, 170, 180, 190, 200, 201, 202, 210, 220]
class GeomorphoLookup:
"""Geomorpho90m pre-processed slope file in data/geomorpho90m/classified_*.tif.
There is a band in the TIF for each slope class defined in GAEZ 3.0.
"""
gaez_slopes = ["0-0.5%", "0.5-2%", "2-5%", "5-10%", "10-15%", "15-30%", "30-45%", ">45%"]
def __init__(self, mapfilename, maskdim='1km'):
self.maskdim = maskdim
self.img = osgeo.gdal.Open(mapfilename, osgeo.gdal.GA_ReadOnly)
def km2(self, x, y, ncols, nrows, maskblock, km2block, df, admin):
for b in range(1, 9):
block = self.img.GetRasterBand(b).ReadAsArray(x, y, ncols, nrows).astype(np.float)
mask = np.logical_or(np.logical_not(maskblock), block == 127)
masked = np.ma.masked_array(block, mask=mask, fill_value=0.0)
typ = self.gaez_slopes[b - 1]
df.loc[admin, typ] += (km2block * (masked / 100.0)).sum()
def get_columns(self):
"""Return list of GAEZ slope classes."""
return self.gaez_slopes
class FaoSlopeLookup:
"""FAO GAEZ 3.0 slope files in data/FAO/GloSlopesCl*_30as.tif.
"""
gaez_slopes = ["0-0.5%", "0.5-2%", "2-5%", "5-8%", "8-15%", "15-30%", "30-45%", ">45%"]
def __init__(self, maskdim='1km'):
self.maskdim = maskdim
self.img = {}
for i in range(1, 9):
mapfilename = f"data/FAO/GloSlopesCl{i}_30as.tif"
self.img[i] = osgeo.gdal.Open(mapfilename, osgeo.gdal.GA_ReadOnly)
def km2(self, x, y, ncols, nrows, maskblock, km2block, df, admin):
for i in range(1, 9):
block = self.img[i].GetRasterBand(1).ReadAsArray(x, y, ncols, nrows).astype(np.float)
mask = np.logical_or(np.logical_not(maskblock), block == 255)
masked = np.ma.masked_array(block, mask=mask).filled(0.0)
typ = self.gaez_slopes[i - 1]
df.loc[admin, typ] += np.nansum(km2block * (masked / 100.0))
def get_columns(self):
"""Return list of GAEZ slope classes."""
return self.gaez_slopes
class WorkabilityLookup:
"""Workability TIF has been pre-processed, pixel values are workability class.
"""
def __init__(self, mapfilename, maskdim='1km'):
self.maskdim = maskdim
self.img = osgeo.gdal.Open(mapfilename, osgeo.gdal.GA_ReadOnly)
self.band = self.img.GetRasterBand(1)
def km2(self, x, y, ncols, nrows, maskblock, km2block, df, admin):
block = self.band.ReadAsArray(x, y, ncols, nrows)
masked = np.ma.masked_array(block, mask=np.logical_not(maskblock))
for label in np.unique(masked):
if label is np.ma.masked or label == 0 or label == 255:
# label 0 (black) == no land cover (like water), just skip it.
continue
df.loc[admin, label] += km2block[masked == label].sum()
def get_columns(self):
return range(1, 8)
class DegradedLandLookup:
"""Binary indication of soil in LDPclass 1, 2, or 3."""
def __init__(self, mapfilename, maskdim='1km'):
self.maskdim = maskdim
self.img = osgeo.gdal.Open(mapfilename, osgeo.gdal.GA_ReadOnly)
self.band = self.img.GetRasterBand(1)
def km2(self, x, y, ncols, nrows, maskblock, km2block, df, admin):
block = self.band.ReadAsArray(x, y, ncols, nrows)
masked = np.ma.masked_array(block, mask=np.logical_not(maskblock))
for label in np.unique(masked):
if label is np.ma.masked:
continue
if label == 0.0:
df.loc[admin, "nondegraded"] += km2block[masked == label].sum()
else:
df.loc[admin, "degraded"] += km2block[masked == label].sum()
def get_columns(self):
return ["degraded", "nondegraded"]
def start_pdb(sig, frame):
"""Start PDB on a signal."""
pdb.Pdb().set_trace(frame)
def process_map(lookupobj, csvfilename):
"""Produce a CSV file of areas per country from a dataset."""
shapefilename = 'data/ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp'
df = pd.DataFrame(columns=lookupobj.get_columns(), dtype=float)
df.index.name = 'Country'
shapefile = osgeo.ogr.Open(shapefilename)
assert shapefile.GetLayerCount() == 1
layer = shapefile.GetLayerByIndex(0)
for idx, feature in enumerate(layer):
admin = admin_names.lookup(feature.GetField("ADMIN"))
if admin is None:
continue
a3 = feature.GetField("SOV_A3")
if admin not in df.index:
df.loc[admin] = [0] * len(df.columns)
print(f"Processing {admin:<41} #{a3}_{idx}")
maskfilename = f"masks/{a3}_{idx}_{lookupobj.maskdim}_mask._tif"
maskimg = osgeo.gdal.Open(maskfilename, osgeo.gdal.GA_ReadOnly)
maskband = maskimg.GetRasterBand(1)
x_siz = maskband.XSize
y_siz = maskband.YSize
x_blksiz, y_blksiz = maskband.GetBlockSize()
for y in range(0, y_siz, y_blksiz):
nrows = geoutil.blklim(coord=y, blksiz=y_blksiz, totsiz=y_siz)
for x in range(0, x_siz, x_blksiz):
ncols = geoutil.blklim(coord=x, blksiz=x_blksiz, totsiz=x_siz)
if geoutil.is_sparse(band=maskband, x=x, y=y, ncols=ncols, nrows=nrows):
# sparse hole in image, no data to process
continue
maskblock = maskband.ReadAsArray(x, y, ncols, nrows)
km2block = geoutil.km2_block(nrows=nrows, ncols=ncols, y_off=y, img=maskimg)
lookupobj.km2(x=x, y=y, ncols=ncols, nrows=nrows, maskblock=maskblock,
km2block=km2block, df=df, admin=admin)
outputfilename = os.path.join('results', csvfilename)
df.sort_index(axis='index').to_csv(outputfilename, float_format='%.2f')
return df
def output_by_region(df, csvfilename):
regions = ['OECD90', 'Eastern Europe', 'Asia (Sans Japan)', 'Middle East and Africa',
'Latin America', 'China', 'India', 'EU', 'USA']
df_region = pd.DataFrame(0, index=regions, columns=df.columns.copy())
df_region.index.name = 'Region'
for country, row in df.iterrows():
region = admin_names.region_mapping[country]
if region is not None:
df_region.loc[region, :] += row
df_region.to_csv(csvfilename, float_format='%.2f')
if __name__ == '__main__':
signal.signal(signal.SIGUSR1, start_pdb)
os.environ['GDAL_CACHEMAX'] = '128'
parser = argparse.ArgumentParser(description='Process GeoTIFF datasets for Project Drawdown')
parser.add_argument('--lc', default=False, required=False,
action='store_true', help='process land cover')
parser.add_argument('--kg', default=False, required=False,
action='store_true', help='process Köppen-Geiger')
parser.add_argument('--sl', default=False, required=False,
action='store_true', help='process slope')
parser.add_argument('--wk', default=False, required=False,
action='store_true', help='process workability')
parser.add_argument('--dg', default=False, required=False,
action='store_true', help='process degraded land')
parser.add_argument('--all', default=False, required=False,
action='store_true', help='process all')
args = parser.parse_args()
processed = False
if args.lc or args.all:
mapfilename = 'data/copernicus/C3S-LC-L4-LCCS-Map-300m-P1Y-2018-v2.1.1.tif'
countrycsv = 'Land-Cover-by-country.csv'
regioncsv = 'Land-Cover-by-region.csv'
lookupobj = ESA_LC_lookup(mapfilename)
df = process_map(lookupobj=lookupobj, csvfilename=countrycsv)
output_by_region(df=df, csvfilename=regioncsv)
print('\n')
processed = True
if args.kg or args.all:
mapfilename = 'data/Beck_KG_V1/Beck_KG_V1_present_0p0083.tif'
countrycsv = 'Köppen-Geiger-present-by-country.csv'
regioncsv = 'Köppen-Geiger-present-by-region.csv'
print(mapfilename)
lookupobj = KGlookup(mapfilename)
df = process_map(lookupobj=lookupobj, csvfilename=countrycsv)
output_by_region(df=df, csvfilename=regioncsv)
print('\n')
mapfilename = 'data/Beck_KG_V1/Beck_KG_V1_future_0p0083.tif'
countrycsv = 'Köppen-Geiger-future-by-country.csv'
regioncsv = 'Köppen-Geiger-future-by-region.csv'
print(mapfilename)
lookupobj = KGlookup(mapfilename)
df = process_map(lookupobj=lookupobj, csvfilename=countrycsv)
output_by_region(df=df, csvfilename=regioncsv)
print('\n')
processed = True
if args.sl or args.all:
mapfilename = 'data/geomorpho90m/classified_slope_merit_dem_1km_s0..0cm_2018_v1.0.tif'
countrycsv = 'Slope-by-country.csv'
regioncsv = 'Slope-by-region.csv'
print(mapfilename)
lookupobj = GeomorphoLookup(mapfilename=mapfilename)
df = process_map(lookupobj=lookupobj, csvfilename=countrycsv)
output_by_region(df=df, csvfilename=regioncsv)
print('\n')
processed = True
countrycsv = 'FAO-Slope-by-country.csv'
regioncsv = 'FAO-Slope-by-region.csv'
print('data/FAO/GloSlopesCl*_30as.tif')
lookupobj = FaoSlopeLookup()
df = process_map(lookupobj=lookupobj, csvfilename=countrycsv)
output_by_region(df=df, csvfilename=regioncsv)
print('\n')
processed = True
if args.wk or args.all:
mapfilename = 'data/FAO/workability_FAO_sq7_1km.tif'
countrycsv = 'Workability-by-country.csv'
regioncsv = 'Workability-by-region.csv'
print(mapfilename)
lookupobj = WorkabilityLookup(mapfilename)
df = process_map(lookupobj=lookupobj, csvfilename=countrycsv)
output_by_region(df=df, csvfilename=regioncsv)
print('\n')
processed = True
if not processed:
print('Select one of:')
print('\t-lc : Land Cover')
print('\t-kg : Köppen-Geiger')
print('\t-sl : Slope')
print('\t-wk : Workability')
print('\t-dg : Degraded Land')
print('\t-all')
sys.exit(1)