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degraded_analysis.py
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degraded_analysis.py
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#!/usr/bin/python
# vim: set fileencoding=utf-8 :
import argparse
import math
import os.path
import pdb
import signal
import subprocess
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)
def start_pdb(sig, frame):
"""Start PDB on a signal."""
pdb.Pdb().set_trace(frame)
def produce_CSV():
"""Produce a CSV file of degraded land for {forest, cropland, grassland}."""
columns = [
'forest:good:degraded', 'forest:marginal:degraded',
'forest:poor:degraded', 'forest:verypoor:degraded',
'forest:good:nondegraded', 'forest:marginal:nondegraded',
'forest:poor:nondegraded', 'forest:verypoor:nondegraded',
'cropland:good:degraded', 'cropland:marginal:degraded',
'cropland:poor:degraded', 'cropland:verypoor:degraded',
'cropland:good:nondegraded', 'cropland:marginal:nondegraded',
'cropland:poor:nondegraded', 'cropland:verypoor:nondegraded',
'grassland:good:degraded', 'grassland:marginal:degraded',
'grassland:poor:degraded', 'grassland:verypoor:degraded',
'grassland:good:nondegraded', 'grassland:marginal:nondegraded',
'grassland:poor:nondegraded', 'grassland:verypoor:nondegraded',
'bare:good:degraded', 'bare:marginal:degraded',
'bare:poor:degraded', 'bare:verypoor:degraded',
'bare:good:nondegraded', 'bare:marginal:nondegraded',
'bare:poor:nondegraded', 'bare:verypoor:nondegraded',
'urban:good:degraded', 'urban:marginal:degraded',
'urban:poor:degraded', 'urban:verypoor:degraded',
'urban:good:nondegraded', 'urban:marginal:nondegraded',
'urban:poor:nondegraded', 'urban:verypoor:nondegraded',
'water:good:degraded', 'water:marginal:degraded',
'water:poor:degraded', 'water:verypoor:degraded',
'water:good:nondegraded', 'water:marginal:nondegraded',
'water:poor:nondegraded', 'water:verypoor:nondegraded',
'ice:good:degraded', 'ice:marginal:degraded',
'ice:poor:degraded', 'ice:verypoor:degraded',
'ice:good:nondegraded', 'ice:marginal:nondegraded',
'ice:poor:nondegraded', 'ice:verypoor:nondegraded',
]
df = pd.DataFrame(columns=columns, dtype='float')
df.index.name = 'Country'
shapefilename = 'data/ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp'
shapefile = osgeo.ogr.Open(shapefilename)
assert shapefile.GetLayerCount() == 1
features = shapefile.GetLayerByIndex(0)
lc_filename = 'data/copernicus/ESACCI-LC-L4-LCCS-Map-300m-P1Y-2015-v2.0.7.tif'
lc_img = osgeo.gdal.Open(lc_filename, osgeo.gdal.GA_ReadOnly)
lc_band = lc_img.GetRasterBand(1)
lpd_filename = 'data/lpd_int2/lpd_int2.tif'
lpd_img = osgeo.gdal.Open(lpd_filename, osgeo.gdal.GA_ReadOnly)
lpd_band = lpd_img.GetRasterBand(1)
wk_filename = 'data/FAO/workability_FAO_sq7_1km.tif'
wk_img = osgeo.gdal.Open(wk_filename, osgeo.gdal.GA_ReadOnly)
wk_band = wk_img.GetRasterBand(1)
for idx, feature in enumerate(features):
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}_1km_mask._tif"
maskimg = osgeo.gdal.Open(maskfilename, osgeo.gdal.GA_ReadOnly)
mask_band = maskimg.GetRasterBand(1)
x_siz = mask_band.XSize
y_siz = mask_band.YSize
x_blksiz, y_blksiz = mask_band.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=mask_band, x=x, y=y, ncols=ncols, nrows=nrows):
# sparse hole in image, no data to process
continue
mask_blk = mask_band.ReadAsArray(x, y, ncols, nrows)
k = geoutil.km2_block(nrows=nrows, ncols=ncols, y_off=y, img=maskimg)
k[np.logical_not(mask_blk)] = 0.0
km2_blk = (np.repeat(np.repeat(k, 3, axis=1), 3, axis=0)) / 9.0
lc_blk = lc_band.ReadAsArray(3*x, 3*y, 3*ncols, 3*nrows)
lc = {}
lc['forest'] = np.logical_or.reduce((lc_blk == 12, lc_blk == 50,
lc_blk == 60, lc_blk == 61, lc_blk == 62,
lc_blk == 70, lc_blk == 71, lc_blk == 72,
lc_blk == 80, lc_blk == 81, lc_blk == 82,
lc_blk == 90, lc_blk == 160, lc_blk == 170))
lc['cropland'] = np.logical_or.reduce((lc_blk == 10, lc_blk == 30,
lc_blk == 20))
lc['grassland'] = np.logical_or.reduce((lc_blk == 11, lc_blk == 40,
lc_blk == 100, lc_blk == 110, lc_blk == 120, lc_blk == 121, lc_blk == 122,
lc_blk == 130, lc_blk == 150, lc_blk == 151, lc_blk == 152, lc_blk == 153,
lc_blk == 180))
lc['bare'] = np.logical_or.reduce((lc_blk == 140, lc_blk == 200,
lc_blk == 201, lc_blk == 202))
lc['urban'] = (lc_blk == 190)
lc['water'] = (lc_blk == 210)
lc['ice'] = (lc_blk == 220)
k = lpd_band.ReadAsArray(x, y, ncols, nrows)
lpd = {}
lpd_blk = np.repeat(np.repeat(k, 3, axis=1), 3, axis=0)
lpd['degraded'] = (lpd_blk != 0.0)
lpd['nondegraded'] = (lpd_blk == 0.0)
k = wk_band.ReadAsArray(x, y, ncols, nrows)
wk_blk = np.repeat(np.repeat(k, 3, axis=1), 3, axis=0)
work = {}
work['good'] = (wk_blk == 1)
work['marginal'] = (wk_blk == 2)
work['poor'] = (wk_blk == 3)
work['verypoor'] = (wk_blk == 4)
for cover in lc.keys():
for degraded in lpd.keys():
for soil in work.keys():
key = f'{cover}:{soil}:{degraded}'
df.loc[admin, key] += (np.logical_and.reduce((lc[cover], lpd[degraded],
work[soil])) * km2_blk).sum()
csvfilename = 'results/degraded-cover-by-country.csv'
df.sort_index(axis='index').to_csv(csvfilename, float_format='%.2f')
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
csvfilename = f"results/degraded-cover-by-region.csv"
df_region.to_csv(csvfilename, float_format='%.2f')
if __name__ == '__main__':
signal.signal(signal.SIGUSR1, start_pdb)
os.environ['GDAL_CACHEMAX'] = '128'
produce_CSV()