### This script calculates the cumulative above and belowground carbon gain in mangrove forest pixels from 2001-2015.
### It multiplies the annual biomass gain rate by the number of years of gain by the biomass-to-carbon conversion.
import multiprocessing
import cumulative_gain_mangrove
import sys
sys.path.append('../')
import constants_and_names as cn
import universal_util as uu
mangrove_biomass_tile_list = uu.tile_list(cn.annual_gain_AGB_mangrove_dir)
# biomass_tile_list = ['20S_110E', '30S_110E'] # test tiles
# mangrove_biomass_tile_list = ['10N_080W'] # test tiles
print mangrove_biomass_tile_list
print "There are {} tiles to process".format(
str(len(mangrove_biomass_tile_list)))
# For downloading all tiles in the input folders
download_list = [
cn.annual_gain_AGB_mangrove_dir, cn.annual_gain_BGB_mangrove_dir,
cn.gain_year_count_mangrove_dir
]
for input in download_list:
uu.s3_folder_download(input, '.')
# # For copying individual tiles to spot machine for testing
# for tile in mangrove_biomass_tile_list:
#
# uu.s3_file_download('{0}{1}_{2}.tif'.format(cn.annual_gain_AGB_mangrove_dir, tile, cn.pattern_annual_gain_AGB_mangrove), '.') # annual AGB gain rate tiles
# uu.s3_file_download('{0}{1}_{2}.tif'.format(cn.annual_gain_BGB_mangrove_dir, tile, cn.pattern_annual_gain_BGB_mangrove), '.') # annual AGB gain rate tiles
import constants_and_names as cn
import universal_util as uu
# Downloads zipped raw mangrove files
uu.s3_file_download(
os.path.join(cn.mangrove_biomass_raw_dir, cn.mangrove_biomass_raw_file),
'.')
# Unzips mangrove images into a flat structure (all tifs into main folder using -j argument)
# NOTE: Unzipping some tifs (e.g., Australia, Indonesia) takes a very long time, so don't worry if the script appears to stop on that.
cmd = ['unzip', '-j', cn.mangrove_biomass_raw_file]
subprocess.check_call(cmd)
# Creates vrt of all raw mangrove tifs
utilities.build_vrt(utilities.mangrove_vrt)
# Iterates through all possible tiles (not just WHRC biomass tiles) to create mangrove biomass tiles that don't have analogous WHRC tiles
total_tile_list = uu.tile_list(cn.pixel_area_dir)
# biomass_tile_list = ['00N_000E', '20S_120W', '00N_120E'] # test tile
print total_tile_list
# For multiprocessor use
# This script worked with count/4 on an r3.16xlarge machine.
count = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processes=count / 4)
pool.map(mangrove_processing.create_mangrove_tiles, total_tile_list)
# # For single processor use, for testing purposes
# for tile in total_tile_list:
#
# mangrove_processing.create_mangrove_tiles(tile)
### This script calculates the cumulative above and belowground carbon gain in non-mangrove, non-planted natural forest pixels from 2001-2015.
### It multiplies the annual biomass gain rate by the number of years of gain by the biomass-to-carbon conversion.
import multiprocessing
import cumulative_gain_natrl_forest
import sys
sys.path.append('../')
import constants_and_names as cn
import universal_util as uu
biomass_tile_list = uu.tile_list(cn.WHRC_biomass_2000_non_mang_non_planted_dir)
# biomass_tile_list = ['20S_110E', '30S_110E'] # test tiles
# biomass_tile_list = ['20S_110E'] # test tiles
print biomass_tile_list
print "There are {} tiles to process".format(str(len(biomass_tile_list)))
# For downloading all tiles in the input folders
download_list = [
cn.annual_gain_AGB_natrl_forest_dir, cn.annual_gain_BGB_natrl_forest_dir,
cn.gain_year_count_natrl_forest_dir
]
for input in download_list:
uu.s3_folder_download(input, '.')
# # For copying individual tiles to spot machine for testing
# for tile in biomass_tile_list:
#
# uu.s3_file_download('{0}{1}_{2}.tif'.format(cn.annual_gain_AGB_natrl_forest_dir, tile, cn.pattern_annual_gain_AGB_natrl_forest), '.') # annual AGB gain rate tiles
# uu.s3_file_download('{0}{1}_{2}.tif'.format(cn.annual_gain_BGB_natrl_forest_dir, tile, cn.pattern_annual_gain_BGB_natrl_forest), '.') # annual AGB gain rate tiles
### Calculates the net emissions over the study period, with units of CO2/ha on a pixel-by-pixel basis
import multiprocessing
import utilities
import net_emissions
import sys
sys.path.append('../')
import constants_and_names as cn
import universal_util as uu
biomass_tile_list = uu.tile_list(cn.natrl_forest_biomass_2000_dir)
# biomass_tile_list = ['10N_080W', '40N_120E'] # test tiles
# biomass_tile_list = ['00N_000E'] # test tiles
print biomass_tile_list
print "There are {} tiles to process".format(str(len(biomass_tile_list)))
# For downloading all tiles in the input folders
download_list = [cn.cumul_gain_combo_dir, cn.gross_emissions_dir]
for input in download_list:
utilities.s3_folder_download('{}'.format(input), '.')
# # For copying individual tiles to spot machine for testing
# for tile in biomass_tile_list:
#
# utilities.s3_file_download('{0}{1}_{2}.tif'.format(cn.cumul_gain_combo_dir, cn.pattern_cumul_gain_combo, tile), '.') # cumulative aboveand belowground carbon gain for all forest types
# utilities.s3_file_download('{0}{1}_{2}.tif'.format(cn.gross_emissions_dir, tile, cn.pattern_gross_emissions), '.') # emissions from all drivers
count = multiprocessing.cpu_count()
pool = multiprocessing.Pool(count / 4)
pool.map(net_emissions.net_calc, biomass_tile_list)
from multiprocessing.pool import Pool
from functools import partial
import annual_gain_rate_mangrove
import pandas as pd
import subprocess
import os
import sys
sys.path.append('../')
import constants_and_names as cn
import universal_util as uu
pd.options.mode.chained_assignment = None
# Lists the tiles that have both mangrove biomass and FAO ecozone information because both of these are necessary for
# calculating mangrove gain
mangrove_biomass_tile_list = uu.tile_list(cn.mangrove_biomass_2000_dir)
ecozone_tile_list = uu.tile_list(cn.cont_eco_dir)
mangrove_ecozone_list = list(
set(mangrove_biomass_tile_list).intersection(ecozone_tile_list))
# mangrove_ecozone_list = ['10N_080W', '00N_110E'] # test tiles
# mangrove_ecozone_list = ['10N_080W'] # test tiles
print mangrove_ecozone_list
print "There are {} tiles to process".format(str(len(mangrove_ecozone_list)))
# For downloading all tiles in the input folders
download_list = [cn.cont_eco_dir, cn.mangrove_biomass_2000_dir]
for input in download_list:
uu.s3_folder_download(input, '.')
# # For copying individual tiles to spot machine for testing
### they are the best we have for parsing planted forests into the component values.
### We want to separate the above+below rate into above and below and convert to biomass so that we can make global
### maps of annual above and below biomass gain rates separately; the natural forests and mangroves already use
### separate above and below annual biomass gain rate files, so this brings planted forests into line with them.
import multiprocessing
import annual_gain_rate_planted_forest
import pandas as pd
import sys
sys.path.append('../')
import constants_and_names as cn
import universal_util as uu
pd.options.mode.chained_assignment = None
tile_list = uu.tile_list(cn.annual_gain_AGC_BGC_planted_forest_unmasked_dir)
# tile_list = ['80N_020E', '00N_000E', '00N_020E', '00N_110E'] # test tiles: no mangrove or planted forest, mangrove only, planted forest only, mangrove and planted forest
# tile_list = ['00N_020E', '00N_110E'] # test tiles: mangrove and planted forest
print tile_list
print "There are {} tiles to process".format(str(len(tile_list)))
# For downloading all tiles in the input folders
download_list = [
cn.annual_gain_AGC_BGC_planted_forest_unmasked_dir,
cn.mangrove_biomass_2000_dir
]
for input in download_list:
uu.s3_folder_download(input, '.')
# # For copying individual tiles to spot machine for testing
### Masks out mangrove and planted forest pixels from WHRC biomass 2000 raster so that
### only non-mangrove, non-planted forest pixels are left of the WHRC biomass 2000 raster
import multiprocessing
import non_mangrove_non_planted_WHRC_biomass_2000
import pandas as pd
import sys
sys.path.append('../')
import constants_and_names as cn
import universal_util as uu
biomass_tile_list = uu.tile_list(cn.WHRC_biomass_2000_unmasked_dir)
# biomass_tile_list = ['80N_020E', '00N_000E', '00N_020E', '00N_110E'] # test tiles: no mangrove or planted forest, mangrove only, planted forest only, mangrove and planted forest
# biomass_tile_list = ['80N_020E', '00N_020E', '00N_000E', '00N_110E'] # test tiles: no mangrove or planted forest, planted forest only, mangrove only, mangrove and planted forest
# biomass_tile_list = ['00N_000E']
print biomass_tile_list
print "There are {} tiles to process".format(str(len(biomass_tile_list)))
# For downloading all tiles in the input folders.
# Mangrove biomass and full-extent planted forests are used to mask out mangrove and planted forests from the natural forests.
download_list = [
cn.mangrove_biomass_2000_dir,
cn.annual_gain_AGC_BGC_planted_forest_unmasked_dir,
cn.WHRC_biomass_2000_unmasked_dir
]
for input in download_list:
uu.s3_folder_download(input, '.')
# # For copying individual tiles to spot machine for testing
# for tile in biomass_tile_list:
### The gain years for each of these conditions are calculated according to rules that are found in the function called by the multiprocessor commands.
### More gdalcalc commands can be run at the same time than gdalmerge so that's why the number of processors being used is higher
### for the first four processing steps (which use gdalcalc).
### At this point, those rules are the same as for mangrove forests.
### Then it combines those four rasters into a single gain year raster for each tile using gdalmerge.
### If different input rasters for loss (e.g., 2001-2017) and gain (e.g., 2000-2018) are used, the year count constants in constants_and_names.py must be changed.
import multiprocessing
import gain_year_count_planted_forest
import sys
sys.path.append('../')
import constants_and_names as cn
import universal_util as uu
# The list of tiles to iterate through
tile_list = uu.tile_list(cn.annual_gain_AGB_planted_forest_non_mangrove_dir)
# biomass_tile_list = ['10N_080W'] # test tile
print tile_list
print "There are {} tiles to process".format(str(len(tile_list)))
# For downloading all tiles in the folders
download_list = [
cn.loss_dir, cn.gain_dir, cn.ifl_dir,
cn.annual_gain_AGB_planted_forest_non_mangrove_dir
]
for input in download_list:
uu.s3_folder_download(input, '.')
# # For copying individual tiles to s3 for testing
# for tile in tile_list:
def main():
parser = argparse.ArgumentParser(
description='Create planted forest carbon gain rate tiles')
parser.add_argument(
'--gadm-tile-index',
'-gi',
required=True,
help=
'Shapefile of 1x1 degree tiles of countries that contain planted forests (i.e. countries with planted forests rasterized to 1x1 deg). If no shapefile, write None.'
)
parser.add_argument(
'--planted-tile-index',
'-pi',
required=True,
help=
'Shapefile of 1x1 degree tiles of that contain planted forests (i.e. planted forest extent rasterized to 1x1 deg). If no shapefile, write None.'
)
args = parser.parse_args()
# Creates the directory and shapefile names for the two possible arguments (index shapefiles)
gadm_index = os.path.split(args.gadm_tile_index)
gadm_index_path = gadm_index[0]
gadm_index_shp = gadm_index[1]
gadm_index_shp = gadm_index_shp[:-4]
planted_index = os.path.split(args.planted_tile_index)
planted_index_path = planted_index[0]
planted_index_shp = planted_index[1]
planted_index_shp = planted_index_shp[:-4]
# Checks the validity of the two arguments. If either one is invalid, the script ends.
if (gadm_index_path not in cn.gadm_plant_1x1_index_dir
or planted_index_path not in cn.gadm_plant_1x1_index_dir):
raise Exception(
'Invalid inputs. Please provide None or s3 shapefile locations for both arguments.'
)
# List of all possible 10x10 Hansen tiles except for those at very extreme latitudes (not just WHRC biomass tiles)
total_tile_list = uu.tile_list(cn.pixel_area_dir)
print "Number of possible 10x10 tiles to evaluate:", len(total_tile_list)
# Removes the latitude bands that don't have any planted forests in them according to Liz Goldman.
# i.e., Liz Goldman said by Slack on 1/2/19 that the nothernmost planted forest is 69.5146 and the southernmost is -46.938968.
# This creates a more focused list of 10x10 tiles to iterate through (removes ones that definitely don't have planted forest).
# NOTE: If the planted forest gdb is updated, the list of latitudes to exclude below may need to be changed to not exclude certain latitude bands.
planted_lat_tile_list = [
tile for tile in total_tile_list if '90N' not in tile
]
planted_lat_tile_list = [
tile for tile in planted_lat_tile_list if '80N' not in tile
]
planted_lat_tile_list = [
tile for tile in planted_lat_tile_list if '50S' not in tile
]
planted_lat_tile_list = [
tile for tile in planted_lat_tile_list if '60S' not in tile
]
planted_lat_tile_list = [
tile for tile in planted_lat_tile_list if '70S' not in tile
]
planted_lat_tile_list = [
tile for tile in planted_lat_tile_list if '80S' not in tile
]
# planted_lat_tile_list = ['10N_080W']
print planted_lat_tile_list
print "Number of 10x10 tiles to evaluate after extreme latitudes have been removed:", len(
planted_lat_tile_list)
# If a planted forest extent 1x1 tile index shapefile isn't supplied
if 'None' in args.planted_tile_index:
### Entry point 1:
# If no shapefile of 1x1 tiles for countries with planted forests is supplied, 1x1 tiles of country extents will be created.
# This runs the process from the very beginning and will take a few days.
if 'None' in args.gadm_tile_index:
print "No GADM 1x1 tile index shapefile provided. Creating 1x1 planted forest country tiles from scratch..."
# Downloads and unzips the GADM shapefile, which will be used to create 1x1 tiles of land areas
uu.s3_file_download(cn.gadm_path, '.')
cmd = ['unzip', cn.gadm_zip]
subprocess.check_call(cmd)
# Creates a new GADM shapefile with just the countries that have planted forests in them.
# This limits creation of 1x1 rasters of land area on the countries that have planted forests rather than on all countries.
# NOTE: If the planted forest gdb is updated and has new countries added to it, the planted forest country list
# in constants_and_names.py must be updated, too.
print "Creating shapefile of countries with planted forests..."
os.system(
'''ogr2ogr -sql "SELECT * FROM gadm_3_6_adm2_final WHERE iso IN ({0})" {1} gadm_3_6_adm2_final.shp'''
.format(str(cn.plantation_countries)[1:-1], cn.gadm_iso))
# Creates 1x1 degree tiles of countries that have planted forests in them.
# I assume this can handle using 50 processors because it's not trying to upload files to s3 and the tiles are small.
# This takes several days to run because it iterates through at least 250 10x10 tiles.
# For multiprocessor use.
num_of_processes = 50
pool = Pool(num_of_processes)
pool.map(plantation_preparation.rasterize_gadm_1x1,
planted_lat_tile_list)
pool.close()
pool.join()
# # Creates 1x1 degree tiles of countries that have planted forests in them.
# # For single processor use.
# for tile in planted_lat_tile_list:
#
# plantation_preparation.rasterize_gadm_1x1(tile)
# Creates a shapefile of the boundaries of the 1x1 GADM tiles in countries with planted forests
os.system('''gdaltindex {0}_{1}.shp GADM_*.tif'''.format(
cn.pattern_gadm_1x1_index, uu.date))
cmd = [
'aws', 's3', 'cp', '.', cn.gadm_plant_1x1_index_dir,
'--exclude', '*', '--include',
'{}*'.format(cn.pattern_gadm_1x1_index), '--recursive'
]
subprocess.check_call(cmd)
# # Saves the 1x1 country extent tiles to s3
# # Only use if the entire process can't run in one go on the spot machine
# cmd = ['aws', 's3', 'cp', '.', 's3://gfw2-data/climate/carbon_model/temp_spotmachine_output/', '--exclude', '*', '--include', 'GADM_*.tif', '--recursive']
# subprocess.check_call(cmd)
# Delete the aux.xml files
os.system('''rm GADM*.tif.*''')
# List of all 1x1 degree countey extent tiles created
gadm_list_1x1 = uu.tile_list_spot_machine(".", "GADM_")
print "List of 1x1 degree tiles in countries that have planted forests, with defining coordinate in the northwest corner:", gadm_list_1x1
print len(gadm_list_1x1)
### Entry point 2:
# If a shapefile of the boundaries of 1x1 degree tiles of countries with planted forests is supplied,
# a list of the 1x1 tiles is created from the shapefile.
# This avoids creating the 1x1 country extent tiles all over again because the relevant tile extent are supplied
# in the shapefile.
elif cn.gadm_plant_1x1_index_dir in args.gadm_tile_index:
print "Country extent 1x1 tile index shapefile supplied. Using that to create 1x1 planted forest tiles..."
# Copies the shapefile of 1x1 tiles of extent of countries with planted forests
cmd = [
'aws', 's3', 'cp', '{}/'.format(gadm_index_path), '.',
'--recursive', '--exclude', '*', '--include',
'{}*'.format(gadm_index_shp), '--recursive'
]
subprocess.check_call(cmd)
# Gets the attribute table of the country extent 1x1 tile shapefile
gadm = glob.glob('{}*.dbf'.format(cn.pattern_gadm_1x1_index))[0]
# Converts the attribute table to a dataframe
dbf = Dbf5(gadm)
df = dbf.to_dataframe()
# Converts the column of the dataframe with the names of the tiles (which contain their coordinates) to a list
gadm_list_1x1 = df['location'].tolist()
gadm_list_1x1 = [str(y) for y in gadm_list_1x1]
print "List of 1x1 degree tiles in countries that have planted forests, with defining coordinate in the northwest corner:", gadm_list_1x1
print "There are", len(
gadm_list_1x1), "1x1 country extent tiles to iterate through."
# In case some other arguments are provided
else:
raise Exception(
'Invalid GADM tile index shapefile provided. Please provide a valid shapefile.'
)
# Creates 1x1 degree tiles of plantation growth wherever there are plantations.
# Because this is iterating through all 1x1 tiles in countries with planted forests, it first checks
# whether each 1x1 tile intersects planted forests before creating a 1x1 planted forest tile for that
# 1x1 country extent tile.
# For multiprocessor use
num_of_processes = 30
pool = Pool(num_of_processes)
pool.map(plantation_preparation.create_1x1_plantation_from_1x1_gadm,
gadm_list_1x1)
pool.close()
pool.join()
# # Creates 1x1 degree tiles of plantation growth wherever there are plantations
# # For single processor use
# for tile in gadm_list_1x1:
#
# plantation_preparation.create_1x1_plantation(tile)
# Creates a shapefile in which each feature is the extent of a plantation extent tile.
# This index shapefile can be used the next time this process is run if starting with Entry Point 3.
os.system('''gdaltindex {0}_{1}.shp plant_*.tif'''.format(
cn.pattern_plant_1x1_index, uu.date))
cmd = [
'aws', 's3', 'cp', '.', cn.gadm_plant_1x1_index_dir, '--exclude',
'*', '--include', '{}*'.format(cn.pattern_plant_1x1_index),
'--recursive'
]
subprocess.check_call(cmd)
### Entry point 3
# If a shapefile of the extents of 1x1 planted forest tiles is provided
if cn.pattern_plant_1x1_index in args.planted_tile_index:
print "Planted forest 1x1 tile index shapefile supplied. Using that to create 1x1 planted forest growth tiles..."
# Copies the shapefile of 1x1 tiles of extent of planted forests
cmd = [
'aws', 's3', 'cp', '{}/'.format(planted_index_path), '.',
'--recursive', '--exclude', '*', '--include',
'{}*'.format(planted_index_shp), '--recursive'
]
subprocess.check_call(cmd)
# Gets the attribute table of the planted forest extent 1x1 tile shapefile
gadm = glob.glob('{}*.dbf'.format(cn.pattern_plant_1x1_index))[0]
# Converts the attribute table to a dataframe
dbf = Dbf5(gadm)
df = dbf.to_dataframe()
# Converts the column of the dataframe with the names of the tiles (which contain their coordinates) to a list
planted_list_1x1 = df['location'].tolist()
planted_list_1x1 = [str(y) for y in planted_list_1x1]
print "List of 1x1 degree tiles in countries that have planted forests, with defining coordinate in the northwest corner:", planted_list_1x1
print "There are", len(
planted_list_1x1
), "1x1 planted forest extent tiles to iterate through."
# Creates 1x1 degree tiles of plantation growth wherever there are plantations.
# Because this is iterating through only 1x1 tiles that are known to have planted forests (from a previous run
# of this script), it does not need to check whether there are planted forests in this tile. It goes directly
# to intersecting the planted forest table with the 1x1 tile.
# For multiprocessor use
# This works with 30 processors on an r4.16xlarge.
num_of_processes = 50
pool = Pool(num_of_processes)
pool.map(plantation_preparation.create_1x1_plantation_from_1x1_planted,
planted_list_1x1)
pool.close()
pool.join()
### All entry points meet here: creation of 10x10 degree planted forest tiles from 1x1 degree planted forest tiles
# Name of the vrt of 1x1 planted forest tiles
plant_1x1_vrt = 'plant_1x1.vrt'
# Creates a mosaic of all the 1x1 plantation growth rate tiles
print "Creating vrt of 1x1 plantation growth rate tiles"
os.system('gdalbuildvrt {} plant_*.tif'.format(plant_1x1_vrt))
# Creates 10x10 degree tiles of plantation growth by iterating over the pixel area tiles that are in latitudes with planted forests
# For multiprocessor use
num_of_processes = 20
pool = Pool(num_of_processes)
pool.map(
partial(plantation_preparation.create_10x10_plantation,
plant_1x1_vrt=plant_1x1_vrt), planted_lat_tile_list)
pool.close()
pool.join()