def restoration_carbon(rest_type, length_yr, crs, geojsons, EXECUTION_ID,
logger):
logger.debug("Entering restoration_carbon function.")
# biomass
agb_30m = ee.Image(
"users/geflanddegradation/toolbox_datasets/forest_agb_30m_woodhole")
agb_1km = ee.Image(
"users/geflanddegradation/toolbox_datasets/forest_agb_1km_geocarbon")
# c sequestration coefficients by intervention from winrock paper
agfor0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b1").divide(100)
agfor2060 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b2").divide(100)
mshrr0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b3").divide(100)
mshrr2060 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b4").divide(100)
mtrer0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b5").divide(100)
mtrer2060 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b6").divide(100)
natre0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b7").divide(100)
natre2060 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b8").divide(100)
pwobr0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b9").divide(100)
pweuc0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b10").divide(100)
pwoak0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b11").divide(100)
pwoco0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b12").divide(100)
pwpin0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b13").divide(100)
pwtea0020 = ee.Image(
"users/geflanddegradation/toolbox_datasets/winrock_co2_seq_coeff_adm1"
).select("b14").divide(100)
# combine the global 1km and the pantropical 30m datasets into one layer for analysis
agb = agb_30m.float().unmask(0).where(
ee.Image.pixelLonLat().select('latitude').abs().gte(29.999), agb_1km)
# calculate below ground biomass following Mokany et al. 2006 = (0.489)*(AGB)^(0.89)
bgb = agb.expression('0.489 * BIO**(0.89)', {'BIO': agb})
# calculate total biomass (t/ha) then convert to carbon equilavent (*0.5) to get Total Carbon (t ha-1) = (AGB+BGB)*0.5
tbc = agb.expression('(bgb + abg ) * 0.5 ', {'bgb': bgb, 'abg': agb})
# convert Total carbon to total CO2 eq (One ton of carbon equals 44/12 = 11/3 = 3.67 tons of carbon dioxide)
current_co2 = tbc.expression('totalcarbon * 3.67 ', {'totalcarbon': tbc})
if rest_type == "terrestrial":
if length_yr <= 20:
d_natre = natre0020.multiply(length_yr).subtract(current_co2)
d_natre = d_natre.where(d_natre.lte(0), 0)
d_agfor = agfor0020.multiply(length_yr).subtract(current_co2)
d_agfor = d_agfor.where(d_agfor.lte(0), 0)
d_pwtea = pwtea0020.multiply(length_yr).subtract(current_co2)
d_pweuc = pweuc0020.multiply(length_yr).subtract(current_co2)
d_pwoak = pwoak0020.multiply(length_yr).subtract(current_co2)
d_pwobr = pwobr0020.multiply(length_yr).subtract(current_co2)
d_pwpin = pwpin0020.multiply(length_yr).subtract(current_co2)
d_pwoco = pwoco0020.multiply(length_yr).subtract(current_co2)
if length_yr > 20:
d_natre = natre0020.multiply(20).add(
natre2060.multiply(length_yr - 20)).subtract(current_co2)
d_natre = d_natre.where(d_natre.lte(0), 0)
d_agfor = agfor0020.multiply(20).add(
agfor2060.multiply(length_yr - 20)).subtract(current_co2)
d_agfor = d_agfor.where(d_agfor.lte(0), 0)
d_pwtea = pwtea0020.multiply(20).subtract(current_co2)
d_pweuc = pweuc0020.multiply(20).subtract(current_co2)
d_pwoak = pwoak0020.multiply(20).subtract(current_co2)
d_pwobr = pwobr0020.multiply(20).subtract(current_co2)
d_pwpin = pwpin0020.multiply(20).subtract(current_co2)
d_pwoco = pwoco0020.multiply(20).subtract(current_co2)
output = current_co2.addBands(d_natre).addBands(d_agfor) \
.addBands(d_pwtea).addBands(d_pweuc) \
.addBands(d_pwoak).addBands(d_pwobr) \
.addBands(d_pwpin).addBands(d_pwoco) \
.rename(['current','natreg','agrfor','pwteak','pweuca','pwoaks','pwobro','pwpine','pwocon'])
logger.debug("Setting up output for terrestrial restoration.")
out = TEImage(output, [
BandInfo("Biomass (tonnes CO2e per ha)",
add_to_map=True,
metadata={'year': 'current'}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'natural regeneration'
}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'agroforestry'
}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'teak plantation'
}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'eucalyptus plantation'
}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'oak plantation'
}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'other broadleaf plantation'
}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'pine plantation'
}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'conifer plantation'
})
])
elif rest_type == "coastal":
if length_yr <= 20:
d_mshrr = mshrr0020.multiply(length_yr).subtract(current_co2)
d_mshrr = d_mshrr.where(d_mshrr.lte(0), 0)
d_mtrer = mtrer0020.multiply(length_yr).subtract(current_co2)
d_mtrer = d_mtrer.where(d_mtrer.lte(0), 0)
if length_yr > 20:
d_mshrr = mshrr0020.multiply(20).add(
mshrr2060.multiply(length_yr - 20)).subtract(current_co2)
d_mshrr = d_mshrr.where(d_mshrr.lte(0), 0)
d_mtrer = mtrer0020.multiply(20).add(
mtrer2060.multiply(length_yr - 20)).subtract(current_co2)
d_mtrer = d_mtrer.where(d_mtrer.lte(0), 0)
logger.debug("Setting up output for coastal restoration.")
out = TEImage(
current_co2.addBands(d_mshrr).addBands(d_mtrer).rename(
['current', 'mshrr', 'mtrer']),
[
BandInfo("Biomass (tonnes CO2e per ha)",
add_to_map=True,
metadata={'year': 'current'}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'mangrove shrub'
}),
BandInfo("Restoration biomass difference (tonnes CO2e per ha)",
metadata={
'years': length_yr,
'type': 'mangrove tree'
})
])
else:
raise
out.image = out.image.reproject(crs=agb_30m.projection())
return out
def soc(geometry, year_start, year_end, fl, remap_matrix, dl_annual_lc,
EXECUTION_ID, logger):
"""
Calculate SOC indicator.
"""
logger.debug("Entering soc function.")
geom = ee.Geometry.Polygon(geometry)
# Location
area = ee.FeatureCollection(geom)
# soc
soc = ee.Image(
"users/geflanddegradation/toolbox_datasets/soc_sgrid_30cm").clip(area)
soc_t0 = soc.updateMask(soc.neq(-32768))
# land cover - note it needs to be reprojected to match soc so that it can
# be output to cloud storage in the same stack
lc = ee.Image("users/geflanddegradation/toolbox_datasets/lcov_esacc_1992_2018") \
.select(ee.List.sequence(year_start - 1992, year_end - 1992, 1)) \
.clip(area) \
.reproject(crs=soc.projection())
lc = lc.where(lc.eq(9999), -32768)
lc = lc.updateMask(lc.neq(-32768))
if fl == 'per pixel':
# Setup a raster of climate regimes to use for coding Fl automatically
climate = ee.Image("users/geflanddegradation/toolbox_datasets/ipcc_climate_zones") \
.clip(area) \
.remap([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
[0, 2, 1, 2, 1, 2, 1, 2, 1, 5, 4, 4, 3])
clim_fl = climate.remap([0, 1, 2, 3, 4, 5],
[0, 0.8, 0.69, 0.58, 0.48, 0.64])
# create empty stacks to store annual land cover maps
stack_lc = ee.Image().select()
# create empty stacks to store annual soc maps
stack_soc = ee.Image().select()
# loop through all the years in the period of analysis to compute changes in SOC
for k in range(year_end - year_start):
# land cover map reclassified to UNCCD 7 classes (1: forest, 2:
# grassland, 3: cropland, 4: wetland, 5: artifitial, 6: bare, 7: water)
lc_t0 = lc.select(k).remap(remap_matrix[0], remap_matrix[1])
lc_t1 = lc.select(k + 1).remap(remap_matrix[0], remap_matrix[1])
if (k == 0):
# compute transition map (first digit for baseline land cover, and
# second digit for target year land cover)
lc_tr = lc_t0.multiply(10).add(lc_t1)
# compute raster to register years since transition
tr_time = ee.Image(2).where(lc_t0.neq(lc_t1), 1)
else:
# Update time since last transition. Add 1 if land cover remains
# constant, and reset to 1 if land cover changed.
tr_time = tr_time.where(lc_t0.eq(lc_t1), tr_time.add(ee.Image(1))) \
.where(lc_t0.neq(lc_t1), ee.Image(1))
# compute transition map (first digit for baseline land cover, and
# second digit for target year land cover), but only update where
# changes actually ocurred.
lc_tr_temp = lc_t0.multiply(10).add(lc_t1)
lc_tr = lc_tr.where(lc_t0.neq(lc_t1), lc_tr_temp)
# stock change factor for land use - note the 99 and -99 will be
# recoded using the chosen Fl option
lc_tr_fl_0 = lc_tr.remap([
11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24, 25, 26, 27, 31, 32, 33,
34, 35, 36, 37, 41, 42, 43, 44, 45, 46, 47, 51, 52, 53, 54, 55, 56,
57, 61, 62, 63, 64, 65, 66, 67, 71, 72, 73, 74, 75, 76, 77
], [
1, 1, 99, 1, 0.1, 0.1, 1, 1, 1, 99, 1, 0.1, 0.1, 1, -99, -99, 1,
1 / 0.71, 0.1, 0.1, 1, 1, 1, 0.71, 1, 0.1, 0.1, 1, 2, 2, 2, 2, 1,
1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
])
if fl == 'per pixel':
lc_tr_fl = lc_tr_fl_0.where(lc_tr_fl_0.eq(99), clim_fl)\
.where(lc_tr_fl_0.eq(-99), ee.Image(1).divide(clim_fl))
else:
lc_tr_fl = lc_tr_fl_0.where(lc_tr_fl_0.eq(99), fl)\
.where(lc_tr_fl_0.eq(-99), ee.Image(1).divide(fl))
# stock change factor for management regime
lc_tr_fm = lc_tr.remap([
11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24, 25, 26, 27, 31, 32, 33,
34, 35, 36, 37, 41, 42, 43, 44, 45, 46, 47, 51, 52, 53, 54, 55, 56,
57, 61, 62, 63, 64, 65, 66, 67, 71, 72, 73, 74, 75, 76, 77
], [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1
])
# stock change factor for input of organic matter
lc_tr_fo = lc_tr.remap([
11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24, 25, 26, 27, 31, 32, 33,
34, 35, 36, 37, 41, 42, 43, 44, 45, 46, 47, 51, 52, 53, 54, 55, 56,
57, 61, 62, 63, 64, 65, 66, 67, 71, 72, 73, 74, 75, 76, 77
], [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1
])
if (k == 0):
soc_chg = (soc_t0.subtract((soc_t0.multiply(lc_tr_fl).multiply(
lc_tr_fm).multiply(lc_tr_fo)))).divide(20)
# compute final SOC stock for the period
soc_t1 = soc_t0.subtract(soc_chg)
# add to land cover and soc to stacks from both dates for the first
# period
stack_lc = stack_lc.addBands(lc_t0).addBands(lc_t1)
stack_soc = stack_soc.addBands(soc_t0).addBands(soc_t1)
else:
# compute annual change in soc (updates from previous period based
# on transition and time <20 years)
soc_chg = soc_chg.where(lc_t0.neq(lc_t1),
(stack_soc.select(k).subtract(stack_soc.select(k) \
.multiply(lc_tr_fl) \
.multiply(lc_tr_fm) \
.multiply(lc_tr_fo))).divide(20)) \
.where(tr_time.gt(20), 0)
# compute final SOC for the period
socn = stack_soc.select(k).subtract(soc_chg)
# add land cover and soc to stacks only for the last year in the
# period
stack_lc = stack_lc.addBands(lc_t1)
stack_soc = stack_soc.addBands(socn)
# compute soc percent change for the analysis period
soc_pch = ((stack_soc.select(year_end - year_start) \
.subtract(stack_soc.select(0))) \
.divide(stack_soc.select(0))) \
.multiply(100)
logger.debug("Setting up output.")
out = TEImage(soc_pch, [
BandInfo("Soil organic carbon (degradation)",
add_to_map=True,
metadata={
'year_start': year_start,
'year_end': year_end
})
])
logger.debug("Adding annual SOC layers.")
# Output all annual SOC layers
d_soc = []
for year in range(year_start, year_end + 1):
if (year == year_start) or (year == year_end):
add_to_map = True
else:
add_to_map = False
d_soc.append(
BandInfo("Soil organic carbon",
add_to_map=add_to_map,
metadata={'year': year}))
out.addBands(stack_soc, d_soc)
if dl_annual_lc:
logger.debug("Adding all annual LC layers.")
d_lc = []
for year in range(year_start, year_end + 1):
d_lc.append(
BandInfo("Land cover (7 class)", metadata={'year': year}))
out.addBands(stack_lc, d_lc)
else:
logger.debug("Adding initial and final LC layers.")
out.addBands(
stack_lc.select(0).addBands(
stack_lc.select(len(stack_lc.getInfo()['bands']) - 1)),
[
BandInfo("Land cover (7 class)", metadata={'year': year_start
}),
BandInfo("Land cover (7 class)", metadata={'year': year_end})
])
out.image = out.image.unmask(-32768).int16()
return out
def land_cover(year_baseline, year_target, trans_matrix, remap_matrix,
EXECUTION_ID, logger):
"""
Calculate land cover indicator.
"""
logger.debug("Entering land_cover function.")
## land cover
lc = ee.Image(
"users/geflanddegradation/toolbox_datasets/lcov_esacc_1992_2018")
lc = lc.where(lc.eq(9999), -32768)
lc = lc.updateMask(lc.neq(-32768))
# Remap LC according to input matrix
lc_remapped = lc.select('y{}'.format(year_baseline)).remap(
remap_matrix[0], remap_matrix[1])
for year in range(year_baseline + 1, year_target + 1):
lc_remapped = lc_remapped.addBands(
lc.select('y{}'.format(year)).remap(remap_matrix[0],
remap_matrix[1]))
## target land cover map reclassified to IPCC 6 classes
lc_bl = lc_remapped.select(0)
## baseline land cover map reclassified to IPCC 6 classes
lc_tg = lc_remapped.select(len(lc_remapped.getInfo()['bands']) - 1)
## compute transition map (first digit for baseline land cover, and second digit for target year land cover)
lc_tr = lc_bl.multiply(10).add(lc_tg)
## definition of land cover transitions as degradation (-1), improvement (1), or no relevant change (0)
lc_dg = lc_tr.remap([
11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24, 25, 26, 27, 31, 32, 33, 34,
35, 36, 37, 41, 42, 43, 44, 45, 46, 47, 51, 52, 53, 54, 55, 56, 57, 61,
62, 63, 64, 65, 66, 67, 71, 72, 73, 74, 75, 76, 77
], trans_matrix)
## Remap persistence classes so they are sequential. This
## makes it easier to assign a clear color ramp in QGIS.
lc_tr = lc_tr.remap([
11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24, 25, 26, 27, 31, 32, 33, 34,
35, 36, 37, 41, 42, 43, 44, 45, 46, 47, 51, 52, 53, 54, 55, 56, 57, 61,
62, 63, 64, 65, 66, 67, 71, 72, 73, 74, 75, 76, 77
], [
1, 12, 13, 14, 15, 16, 17, 21, 2, 23, 24, 25, 26, 27, 31, 32, 3, 34,
35, 36, 37, 41, 42, 43, 4, 45, 46, 47, 51, 52, 53, 54, 5, 56, 57, 61,
62, 63, 64, 65, 6, 67, 71, 72, 73, 74, 75, 76, 7
])
logger.debug("Setting up output.")
out = TEImage(
lc_dg.addBands(lc.select('y{}'.format(year_baseline))).addBands(
lc.select('y{}'.format(year_target))).addBands(lc_tr), [
BandInfo("Land cover (degradation)",
add_to_map=True,
metadata={
'year_baseline': year_baseline,
'year_target': year_target
}),
BandInfo("Land cover (ESA classes)",
metadata={'year': year_baseline}),
BandInfo("Land cover (ESA classes)",
metadata={'year': year_target}),
BandInfo("Land cover transitions",
add_to_map=True,
metadata={
'year_baseline': year_baseline,
'year_target': year_target
})
])
# Return the full land cover timeseries so it is available for reporting
logger.debug("Adding annual lc layers.")
d_lc = []
for year in range(year_baseline, year_target + 1):
if (year == year_baseline) or (year == year_target):
add_to_map = True
else:
add_to_map = False
d_lc.append(
BandInfo("Land cover (7 class)",
add_to_map=add_to_map,
metadata={'year': year}))
out.addBands(lc_remapped, d_lc)
out.image = out.image.unmask(-32768).int16()
return out