Reproducing L4A AGBD estimates from GEDI L2A RH metrics#
This tutorial shows how to reconstruct L4A aboveground biomass density (AGBD) estimates [Dubayah et al., 2022] using L2A relative height (RH) metrics [Dubayah et al., 2021]. We will use a GEDI L4A file GEDI04_A_2020207182449_O09168_03_T03028_02_002_02_V002.h5 and corresponding GEDI L2A file GEDI02_A_2020207182449_O09168_03_T03028_02_003_01_V002.h5 from the orbit 09168 and track 03028 for this purpose.
Learning Objectives
Reconstruct L4A AGBD value for a single shot
Apply for all the shots within a single L2A beam to reconstruct AGBD value
Requirements#
Additional prerequisites are provided here.
# import all the modules
from os import path
import earthaccess
import h5py
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
sns.set(style='whitegrid')
We will use earthaccess module to authenticate to NASA Earthdata Login and download the GEDI L2A and L4A files to the full_orbits directory.
# authenticate using the EDL login
try:
auth = earthaccess.login(strategy="netrc")
except FileNotFoundError:
auth = earthaccess.login(strategy="interactive", persist=True)
def search_download(doi):
"""searches and downloads GEDI granules"""
granules = earthaccess.search_data(doi=doi,
granule_name=f"*O09168_03_T03028*")
return earthaccess.download(granules, local_path="full_orbits")
l4af = search_download("10.3334/ORNLDAAC/2056")# GEDI L4A DOI
l2af = search_download("10.5067/GEDI/GEDI02_A.002")# GEDI L2A DOI
1. Reconstruct AGBD for a single shot#
We will use a single shot (shot number 91680600300633870) from the beam BEAM0110. First, open the L4A file, and find out the predict_stratum and selected_algorithm for the shot.
# read the L4A file
hf_l4a = h5py.File(l4af[0], 'r')
sn = 158560 # index of shot 91680600300633870
beam = 'BEAM0110' # selected beam
shot_number_l4a = hf_l4a[beam]['shot_number'][sn:sn+1][0]
predict_stratum_l4a = hf_l4a[beam]['predict_stratum'][sn:sn+1][0]
selected_algorithm_l4a = hf_l4a[beam]['selected_algorithm'][sn:sn+1][0]
print_s = f"""For GEDI shot number {shot_number_l4a}, \
the predict stratum is {predict_stratum_l4a.decode('UTF-8')}, \
and the selected algorithm group is {selected_algorithm_l4a}."""
print(print_s)
For GEDI shot number 91680600300633870, the predict stratum is EBT_SAs, and the selected algorithm group is 2.
L4A model parameters: Let’s print the model_data information for the EBT_SAs stratum.
model_data_l4a = hf_l4a['ANCILLARY']['model_data']
model_stratum_l4a = model_data_l4a[12] # EBT_SAs
for v in model_stratum_l4a.dtype.names:
print(v, " : ", model_stratum_l4a[v])
predict_stratum : b'EBT_SAs'
model_group : 4
model_name : b'EBT_coarse'
model_id : 1
x_transform : b'sqrt'
y_transform : b'sqrt'
bias_correction_name : b'Snowdon'
fit_stratum : b'EBT'
rh_index : [50 98 0 0 0 0 0 0]
predictor_id : [1 2 0 0 0 0 0 0]
predictor_max_value : [12.66807 13.052969 0. 0. 0. 0. 0.
0. ]
vcov : [[ 1.6210171 -0.10003732 -0.047196 0. 0. ]
[-0.10003732 0.05625523 -0.04469026 0. 0. ]
[-0.047196 -0.04469026 0.04664591 0. 0. ]
[ 0. 0. 0. 0. 0. ]
[ 0. 0. 0. 0. 0. ]]
par : [-104.9654541 6.80217409 3.95531225 0. 0. ]
rse : 3.9209127
dof : 4811
response_max_value : 1578.0
bias_correction_value : 1.1133657
npar : 3
A square-root transformation is applied to both predictor and response variables as indicated by x_transform and y_transform above. Take a note of the bias_correction_value and bias_correction_name above. We will use the bias_correction_value later to back-transform AGBD values. Now, read all the model parameters and print the AGBD model.
intercept = model_stratum_l4a['par'][0] # intercept
coeff_1 = model_stratum_l4a['par'][1] # first coeff
coeff_2 = model_stratum_l4a['par'][2] # first coeff
predict_1 = model_stratum_l4a['rh_index'][0] # predictor 1
predict_2 = model_stratum_l4a['rh_index'][1] # predictor 1
# print
print_s = f"""The predict stratum {predict_stratum_l4a.decode('UTF-8')} uses following model:
AGBD = {intercept} + {coeff_1} x RH_{predict_1} + {coeff_2} x RH_{predict_2}"""
print(print_s)
The predict stratum EBT_SAs uses following model:
AGBD = -104.9654541015625 + 6.802174091339111 x RH_50 + 3.9553122520446777 x RH_98
Also, read the predictor and response offsets from the L4A file.
predictor_offset = hf_l4a[beam]['agbd_prediction'].attrs['predictor_offset']
response_offset = hf_l4a[beam]['agbd_prediction'].attrs['response_offset']
# print
print_s = f"""The predictor offset is {predictor_offset},
and the response offset is {response_offset}"""
print(print_s)
The predictor offset is 100,
and the response offset is 0
We will use the predictor offset later when transforming the variables.
L2A file and RH metrics
Now, let’s open the L2A file and read the RH metrics.
hf_l2a = h5py.File(l2af[0], 'r')
rh_50 = hf_l2a[beam]['rh'][sn:sn+1,50][0] # rh_50
rh_98 = hf_l2a[beam]['rh'][sn:sn+1,98][0] # rh_98
rh_units = hf_l2a[beam]['rh'].attrs['units']
shot_number_l2a = hf_l2a[beam]['shot_number'][sn:sn+1][0]
selected_algorithm_l2a = hf_l2a[beam]['selected_algorithm'][sn:sn+1][0]
print_s = f"""For GEDI shot number {shot_number_l2a},
L2A rh_50 is {rh_50} {rh_units}.
L2A rh_98 is {rh_98} {rh_units}.
Selected algorithm group is {selected_algorithm_l2a}."""
print(print_s)
For GEDI shot number 91680600300633870,
L2A rh_50 is 19.149999618530273 m.
L2A rh_98 is 37.150001525878906 m.
Selected algorithm group is 2.
xvar
We can now compute predictor variables xvar from the L2A RH metrics and compare them with the L4A xvar values.
xvar_1_l2a = np.sqrt(rh_50 + predictor_offset) # sqrt x transformation
xvar_2_l2a = np.sqrt(rh_98 + predictor_offset) # sqrt x transformation
xvar_1_l4a = hf_l4a[beam]['xvar'][158560:158561,0][0]
xvar_2_l4a = hf_l4a[beam]['xvar'][158560:158561,1][0]
print_s = f"""For GEDI shot number {shot_number_l2a},
L2A derived xvars: {xvar_1_l2a}, {xvar_2_l2a}.
L4A xvars: {xvar_1_l4a}, {xvar_2_l4a} """
print(print_s)
For GEDI shot number 91680600300633870,
L2A derived xvars: 10.91558517068738, 11.711105905331012.
L4A xvars: 10.9155855178833, 11.711106300354004
agbd_t
Let’s use the L2A-derived xvars from the above to compute AGBD in the transformed space and compare the transformed AGBD with that of L4A.
agbd_t_l4a = intercept + (coeff_1 * xvar_1_l4a) + (coeff_2 * xvar_2_l4a) # l2a agbd_t
agbd_t_l2a = intercept + (coeff_1 * xvar_1_l2a) + (coeff_2 * xvar_2_l2a) # l2a agbd_t
print_s = f"""For GEDI shot number {shot_number_l2a},
L2A derived agbd_t: {agbd_t_l2a}.
L4A agbd_t: {agbd_t_l4a} """
print(print_s)
For GEDI shot number 91680600300633870,
L2A derived agbd_t: 15.605337210641139.
L4A agbd_t: 15.605341134767514
agbd
Now, let’s back-transform L2A derived agbd_t and compare it with the AGBD from L4A.
agbd_units = hf_l4a[beam]['agbd'].attrs['units']
bias_correction_value = model_stratum_l4a['bias_correction_value'] # bias_correction_value
agbd_l2a = (agbd_t_l2a**2) * bias_correction_value # snowdon model with sqrt y transform
agbd_l4a = (agbd_t_l4a**2) * bias_correction_value # snowdon model with sqrt y transform
# closing files
hf_l4a.close()
hf_l2a.close()
print_s = f"""For GEDI shot number {shot_number_l2a},
L2A derived estimated AGBD is {agbd_l2a} {agbd_units}.
L4A estimated AGBD {agbd_l4a} {agbd_units}."""
print(print_s)
For GEDI shot number 91680600300633870,
L2A derived estimated AGBD is 271.13409507246865 Mg / ha.
L4A estimated AGBD 271.1342314315326 Mg / ha.
2. Reconstruct AGBD for a single beam#
We will reconstruct AGBD for all the shots within beam BEAM0110. Let’s open the L4A file and read model parameters into a pandas dataframe.
# read the L4A file
hf_l4a = h5py.File(l4af[0], 'r')
# model_parameters
model_data=hf_l4a['ANCILLARY']['model_data']
predict_stratum = model_data['predict_stratum'].astype('U13')
bias_correction_name = model_data['bias_correction_name'].astype('U13')
bias_correction_value = model_data['bias_correction_value']
x_transform = model_data['x_transform'].astype('U13')
y_transform = model_data['y_transform'].astype('U13')
npar = model_data['npar']
par = model_data['par']
rh_index = model_data['rh_index']
# pandas dataframe
df_l4a_model = pd.DataFrame(list(zip(predict_stratum, bias_correction_name, bias_correction_value,
x_transform,y_transform, npar, par, rh_index)),
columns=['predict_stratum', 'bias_correction_name', 'bias_correction_value',
'x_transform', 'y_transform', 'npar', 'par', 'rh_index'])
df_l4a_model.set_index('predict_stratum', inplace=True)
# print the header rows
df_l4a_model.head()
| bias_correction_name | bias_correction_value | x_transform | y_transform | npar | par | rh_index | |
|---|---|---|---|---|---|---|---|
| predict_stratum | |||||||
| DBT_Af | Snowdon | 1.092463 | sqrt | sqrt | 3 | [-118.40806579589844, 1.956794023513794, 9.961... | [50, 98, 0, 0, 0, 0, 0, 0] |
| DBT_Au | Snowdon | 1.017920 | sqrt | sqrt | 3 | [-155.41419982910156, 7.816701889038086, 7.709... | [70, 98, 0, 0, 0, 0, 0, 0] |
| DBT_Eu | Snowdon | 0.962632 | sqrt | sqrt | 3 | [-96.53070068359375, 7.175395488739014, 2.9214... | [70, 98, 0, 0, 0, 0, 0, 0] |
| DBT_NAs | Snowdon | 1.016632 | sqrt | sqrt | 3 | [-110.05912780761719, 5.133802890777588, 6.171... | [60, 98, 0, 0, 0, 0, 0, 0] |
| DBT_SA | Snowdon | 1.105528 | sqrt | sqrt | 3 | [-134.77015686035156, 6.653591632843018, 6.687... | [50, 98, 0, 0, 0, 0, 0, 0] |
Now, read the data from BEAM0110 into the pandas dataframe df_l4a.
shot_number_l4a = hf_l4a[beam]['shot_number'] # selected algorithm
agbd_l4a = hf_l4a[beam]['agbd'] # l4a agbd
selected_algorithm_l4a = hf_l4a[beam]['selected_algorithm'] # selected algorithm
predict_stratum = hf_l4a[beam]['predict_stratum'] # selected algorithm
df_l4a = pd.DataFrame(list(zip(shot_number_l4a[:], agbd_l4a[:], selected_algorithm_l4a[:],
predict_stratum[:].astype('U13'))),
columns=['shot_number_l4a', 'agbd_l4a', 'selected_algorithm_l4a', 'predict_stratum'])
df_l4a['shot_number_l4a'] = df_l4a['shot_number_l4a'].astype(str).str[-8:]
df_l4a = df_l4a.set_index('shot_number_l4a')
df_l4a.head()
| agbd_l4a | selected_algorithm_l4a | predict_stratum | |
|---|---|---|---|
| shot_number_l4a | |||
| 00475310 | -9999.000000 | 2 | ENT_Eu |
| 00475311 | 25.602335 | 1 | ENT_Eu |
| 00475312 | 24.043011 | 1 | ENT_Eu |
| 00475313 | -9999.000000 | 2 | ENT_Eu |
| 00475314 | -9999.000000 | 1 | ENT_Eu |
We can plot total shots by the prediction strata to check how many prediction strata are in the beam BEAM0110.
ax = df_l4a.predict_stratum.value_counts().plot(kind='bar', ylabel='Total Shots', figsize=(20, 4))
ax.set_title(f'Total shots by prediction strata in {beam}')
ax.bar_label(ax.containers[0])
plt.show()
There are ten prediction strata in the BEAM0110. Let’s also plot total shots by the selected algorithm setting groups.
ax = df_l4a.selected_algorithm_l4a.value_counts().plot(kind='bar', ylabel='Total Shots', figsize=(20, 4))
ax.set_title(f'Total shots by selected algorithm setting groups in {beam}')
ax.bar_label(ax.containers[0])
plt.show()
There are four algorithm setting groups selected for the beam BEAM0110. Now, open the corresponding GEDI L2A file.
# read GEDI L2a file
hf_l2a = h5py.File(l2af[0], 'r')
shot_number_l2a = hf_l2a[beam]['shot_number'][:]
selected_algorithm_l2a = hf_l2a[beam]['selected_algorithm'][:]
# selected_algorithm for a shot may be different for L2A and L4A
# it is always advisable to get the RH metrics directly for each
# algorithm setting groups.
rh_a1 = hf_l2a[beam]['geolocation']['rh_a1'][:]/100
rh_a2 = hf_l2a[beam]['geolocation']['rh_a2'][:]/100
rh_a3 = hf_l2a[beam]['geolocation']['rh_a3'][:]/100
rh_a4 = hf_l2a[beam]['geolocation']['rh_a4'][:]/100
rh_a5 = hf_l2a[beam]['geolocation']['rh_a5'][:]/100
rh_a6 = hf_l2a[beam]['geolocation']['rh_a6'][:]/100
# Algorithm setting group 10 indicates that agorithm setting 5
# has been used but that the lowest detected mode in this
# algorithm is likely a noise detection and that a higher mode
# has been subsequently used to calculate the metrics.
# Please refer to Level 2A user guide for details
# https://lpdaac.usgs.gov/documents/998/GEDI02_UserGuide_V21.pdf
selected_mode = hf_l4a[beam]['selected_mode'][:] # using L4A selected_mode
elevs_allmodes_a5 = hf_l2a[beam]['geolocation']['elevs_allmodes_a5'][:]
elev_lowestmode_a5 = hf_l2a[beam]['geolocation']['elev_lowestmode_a5'][:]
elev_lowestmode =[]
for i in range(selected_mode.shape[0]):
elev_lowestmode.append(elevs_allmodes_a5[i, selected_mode[i]])
elev_lowestmode = np.array(elev_lowestmode)
rh_a10 = rh_a5 - (elev_lowestmode[:, None] - elev_lowestmode_a5[:, None])
df_l2a = pd.DataFrame(list(zip(shot_number_l2a, rh_a1, rh_a2, rh_a3, rh_a4, rh_a5, rh_a6, rh_a10,
selected_algorithm_l2a)),
columns=['shot_number_l2a', 'rh_a1', 'rh_a2', 'rh_a3', 'rh_a4', 'rh_a5', 'rh_a6', 'rh_a10',
'selected_algorithm_l2a'])
df_l2a['shot_number_l2a'] = df_l2a['shot_number_l2a'].astype(str).str[-8:]
df_l2a.set_index('shot_number_l2a', inplace=True)
df_l2a.head()
| rh_a1 | rh_a2 | rh_a3 | rh_a4 | rh_a5 | rh_a6 | rh_a10 | selected_algorithm_l2a | |
|---|---|---|---|---|---|---|---|---|
| shot_number_l2a | ||||||||
| 00475310 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [-1.08, -1.04, -1.0, -0.97, -0.93, -0.89, -0.8... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [-1.75, -1.68, -1.6, -1.53, -1.45, -1.38, -1.3... | [-0.33, -0.33, -0.29, -0.29, -0.26, -0.22, -0.... | [-4.177726745605469, -4.1077267456054685, -4.0... | 2 |
| 00475311 | [-1.19, -1.15, -1.12, -1.08, -1.04, -1.04, -1.... | [-2.35, -2.27, -2.16, -2.09, -2.01, -1.94, -1.... | [-1.26, -1.23, -1.19, -1.15, -1.15, -1.12, -1.... | [-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1.... | [-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2.... | [-1.97, -1.94, -1.86, -1.79, -1.75, -1.68, -1.... | [-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2.... | 1 |
| 00475312 | [-0.97, -0.97, -0.93, -0.89, -0.85, -0.85, -0.... | [-2.46, -2.39, -2.31, -2.24, -2.16, -2.09, -2.... | [-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1.... | [-0.97, -0.97, -0.93, -0.93, -0.89, -0.89, -0.... | [-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3... | [-2.01, -1.97, -1.9, -1.86, -1.83, -1.75, -1.7... | [-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3... | 1 |
| 00475313 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [-1.34, -1.3, -1.26, -1.23, -1.19, -1.15, -1.1... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [-0.33, -0.29, -0.22, -0.18, -0.11, -0.03, 0.0... | [-0.18, -0.18, -0.18, -0.14, -0.14, -0.14, -0.... | [-3.1685696411132813, -3.1285696411132813, -3.... | 2 |
| 00475314 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... | [158.7861328125, 158.7861328125, 158.786132812... | 1 |
We will now join the L4A dataframe (df_l4a) with the L2A dataframe (df_2a) using pandas’ join.
df_l4a = df_l4a.join(df_l2a)
df_l4a = df_l4a[df_l4a.agbd_l4a != -9999] # removing Nan L4A agbd values
df_l4a.head()
| agbd_l4a | selected_algorithm_l4a | predict_stratum | rh_a1 | rh_a2 | rh_a3 | rh_a4 | rh_a5 | rh_a6 | rh_a10 | selected_algorithm_l2a | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| shot_number_l4a | |||||||||||
| 00475311 | 25.602335 | 1 | ENT_Eu | [-1.19, -1.15, -1.12, -1.08, -1.04, -1.04, -1.... | [-2.35, -2.27, -2.16, -2.09, -2.01, -1.94, -1.... | [-1.26, -1.23, -1.19, -1.15, -1.15, -1.12, -1.... | [-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1.... | [-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2.... | [-1.97, -1.94, -1.86, -1.79, -1.75, -1.68, -1.... | [-2.87, -2.68, -2.53, -2.39, -2.27, -2.16, -2.... | 1 |
| 00475312 | 24.043011 | 1 | ENT_Eu | [-0.97, -0.97, -0.93, -0.89, -0.85, -0.85, -0.... | [-2.46, -2.39, -2.31, -2.24, -2.16, -2.09, -2.... | [-1.19, -1.19, -1.15, -1.12, -1.08, -1.08, -1.... | [-0.97, -0.97, -0.93, -0.93, -0.89, -0.89, -0.... | [-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3... | [-2.01, -1.97, -1.9, -1.86, -1.83, -1.75, -1.7... | [-3.06, -2.95, -2.8, -2.68, -2.57, -2.46, -2.3... | 1 |
| 00475317 | 24.283388 | 1 | ENT_Eu | [-1.94, -1.9, -1.83, -1.79, -1.75, -1.68, -1.6... | [-3.13, -3.02, -2.87, -2.76, -2.65, -2.57, -2.... | [-2.24, -2.2, -2.12, -2.09, -2.01, -1.97, -1.9... | [-1.94, -1.9, -1.86, -1.79, -1.75, -1.71, -1.6... | [-3.54, -3.36, -3.17, -3.02, -2.87, -2.76, -2.... | [-2.8, -2.72, -2.61, -2.53, -2.46, -2.35, -2.3... | [-3.54, -3.36, -3.17, -3.02, -2.87, -2.76, -2.... | 1 |
| 00475318 | 23.047647 | 2 | ENT_Eu | [-0.14, -0.14, -0.14, -0.11, -0.11, -0.07, -0.... | [-2.2, -2.16, -2.09, -2.01, -1.97, -1.9, -1.86... | [-0.07, -0.07, -0.07, -0.03, -0.03, 0.0, 0.0, ... | [-0.14, -0.14, -0.14, -0.14, -0.14, -0.14, -0.... | [-3.06, -2.95, -2.83, -2.72, -2.61, -2.5, -2.4... | [-1.56, -1.53, -1.49, -1.45, -1.38, -1.34, -1.... | [-3.06, -2.95, -2.83, -2.72, -2.61, -2.5, -2.4... | 2 |
| 00475320 | 23.937260 | 1 | ENT_Eu | [-0.59, -0.59, -0.56, -0.56, -0.52, -0.52, -0.... | [-2.65, -2.57, -2.53, -2.46, -2.39, -2.31, -2.... | [-0.85, -0.85, -0.82, -0.82, -0.78, -0.78, -0.... | [-0.59, -0.59, -0.59, -0.59, -0.56, -0.56, -0.... | [-3.36, -3.24, -3.13, -3.06, -2.95, -2.87, -2.... | [-2.05, -2.01, -1.97, -1.9, -1.86, -1.83, -1.7... | [-3.36, -3.24, -3.13, -3.06, -2.95, -2.87, -2.... | 1 |
Let’s calculate AGBD values from L2A RH metrics and save them to a new column ‘agbd_l2a’ in the dataframe.
def calculate_agbd(ps, sag, rh1, rh2, rh3, rh4, rh5, rh6, rh10):
shot_model = df_l4a_model.loc[ps]
agbd_t_l2a = shot_model['par'][0] # intercept
for i in range(shot_model['npar']-1):
r = shot_model['rh_index'][i]
if (sag == 1):
rh = rh1
elif (sag == 2):
rh = rh2
elif (sag == 3):
rh = rh3
elif (sag == 4):
rh = rh4
elif (sag == 5):
rh = rh5
elif (sag == 6):
rh = rh6
elif (sag == 10):
rh = rh10
xvar_x = np.sqrt(rh[r] + predictor_offset)
agbd_t_l2a += shot_model['par'][i+1] * xvar_x
return (agbd_t_l2a**2) * shot_model['bias_correction_value']
predictor_offset = hf_l4a[beam]['agbd_prediction'].attrs['predictor_offset']
df_l4a['agbd_l2a']= df_l4a.apply(lambda x: calculate_agbd(x['predict_stratum'], x['selected_algorithm_l4a'],
x['rh_a1'], x['rh_a2'], x['rh_a3'], x['rh_a4'],
x['rh_a5'], x['rh_a6'], x['rh_a10'] ), axis=1)
# closing files
hf_l4a.close()
hf_l2a.close()
We will plot the L4A AGBD values against the AGBD derived from L2A RH metrics by the algorithm setting groups.
sns.relplot(data=df_l4a, x="agbd_l4a", y="agbd_l2a", hue="predict_stratum",
col="selected_algorithm_l4a", col_wrap=2, palette="tab20", facet_kws={'sharey': False, 'sharex': False})
sns.despine(top=False, right=False, left=False, bottom=False, offset=None, trim=False)
We will plot the L4A AGBD values against the AGBD derived from L2A RH metrics by the prediction strata to check if the AGBD values look all right.
sns.relplot(data=df_l4a, x="agbd_l4a", y="agbd_l2a", hue="selected_algorithm_l4a",
col="predict_stratum", col_wrap=3, palette="tab20", facet_kws={'sharey': False, 'sharex': False})
sns.despine(top=False, right=False, left=False, bottom=False, offset=None, trim=False)
References#
[1]
R. Dubayah, M. Hofton, J. Blair, J. Armston, H. Tang, and S. Luthcke. GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002. 2021. doi:10.5067/GEDI/GEDI02_A.002.
[2]
R.O. Dubayah, J. Armston, J.R. Kellner, L. Duncanson, S.P. Healey, P.L. Patterson, S. Hancock, H. Tang, J. Bruening, M.A. Hofton, J.B. Blair, and S.B. Luthcke. GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1. 2022. doi:10.3334/ORNLDAAC/2056.