# Import python libraries
import getpass
import json
import os
import pathlib
from glob import glob

import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

import earthpy.appeears as eaapp
import hvplot.pandas
import hvplot.xarray
import rioxarray as rxr
import xarray as xr

asia_fires_url = "https://data.cyverse.org/dav-anon/iplant/projects/earthlab/firedpy_north_america/fired_central_asia_to2021182_events.gpkg"

asia_fires_gdf = gpd.read_file(asia_fires_url)
asia_fires_gdf

fires_2021 = asia_fires_gdf[asia_fires_gdf["ig_year"]==2021]
fires_2021

fires_2021.explore()

pak_url = "https://stacks.stanford.edu/file/druid:st456dv9938/data.zip"
pak_gdf = gpd.read_file(pak_url)
pak_gdf

pak_gdf.explore()

fata = pak_gdf[pak_gdf["NAME_1"]=="F.A.T.A."]
fata

fata.plot()
print(fata.crs)

EPSG:4326

print(fires_2021.crs)

PROJCS["unknown",GEOGCS["unknown",DATUM["unknown",SPHEROID["unknown",6371007.181,0]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]

# Reproject fire data 
fires_2021_4326 = fires_2021.to_crs('epsg:4326')
fires_2021_4326

# Plot fires (2021) and F.A.T.A. boundary on same axis
fig, ax = plt.subplots()
fires_2021_4326.plot(ax=ax)
fata.plot(color='grey', edgecolor='black', ax=ax)

<Axes: >

# Clip fires to F.A.T.A. boundary
fata_fires = gpd.clip(fires_2021_4326, fata)
fata_fires

# Plot F.A.T.A. fires (2021)
fig, ax = plt.subplots(figsize=(10,20))
fata.plot(color='grey', edgecolor='black', ax=ax)
fata_fires.plot(color='red', edgecolor='black', ax=ax)
ax.set(title="F.A.T.A fires - 2021")
plt.show()

# Largest fire 2021
big_fire = fata_fires[fata_fires["tot_pix"]==172]
big_fire

fata_fires.explore()

# Create a path to the data
fata_dir = os.path.join(pathlib.Path.home(), 'fata-data')

# Make the data directory
os.makedirs(fata_dir, exist_ok=True)

fata_dir

'/home/jovyan/fata-data'

# Initialize AppeearsDownloader for MODIS NDVI data
ndvi_downloader = eaapp.AppeearsDownloader(
    download_key='fata-ndvi',
    ea_dir=fata_dir,
    product='MOD13Q1.061',
    layer='_250m_16_days_NDVI',
    start_date="07-01",
    end_date="07-31",
    recurring=True,
    year_range=[2019, 2023],
    polygon=fata
)

ndvi_downloader.download_files(cache=True)

# Get a list of NDVI tif file paths
fata_paths = sorted(glob(os.path.join(fata_dir, 'fata-ndvi', '*', '*NDVI*.tif')))
print(fata_paths)
len(fata_paths)

['/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2019177_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2019193_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2019209_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2020177_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2020193_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2020209_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2021177_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2021193_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2021209_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2022177_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2022193_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2022209_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2023177_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2023193_aid0001.tif', '/home/jovyan/fata-data/fata-ndvi/MOD13Q1.061_2019167_to_2023212/MOD13Q1.061__250m_16_days_NDVI_doy2023209_aid0001.tif']

15

scale_factor = 10000
doy_start = -19
doy_end = -12

# Build data list of data arrays
fata_das = []
for fata_path in fata_paths:
    # Get date from file name
    doy = fata_path[doy_start:doy_end]
    date = pd.to_datetime(doy, format='%Y%j')

    # Open dataset
    da = rxr.open_rasterio(fata_path, masked=True).squeeze()

    # Add date dimension and clean up metadata
    da = da.assign_coords({'date': date})
    da = da.expand_dims({'date': 1})
    da.name = 'NDVI'

    # Multiple by scale factor
    da = da / scale_factor

    # Prepare for concatenation
    fata_das.append(da)

len(fata_das)

15

# Combine into single array by date
fata_da = xr.combine_by_coords(fata_das, coords=['date'])
fata_da

<xarray.Dataset>
Dimensions:      (x: 1230, y: 1870, date: 15)
Coordinates:
    band         int64 1
  * x            (x) float64 69.24 69.24 69.24 69.24 ... 71.79 71.79 71.8 71.8
  * y            (y) float64 34.96 34.96 34.96 34.96 ... 31.07 31.07 31.07 31.07
    spatial_ref  int64 0
  * date         (date) datetime64[ns] 2019-06-26 2019-07-12 ... 2023-07-28
Data variables:
    NDVI         (date, y, x) float32 0.6893 0.6074 0.6154 ... 0.0442 0.0442

array(1)

array([69.238542, 69.240625, 69.242708, ..., 71.794792, 71.796875, 71.798958])

array([34.961458, 34.959375, 34.957292, ..., 31.071875, 31.069792, 31.067708])

array(0)

array(['2019-06-26T00:00:00.000000000', '2019-07-12T00:00:00.000000000',
       '2019-07-28T00:00:00.000000000', '2020-06-25T00:00:00.000000000',
       '2020-07-11T00:00:00.000000000', '2020-07-27T00:00:00.000000000',
       '2021-06-26T00:00:00.000000000', '2021-07-12T00:00:00.000000000',
       '2021-07-28T00:00:00.000000000', '2022-06-26T00:00:00.000000000',
       '2022-07-12T00:00:00.000000000', '2022-07-28T00:00:00.000000000',
       '2023-06-26T00:00:00.000000000', '2023-07-12T00:00:00.000000000',
       '2023-07-28T00:00:00.000000000'], dtype='datetime64[ns]')

array([[[0.6893, 0.6074, 0.6154, ..., 0.4311, 0.3889, 0.4265],
        [0.6362, 0.659 , 0.6568, ..., 0.3889, 0.3889, 0.3889],
        [0.642 , 0.6306, 0.6306, ..., 0.4253, 0.4239, 0.4575],
        ...,
        [0.1456, 0.1456, 0.1107, ..., 0.0502, 0.0544, 0.0543],
        [0.1091, 0.1091, 0.0711, ..., 0.0477, 0.0543, 0.0555],
        [0.1038, 0.0729, 0.0729, ..., 0.0483, 0.0488, 0.0488]],

       [[0.6664, 0.5904, 0.6547, ..., 0.4585, 0.4585, 0.4732],
        [0.5904, 0.6467, 0.6419, ..., 0.4485, 0.4625, 0.4625],
        [0.6127, 0.6224, 0.6224, ..., 0.4734, 0.5024, 0.5412],
        ...,
        [0.1674, 0.1674, 0.1192, ..., 0.06  , 0.06  , 0.0642],
        [0.1225, 0.0959, 0.0881, ..., 0.0543, 0.0498, 0.0498],
        [0.1225, 0.0887, 0.0887, ..., 0.0548, 0.0498, 0.0498]],

       [[0.6526, 0.6316, 0.6264, ..., 0.6513, 0.6513, 0.7442],
        [0.6455, 0.6547, 0.6547, ..., 0.614 , 0.7341, 0.7341],
        [0.6353, 0.6054, 0.6054, ..., 0.614 , 0.7562, 0.7254],
        ...,
...
        [0.2038, 0.2038, 0.2038, ..., 0.06  , 0.06  , 0.0702],
        [0.1486, 0.1272, 0.0964, ..., 0.0538, 0.0702, 0.0702],
        [0.1355, 0.0891, 0.0891, ..., 0.0538, 0.0499, 0.0499]],

       [[0.5534, 0.5057, 0.5133, ..., 0.4965, 0.4578, 0.5193],
        [0.5528, 0.5138, 0.5202, ..., 0.4531, 0.4399, 0.4399],
        [0.5462, 0.5302, 0.5302, ..., 0.4858, 0.4854, 0.4836],
        ...,
        [0.2142, 0.2142, 0.1444, ..., 0.0445, 0.0533, 0.0555],
        [0.1627, 0.1627, 0.1168, ..., 0.0459, 0.0566, 0.0597],
        [0.1254, 0.1069, 0.1069, ..., 0.0479, 0.0435, 0.0435]],

       [[0.6312, 0.5675, 0.5786, ..., 0.7258, 0.721 , 0.6796],
        [0.6154, 0.6269, 0.6311, ..., 0.721 , 0.7144, 0.7144],
        [0.6104, 0.5632, 0.5632, ..., 0.7026, 0.6885, 0.7068],
        ...,
        [0.2075, 0.2075, 0.1519, ..., 0.0588, 0.0588, 0.0636],
        [0.1756, 0.1724, 0.1389, ..., 0.0522, 0.052 , 0.0521],
        [0.1269, 0.1189, 0.1189, ..., 0.0467, 0.0442, 0.0442]]],
      dtype=float32)

PandasIndex(Index([69.23854166046391, 69.24062499379706,  69.2427083271302,
       69.24479166046335,  69.2468749937965, 69.24895832712964,
       69.25104166046279, 69.25312499379594, 69.25520832712908,
       69.25729166046223,
       ...
       71.78020832690288, 71.78229166023603, 71.78437499356917,
       71.78645832690232, 71.78854166023547, 71.79062499356861,
       71.79270832690176,  71.7947916602349, 71.79687499356805,
        71.7989583269012],
      dtype='float64', name='x', length=1230))

PandasIndex(Index([  34.9614583302013, 34.959374996868156,  34.95729166353501,
        34.95520833020186, 34.953124996868716,  34.95104166353557,
        34.94895833020242, 34.946874996869276,  34.94479166353613,
        34.94270833020298,
       ...
       31.086458330548446,   31.0843749972153, 31.082291663882152,
       31.080208330549006,  31.07812499721586, 31.076041663882712,
       31.073958330549566,  31.07187499721642, 31.069791663883272,
       31.067708330550126],
      dtype='float64', name='y', length=1870))

PandasIndex(DatetimeIndex(['2019-06-26', '2019-07-12', '2019-07-28', '2020-06-25',
               '2020-07-11', '2020-07-27', '2021-06-26', '2021-07-12',
               '2021-07-28', '2022-06-26', '2022-07-12', '2022-07-28',
               '2023-06-26', '2023-07-12', '2023-07-28'],
              dtype='datetime64[ns]', name='date', freq=None))

# Compute the difference in NDVI before and 2021
fata_diff = (
    fata_da
        .sel(date=slice('2021', '2023'))
        .mean('date')
        .NDVI 
   - fata_da
        .sel(date=slice('2019', '2021'))
        .mean('date')
        .NDVI
)

fata_diff

<xarray.DataArray 'NDVI' (y: 1870, x: 1230)>
array([[-0.03661114, -0.03126669, -0.03695565, ...,  0.02910003,
         0.02556667,  0.01021111],
       [-0.03023332, -0.03587776, -0.04015559, ...,  0.02942222,
         0.00923336,  0.00923336],
       [-0.02385557, -0.03790003, -0.03790003, ...,  0.03593332,
         0.01067775,  0.01486659],
       ...,
       [ 0.02211113,  0.02211113,  0.01124446, ..., -0.0048    ,
        -0.00538889,  0.00071111],
       [ 0.01797779,  0.02203332,  0.0165    , ..., -0.00223334,
        -0.00221111,  0.00073333],
       [ 0.01944444,  0.01374444,  0.01374444, ..., -0.00487778,
        -0.00511111, -0.00511111]], dtype=float32)
Coordinates:
    band         int64 1
  * x            (x) float64 69.24 69.24 69.24 69.24 ... 71.79 71.79 71.8 71.8
  * y            (y) float64 34.96 34.96 34.96 34.96 ... 31.07 31.07 31.07 31.07
    spatial_ref  int64 0

array([[-0.03661114, -0.03126669, -0.03695565, ...,  0.02910003,
         0.02556667,  0.01021111],
       [-0.03023332, -0.03587776, -0.04015559, ...,  0.02942222,
         0.00923336,  0.00923336],
       [-0.02385557, -0.03790003, -0.03790003, ...,  0.03593332,
         0.01067775,  0.01486659],
       ...,
       [ 0.02211113,  0.02211113,  0.01124446, ..., -0.0048    ,
        -0.00538889,  0.00071111],
       [ 0.01797779,  0.02203332,  0.0165    , ..., -0.00223334,
        -0.00221111,  0.00073333],
       [ 0.01944444,  0.01374444,  0.01374444, ..., -0.00487778,
        -0.00511111, -0.00511111]], dtype=float32)

array(1)

array([69.238542, 69.240625, 69.242708, ..., 71.794792, 71.796875, 71.798958])

array([34.961458, 34.959375, 34.957292, ..., 31.071875, 31.069792, 31.067708])

array(0)

PandasIndex(Index([69.23854166046391, 69.24062499379706,  69.2427083271302,
       69.24479166046335,  69.2468749937965, 69.24895832712964,
       69.25104166046279, 69.25312499379594, 69.25520832712908,
       69.25729166046223,
       ...
       71.78020832690288, 71.78229166023603, 71.78437499356917,
       71.78645832690232, 71.78854166023547, 71.79062499356861,
       71.79270832690176,  71.7947916602349, 71.79687499356805,
        71.7989583269012],
      dtype='float64', name='x', length=1230))

PandasIndex(Index([  34.9614583302013, 34.959374996868156,  34.95729166353501,
        34.95520833020186, 34.953124996868716,  34.95104166353557,
        34.94895833020242, 34.946874996869276,  34.94479166353613,
        34.94270833020298,
       ...
       31.086458330548446,   31.0843749972153, 31.082291663882152,
       31.080208330549006,  31.07812499721586, 31.076041663882712,
       31.073958330549566,  31.07187499721642, 31.069791663883272,
       31.067708330550126],
      dtype='float64', name='y', length=1870))

# Plot the difference
fig, ax = plt.subplots(figsize=(12,20))

fata_diff.plot(x='x', y='y', cmap='PiYG', robust=True, ax=ax)
fata.plot(edgecolor='black', color='none', ax=ax)
fata_fires.plot(edgecolor='black', color='none', ax=ax)
plt.title('NDVI Difference (July)\nFederally Administered Tribal Areas, Pakistan\n2021-2023 vs. 2019-2021', fontsize=20)
plt.show()

The Kernel crashed while executing code in the current cell or a previous cell. 

Please review the code in the cell(s) to identify a possible cause of the failure. 

Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. 

View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.

%%capture
%%bash
jupyter nbconvert *.ipynb --to html

	id	ig_date	ig_day	ig_month	ig_year	last_date	event_dur	tot_pix	tot_ar_km2	fsr_px_dy	...	lc_name	lc_desc	lc_type	eco_mode	eco_name	eco_type	ig_utm_x	ig_utm_y	tot_perim	geometry
0	1	2000-11-02	307	11	2000	2000-11-02	1	2	0.429317	2.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Kazakh semi-desert	WWF Terrestrial Ecoregions of the World	4.487647e+06	5.234044e+06	1861.250866	MULTIPOLYGON (((4487879.629 5233811.102, 44874...
1	2	2000-11-02	307	11	2000	2000-11-02	1	2	0.429317	2.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Kazakh semi-desert	WWF Terrestrial Ecoregions of the World	4.490427e+06	5.235897e+06	1861.250866	MULTIPOLYGON (((4490659.505 5235664.353, 44901...
2	3	2000-11-02	307	11	2000	2000-11-02	1	2	0.429317	2.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Kazakh semi-desert	WWF Terrestrial Ecoregions of the World	4.488574e+06	5.228484e+06	1861.250866	MULTIPOLYGON (((4488806.254 5228251.350, 44883...
3	5	2000-11-02	307	11	2000	2000-11-02	1	1	0.214659	1.0	...	Barren	At least 60% of area is non-vegetated barren(s...	IGBP global vegetation classification scheme	18.0	Kazakh semi-desert	WWF Terrestrial Ecoregions of the World	4.707721e+06	5.251186e+06	1861.250866	MULTIPOLYGON (((4707487.856 5250953.673, 47079...
4	6	2000-11-02	307	11	2000	2000-11-02	1	3	0.643976	3.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Kazakh semi-desert	WWF Terrestrial Ecoregions of the World	4.643320e+06	5.306321e+06	4641.127165	MULTIPOLYGON (((4644016.014 5306089.886, 46444...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
212184	817204	2021-08-01	213	8	2021	2021-08-01	1	6	1.287952	6.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	10.0	Kazakh steppe	WWF Terrestrial Ecoregions of the World	4.309272e+06	5.597744e+06	7421.003464	MULTIPOLYGON (((4310894.171 5597050.272, 43113...
212185	817205	2021-08-01	213	8	2021	2021-08-01	1	3	0.643976	3.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	11.0	Central Asian riparian woodlands	WWF Terrestrial Ecoregions of the World	4.830962e+06	4.826792e+06	1861.250866	MULTIPOLYGON (((4831194.352 4826559.224, 48307...
212186	817209	2021-08-01	213	8	2021	2021-08-01	1	1	0.214659	1.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Kazakh semi-desert	WWF Terrestrial Ecoregions of the World	4.688261e+06	5.578285e+06	1861.250866	MULTIPOLYGON (((4688028.722 5578052.451, 46884...
212187	817217	2021-08-01	213	8	2021	2021-08-01	1	1	0.214659	1.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	9.0	Kazakh forest steppe	WWF Terrestrial Ecoregions of the World	4.153599e+06	5.775193e+06	1861.250866	MULTIPOLYGON (((4153365.847 5774960.355, 41538...
212188	817224	2021-08-01	213	8	2021	2021-08-01	1	3	0.643976	3.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	10.0	Kazakh steppe	WWF Terrestrial Ecoregions of the World	5.187712e+06	5.736275e+06	3714.501732	MULTIPOLYGON (((5187943.143 5735578.774, 51874...

	id	ig_date	ig_day	ig_month	ig_year	last_date	event_dur	tot_pix	tot_ar_km2	fsr_px_dy	...	lc_name	lc_desc	lc_type	eco_mode	eco_name	eco_type	ig_utm_x	ig_utm_y	tot_perim	geometry
207928	801338	2021-01-01	1	1	2021	2021-01-04	4	20	4.293173	5.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	3.0	Northwestern thorn scrub forests	WWF Terrestrial Ecoregions of the World	6.661974e+06	3.670827e+06	7421.003464	MULTIPOLYGON (((6662204.207 3670593.997, 66617...
207929	801339	2021-01-01	1	1	2021	2021-01-10	10	20	4.293173	2.0	...	Croplands	At least 60% of area is cultivated cropland	IGBP global vegetation classification scheme	2.0	Western Himalayan subalpine conifer forests	WWF Terrestrial Ecoregions of the World	6.683286e+06	3.821403e+06	7421.003464	MULTIPOLYGON (((6683518.592 3820707.317, 66830...
207930	801369	2021-01-02	2	1	2021	2021-01-04	3	18	3.863856	6.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.507690e+06	3.734764e+06	6494.378031	MULTIPOLYGON (((6507459.760 3734067.839, 65074...
207931	801371	2021-01-02	2	1	2021	2021-01-02	1	1	0.214659	1.0	...	Croplands	At least 60% of area is cultivated cropland	IGBP global vegetation classification scheme	3.0	Northwestern thorn scrub forests	WWF Terrestrial Ecoregions of the World	6.898726e+06	2.841034e+06	1861.250866	MULTIPOLYGON (((6898493.693 2840800.921, 68989...
207932	801375	2021-01-02	2	1	2021	2021-01-05	4	10	2.146587	2.5	...	Barren	At least 60% of area is non-vegetated barren(s...	IGBP global vegetation classification scheme	26.0	Registan-North Pakistan sandy desert	WWF Terrestrial Ecoregions of the World	5.882682e+06	3.477625e+06	6494.378031	MULTIPOLYGON (((5883377.531 3476465.968, 58829...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
212184	817204	2021-08-01	213	8	2021	2021-08-01	1	6	1.287952	6.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	10.0	Kazakh steppe	WWF Terrestrial Ecoregions of the World	4.309272e+06	5.597744e+06	7421.003464	MULTIPOLYGON (((4310894.171 5597050.272, 43113...
212185	817205	2021-08-01	213	8	2021	2021-08-01	1	3	0.643976	3.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	11.0	Central Asian riparian woodlands	WWF Terrestrial Ecoregions of the World	4.830962e+06	4.826792e+06	1861.250866	MULTIPOLYGON (((4831194.352 4826559.224, 48307...
212186	817209	2021-08-01	213	8	2021	2021-08-01	1	1	0.214659	1.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Kazakh semi-desert	WWF Terrestrial Ecoregions of the World	4.688261e+06	5.578285e+06	1861.250866	MULTIPOLYGON (((4688028.722 5578052.451, 46884...
212187	817217	2021-08-01	213	8	2021	2021-08-01	1	1	0.214659	1.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	9.0	Kazakh forest steppe	WWF Terrestrial Ecoregions of the World	4.153599e+06	5.775193e+06	1861.250866	MULTIPOLYGON (((4153365.847 5774960.355, 41538...
212188	817224	2021-08-01	213	8	2021	2021-08-01	1	3	0.643976	3.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	10.0	Kazakh steppe	WWF Terrestrial Ecoregions of the World	5.187712e+06	5.736275e+06	3714.501732	MULTIPOLYGON (((5187943.143 5735578.774, 51874...

	ID_0	ISO	NAME_0	ID_1	NAME_1	HASC_1	CCA_1	TYPE_1	ENGTYPE_1	NL_NAME_1	VARNAME_1	geometry
0	171	PAK	Pakistan	1	Azad Kashmir	PK.JK	None	Centrally Administered Area	Centrally Administered Area	None	Kashmir	POLYGON ((74.64930 35.13991, 74.66885 35.12403...
1	171	PAK	Pakistan	2	Baluchistan	PK.BA	None	Province	Province	None	Balochistan\|BéloutchistanBeluchistan\|Baluchistão	MULTIPOLYGON (((66.60542 24.90264, 66.60542 24...
2	171	PAK	Pakistan	3	F.A.T.A.	PK.TA	None	Territory	Territory	None	Federally Administered Tribal Areas	POLYGON ((71.55066 34.93081, 71.56419 34.91536...
3	171	PAK	Pakistan	4	F.C.T.	PK.IS	None	Capital Territory	Capital Territory	None	Islamabad\|Federal Capital Territory\|Federal Ca...	POLYGON ((72.88009 33.75444, 72.90147 33.75299...
4	171	PAK	Pakistan	5	N.W.F.P.	PK.NW	None	Province	Province	None	North-West Frontier Province	POLYGON ((73.66302 36.88824, 73.66410 36.88670...
5	171	PAK	Pakistan	6	Northern Areas	PK.NA	None	Centrally Administered Area	Centrally Administered Area	None	None	POLYGON ((74.74200 37.02206, 74.74920 37.02148...
6	171	PAK	Pakistan	7	Punjab	PK.PB	None	Province	Province	None	Pendjab\|Penjab	POLYGON ((73.53603 33.66520, 73.53673 33.65730...
7	171	PAK	Pakistan	8	Sind	PK.SD	None	Province	Province	None	Sindh	MULTIPOLYGON (((68.07542 23.70542, 68.07542 23...

	id	ig_date	ig_day	ig_month	ig_year	last_date	event_dur	tot_pix	tot_ar_km2	fsr_px_dy	...	lc_name	lc_desc	lc_type	eco_mode	eco_name	eco_type	ig_utm_x	ig_utm_y	tot_perim	geometry
207928	801338	2021-01-01	1	1	2021	2021-01-04	4	20	4.293173	5.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	3.0	Northwestern thorn scrub forests	WWF Terrestrial Ecoregions of the World	6.661974e+06	3.670827e+06	7421.003464	MULTIPOLYGON (((71.44837 33.01041, 71.44340 33...
207929	801339	2021-01-01	1	1	2021	2021-01-10	10	20	4.293173	2.0	...	Croplands	At least 60% of area is cultivated cropland	IGBP global vegetation classification scheme	2.0	Western Himalayan subalpine conifer forests	WWF Terrestrial Ecoregions of the World	6.683286e+06	3.821403e+06	7421.003464	MULTIPOLYGON (((72.81162 34.36041, 72.80657 34...
207930	801369	2021-01-02	2	1	2021	2021-01-04	3	18	3.863856	6.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.507690e+06	3.734764e+06	6494.378031	MULTIPOLYGON (((70.24698 33.58124, 70.24696 33...
207931	801371	2021-01-02	2	1	2021	2021-01-02	1	1	0.214659	1.0	...	Croplands	At least 60% of area is cultivated cropland	IGBP global vegetation classification scheme	3.0	Northwestern thorn scrub forests	WWF Terrestrial Ecoregions of the World	6.898726e+06	2.841034e+06	1861.250866	MULTIPOLYGON (((68.76286 25.54791, 68.76750 25...
207932	801375	2021-01-02	2	1	2021	2021-01-05	4	10	2.146587	2.5	...	Barren	At least 60% of area is non-vegetated barren(s...	IGBP global vegetation classification scheme	26.0	Registan-North Pakistan sandy desert	WWF Terrestrial Ecoregions of the World	5.882682e+06	3.477625e+06	6494.378031	MULTIPOLYGON (((61.89947 31.26457, 61.89458 31...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
212184	817204	2021-08-01	213	8	2021	2021-08-01	1	6	1.287952	6.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	10.0	Kazakh steppe	WWF Terrestrial Ecoregions of the World	4.309272e+06	5.597744e+06	7421.003464	MULTIPOLYGON (((60.73828 50.33543, 60.74481 50...
212185	817205	2021-08-01	213	8	2021	2021-08-01	1	3	0.643976	3.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	11.0	Central Asian riparian woodlands	WWF Terrestrial Ecoregions of the World	4.830962e+06	4.826792e+06	1861.250866	MULTIPOLYGON (((59.80445 43.40624, 59.79869 43...
212186	817209	2021-08-01	213	8	2021	2021-08-01	1	1	0.214659	1.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Kazakh semi-desert	WWF Terrestrial Ecoregions of the World	4.688261e+06	5.578285e+06	1861.250866	MULTIPOLYGON (((65.81552 50.16457, 65.82205 50...
212187	817217	2021-08-01	213	8	2021	2021-08-01	1	1	0.214659	1.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	9.0	Kazakh forest steppe	WWF Terrestrial Ecoregions of the World	4.153599e+06	5.775193e+06	1861.250866	MULTIPOLYGON (((60.58245 51.93541, 60.58923 51...
212188	817224	2021-08-01	213	8	2021	2021-08-01	1	3	0.643976	3.0	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	10.0	Kazakh steppe	WWF Terrestrial Ecoregions of the World	5.187712e+06	5.736275e+06	3714.501732	MULTIPOLYGON (((75.08194 51.58124, 75.07523 51...

	id	ig_date	ig_day	ig_month	ig_year	last_date	event_dur	tot_pix	tot_ar_km2	fsr_px_dy	...	lc_name	lc_desc	lc_type	eco_mode	eco_name	eco_type	ig_utm_x	ig_utm_y	tot_perim	geometry
208419	802235	2021-02-14	45	2	2021	2021-02-14	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.522053e+06	3.637468e+06	1861.250866	POLYGON ((69.70667 32.71041, 69.70994 32.71459...
208523	802679	2021-03-01	60	3	2021	2021-03-01	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.522053e+06	3.637931e+06	1861.250866	POLYGON ((69.70993 32.71457, 69.71320 32.71876...
208517	802594	2021-02-28	59	2	2021	2021-02-28	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.521126e+06	3.638395e+06	1861.250866	POLYGON ((69.70328 32.71874, 69.70655 32.72293...
208377	802088	2021-02-11	42	2	2021	2021-02-15	5	18	3.863856	3.600000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.521590e+06	3.638858e+06	7421.003464	POLYGON ((69.70823 32.71874, 69.71149 32.72291...
211529	815782	2021-06-29	180	6	2021	2021-07-06	8	36	7.727712	4.500000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.499351e+06	3.681020e+06	11127.505197	POLYGON ((69.77861 33.09791, 69.77364 33.09791...
211665	816028	2021-07-04	185	7	2021	2021-07-04	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.644368e+06	3.493378e+06	1861.250866	POLYGON ((70.01500 31.41457, 70.01812 31.41876...
208528	802699	2021-03-01	60	3	2021	2021-03-01	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.571628e+06	3.624495e+06	1861.250866	POLYGON ((70.14495 32.59374, 70.14822 32.59793...
208515	802583	2021-02-28	59	2	2021	2021-03-04	5	48	10.303616	9.600000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.555412e+06	3.732911e+06	19467.134094	POLYGON ((70.75005 33.56041, 70.75003 33.56041...
208341	802039	2021-02-08	39	2	2021	2021-02-10	3	10	2.146587	3.333333	...	Savannas	Tree cover 10-30% (canopy>2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.532709e+06	3.738007e+06	5567.752598	POLYGON ((70.54248 33.60624, 70.54589 33.61041...
208475	802426	2021-02-23	54	2	2021	2021-03-08	14	58	12.450203	4.142857	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.535026e+06	3.737080e+06	21320.384960	POLYGON ((70.56749 33.60624, 70.56249 33.60624...
208993	804627	2021-03-29	88	3	2021	2021-03-29	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.537342e+06	3.739397e+06	1861.250866	POLYGON ((70.60457 33.62707, 70.60800 33.63126...
208617	803704	2021-03-18	77	3	2021	2021-03-18	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.536879e+06	3.739397e+06	1861.250866	POLYGON ((70.59956 33.62707, 70.60299 33.63126...
208526	802695	2021-03-01	60	3	2021	2021-03-04	4	24	5.151808	6.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.568848e+06	3.624959e+06	7421.003464	POLYGON ((70.12517 32.59374, 70.12022 32.59374...
207945	801397	2021-01-03	3	1	2021	2021-01-04	2	8	1.717269	4.000000	...	Open Shrublands	Dominated by woody perennials (1-2m height)10-...	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.526223e+06	3.682409e+06	4641.127165	POLYGON ((70.07043 33.11457, 70.06545 33.11457...
208518	802595	2021-02-28	59	2	2021	2021-03-02	3	172	36.921292	57.333333	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.526686e+06	3.719011e+06	25953.512126	POLYGON ((70.34514 33.44374, 70.34017 33.44374...
208176	801763	2021-01-23	23	1	2021	2021-01-23	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.513250e+06	3.726887e+06	1861.250866	POLYGON ((70.25275 33.51457, 70.25615 33.51876...
208006	801519	2021-01-11	11	1	2021	2021-01-13	3	56	12.020886	18.666667	...	Open Shrublands	Dominated by woody perennials (1-2m height)10-...	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.515567e+06	3.727351e+06	15752.632362	POLYGON ((70.26936 33.51041, 70.26436 33.51041...
208594	803272	2021-03-09	68	3	2021	2021-03-17	9	150	32.198801	16.666667	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.505374e+06	3.729204e+06	23173.635826	POLYGON ((70.21777 33.51457, 70.22115 33.51874...
208173	801758	2021-01-23	23	1	2021	2021-01-23	1	2	0.429317	2.000000	...	Open Shrublands	Dominated by woody perennials (1-2m height)10-...	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.514177e+06	3.729204e+06	1861.250866	POLYGON ((70.27967 33.53541, 70.28308 33.53959...
208508	802532	2021-02-27	58	2	2021	2021-03-05	7	94	20.177915	13.428571	...	Savannas	Tree cover 10-30% (canopy>2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.504911e+06	3.732911e+06	16687.257795	POLYGON ((70.21196 33.58126, 70.21198 33.58126...
208081	801626	2021-01-15	15	1	2021	2021-01-23	9	88	18.889963	9.777778	...	Savannas	Tree cover 10-30% (canopy>2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.507690e+06	3.733837e+06	14834.006929	POLYGON ((70.22987 33.54791, 70.22985 33.54791...
208031	801559	2021-01-12	12	1	2021	2021-01-12	1	2	0.429317	2.000000	...	Savannas	Tree cover 10-30% (canopy>2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.505837e+06	3.733837e+06	1861.250866	POLYGON ((70.22356 33.57707, 70.22697 33.58126...
207930	801369	2021-01-02	2	1	2021	2021-01-04	3	18	3.863856	6.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.507690e+06	3.734764e+06	6494.378031	POLYGON ((70.24696 33.58124, 70.24198 33.58124...
208602	803404	2021-03-12	71	3	2021	2021-03-14	3	6	1.287952	2.000000	...	Savannas	Tree cover 10-30% (canopy>2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.504911e+06	3.735227e+06	3714.501732	POLYGON ((70.22534 33.58541, 70.22034 33.58541...
207967	801458	2021-01-07	7	1	2021	2021-01-07	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.521126e+06	3.741713e+06	1861.250866	POLYGON ((70.44647 33.64791, 70.44989 33.65209...
208092	801640	2021-01-16	16	1	2021	2021-01-26	11	132	28.334945	12.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.494254e+06	3.743103e+06	47265.897086	POLYGON ((70.16635 33.66041, 70.16975 33.66457...
207935	801379	2021-01-02	2	1	2021	2021-01-05	4	78	16.743377	19.500000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.494254e+06	3.748663e+06	15760.632362	POLYGON ((70.21019 33.68957, 70.20520 33.68957...
208892	804375	2021-03-27	86	3	2021	2021-03-27	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.536416e+06	3.739860e+06	1861.250866	POLYGON ((70.59797 33.63124, 70.60140 33.63543...
208053	801587	2021-01-13	13	1	2021	2021-01-17	5	14	3.005221	2.800000	...	Savannas	Tree cover 10-30% (canopy>2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.521126e+06	3.741250e+06	6494.378031	POLYGON ((70.44966 33.63957, 70.45307 33.64374...
208214	801831	2021-01-29	29	1	2021	2021-01-29	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.568384e+06	3.743103e+06	1861.250866	POLYGON ((70.96731 33.66041, 70.97076 33.66459...
208435	802285	2021-02-16	47	2	2021	2021-02-22	7	18	3.863856	2.571429	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.554022e+06	3.742177e+06	9274.254331	POLYGON ((70.81717 33.66043, 70.81374 33.65626...
207949	801406	2021-01-04	4	1	2021	2021-01-10	7	36	7.727712	5.142857	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.542902e+06	3.743103e+06	11127.505197	POLYGON ((70.70518 33.65207, 70.70516 33.65207...
208450	802325	2021-02-18	49	2	2021	2021-02-18	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.568848e+06	3.743567e+06	1861.250866	POLYGON ((70.97575 33.66457, 70.97921 33.66876...
208332	802019	2021-02-07	38	2	2021	2021-02-07	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.538732e+06	3.743567e+06	1861.250866	POLYGON ((70.65035 33.66457, 70.65379 33.66876...
208728	804063	2021-03-24	83	3	2021	2021-03-24	1	4	0.858635	4.000000	...	Croplands	At least 60% of area is cultivated cropland	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.529929e+06	3.757003e+06	3714.501732	POLYGON ((70.65463 33.78541, 70.65807 33.78957...
211353	815448	2021-06-21	172	6	2021	2021-06-28	8	92	19.748598	11.500000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	18.0	Sulaiman Range alpine meadows	WWF Terrestrial Ecoregions of the World	6.539659e+06	3.776925e+06	15760.632362	POLYGON ((70.88970 33.94791, 70.88468 33.94791...
211072	814599	2021-06-02	153	6	2021	2021-06-02	1	2	0.429317	2.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.556338e+06	3.785728e+06	1861.250866	POLYGON ((71.15576 34.04374, 71.15927 34.04793...
211178	814984	2021-06-10	161	6	2021	2021-06-13	4	32	6.869078	8.000000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.535026e+06	3.818160e+06	16679.257795	POLYGON ((71.16179 34.32707, 71.17109 34.33892...
208367	802072	2021-02-10	41	2	2021	2021-02-13	4	58	12.450203	14.500000	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.487305e+06	3.749590e+06	17613.883228	MULTIPOLYGON (((70.15732 33.72293, 70.15656 33...
208478	802439	2021-02-23	54	2	2021	2021-03-01	7	12	2.575904	1.714286	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.490548e+06	3.750980e+06	5567.752598	POLYGON ((70.19075 33.72707, 70.18574 33.72707...
208386	802111	2021-02-11	42	2	2021	2021-02-16	6	80	17.172694	13.333333	...	Grasslands	Dominated by herbaceous annuals (<2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.482208e+06	3.751906e+06	20393.759527	POLYGON ((70.08233 33.73541, 70.08573 33.73957...
208509	802535	2021-02-27	58	2	2021	2021-03-05	7	22	4.722491	3.142857	...	Savannas	Tree cover 10-30% (canopy>2m)	IGBP global vegetation classification scheme	7.0	Baluchistan xeric woodlands	WWF Terrestrial Ecoregions of the World	6.500741e+06	3.754223e+06	10200.879764	POLYGON ((70.32488 33.75624, 70.32829 33.76041...