EO-LINCS project: Cube generation for Scientific Case Study (SCS) 2¶

Forest recovery post disturbance¶

Objective: The SCS2 aims to estimate canopy height and above-ground biomass maps using deep learning methods based on remote sensing data. The canopy height and above-ground biomass maps are subsequently used to calculate recovery curves, which in turn can be used to estimate carbon budget.

Outcomes: New high-resolution height/biomass maps that are expected to enable the monitoring of biomass at finer scales, in particular the impact of fine scale forest disturbances due to management practices such as thinning and the impact of natural disturbances (insects attacks, droughts, fires and windthrown in regions of interest). Analysis of forest recovery depending on environmental factors (such as climate, soil composition and pH) and the nature and intensity of disturbance shall aid in the optimization of forest management considering potential increased future disturbances.

Required Datasets:

• Global Age Mapping Integration (GAMI) dataset
• European Forest Disturbance Atlas
• Canopy height and biomass map for Europe
• Copernicus Tree cover density 2015
• Copernicus Dominant Leaf Type 2015
• Copernicus Forest Type 2015

Note: the last three datassets are excluded until the CLMS dataset migration is finalized.

---

The following notebook shows how the users can load data from various sources defined in scs2_config.yml using the MultiSourceDataStore tool.

What You Can Do with This Notebook¶

• Load datasets from various sources as defined in the scs2_config.yml
• View the progress of each data request to the MultiSourceDataStore
• Quickly preview the datasets by downsampling and plotting them.

Requirements¶

Before you begin, ensure that all necessary dependencies are installed and that you have generated API token credentials for CLMS:

• Install xcube-multistore via conda-forge by running: conda install --channel conda-forge xcube-multistore
• Generate CLMS API token credentials by following the offical instructions. Save the credentials in a file named clms-credentials.json, placing it in the same directory as this notebook. Note that the filename can be customized in the scs2_config.yml configuration file.

Once you have it installed, you are ready to proceed.

This Multistore mainly works with a file called scs2_config.yml which is at the same file level as this notebook. To understand what goes into the schema, you can read more here.

---

Let's import the MultiSourceDataStore

In [1]:

from xcube_multistore import MultiSourceDataStore

from xcube_multistore import MultiSourceDataStore

You can find out how to fill out the config file by also using this super helpful function get_config_schema(). Run it and try expand the fields to learn more about the possible properties that the configuration file accepts along with the Configuration Guide.

In [2]:

MultiSourceDataStore.get_config_schema()

MultiSourceDataStore.get_config_schema()

Out[2]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7e5aa4f5ac10>

To set up the config file, please refer to the notebook setup_config.ipynb or the Configuration Guide in the documentation.

Now, we can initialize the MultiSourceDataStore by passing the path to the scs2_config.yml which currently is on the same level as this notebook. It shows, that for the user-defined dataset ID clms and senf, some data fusion of multiple data sources will be performed.

In [3]:

msds = MultiSourceDataStore("scs2_config.yml")

msds = MultiSourceDataStore("scs2_config.yml")

And we can display the overview of the configuration file for each dataset.

In [4]:

msds.display_config()

msds.display_config()

Configuration

User-defined ID	Data Store ID	Data Store Params	Data ID	Open Data Params	Grid-Mapping	Format
senf	zenodo	root: 13333034	france/annual_disturbances_1985_2023_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
senf	zenodo	root: 13333034	france/disturbance_agent_1985_2023_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
senf	zenodo	root: 13333034	france/greatest_disturbance_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
senf	zenodo	root: 13333034	france/forest_mask_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
senf	zenodo	root: 13333034	france/disturbance_agent_aggregated_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
senf	zenodo	root: 13333034	france/latest_disturbance_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
senf	zenodo	root: 13333034	france/number_disturbances_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
senf	zenodo	root: 13333034	france/disturbance_probability_1985_2023_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
senf	zenodo	root: 13333034	france/disturbance_severity_1985_2023_france.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
gami	s3	root: dog.atlaseo-glm.eo-gridded-data; storage_options: {'anon': True, 'endpoint_url': 'https://s3.gfz-potsdam.de'}	collections/GAMI/GAMI_v2.1.zarr	-	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
liu	zenodo	root: 8154445	planet_agb_30m_v0.1.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
liu	zenodo	root: 8154445	planet_canopy_cover_30m_v0.1.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr
liu	zenodo	root: 8154445	planet_canopy_height_30m_v0.1.tif	band_as_variable: False	bbox: [-1.25, 43.5, 0.5, 44.8]; spatial_res: 0.00025; crs: EPSG:4326; tile_size: 4000	Zarr

We can display the selected bounding box as shown in the following cell.

In [5]:

msds.display_geolocations()

msds.display_geolocations()

Out[5]:

Make this Notebook Trusted to load map: File -> Trust Notebook

We can now generate the datacubes:

NOTE: In the scs2_config.yml we are also using the custom_processing feature of this tool that allows us to run a function for processing each dataset separately. In this example, we have defined a module called modify_dataset that does some custom processing which takes a xarray.Dataset argument as input and returns a new xarray.Dataset object. To read more about this custom_processing function, you can see more here.

In [6]:

msds.generate()

msds.generate()

Preload Datasets from store 'zenodo_senf'

Dataset identifier	Status	Message	Exception
france.zip	COMPLETED	Already preloaded.	-

Cube Generation

Dataset identifier	Status	Message	Exception
senf	COMPLETED	Dataset 'senf' finished: 0:24:48	-
gami	COMPLETED	Dataset 'gami' finished: 0:00:35	-
liu	COMPLETED	Dataset 'liu' finished: 0:17:30	-

We can now open the data using the xcube datastore framework API as usual. Note that the multi-source data store requires a data store called storage, which is configured in our scs2_config.yml under the data_stores section.

In [7]:

ds = msds.stores.storage.open_data("gami.zarr")
ds

ds = msds.stores.storage.open_data("gami.zarr") ds

Out[7]:

<xarray.Dataset> Size: 6GB
Dimensions:      (members: 20, time: 2, latitude: 5199, longitude: 7000,
                  lat: 5199, lon: 7000)
Coordinates:
  * members      (members) int64 160B 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19
  * time         (time) datetime64[ns] 16B 2010-01-01 2020-01-01
  * lat          (lat) float64 42kB 44.8 44.8 44.8 44.8 ... 43.5 43.5 43.5 43.5
  * lon          (lon) float64 56kB -1.25 -1.25 -1.249 ... 0.4994 0.4996 0.4999
    spatial_ref  int64 8B ...
Dimensions without coordinates: latitude, longitude
Data variables:
    forest_age   (members, time, latitude, longitude) float32 6GB dask.array<chunksize=(5, 2, 4000, 4000), meta=np.ndarray>
Attributes:
    _FillValue:          -9999
    contact:             Simon Besnard (GFZ Potsdam)
    created_by:          Simon Besnard
    creation_date:       2025-12-16 12:33
    crs:                 EPSG:4326
    institution:         Helmholtz Centre Potsdam – GFZ German Research Centr...
    product_name:        Global Age Mapping Integration (GAMI)
    spatial_resolution:  100 m
    title:               Global Age Mapping Integration (GAMI) v2.1
    version:             2.1

xarray.Dataset

• Dimensions:
  • members: 20
  • time: 2
  • latitude: 5199
  • longitude: 7000
  • lat: 5199
  • lon: 7000
• Coordinates: (5)
  • members
    (members)
    int64
    0 1 2 3 4 5 6 ... 14 15 16 17 18 19

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19])

  • time
    (time)
    datetime64[ns]
    2010-01-01 2020-01-01

    array(['2010-01-01T00:00:00.000000000', '2020-01-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    44.8 44.8 44.8 ... 43.5 43.5 43.5

    array([44.799625, 44.799375, 44.799125, ..., 43.500625, 43.500375, 43.500125],
          shape=(5199,))

  • lon
    (lon)
    float64
    -1.25 -1.25 ... 0.4996 0.4999

    array([-1.249875, -1.249625, -1.249375, ...,  0.499375,  0.499625,  0.499875],
          shape=(7000,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["WGS 84",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]]

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    latitude_longitude

    horizontal_datum_name :

    World Geodetic System 1984

    inverse_flattening :

    298.257223563

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    reference_ellipsoid_name :

    WGS 84

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    [1 values with dtype=int64]

• Data variables: (1)
  • forest_age
    (members, time, latitude, longitude)
    float32
    dask.array<chunksize=(5, 2, 4000, 4000), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    long_name :

    Forest age using ML + Landsat-based time since disturbance fusion

    units :

    years

    valid_max :

    300

    valid_min :

    1

    Array Chunk Bytes 5.42 GiB 610.35 MiB Shape (20, 2, 5199, 7000) (5, 2, 4000, 4000) Dask graph 16 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (10)

  _FillValue :

  -9999

  contact :

  Simon Besnard (GFZ Potsdam)

  created_by :

  Simon Besnard

  creation_date :

  2025-12-16 12:33

  crs :

  EPSG:4326

  institution :

  Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences; Max Planck Institute for Biogeochemistry

  product_name :

  Global Age Mapping Integration (GAMI)

  spatial_resolution :

  100 m

  title :

  Global Age Mapping Integration (GAMI) v2.1

  version :

  2.1

We can now select a variable for one timestep, downsample and plot it for a quick preview of the data

In [8]:

ds["forest_age"].isel(members=0, time=0)[::5, ::5].plot(vmin=0, vmax=120)

ds["forest_age"].isel(members=0, time=0)[::5, ::5].plot(vmin=0, vmax=120)

Out[8]:

<matplotlib.collections.QuadMesh at 0x7e5aa1cb8830>

In [9]:

ds["forest_age"].isel(members=0, time=1)[::5, ::5].plot(vmin=0, vmax=120)

ds["forest_age"].isel(members=0, time=1)[::5, ::5].plot(vmin=0, vmax=120)

Out[9]:

<matplotlib.collections.QuadMesh at 0x7e5aa1e8da90>

In [10]:

ds = msds.stores.storage.open_data("liu.zarr")
ds

ds = msds.stores.storage.open_data("liu.zarr") ds

Out[10]:

<xarray.Dataset> Size: 874MB
Dimensions:        (lat: 5199, lon: 7000)
Coordinates:
  * lat            (lat) float64 42kB 44.8 44.8 44.8 44.8 ... 43.5 43.5 43.5
  * lon            (lon) float64 56kB -1.25 -1.25 -1.249 ... 0.4996 0.4999
    spatial_ref    int64 8B ...
Data variables:
    agb            (lat, lon) float64 291MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    canopy_cover   (lat, lon) float64 291MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    canopy_height  (lat, lon) float64 291MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • lat: 5199
  • lon: 7000
• Coordinates: (3)
  • lat
    (lat)
    float64
    44.8 44.8 44.8 ... 43.5 43.5 43.5

    array([44.799625, 44.799375, 44.799125, ..., 43.500625, 43.500375, 43.500125],
          shape=(5199,))

  • lon
    (lon)
    float64
    -1.25 -1.25 ... 0.4996 0.4999

    array([-1.249875, -1.249625, -1.249375, ...,  0.499375,  0.499625,  0.499875],
          shape=(7000,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    latitude_longitude

    horizontal_datum_name :

    World Geodetic System 1984 ensemble

    inverse_flattening :

    298.257223563

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    reference_ellipsoid_name :

    WGS 84

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    [1 values with dtype=int64]

• Data variables: (3)
  • agb
    (lat, lon)
    float64
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 277.66 MiB 122.07 MiB Shape (5199, 7000) (4000, 4000) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • canopy_cover
    (lat, lon)
    float64
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 277.66 MiB 122.07 MiB Shape (5199, 7000) (4000, 4000) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • canopy_height
    (lat, lon)
    float64
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 277.66 MiB 122.07 MiB Shape (5199, 7000) (4000, 4000) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

In [11]:

ds["agb"][::5, ::5].plot(vmax=20000)

ds["agb"][::5, ::5].plot(vmax=20000)

Out[11]:

<matplotlib.collections.QuadMesh at 0x7e5aa0636d50>

In [12]:

ds["canopy_cover"][::5, ::5].plot()

ds["canopy_cover"][::5, ::5].plot()

Out[12]:

<matplotlib.collections.QuadMesh at 0x7e5aa0af5e50>

In [13]:

ds["canopy_height"][::5, ::5].plot()

ds["canopy_height"][::5, ::5].plot()

Out[13]:

<matplotlib.collections.QuadMesh at 0x7e5aa1cf3610>

In [14]:

ds = msds.stores.storage.open_data("senf.zarr")
ds

ds = msds.stores.storage.open_data("senf.zarr") ds

Out[14]:

<xarray.Dataset> Size: 47GB
Dimensions:                       (time: 39, lat: 5199, lon: 7000)
Coordinates:
  * time                          (time) datetime64[ns] 312B 1985-01-01 ... 2...
  * lat                           (lat) float64 42kB 44.8 44.8 ... 43.5 43.5
  * lon                           (lon) float64 56kB -1.25 -1.25 ... 0.4999
    spatial_ref                   int64 8B ...
Data variables:
    annual_disturbances           (time, lat, lon) float64 11GB dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>
    disturbance_agent             (time, lat, lon) float64 11GB dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>
    disturbance_agent_aggregated  (lat, lon) float64 291MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    disturbance_probability       (time, lat, lon) float64 11GB dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>
    disturbance_severity          (time, lat, lon) float64 11GB dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>
    forest_mask                   (lat, lon) float64 291MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    greatest_disturbance          (lat, lon) float64 291MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    latest_disturbance            (lat, lon) float64 291MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    number_disturbances           (lat, lon) float64 291MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 39
  • lat: 5199
  • lon: 7000
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1985-01-01 ... 2023-01-01

    array(['1985-01-01T00:00:00.000000000', '1986-01-01T00:00:00.000000000',
           '1987-01-01T00:00:00.000000000', '1988-01-01T00:00:00.000000000',
           '1989-01-01T00:00:00.000000000', '1990-01-01T00:00:00.000000000',
           '1991-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000',
           '1993-01-01T00:00:00.000000000', '1994-01-01T00:00:00.000000000',
           '1995-01-01T00:00:00.000000000', '1996-01-01T00:00:00.000000000',
           '1997-01-01T00:00:00.000000000', '1998-01-01T00:00:00.000000000',
           '1999-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',
           '2001-01-01T00:00:00.000000000', '2002-01-01T00:00:00.000000000',
           '2003-01-01T00:00:00.000000000', '2004-01-01T00:00:00.000000000',
           '2005-01-01T00:00:00.000000000', '2006-01-01T00:00:00.000000000',
           '2007-01-01T00:00:00.000000000', '2008-01-01T00:00:00.000000000',
           '2009-01-01T00:00:00.000000000', '2010-01-01T00:00:00.000000000',
           '2011-01-01T00:00:00.000000000', '2012-01-01T00:00:00.000000000',
           '2013-01-01T00:00:00.000000000', '2014-01-01T00:00:00.000000000',
           '2015-01-01T00:00:00.000000000', '2016-01-01T00:00:00.000000000',
           '2017-01-01T00:00:00.000000000', '2018-01-01T00:00:00.000000000',
           '2019-01-01T00:00:00.000000000', '2020-01-01T00:00:00.000000000',
           '2021-01-01T00:00:00.000000000', '2022-01-01T00:00:00.000000000',
           '2023-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    44.8 44.8 44.8 ... 43.5 43.5 43.5

    array([44.799625, 44.799375, 44.799125, ..., 43.500625, 43.500375, 43.500125],
          shape=(5199,))

  • lon
    (lon)
    float64
    -1.25 -1.25 ... 0.4996 0.4999

    array([-1.249875, -1.249625, -1.249375, ...,  0.499375,  0.499625,  0.499875],
          shape=(7000,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    latitude_longitude

    horizontal_datum_name :

    World Geodetic System 1984 ensemble

    inverse_flattening :

    298.257223563

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    reference_ellipsoid_name :

    WGS 84

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    [1 values with dtype=int64]

• Data variables: (9)
  • annual_disturbances
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.57 GiB 122.07 MiB Shape (39, 5199, 7000) (1, 4000, 4000) Dask graph 156 chunks in 2 graph layers Data type float64 numpy.ndarray

  • disturbance_agent
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.57 GiB 122.07 MiB Shape (39, 5199, 7000) (1, 4000, 4000) Dask graph 156 chunks in 2 graph layers Data type float64 numpy.ndarray

  • disturbance_agent_aggregated
    (lat, lon)
    float64
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 277.66 MiB 122.07 MiB Shape (5199, 7000) (4000, 4000) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • disturbance_probability
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.57 GiB 122.07 MiB Shape (39, 5199, 7000) (1, 4000, 4000) Dask graph 156 chunks in 2 graph layers Data type float64 numpy.ndarray

  • disturbance_severity
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.57 GiB 122.07 MiB Shape (39, 5199, 7000) (1, 4000, 4000) Dask graph 156 chunks in 2 graph layers Data type float64 numpy.ndarray

  • forest_mask
    (lat, lon)
    float64
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 277.66 MiB 122.07 MiB Shape (5199, 7000) (4000, 4000) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • greatest_disturbance
    (lat, lon)
    float64
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 277.66 MiB 122.07 MiB Shape (5199, 7000) (4000, 4000) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • latest_disturbance
    (lat, lon)
    float64
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 277.66 MiB 122.07 MiB Shape (5199, 7000) (4000, 4000) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • number_disturbances
    (lat, lon)
    float64
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 277.66 MiB 122.07 MiB Shape (5199, 7000) (4000, 4000) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

In [18]:

ds.annual_disturbances.encoding

ds.annual_disturbances.encoding

Out[18]:

{'chunks': (1, 4000, 4000),
 'preferred_chunks': {'time': 1, 'lat': 4000, 'lon': 4000},
 'compressor': Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0),
 'filters': None,
 '_FillValue': np.uint8(255),
 'scale_factor': 1.0,
 'add_offset': 0.0,
 'dtype': dtype('uint8'),
 'coordinates': 'spatial_ref'}

In [15]:

ds["annual_disturbances"].isel(time=10)[::5, ::5].plot()

ds["annual_disturbances"].isel(time=10)[::5, ::5].plot()

Out[15]:

<matplotlib.collections.QuadMesh at 0x7e5aa0881d10>

In [16]:

ds["disturbance_probability"].isel(time=10)[::5, ::5].plot()

ds["disturbance_probability"].isel(time=10)[::5, ::5].plot()

Out[16]:

<matplotlib.collections.QuadMesh at 0x7e5aa08ff4d0>