Notebook page

Cube generation for SCS2: Forest recovery post disturbance in the EO-LINCS project¶

Scientific Case Study 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.

Requried datasets:

• CLMS Forest Type 2015
• Global Age Mapping Integration (GAMI) – Global forest age ensemble
• ForestPaths – European Tree Genus Map 2020
• Liu et al. – Europe aboveground biomass, canopy cover, and canopy height (30 m)
• ESA-CCI Biomass v6.0
• European Forest Disturbance Atlas

---

Overview of the Cube Generation Pipeline¶

This notebook demonstrates how users can access the required datasets via the xcube Multi-Source Data Store. All parameters controlling the cube generation workflow are defined in the configuration file config.yml.

Comprehensive documentation of the configuration schema is available on the documentation webpage. A step-by-step example showing how to set up a configuration file—while extracting information from multiple data stores—is provided here:
Setup Config YAML File.

In this notebook, we provide:

• A concise preview of the configuration file (summarized in a table)
• The requested geospatial domain
• A walkthrough of the cube generation process

---

Requirements¶

Before proceeding, ensure that all required dependencies are installed. The recommended approach is to create a Conda/Mamba environment using the provided environment specification:

conda/mamba env create -f environment.yml

The corresponding file can be found here: environment.yml

After creation, activate the environment:

conda activate eo-lincs-scs2

Next, access to CLMS data requires authentication credentials for the CLMS API, as described in the xcube-clms documentation.

These credentials should be stored in a JSON file (in this example clms-credentials.json) and are loaded during the cube generation process as specified in the configuration file config.yml.

Note that both the filename and the relative path to the credentials file are user-defined and can be adjusted if needed, provided they are correctly referenced in the configuration.

---

Running the Pipeline¶

Once all dependencies are installed and the environment is activated, the final data cubes can be generated by executing the provided notebook.

The resulting outputs will be written to the data directory, as specified in the configuration file config.yml. Users can modify the configuration to change the root directory of the storage data store if needed.

At the end of the notebook, basic visualizations of the generated data cubes are provided for quick inspection and validation.

---

Imports and cluster setup¶

Let's import the MultiSourceDataStore

In [1]:

from dask.distributed import Client, LocalCluster
import matplotlib.pyplot as plt
from xcube_multistore.multistore import MultiSourceDataStore

from dask.distributed import Client, LocalCluster import matplotlib.pyplot as plt from xcube_multistore.multistore import MultiSourceDataStore

We create a local Dask cluster to limit resource usage during cube generation. A larger memory allocation per worker is necessary because the GAMI dataset is relatively large, containing 20 members that are aggregated during the generation process. The number of workers can be reduced if insufficient RAM is available, although this will come at the cost of slower processing.

In [2]:

cluster = LocalCluster(
    n_workers=4,
    threads_per_worker=1,
    memory_limit="6GB",   # per worker
    dashboard_address=':8787',
)
client = Client(cluster)
client.dashboard_link

cluster = LocalCluster( n_workers=4, threads_per_worker=1, memory_limit="6GB", # per worker dashboard_address=':8787', ) client = Client(cluster) client.dashboard_link

Out[2]:

'http://127.0.0.1:8787/status'

---

Configuration of the Cube Generation Process¶

You can explore the available options for the configuration file using the function get_config_schema(). Run it and expand the fields to see all the properties that can be set, in combination with the Configuration Guide.

For guidance on creating your configuration file, you can also refer to the notebook setup_config.ipynb or consult the Configuration Guide in the documentation.

In [3]:

MultiSourceDataStore.get_config_schema()

MultiSourceDataStore.get_config_schema()

Out[3]:

<xcube.util.jsonschema.JsonObjectSchema at 0x79998eafb930>

We can now initialize the MultiSourceDataStore by providing the path to config.yml, which is located in the same directory as this notebook.

Here are a few notes explaining what happens during cube generation:

• The dataset forest-type-2015 from CLMS is preloaded using xcube-clms. Preloading is required because accessing the dataset involves sending a request to the CLMS API, queuing the request, and downloading the data before processing can begin. Once downloaded, the data is stored in the local cache datastore for subsequent use. (We focus on the 2015 dataset because more recent datasets have already been migrated to WekEO or CDSE. The migration for additional datasets is not yet complete. We contacted CLMS support regarding a roadmap, but did not receive a definitive timeline. As a result, we decided to wait for further updates before implementing access to newer CLMS datasets.)

• The config.yml also uses the custom_processing feature of xcube-multistore. This allows a custom processing function to be applied to each dataset individually. In this example, we define a module called utils, which contains a function that takes an xarray.Dataset as input and returns a modified xarray.Dataset. More details about custom_processing can be found in the xcube-multistore documentation.

Note: The following cell will produce a NoConformsTo warning. This occurs because the EO Forest STAC is a static STAC catalog rather than a full STAC API. It does not expose search capabilities or advertise STAC API conformance classes. This warning is harmless, as xcube-stac can also work with static STAC catalogs.

In [3]:

msds = MultiSourceDataStore("config.yml")

msds = MultiSourceDataStore("config.yml")

/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/pystac_client/client.py:191: NoConformsTo: Server does not advertise any conformance classes.
  warnings.warn(NoConformsTo())

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

In [5]:

msds.display_config()

msds.display_config()

Configuration

User-defined ID	Data Store ID	Data Store Params	Data ID	Open Data Params	Grid-Mapping	Format
forest_type_2015	clms	CLMS credentials	forest-type-2015|FTY_2015_020m_eu_03035_d04_E30N20.zarr	-	bbox: [3401404, 2346797, 3556068, 2539337]; spatial_res: 30; crs: EPSG:3035; tile_size: 2048	Zarr
gami	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	structure-demography/GAMI/GAMI_v3.1/GAMI_v3.1.json	-	bbox: [3401404, 2346797, 3556068, 2539337]; spatial_res: 30; crs: EPSG:3035; tile_size: 2048	Zarr
forestpaths_genus	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	structure-demography/FORESTPATHS_GENUS/FORESTPATHS_GENUS_v0.0.1/FORESTPATHS_GENUS_v0.0.1.json	-	bbox: [3401404, 2346797, 3556068, 2539337]; spatial_res: 30; crs: EPSG:3035; tile_size: 2048	Zarr
liu	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	biomass-carbon/LIU_BIOMASS/LIU_BIOMASS_v0.1/LIU_BIOMASS_v0.1.json	-	bbox: [3401404, 2346797, 3556068, 2539337]; spatial_res: 30; crs: EPSG:3035; tile_size: 2048	Zarr
esa_cci_biomass	cciodp	-	esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.100m	-	bbox: [3401404, 2346797, 3556068, 2539337]; spatial_res: 30; crs: EPSG:3035; tile_size: 2048	Zarr
efda	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	disturbance-change/EFDA/EFDA_v2.1.1/EFDA_v2.1.1.json	-	bbox: [3401404, 2346797, 3556068, 2539337]; spatial_res: 30; crs: EPSG:3035; tile_size: 2048	Zarr

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

In [6]:

msds.display_geolocations()

msds.display_geolocations()

Out[6]:

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

---

Cube Generation¶

Cube generation is triggered with a single command and proceeds in two stages:

1. Preloading¶

The workflow first preloads all required datasets from their respective stores:

• zenodo: Preloads the European Forest Disturbance Atlas.
• clms: Preloads the Forest Type map for 2015.

Preloading ensures that all input files, including archives or TIFFs, are available locally for processing.

2. Sequential Processing¶

Once preloading is complete, each dataset is processed sequentially. The workflow applies the following steps as needed:

• Spatial resampling to a common target grid
• Temporal aggregation or other dataset-specific preprocessing
• Writing the final datacube to the data/ output directory

Dataset Notes¶

• forest_type_2015: Loaded from the local CLMS cache (preloading output). User-defined processing specifications are applied during cube generation.

• gami: Aggregated using the custom function utils.modify_gami and resampled according to workflow specifications.

• forestpaths_genus, liu and efda: Accessed lazily via the EOForest STAC store using xcube’s STAC datastore, with resampling performed on the fly.

• biomass_esa_cci: Accessed lazily via the xcube ESA CCI datastore, with on-the-fly resampling.

Note: The warning "RuntimeWarning: invalid value encountered in divide" originates from NumPy when performing calculations involving NaN values. These warnings are harmless and can be safely ignored.

In [7]:

msds.generate()

msds.generate()

Preload Datasets from store 'clms'

Data ID	Status	Progress	Message	Exception
forest-type-2015|FTY_2015_020m_eu_03035_d04_E30N20	COMPLETED	100%	Task ID 81148080445: Preloading Complete.	-

Cube Generation

Dataset identifier	Status	Message	Exception
forest_type_2015	COMPLETED	Dataset 'forest_type_2015' finished: 0:00:09	-
gami	COMPLETED	Dataset 'gami' finished: 0:04:55	-
forestpaths_genus	COMPLETED	Dataset 'forestpaths_genus' finished: 0:00:36	-
liu	COMPLETED	Dataset 'liu' finished: 0:00:26	-
esa_cci_biomass	COMPLETED	Dataset 'esa_cci_biomass' finished: 0:07:28	-
efda	COMPLETED	Dataset 'efda' finished: 0:03:07	-

/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/distributed/client.py:3398: UserWarning: Sending large graph of size 877.13 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
  warnings.warn(
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/dask/_task_spec.py:768: RuntimeWarning: invalid value encountered in divide
  return self.func(*new_argspec)
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/dask/_task_spec.py:768: RuntimeWarning: invalid value encountered in divide
  return self.func(*new_argspec)
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/dask/_task_spec.py:768: RuntimeWarning: invalid value encountered in divide
  return self.func(*new_argspec)
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/dask/_task_spec.py:768: RuntimeWarning: invalid value encountered in divide
  return self.func(*new_argspec)
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/distributed/client.py:3398: UserWarning: Sending large graph of size 97.09 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
  warnings.warn(
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/distributed/client.py:3398: UserWarning: Sending large graph of size 78.03 MiB.
This may cause some slowdown.
Consider loading the data with Dask directly
 or using futures or delayed objects to embed the data into the graph without repetition.
See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.
  warnings.warn(
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/dask/array/chunk.py:288: RuntimeWarning: invalid value encountered in cast
  return x.astype(astype_dtype, **kwargs)
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/dask/array/chunk.py:288: RuntimeWarning: invalid value encountered in cast
  return x.astype(astype_dtype, **kwargs)
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/dask/array/chunk.py:288: RuntimeWarning: invalid value encountered in cast
  return x.astype(astype_dtype, **kwargs)
/home/konstantin/micromamba/envs/eo-lincs-scs2/lib/python3.13/site-packages/dask/array/chunk.py:288: RuntimeWarning: invalid value encountered in cast
  return x.astype(astype_dtype, **kwargs)

---

Initial Inspection of the Generated Datasets¶

After cube generation has completed, the datasets can be opened using the xcube datastore framework API as usual.

Note that the multi-source datastore expects a datastore named storage. This datastore is defined in the data_stores section of the config.yml configuration file.

In [8]:

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

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

Out[8]:

<xarray.Dataset> Size: 132MB
Dimensions:      (y: 6418, x: 5156)
Coordinates:
  * y            (y) float64 51kB 2.539e+06 2.539e+06 ... 2.347e+06 2.347e+06
  * x            (x) float64 41kB 3.401e+06 3.401e+06 ... 3.556e+06 3.556e+06
    spatial_ref  int64 8B ...
    time         datetime64[ns] 8B ...
Data variables:
    forest_age   (y, x) float32 132MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        GAMI_v3.1
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • y: 6418
  • x: 5156
• Coordinates: (4)
  • y
    (y)
    float64
    2.539e+06 2.539e+06 ... 2.347e+06

    array([2539322., 2539292., 2539262., ..., 2346872., 2346842., 2346812.],
          shape=(6418,))

  • x
    (x)
    float64
    3.401e+06 3.401e+06 ... 3.556e+06

    array([3401419., 3401449., 3401479., ..., 3556009., 3556039., 3556069.],
          shape=(5156,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",ENSEMBLE["European Terrestrial Reference System 1989 ensemble",MEMBER["European Terrestrial Reference Frame 1989"],MEMBER["European Terrestrial Reference Frame 1990"],MEMBER["European Terrestrial Reference Frame 1991"],MEMBER["European Terrestrial Reference Frame 1992"],MEMBER["European Terrestrial Reference Frame 1993"],MEMBER["European Terrestrial Reference Frame 1994"],MEMBER["European Terrestrial Reference Frame 1996"],MEMBER["European Terrestrial Reference Frame 1997"],MEMBER["European Terrestrial Reference Frame 2000"],MEMBER["European Terrestrial Reference Frame 2005"],MEMBER["European Terrestrial Reference Frame 2014"],MEMBER["European Terrestrial Reference Frame 2020"],ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[0.1]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["Europe Equal Area 2001",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing (Y)",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting (X)",east,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Statistical analysis."],AREA["Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Türkiye (Turkey); United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State."],BBOX[24.6,-35.58,84.73,44.83]],ID["EPSG",3035]]

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    geographic_crs_name :

    ETRS89

    grid_mapping_name :

    lambert_azimuthal_equal_area

    horizontal_datum_name :

    European Terrestrial Reference System 1989 ensemble

    inverse_flattening :

    298.257222101

    latitude_of_projection_origin :

    52.0

    longitude_of_prime_meridian :

    0.0

    longitude_of_projection_origin :

    10.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    reference_ellipsoid_name :

    GRS 1980

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    [1 values with dtype=int64]

  • time
    ()
    datetime64[ns]
    ...

    [1 values with dtype=datetime64[ns]]

• Data variables: (1)
  • forest_age
    (y, x)
    float32
    dask.array<chunksize=(2048, 2048), 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 126.23 MiB 16.00 MiB Shape (6418, 5156) (2048, 2048) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  GAMI_v3.1

  xcube_stac_version :

  1.3.1

We select a single variable at one time step, downsample it, and plot the result to quickly preview the data.

In [9]:

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

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

Out[9]:

<matplotlib.collections.QuadMesh at 0x799661a92120>

The same quicklook can be created for any generated dataset.

In [10]:

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

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

Out[10]:

<xarray.Dataset> Size: 265MB
Dimensions:      (y: 6418, x: 5156)
Coordinates:
  * y            (y) float64 51kB 2.539e+06 2.539e+06 ... 2.347e+06 2.347e+06
  * x            (x) float64 41kB 3.401e+06 3.401e+06 ... 3.556e+06 3.556e+06
    spatial_ref  int64 8B ...
    time         datetime64[ns] 8B ...
Data variables:
    genus        (y, x) float64 265MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        FORESTPATHS_GENUS_v0.0.1
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • y: 6418
  • x: 5156
• Coordinates: (4)
  • y
    (y)
    float64
    2.539e+06 2.539e+06 ... 2.347e+06

    array([2539322., 2539292., 2539262., ..., 2346872., 2346842., 2346812.],
          shape=(6418,))

  • x
    (x)
    float64
    3.401e+06 3.401e+06 ... 3.556e+06

    array([3401419., 3401449., 3401479., ..., 3556009., 3556039., 3556069.],
          shape=(5156,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",DATUM["European Terrestrial Reference System 1989",ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["unnamed",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting",east,ORDER[2],LENGTHUNIT["metre",1]],ID["EPSG",3035]]

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    geographic_crs_name :

    ETRS89

    grid_mapping_name :

    lambert_azimuthal_equal_area

    horizontal_datum_name :

    European Terrestrial Reference System 1989

    inverse_flattening :

    298.257222101

    latitude_of_projection_origin :

    52.0

    longitude_of_prime_meridian :

    0.0

    longitude_of_projection_origin :

    10.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    reference_ellipsoid_name :

    GRS 1980

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    [1 values with dtype=int64]

  • time
    ()
    datetime64[ns]
    ...

    [1 values with dtype=datetime64[ns]]

• Data variables: (1)
  • genus
    (y, x)
    float64
    dask.array<chunksize=(2048, 2048), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    European tree genus map at 10 m resolution derived from Sentinel-1 and Sentinel-2 data for reference year 2020. Categorical map describing dominant tree genus or functional group.

    flag_meanings :

    larix picea pinus fagus quercus other_needleleaf other_broadleaf no_trees

    flag_values :

    [0, 1, 2, 3, 4, 5, 6, 7]

    grid_mapping :

    spatial_ref

    long_name :

    Tree genus classification

    reference_year :

    2020

    source :

    European Tree Genus Map (Sentinel-1 & Sentinel-2)

    valid_max :

    7

    valid_min :

    0

    Array Chunk Bytes 252.47 MiB 32.00 MiB Shape (6418, 5156) (2048, 2048) Dask graph 12 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  FORESTPATHS_GENUS_v0.0.1

  xcube_stac_version :

  1.3.1

In [11]:

ds["genus"][::5, ::5].plot(vmax=7)

ds["genus"][::5, ::5].plot(vmax=7)

Out[11]:

<matplotlib.collections.QuadMesh at 0x79993f7d1d10>

In [4]:

ds = msds.stores.storage.open_data("forest_type_2015.zarr")
ds

ds = msds.stores.storage.open_data("forest_type_2015.zarr") ds

Out[4]:

<xarray.Dataset> Size: 265MB
Dimensions:                            (y: 6418, x: 5156)
Coordinates:
  * y                                  (y) float64 51kB 2.539e+06 ... 2.347e+06
  * x                                  (x) float64 41kB 3.401e+06 ... 3.556e+06
    spatial_ref                        int64 8B ...
Data variables:
    FTY_2015_020m_eu_03035_d04_E30N20  (y, x) float64 265MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
Attributes:
    AREA_OR_POINT:  Area
    DataType:       Thematic

xarray.Dataset

• Dimensions:
  • y: 6418
  • x: 5156
• Coordinates: (3)
  • y
    (y)
    float64
    2.539e+06 2.539e+06 ... 2.347e+06

    array([2539322., 2539292., 2539262., ..., 2346872., 2346842., 2346812.],
          shape=(6418,))

  • x
    (x)
    float64
    3.401e+06 3.401e+06 ... 3.556e+06

    array([3401419., 3401449., 3401479., ..., 3556009., 3556039., 3556069.],
          shape=(5156,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",ENSEMBLE["European Terrestrial Reference System 1989 ensemble",MEMBER["European Terrestrial Reference Frame 1989"],MEMBER["European Terrestrial Reference Frame 1990"],MEMBER["European Terrestrial Reference Frame 1991"],MEMBER["European Terrestrial Reference Frame 1992"],MEMBER["European Terrestrial Reference Frame 1993"],MEMBER["European Terrestrial Reference Frame 1994"],MEMBER["European Terrestrial Reference Frame 1996"],MEMBER["European Terrestrial Reference Frame 1997"],MEMBER["European Terrestrial Reference Frame 2000"],MEMBER["European Terrestrial Reference Frame 2005"],MEMBER["European Terrestrial Reference Frame 2014"],MEMBER["European Terrestrial Reference Frame 2020"],ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[0.1]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["Europe Equal Area 2001",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing (Y)",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting (X)",east,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Statistical analysis."],AREA["Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Türkiye (Turkey); United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State."],BBOX[24.6,-35.58,84.73,44.83]],ID["EPSG",3035]]

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    geographic_crs_name :

    ETRS89

    grid_mapping_name :

    lambert_azimuthal_equal_area

    horizontal_datum_name :

    European Terrestrial Reference System 1989 ensemble

    inverse_flattening :

    298.257222101

    latitude_of_projection_origin :

    52.0

    longitude_of_prime_meridian :

    0.0

    longitude_of_projection_origin :

    10.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    reference_ellipsoid_name :

    GRS 1980

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    [1 values with dtype=int64]

• Data variables: (1)
  • FTY_2015_020m_eu_03035_d04_E30N20
    (y, x)
    float64
    dask.array<chunksize=(2048, 2048), meta=np.ndarray>

    RepresentationType :

    THEMATIC

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 252.47 MiB 32.00 MiB Shape (6418, 5156) (2048, 2048) Dask graph 12 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (2)

  AREA_OR_POINT :

  Area

  DataType :

  Thematic

In [5]:

ds["FTY_2015_020m_eu_03035_d04_E30N20"][::5, ::5].plot(vmax=3)

ds["FTY_2015_020m_eu_03035_d04_E30N20"][::5, ::5].plot(vmax=3)

Out[5]:

<matplotlib.collections.QuadMesh at 0x70cf4a1f6ba0>

In [14]:

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

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

Out[14]:

<xarray.Dataset> Size: 794MB
Dimensions:              (y: 6418, x: 5156)
Coordinates:
  * y                    (y) float64 51kB 2.539e+06 2.539e+06 ... 2.347e+06
  * x                    (x) float64 41kB 3.401e+06 3.401e+06 ... 3.556e+06
    spatial_ref          int64 8B ...
    time                 datetime64[ns] 8B ...
Data variables:
    aboveground_biomass  (y, x) float64 265MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    canopy_cover         (y, x) float64 265MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
    canopy_height        (y, x) float64 265MB dask.array<chunksize=(2048, 2048), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        LIU_BIOMASS_v0.1
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • y: 6418
  • x: 5156
• Coordinates: (4)
  • y
    (y)
    float64
    2.539e+06 2.539e+06 ... 2.347e+06

    array([2539322., 2539292., 2539262., ..., 2346872., 2346842., 2346812.],
          shape=(6418,))

  • x
    (x)
    float64
    3.401e+06 3.401e+06 ... 3.556e+06

    array([3401419., 3401449., 3401479., ..., 3556009., 3556039., 3556069.],
          shape=(5156,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",DATUM["European Terrestrial Reference System 1989",ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["unnamed",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting",east,ORDER[2],LENGTHUNIT["metre",1]],ID["EPSG",3035]]

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    geographic_crs_name :

    ETRS89

    grid_mapping_name :

    lambert_azimuthal_equal_area

    horizontal_datum_name :

    European Terrestrial Reference System 1989

    inverse_flattening :

    298.257222101

    latitude_of_projection_origin :

    52.0

    longitude_of_prime_meridian :

    0.0

    longitude_of_projection_origin :

    10.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    reference_ellipsoid_name :

    GRS 1980

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    [1 values with dtype=int64]

  • time
    ()
    datetime64[ns]
    ...

    [1 values with dtype=datetime64[ns]]

• Data variables: (3)
  • aboveground_biomass
    (y, x)
    float64
    dask.array<chunksize=(2048, 2048), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    Aboveground woody biomass estimated from canopy cover and canopy height using allometric equations.

    grid_mapping :

    spatial_ref

    long_name :

    Aboveground biomass

    units :

    kg

    Array Chunk Bytes 252.47 MiB 32.00 MiB Shape (6418, 5156) (2048, 2048) Dask graph 12 chunks in 2 graph layers Data type float64 numpy.ndarray

  • canopy_cover
    (y, x)
    float64
    dask.array<chunksize=(2048, 2048), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    Fractional canopy cover derived from PlanetScope imagery.

    grid_mapping :

    spatial_ref

    long_name :

    Canopy cover

    units :

    percent

    valid_max :

    100

    valid_min :

    0

    Array Chunk Bytes 252.47 MiB 32.00 MiB Shape (6418, 5156) (2048, 2048) Dask graph 12 chunks in 2 graph layers Data type float64 numpy.ndarray

  • canopy_height
    (y, x)
    float64
    dask.array<chunksize=(2048, 2048), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    Mean canopy height derived from fusion of PlanetScope imagery and airborne LiDAR canopy height models.

    grid_mapping :

    spatial_ref

    long_name :

    Canopy height

    units :

    m

    Array Chunk Bytes 252.47 MiB 32.00 MiB Shape (6418, 5156) (2048, 2048) Dask graph 12 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  LIU_BIOMASS_v0.1

  xcube_stac_version :

  1.3.1

In [15]:

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

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

Out[15]:

<matplotlib.collections.QuadMesh at 0x79993e150550>

In [16]:

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

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

Out[16]:

<matplotlib.collections.QuadMesh at 0x79993e21afd0>

In [17]:

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

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

Out[17]:

<matplotlib.collections.QuadMesh at 0x79993e470050>

In [18]:

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

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

Out[18]:

<xarray.Dataset> Size: 1GB
Dimensions:            (time: 39, y: 6418, x: 5156)
Coordinates:
  * time               (time) datetime64[ns] 312B 1985-01-01 ... 2023-01-01
  * y                  (y) float64 51kB 2.539e+06 2.539e+06 ... 2.347e+06
  * x                  (x) float64 41kB 3.401e+06 3.401e+06 ... 3.556e+06
    spatial_ref        int64 8B ...
Data variables:
    disturbance_agent  (time, y, x) uint8 1GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        EFDA_v2.1.1
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • time: 39
  • y: 6418
  • x: 5156
• 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]')

  • y
    (y)
    float64
    2.539e+06 2.539e+06 ... 2.347e+06

    array([2539322., 2539292., 2539262., ..., 2346872., 2346842., 2346812.],
          shape=(6418,))

  • x
    (x)
    float64
    3.401e+06 3.401e+06 ... 3.556e+06

    array([3401419., 3401449., 3401479., ..., 3556009., 3556039., 3556069.],
          shape=(5156,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",DATUM["European Terrestrial Reference System 1989",ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["unnamed",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting",east,ORDER[2],LENGTHUNIT["metre",1]],ID["EPSG",3035]]

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    geographic_crs_name :

    ETRS89

    grid_mapping_name :

    lambert_azimuthal_equal_area

    horizontal_datum_name :

    European Terrestrial Reference System 1989

    inverse_flattening :

    298.257222101

    latitude_of_projection_origin :

    52.0

    longitude_of_prime_meridian :

    0.0

    longitude_of_projection_origin :

    10.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    reference_ellipsoid_name :

    GRS 1980

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    [1 values with dtype=int64]

• Data variables: (1)
  • disturbance_agent
    (time, y, x)
    uint8
    dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    Causal agent of forest disturbance following the EFDA classification.

    flag_descriptions :

    No Disturbance, Wind and bark beetle disturbance complex; Wildfire-related disturbance; Planned or salvage logging; Mixed agents (more than one disturbance agent occurred)

    flag_meanings :

    no_disturbance wind_bark_beetle fire harvest mixed

    flag_values :

    [0, 1, 2, 3, 4]

    grid_mapping :

    spatial_ref

    long_name :

    EFDA disturbance agent

    units :

    categorical

    Array Chunk Bytes 1.20 GiB 4.00 MiB Shape (39, 6418, 5156) (1, 2048, 2048) Dask graph 468 chunks in 2 graph layers Data type uint8 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  EFDA_v2.1.1

  xcube_stac_version :

  1.3.1

In [19]:

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

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

Out[19]:

<matplotlib.collections.QuadMesh at 0x799896416710>

In [20]:

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

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

Out[20]:

<xarray.Dataset> Size: 3GB
Dimensions:      (time: 10, y: 6418, x: 5156)
Coordinates:
  * time         (time) datetime64[ns] 80B 2007-07-02T11:59:59 ... 2022-07-02...
  * y            (y) float64 51kB 2.539e+06 2.539e+06 ... 2.347e+06 2.347e+06
  * x            (x) float64 41kB 3.401e+06 3.401e+06 ... 3.556e+06 3.556e+06
    spatial_ref  int64 8B ...
Data variables:
    agb          (time, y, x) float32 1GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>
    agb_sd       (time, y, x) float32 1GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    date_created:            2026-06-30T10:24:04.780645
    history:                 [{'cube_params': {'time_range': ['2007-01-01T00:...
    processing_level:        L4
    time_coverage_duration:  P5843DT23H59M59S
    time_coverage_end:       2022-12-31T23:59:59
    time_coverage_start:     2007-01-01T00:00:00
    title:                   esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-plat...

xarray.Dataset

• Dimensions:
  • time: 10
  • y: 6418
  • x: 5156
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2007-07-02T11:59:59 ... 2022-07-...

    bounds :

    time_bnds

    standard_name :

    time

    array(['2007-07-02T11:59:59.000000000', '2010-07-02T11:59:59.000000000',
           '2015-07-02T11:59:59.000000000', '2016-07-01T23:59:59.000000000',
           '2017-07-02T11:59:59.000000000', '2018-07-02T11:59:59.000000000',
           '2019-07-02T11:59:59.000000000', '2020-07-01T23:59:59.000000000',
           '2021-07-02T11:59:59.000000000', '2022-07-02T11:59:59.000000000'],
          dtype='datetime64[ns]')

  • y
    (y)
    float64
    2.539e+06 2.539e+06 ... 2.347e+06

    array([2539322., 2539292., 2539262., ..., 2346872., 2346842., 2346812.],
          shape=(6418,))

  • x
    (x)
    float64
    3.401e+06 3.401e+06 ... 3.556e+06

    array([3401419., 3401449., 3401479., ..., 3556009., 3556039., 3556069.],
          shape=(5156,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",ENSEMBLE["European Terrestrial Reference System 1989 ensemble",MEMBER["European Terrestrial Reference Frame 1989"],MEMBER["European Terrestrial Reference Frame 1990"],MEMBER["European Terrestrial Reference Frame 1991"],MEMBER["European Terrestrial Reference Frame 1992"],MEMBER["European Terrestrial Reference Frame 1993"],MEMBER["European Terrestrial Reference Frame 1994"],MEMBER["European Terrestrial Reference Frame 1996"],MEMBER["European Terrestrial Reference Frame 1997"],MEMBER["European Terrestrial Reference Frame 2000"],MEMBER["European Terrestrial Reference Frame 2005"],MEMBER["European Terrestrial Reference Frame 2014"],MEMBER["European Terrestrial Reference Frame 2020"],ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[0.1]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["Europe Equal Area 2001",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing (Y)",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting (X)",east,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Statistical analysis."],AREA["Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Türkiye (Turkey); United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State."],BBOX[24.6,-35.58,84.73,44.83]],ID["EPSG",3035]]

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    geographic_crs_name :

    ETRS89

    grid_mapping_name :

    lambert_azimuthal_equal_area

    horizontal_datum_name :

    European Terrestrial Reference System 1989 ensemble

    inverse_flattening :

    298.257222101

    latitude_of_projection_origin :

    52.0

    longitude_of_prime_meridian :

    0.0

    longitude_of_projection_origin :

    10.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    reference_ellipsoid_name :

    GRS 1980

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    [1 values with dtype=int64]

• Data variables: (2)
  • agb
    (time, y, x)
    float32
    dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>

    ancillary_variables :

    agb_se

    chunk_sizes :

    [1, 1875, 1875]

    data_type :

    int16

    dimensions :

    ['time', 'lat', 'lon']

    file_chunk_sizes :

    [1, 1875, 1875]

    file_dimensions :

    ['time', 'lat', 'lon']

    fill_value :

    -32768

    grid_mapping :

    spatial_ref

    long_name :

    Above-ground biomass

    orig_data_type :

    int16

    shape :

    [10, 157500, 405000]

    size :

    637875000000

    units :

    Mg/ha

    valid_max :

    10000.0

    valid_min :

    0.0

    Array Chunk Bytes 1.23 GiB 16.00 MiB Shape (10, 6418, 5156) (1, 2048, 2048) Dask graph 120 chunks in 2 graph layers Data type float32 numpy.ndarray

  • agb_sd
    (time, y, x)
    float32
    dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>

    chunk_sizes :

    [1, 1875, 1875]

    data_type :

    int16

    dimensions :

    ['time', 'lat', 'lon']

    file_chunk_sizes :

    [1, 1875, 1875]

    file_dimensions :

    ['time', 'lat', 'lon']

    fill_value :

    -32768

    grid_mapping :

    spatial_ref

    long_name :

    Above-ground biomass standard_deviation

    orig_data_type :

    int16

    shape :

    [10, 157500, 405000]

    size :

    637875000000

    units :

    Mg/ha

    valid_max :

    10000.0

    valid_min :

    0.0

    Array Chunk Bytes 1.23 GiB 16.00 MiB Shape (10, 6418, 5156) (1, 2048, 2048) Dask graph 120 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (8)

  Conventions :

  CF-1.7

  date_created :

  2026-06-30T10:24:04.780645

  history :

  [{'cube_params': {'time_range': ['2007-01-01T00:00:00', '2022-12-31T23:59:59'], 'variable_names': ['agb', 'agb_sd', 'crs']}, 'program': 'xcube_cci.chunkstore.CciChunkStore'}]

  processing_level :

  L4

  time_coverage_duration :

  P5843DT23H59M59S

  time_coverage_end :

  2022-12-31T23:59:59

  time_coverage_start :

  2007-01-01T00:00:00

  title :

  esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.100m

In [21]:

agb_coarse = ds["agb"][:, ::5, ::5]

sites = {
    "Site 1": (800, 600),
    "Site 2": (800, 800),
    "Site 3": (1000, 600),
    "Site 4": (1000, 800),
}

fig, (ax_map, ax_ts) = plt.subplots(1, 2, figsize=(16, 6))

agb_coarse.isel(time=0).plot(ax=ax_map)
ax_map.set_title("AGB (t=0) with selected sites")

for name, (iy, ix) in sites.items():
    ts = agb_coarse.isel(y=iy, x=ix)
    ts.plot(ax=ax_ts, label=name)
    ax_map.scatter(ts.x, ts.y, color="white", s=40, marker="x")
    ax_map.text(ts.x + 1000, ts.y + 1000, name, color="white", fontsize=10)

ax_ts.set_title("AGB time series at selected pixels")
ax_ts.legend()

agb_coarse = ds["agb"][:, ::5, ::5] sites = { "Site 1": (800, 600), "Site 2": (800, 800), "Site 3": (1000, 600), "Site 4": (1000, 800), } fig, (ax_map, ax_ts) = plt.subplots(1, 2, figsize=(16, 6)) agb_coarse.isel(time=0).plot(ax=ax_map) ax_map.set_title("AGB (t=0) with selected sites") for name, (iy, ix) in sites.items(): ts = agb_coarse.isel(y=iy, x=ix) ts.plot(ax=ax_ts, label=name) ax_map.scatter(ts.x, ts.y, color="white", s=40, marker="x") ax_map.text(ts.x + 1000, ts.y + 1000, name, color="white", fontsize=10) ax_ts.set_title("AGB time series at selected pixels") ax_ts.legend()

Out[21]:

<matplotlib.legend.Legend at 0x79994604d400>

Lastly, we perform a clean-up by closing the Dask Client and Cluster, ensuring that the notebook can be safely rerun without unexpected side effects.

In [22]:

client.close()
cluster.close()

client.close() cluster.close()