LiDAR-derived Forest Mapping in Mazia valley
Abstract
This dataset contains forest structural and species information derived from RGB-colored LiDAR point clouds collected by Eurac Research. Using a standardized R workflow (lidR, ForestTools), the LiDAR data were processed through ground classification, height normalization, canopy height model (CHM) generation, individual tree detection, crown segmentation, point-cloud tree assignment, and RGB-based unsupervised species clustering. The result is a set of spatial layers representing tree locations, tree crowns, canopy structure, and species group labels. These data products support forest monitoring, digital twin development, ecological analysis, and modelling of forest dynamics.
FAIR Overall Score
Keywords
Digital Object Identifier (DOI)
Citation
Mina, M., Tak, V., & Scuri, G. (2025). LiDAR-derived Forest Mapping in Mazia valley [Data set]. Eurac Research. https://doi.org/10.48784/JYGS-B177
Legal constraints
restricted, internal use only for Eurac Research
FAIR Overall Score: 96%
Evaluated by F-UJI - An Automated FAIR Data Assessment Tool: https://doi.org/10.5281/zenodo.6361400
Findable Advanced
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Metadata identifier follows a defined unique identifier syntax or scheme (IRI, URL, UUID, HASH or PID) (1/1)
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Metadata identifier follows a defined persistent identifier syntax (0.5/0.5)
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Persistent identifier for metadata is registered and maintained by a PID authority (0.5/0.5)
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Core data citation metadata is available (1/1)
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Core descriptive metadata is available (1/1)
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Metadata contains a PID or URL which indicates the location of the downloadable data content (1/1)
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Metadata is given in a way major search engines can ingest it for their catalogues (Dublin Core or schema.org or DCAT encoded in microdata, RDFa, embedded JSON-LD or meta tags see e.g. Google Dataset Search webmaster guidelines) (2/2)
Accessible Advanced
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Information about access restrictions or rights can be identified in metadata (1/1)
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Metadata are retrievable via their specified identifier (1/1)
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Data are retrievable via the identifiers given in metadata (1/1)
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Identifier leading to metadata matches a scheme indicating a standardized web communication protocol. (1/1)
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Identifier leading to data are matching a schema indicating a standardized web communication protocol. (1/1)
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The communication protocol found in identifiers (IRIs) leading to metadata supports authentication. (1/1)
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The communication protocol identified in data links (IRIs) supports authentication. (1/1)
Interoperable Advanced
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Parsable, structured metadata (JSON-LD, RDFa) is embedded in the landing page XHTML/HTML code (2/2)
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Parsable, structured metadata (RDF, JSON-LD) is accessible through content negotiation, typed links or sparql endpoint (2/2)
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Metadata uses terms from registered vocabularies that are identified by their namespaces (2/2)
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Related resources are referenced in plain text within appropriate metadata properties indicating the relation type (2/2)
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Related resources are referenced by machine readable links or identifiers within appropriate metadata properties indicating the relation type (2/2)
Reusable Moderate
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Minimum information (resource type) about the available data content is specified in the metadata (1/1)
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Information on the manner and form (file size and type or service (API) endpoint and protocol) in which data is delivered is provided (0/1)
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Licence information is given in an appropriate metadata element (1/1)
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Metadata contains elements which hold provenance information which can be mapped to PROV based on PROV-DC. (1/1)
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Metadata contains elements which hold provenance information using formal provenance ontologies (PROV, PAV). (0/1)
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Community specific metadata standard is detected using namespaces or schemas found in provided metadata (1/1)
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Multidisciplinary but community endorsed metadata (RDA Metadata Standards Catalog, fairsharing) standard is detected by namespace (1/1)
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Data is available in a file format recommended by the research community (long term file formats, open file formats or scientific file format) (1/1)
Snippet code
##### ----Explore and download STAC data with Python ----- #####
from pystac_client import Client
## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)
## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
print(f"Number of collections: {len(stac_collections)}")
## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
print(f"- {collection.id}")
print("-------------------------")
## Retrieve a specific collection
collection = catalog.get_collection("lidar_forestmapping_mazia_environtwin")
## Search for items in the collection
collection_items = list(catalog.search(collections=['lidar_forestmapping_mazia_environtwin'], max_items=10).items())
print(collection_items)
## Retrieve a list of the first 10 items belonging to a specific collection
item = collection.get_item("NameOfTheItem")
#print(list(item.assets.items())[0:10])
##Print the band name and the href of a specific item retrieved from the previous list
print(item.assets["lidar_forestmapping_mazia_environtwin"].title)
print(item.assets["lidar_forestmapping_mazia_environtwin"].href)
## Print Item’s assets through the assets attribute, which is a dictionary
for asset_key in item.assets:
asset = item.assets[asset_key]
print("{}: {} ({})".format(asset_key, asset.href, asset.media_type))
## ------------- DONWLOAD COG files from a specific collection ------- ####
from pystac_client import Client
import requests
## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)
## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
# print(f"Number of collections: {len(stac_collections)}")
## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
print(f"- {collection.id}")
print("-------------------------")
## Retrieve a specific collection
collection = catalog.get_collection("lidar_forestmapping_mazia_environtwin")
## Search for items in the collection
collection_items = list(catalog.search(collections=['lidar_forestmapping_mazia_environtwin'], max_items=10).items())
print(collection_items)
url_basepath = "https://eurac-eo.s3-eu-west-1.amazonaws.com/"
collection_url = url_basepath + "lidar_forestmapping_mazia_environtwin" + "/"
print(collection_url)
for item_id in collection_items:
# item = item.assets[asset_key]
print(item_id.id)
cog_file = str(item_id.id + '.tif')
url = collection_url + cog_file
response = requests.get(url)
with open(cog_file, "wb") as f:
f.write(response.content)
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| 1 | MOOC Cubes and Clouds | Free Online Course teaching the concepts of data cubes, cloud platforms and open science in geospatial and EO. | Link | March 8, 2024 | OpenEO, STAC |
| 2 | STAC guidelines | Documentation to browse and download items of the STAC catalog | Link | Feb. 22, 2024 | STAC |