# Source: Hansen Global Forest Change (GFC)
# Type: cog
# Catalog: Manual
# Created for: CBGB benchmark problems

source:
  id: umd-hansen-global-forest-change
  name: Hansen Global Forest Change v1.11
  type: cog
  url: https://glad.earthengine.app/view/global-forest-change
  description: >
    Global Forest Change (GFC) dataset from the University of Maryland, mapping
    global tree cover extent, loss, and gain at 30m resolution from 2000-2023.
    Derived from Landsat time-series imagery. Tree cover is defined as canopy
    closure for all vegetation taller than 5m. Loss is defined as stand-replacement
    disturbance (complete removal of canopy cover). Gain is defined as the inverse
    of loss (non-forest to forest). The dataset is organized as 10x10 degree tiles
    hosted on public Google Cloud Storage.
  provider: University of Maryland (UMD)
  license: CC-BY-4.0
  metadata:
    connector_id: null
    tags:
    - Forest
    - Deforestation
    - Tree Cover
    - Global
    - Landsat
    - 30m
    - Hansen
    dataset:
      tier: 1
      thematic_group: Vegetation & Agriculture
      format: cog
      resolution: 30m
      crs: EPSG:4326
      bands:
      - id: treecover2000
        description: Tree canopy cover for year 2000 (percentage 0-100)
        dtype: uint8
      - id: loss
        description: Forest loss during 2001-2023 (binary 0/1)
        dtype: uint8
      - id: gain
        description: Forest gain during 2001-2012 (binary 0/1)
        dtype: uint8
      - id: lossyear
        description: Year of forest loss (1-23 for 2001-2023, 0 = no loss)
        dtype: uint8
      - id: datamask
        description: Data mask (0=no data, 1=mapped land, 2=water)
        dtype: uint8
      - id: first
        description: Landsat spectral composite for circa 2000
        dtype: uint8
      - id: last
        description: Landsat spectral composite for circa 2023
        dtype: uint8
      temporal:
        range: 2000-01-01/2023-12-31
        frequency: annual
        type: time_series
        default_range: latest
      quality:
        methodology: >
          Supervised classification of Landsat 7 ETM+ and Landsat 8 OLI imagery.
          Training data from global sample of Landsat scenes. Per-pixel analysis
          of time-series data to characterize forest extent and change.
        accuracy: >
          Overall accuracy varies by biome; global accuracy ~99% for tree cover
          loss detection. See Hansen et al. 2013, Science 342(6160).
        coverage: global
      layout:
        type: tiled
        spatial:
          scheme: grid-10x10
        uri_template: https://storage.googleapis.com/earthenginepartners-hansen/GFC-2023-v1.11/Hansen_GFC-2023-v1.11_{band}_{lat}_{lon}.tif
        parameters:
        - var: band
          values:
          - treecover2000
          - loss
          - gain
          - lossyear
          - datamask
        - var: lat
          description: Latitude of tile upper-left corner (e.g., 40N, 10S)
        - var: lon
          description: Longitude of tile upper-left corner (e.g., 080W, 100E)
      original_source_url: https://storage.googleapis.com/earthenginepartners-hansen/
      homepage: https://glad.earthengine.app/view/global-forest-change
      gee_asset_id: UMD/hansen/global_forest_change_2023_v1_11
      cbgb_problems:
      - EBA_F5.1_A1
      - EBA_F5.1_A2
      - EBD_F5.1_C1