Skip to content

Living Wales

Wales earth observation monitoring environment policy

  • Data
  • Geo-portal
  • Planet
  • Learning & Art
  • Themes
  • Collaborations
  • About Us

Canopy Cover Retrieval

HomeDataRemote Sensing AlgorithmsVegetation AlgorithmsCanopy Cover Retrieval

Following products are available for canopy cover or fractional canopy cover (FCOVER):

  • Copernicus BioPAR GEOv1
  • Copernicus BioPAR GEOv2
  • Copernicus BioPAR GEOv3
  • USGS Landsat 7 ETM+ Global Tree Canopy Cover (circa 2010)
  • NASA Landsat 5TM/ETM+ Global Tree Canopy Cover
  • NASA MODIS MOD44B VCF
  • CYCLOPES FCOVER

Copernicus BioPAR GEOv1

Period: 1999 – present Sensor: SPOT-VGT and PROBA-V Frequency: 10 days Resolutions: 1km Extent: Global Data can be accessed here. Algorithm description: The algorithm follows same approach as in for LAI and FAPAR GEOv1 product generation. The algorithm involves training of neural nets using CYCLOPES canopy cover data and SPOT-VGT and PROBA-V reflectance products. As researchers have reported underestimation issues in the CYCLOPES FCOVER…

Read more Copernicus BioPAR GEOv1

Copernicus BioPAR GEOv2

Period: 1998 – present Sensor: SPOT-VGT and PROBA-V Frequency: 10 days Resolutions: 1km Extent: Global Data can be accessed here. Algorithm description: The algorithm is based on neural network and similar to that for GEOv1 canopy cover product. However, novel temporal compositing is implemented using time series data from GEOv1 so as to reduce gaps in the dataset. Accuracy: GEOV2 FCOVER product have been…

Read more Copernicus BioPAR GEOv2

Copernicus BioPAR GEOv3

Period: 2014 – present Sensor: PROBA-V Frequency: 10 days Resolutions: 300m Extent: Global Data can be accessed here. Algorithm description: The algorithm is similar to GEOV2 product (neural network trained on CYCLOPES data and temporal compositing using historical time series data). The algorithm is implemented on PROBA-V data at 300m resolution. As the neural nets have been trained on CYCLOPES FCOVER and SPOT-VGT reflectance…

Read more Copernicus BioPAR GEOv3

USGS Landsat 7 ETM+ Global Tree Canopy Cover (circa 2010)

Period: 2010 Sensor: Landsat 7 ETM+ Resolutions: 30m Extent: Global Data can be accessed here. Algorithm description: The precise algorithm for the product has not been described in any one manuscript or ATBD. However, the algorithm is similar to those published in Hansen et al. 2002, 2008 and 2013. The algorithm uses training datasets of continuous forest cover and annual phenological…

Read more USGS Landsat 7 ETM+ Global Tree Canopy Cover (circa 2010)

NASA Landsat 5TM/ETM+ Global Tree Canopy Cover

Period: 2000, 2005, 2010, 2015 Sensor: Landsat 5 TM/ETM+ Frequency: 5 yearly Resolutions: 30m Extent: Global Data can be accessed here. Algorithm description: The Landsat Vegetation Continuous fields (VCF) tree cover maps provide estimates of percentage of horizontal ground covered by woody vegetation greater than 5m in height. The algorithm is based on Sexton et al (2013). The tree cover is estimated…

Read more NASA Landsat 5TM/ETM+ Global Tree Canopy Cover

NASA MODIS MOD44B VCF

Period: 2000 – 2017 Sensor: MODIS Terra Frequency: Yearly Resolutions: 250m Extent: Global Data can be accessed here. Algorithm description: The algorithm for MODIS VCF product is based on Hansen et al (2002, 2003). It is similar to that used for USGS Landsat 7 ETM+ global tree cover product. MODIS reflectance in all 7 bands, NDVI and surface temperature data are used to…

Read more NASA MODIS MOD44B VCF

CYCLOPES FCOVER

Period: 1999 – 2007 Sensor: SPOT-VGT Frequency: 10 days Resolutions: 1km Extent: Global Data can be accessed here. Algorithm description: The algorithm employs neural network based inversion of PROSAIL radiative transfer model (Baret et al, 2009). SPOT-VGT data are converted into top of canopy reflectance in nadir direction through four steps: cloud screening, atmospheric correction, BRDF normalisation and temporal compositing. Finally, these…

Read more CYCLOPES FCOVER

Proudly powered by WordPress | Theme: Edin by WordPress.com.
  • Data
  • Geo-portal
  • Planet
  • Learning & Art
  • Themes
  • Collaborations
  • About Us