Data & Code
LCSC source code and data products are available free of charge to the public. Source code can be accessed via our GitHub account. MODIS data products are archived and available via FTP from the Land Processes Distributed Active Archive Center (DAAC) at EROS Data Center. Other data sets created through funding from NASA’s ABoVE project and the NSF’s Macrosystems Biology Funding are available from the Oak Ridge National DAAC. Specific information provided below.
For more information on science, algorithms and applications, readers are also referred to the papers listed on the “Publications” page.
Before using MCD12 data, users are encouraged to review available documentation, including the user manual and “known issues” document, which are provided below. Extensive information related to the entire suite of MODIS land products, is provided at the DAAC.
ABoVE: Peak Greenness for Canadian Boreal Forest from Landsat 5 TM Imagery, 1984-2011
This dataset provides a 28-year time series of peak greenness (NDVI) data derived from Landsat 5 TM imagery over the boreal forest region of Canada. Landsat 5 TM scenes were collected for 46 selected sidelap sites along gradients in climate, tree cover, and disturbance history from 1984 to 2011. Peak-greenness reflectance was computed for 30-m Landsat pixels using the maximum normalized difference vegetation index (NDVI) along with the normalized burn ratio (NBR) during the period between days of the year (DOY) 180 and 204.
ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014.
This dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas.
Landsat-derived Spring and Autumn Phenology, Eastern US – Canadian Forests, 1984-2013
This dataset provides Landsat phenology algorithm (LPA) derived start and end of growing seasons (SOS and EOS) at 500-m resolution for deciduous and mixed forest areas of 75 selected Landsat sidelap regions across the Eastern United States and Canada. The data are a 30-year time series (1984-2013) of derived spring and autumn phenology for forested areas of the Eastern Temperate Forest, Northern Forest, and Taiga ecoregions.
PhenoCam Dataset v1.0: Vegetation Phenology from Digital Camera Imagery, 2000-2015.
This data set provides a time series of vegetation phenological observations for 133 sites across diverse ecosystems of North America and Europe from 2000-2015. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site.
PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018.
This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 393 sites predominantly in North America from 2000-2018.
PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018.
This data set provides time series of vegetation phenological observations for 393 sites across diverse ecosystems of North America and Europe from 2000-2018. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site.
Annual Phenology Derived from Landsat across the ABoVE Core Domain, 1984-2014.
This dataset provides annual maps of the timing of spring onset with leaf emergence, of autumn onset with leaf senescence, and of peak greenness for each 30 m pixel derived from Landsat time series of Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) observations from 1984 to 2014. The ABoVE core domain includes 169 ABoVE grid tiles across Alaska, USA and Alberta, British Columbia, Northwest Territories, Nunavut, Saskatchewan, and Yukon, Canada. The data provided for deriving seasonality includes the total number of cloud-free observations, r-squared values between observed and spline-predicted Enhanced Vegetation Index (EVI), long-term average minimum EVI, long-term average maximum EVI, long-term average spring onset, long-term average autumn onset, annual spring onset, and annual autumn onset. The data provided for peak greenness includes annual peak Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), annual composite red reflectance, annual composite NIR reflectance, annual composite shortwave infrared reflectance (band 6, SWIR1), annual composite shortwave infrared reflectance (band 7, SWIR2), number of dates used to calculate composites, and day of year of associated maximum composite.
ABoVE: Annual Aboveground Biomass for Boreal Forests of ABoVE Core Domain, 1984-2014
This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests.