Description: High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics.

Description: The global forest age dataset (GFAD v.1.1) provides a correction to GFAD v1.0, as well as its uncertainties. GFAD describes the age distributions of plant functional types (PFT) on a 0.5-degree grid. Each grid cell contains information on the fraction of each PFT within an age class. The four PFTs, needleaf evergreen (NEEV), needleleaf deciduous (NEDE), broadleaf evergreen (BREV) and broadleaf deciduous (BRDC) are mapped from the MODIS Collection 5.1 land cover dataset, crosswalking land cover types to PFT fractions. The source of data for the age distributions is from country-level forest inventory for temperate and high-latitude countries, and from biomass for tropical countries. The inventory and biomass data are related to fifteen age classes defined in ten-year intervals, from 1-10 up to a class greater than 150 years old. The uncertainties are estimated for the inventory derived forest age classes as +/- 40% of the mean age. For the areas where age is derived from aboveground biomass, the uncertainty is derived from the 5th and 95th percentile estimates of biomass, but using the same age-aboveground biomass curves. The GFAD dataset represents the 2000-2010 era.

Description: The global forest age dataset (GFAD) describes the age distributions of plant functional types (PFT) on a 0.5-degree grid. Each grid cell contains information on the fraction of each PFT within an age class. The four PFTs, needleaf evergreen (NEEV), needleleaf deciduous (NEDE), broadleaf evergreen (BREV) and broadleaf deciduous (BRDC) are mapped from the MODIS Collection 5.1 land cover dataset, crosswalking land cover types to PFT fractions. The source of data for the age distributions is from country-level forest inventory for temperate and high-latitude countries, and from biomass for tropical countries. The inventory and biomass data are related to fifteen age classes defined in ten-year intervals, from 1-10 up to a class greater than 150 years old. The GFAD dataset represents the 2000-2010 era.

Description: This project was originally designed and implemented by Ben Poulter in 2003/2004 to investigate vegetation composition across a gradient of saltwater exposure. The study was then replicated by Paul Taillie in 2016/2017 to investigate vegetation change. The files are grouped in an R project and thus are relatively linked. This means that you do not have to change your working directory to get scripts to run on your machine. Simply unzip the folder, open R, select file〉open project〉browse to the unzipped folder〉click the Rproject file called "APPveg_data." Each script starts with a preamble that will clear your workspace and load the required packages. You may have to install packages first if they are not yet installed on your machine. Otherwise, the script should run from start to finish. Non-R users can still access the raw data easily, which is in .csv format.