The System for Terrestrial Ecosystem Parameterization (STEP) is a model for deriving vegetation and land surface parameters from remote sensing data for use in remote sensing-based classification of land cover, ecosystems, and vegetation types. The model defines parameters that relate to important ecological and biogeophysical parameters and that can be reliably measured or inferred from remote sensing, collateral, and field plot data. STEP is maintained as a database of training polygons drawn on high spatial resolution imagery that can be extracted with GIS to produce a global land cover classification. STEP is periodically reviewed to filter out inconsistent sites and augmented to fill gaps in biogeographical coverage. The database was originally created to follow the International Geosphere-Biosphere Programme (IGBP) land cover legend but it has since evolved to support any number of additional classifications.
The model does not assume that all of the site parameters can be measured, estimated and/or inferred to the same level of precision and accuracy. Broadly speaking, the analysts who work on STEP should know which ecosystems and alliances are to be found in a particular region and should be able to make some level of remote sensing-supported observations and inferences about a particular site. Google Earth has become an essential tool for reviewing and creating STEP polygons and instructions detailing this procedure represent a major update for this version.
Important note: this dataset is suitbable for training, not for validation since the samples do not follow a probability sampling scheme.
References:
Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., & Schaaf, C. (2002). Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83, 287-302.
Friedl, M. A., Muchoney, D., McIver, D., Gao, F., Hodges, J. C. F., & Strahler, A. H. (2000). Characterization of North American land cover from NOAA-AVHRR data using the EOS MODIS land cover classification algorithm. Geophysical Research Letters, 27 (7), 977-980.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168-182.
Muchoney, D., Borak, J., Chi, H., Friedl, M., Gopal, S., Hodges, J., Morrow, N., & Strahler, A. (2000). Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America. International Journal of Remote Sensing, 21, 1115-1138.
Schneider, A., Friedl, M. A., & Potere, D. (2009). A new map of global urban extent from MODIS satellite data. Environmental Research Letters, 4, 044003.
Schneider, A., Friedl, M. A., & Potere, D. (2010). Mapping global urban areas using MODIS 500-m data: New methods and datasets based on 'urban ecoregions'. Remote Sensing of Environment, 114, 1733-1746.
Sulla-Menashe, D., Friedl, M. A., Krankina, O. N., Baccini, A., Woodcock, C. E., Sun, G., Kharuk, V., & Elsakov, V. (2011). Hierarchical mapping of Northern Eurasian land cover using MODIS data. Remote Sensing of Environment, 115, 392-403.
Access to the data
We provide an access to a subset of the current version of the dataset. A stratified (by IGBP class legend) random selection of 70% of the samples has been done.
You can download this dataset in different formats via the buttons provided at the bottom of this page.
Last update: September 15, 2014
Spatial distribution of the randomly selected sample plots of the STEP dataset available on the the portal
