Reliable, geographically-referenced data on global vegetative cover is an important requirement for global models of the earth system. Satellite data provide the only truly synoptic view of the earth, and may potentially increase the quality, internal consistency, and reproducibility of global land cover information.
This project initially aimed to develop a coarse resolution, global land cover data set from satellite data for use in climate models. To this end, AVHRR data were resampled to a spatial reolution of one by one degree and used to carry out a conventional, supervised classification of global land cover. Classifications have also proceeded at a finer spatial resolution of 8km at a continental scale. In addition to describing vegetative cover according to topological schemes, the project has explored methodologies to represent vegetative cover more realistically as gradients and mosaics of cover types.
Most recently we have worked with colleagues in the Geography Department at the University of Maryland to develop land cover characterizations for net primary productivity models. Supervised classifications at finer spatial resolutions are underway, drawing particularly on the Pathfinder 1-km and 8-km data sets. The current project aim is to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data.