Advancing process-based land cover mapping in arid rangelands via integration with long-term validation datasets
Classifications of land cover/land use, and land cover change have been useful for documenting forest loss, urbanization and habitat conversion. However, the current global land cover data products are insufficient for arid grasslands for two reasons. First, current classification products have poor performance in arid systems and are extremely unreliable. Second, these classifications are discrete characterizations of dynamic systems and do not provide any information about grassland degradation, or relative condition; rather grassland is classified as a singular static state. Combined, these limitations mean that our ability to discern any information about landscape change in rangelands with current land cover mapping approaches is extremely limited.
At SESYNC, I propose to create and implement a new ontology for arid grassland classification that incorporates spatio-temporal patterns from remote sensing data to represent land-cover dynamics, land-use history, and grassland condition. Working in collaboration with Dr. Deb Peters from the Jornada Basin LTER, I will synthesize long-term vegetation, climate and management data from a series of LTER sites to validate the classifications derived from satellite data. I will extend these classifications by integrating them into a system dynamics model for the study sites.
The idea for this project comes from a need for a more accurate portrayal of grassland dynamics in a spatial context. The end goal of this project is to create a new land cover product for arid rangelands globally, that can serve as a complement and corollary to the current forest cover change product.