Sudden Oak Death Research

Sudden Oak Death is a new and virulent disease affecting hardwood forests in coastal California. The pathogen, Phytophthora ramorum, has reached epidemic levels in several California forests, killing thousands of trees. The pathogen also colonizes the foliage of several other overstory and understory hosts without killing them. The Kellylab has investigated the dynamics of the disease using geospatial technologies in the following areas:

Remote sensing of mortality

We are using digital imagery (ADAR 5500) (Spectral Bands: Blue: 450-550 nm, Green: 520-610 nm, Red: 610-700 nm, Near Infrared (NIR): 780-920 nm), at 1-m spatial resolution to map dead crowns, and forest structure. We are investigating different automated classifiers, including standard MLC and ISODATA, and comparing them to hybrid methods, object oriented methods, and machine learning classifiers. Former students Desheng Liu and Qinghua Guo were involved in this research. John Connors is porting this work to map the new SOD infestation in the East Bay Regional Parks.

Spatial pattern of mortality

This research attempts to explore the hypothesis that links the disease spread with presence of foliar hosts. In other words, by using remote sensing and spatial analysis as the primary tools, the spatial relationship between the suspected foliar hosts and terminal hosts can be explored over a longer time period (and into the past, prior to onset of the disease) and over a larger area than might be allowed using field methods alone. Former students Desheng Liu and Qinghua Guo were involved.

OakMapper webGIS: Monitoring Sudden Oak Death in California

Maintenance of accurate and precise spatial measurement of disease presence in the state is imperative for regulation. The Kelly lab maintains all statewide spatial data describing the confirmed locations of P. ramorum in a GIS format. We produce static maps, and provide an interactive GIS-based website for the public called the OakMapper. We've totally revamped the site using Open-source webGIS and Google mashup tools. John Connors and Shufei Lei are involved in this effort.

Modeling Sudden Oak Death risk across scales

We are using numerous spatial and Machine Learning models to estimate risk across landscape, regional and global scales as an aid to monitoring efforts. Qinghua Guo and Ken-ichi Ueda have been involved in this project.