Broadly, I am interested in the fusion of our ever-evolving technological capabilities with conservation and resource planning objectives. Remote sensing technology provides new types and scales of data collection while increased data capacity and software improvements allow for the development of new analytical tools and the wider implementation of once cost prohibitive techniques.
More specifically, my research involves the use of remotely sensed data, spatially referenced predictive modeling techniques, and various geospatial tools. In addition, I am implementing scripting in Python as a method for handling iterative processes and communication across platforms.
Rangeland Productivity Modelling
I am developing a statewide model of rangeland forage productivity given various precipitation scenarios for CalFire's Fire and Resource Assessment Program (FRAP). This model, built using readily available statewide vegetation and climate data sets is a cost effective solution for examining statewide resource availability.
Development pressure and projected impacts from climate change are two primary challenges facing conservation in California. I am interested in developing a predictive habitat distribution model which integrates climate change models with land use change models to provide a more complete picture of future availability and distribution of rangeland resources.