Assessing Efficacy of Different Probabilistic Softwares with a Bayesian Hierarchal Model
Information on vegetation distributions is a key factor in establishing a baseline for ecological health as well as influencing environmental regulations and policy, but collecting data on these distributions can be costly and difficult. We have developed a Bayesian hierarchical model for three deciduous tree species in order to predict and classify sites according to the dominant vegetation cover with relation to wildfire-driven forest conversion in the Jemez Mountains of New Mexico. The dataset we are using consists of remote sensing data collected with moderate resolution satellites from a pilot study area at 60 reference sites. Currently, we are investigating the efficiency and efficacy of various probabilistic softwares with the application of this model, such as Nimble and STAN.