In our new paper, led by my colleague Henry Scharf at the University of Arizona, we attempt to separate three resprouting deciduous species that all share a similar phenology. Extensive fire over the last several decades has removed large areas of conifer forest from the Jemez Mountains in northern New Mexico. Shrubs found in the understory have the ability to resprout and dominate the vegetation for the foreseeable future, representing a potential vegetation conversion. The work was recently published in the Journal of Agricultural, Biological and Environmental Statistics (open access preprint). We think this work is noteworthy because it combines an entire year’s worth of Sentinel-2 satellite data with field observations in a Bayesian framework. Personally, it was satisfying to see this come to fruition because it was a pandemic collaboration between myself and good friend Henry that extended research from one of my field sites. Jonathan Schierbaum, a graduate student with Henry, did much of the heavy lifting for the analysis and Hana Matsumoto, a former undergraduate student in my lab, ran all kinds of leg work for us (from corralling field data to writing code)!
Accurate information on the distribution of vegetation species is used as a proxy for the health of an ecosystem, a currency of international environmental treaties, and a necessary planning tool for forest preservation and rehabilitation, to name just a few of its applications. However, direct, extensive observation of vegetation across large geographic regions can be very expensive. The extensive coverage and high temporal resolution of remote sensing data collected by satellites like the European Space Agency’s Sentinel-2 system could be a critical component of a solution to this problem. We propose a hierarchical model for predicting vegetation cover that incorporates high resolution satellite imagery, landscape characteristics such as elevation and slope, and direct observation of vegetation cover. Besides providing model-based predictions of vegetation cover with accompanying uncertainty quantification, our proposed model offers inference about the effects of landscape characteristics on vegetation type. Implementation of the model is computationally challenging due to the volume and spatial extent of data involved. Thus, we propose an efficient, approximate method for model fitting that is able to make use of all available observations. We demonstrate our approach with an application to the distribution of three post-fire resprouting deciduous species in the Jemez Mountains of New Mexico.Supplementary materials accompanying this paper appear on-line.
Top image: The post-fire landscape in the Jemez Mountains, with the Sandia Mountains in the distance; New Mexico, USA