Remote sensing canopy condition and habitat

Remote sensing canopy condition and habitat

Led by Jen Swenson, Amanda Schwantes, Chris Kilner

Remote sensing, including NASA products and the NEON AOP are being used to evaluate canopy condition variables related to masting status and habitat for mast consumers.  We have processed lidar data across 42 NEON sites for 2017/2018 using FUSION. We have calculated vegetation structure metrics from the LiDAR point clouds at the NEON plots (~3000 total) for small mammals, birds, vascular plants, beetles, and ticks. We are currently evaluating vegetation structure metrics that are important for mast status and consumer abundances across the continental United States.

The banner for this page shows how understory varies across a transect, where understory is dense to the left of the clearing but relatively open to the right. At each of these NEON sites, we also calculated fine-scale topographic metrics, such as slope, aspect, elevation, standard deviation in elevation, topographic position index, and topographic wetness index to capture microtopographic variability and better explain how species abundance varies across landscapes.

We have generated nine hyperspectral-derived vegetation, soil, and canopy condition indices across all NEON sites for 2016 and 2017: albedo, Leaf Area Index, biomass, Atmospherically Resistant Vegetation Index, Enhanced Vegetation Index, Normalized Difference Lignin Index, Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index, and Photochemical Reflectance Index . Summarized as plot means and variances (Figure at right), these metrics are being used with liar summaries to evaluate predictive performance for NEON biodiversity data and mast.  We are now acquiring and processing the full hyperspectral stacks from NEON AOP imagery for all NEON sites from 2013 – 2018.

We have developed a workflow for downloading remote sensing and environmental data from Google Earth Engine that will be converted into a python package. We can now download environmental data related to land cover, topography, soil condition, climate, soil moisture (SMOS), and Landsat and MODIS vegetation indices/products.