Abstract
The National Institute of Standards and Technology data science evaluation plant identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. I created a pipeline to perform three remote sensing tasks. First, a marker-controlled watershed segmentation thresholded by vegetation index and height was performed to identify individual tree crowns within the canopy height model. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset's dimensionality through principle component analysis and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.
Keywords:
Alignment; Biogeography; Classification; Data science; Ecology; Forestry; Hyperspectral camera; Lidar; Remote sensing; Segmentation.
Grants and funding
The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program. The ECODSE competition was supported, in part, by a research grant from NIST IAD Data Science Research Program to D. Z. Wang, E. P. White, and S. Bohlman, by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through grant GBMF4563 to E. P. White, and by an NSF Dimension of Biodiversity program grant (DEB-1442280) to S. Bohlman. These funding sources allowed the collection of the data used in the competition and the specification of the competition rules. The authors received no resources from these organizations outside of the data provided for the competition. There was no additional internal or external funding received for this study. The data provided for the competition were provided by the National Ecological Observatory Network as described in the Funding Statement. Provision of this data was the only manner in which resources were provided by that organization. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.