NEON NIST data science evaluation challenge: methods and results of team Conor
- Published
- Accepted
- Subject Areas
- Biogeography, Ecology, Natural Resource Management, Forestry, Spatial and Geographic Information Science
- Keywords
- remote sensing, forestry, lidar, hyperspectral camera, segmentation, classification, alignment, ecology
- Copyright
- © 2018 McMahon
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. NEON NIST data science evaluation challenge: methods and results of team Conor. PeerJ Preprints 6:e26977v1 https://doi.org/10.7287/peerj.preprints.26977v1
Abstract
The NIST DSE Plant Identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. To compete in the competition, I created a pipeline to perform three remote sensing tasks. First, a NDVI- and height-thresholded watershed segmentation was performed to identify individual tree crowns using LIDAR height measurements. 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 PCA 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.
Author Comment
This is a submission to PeerJ for review.