Next Article in Journal
Detecting Targets above the Earth’s Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1
Previous Article in Journal
Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy
Open AccessArticle

A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery

1
Department of Social Sciences, California Polytechnic State University, San Luis Obispo, CA 93407, USA
2
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
3
Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
4
Amazon Web Services, Amazon Corp., Seattle, WA 98109, USA
5
U.S. Forest Service, PSW Research Station, Mammoth Lakes, CA 93546, USA
6
Bren School of Environmental Science & Management, University of California, Santa Barbara, CA 93106, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2326; https://doi.org/10.3390/rs11192326
Received: 24 July 2019 / Revised: 28 September 2019 / Accepted: 29 September 2019 / Published: 6 October 2019
(This article belongs to the Section Environmental Remote Sensing)
In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data. View Full-Text
Keywords: deep learning; species distribution modeling; convolutional neural networks; hyperspectral imagery deep learning; species distribution modeling; convolutional neural networks; hyperspectral imagery
Show Figures

Graphical abstract

MDPI and ACS Style

Fricker, G.A.; Ventura, J.D.; Wolf, J.A.; North, M.P.; Davis, F.W.; Franklin, J. A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery. Remote Sens. 2019, 11, 2326.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3470250
    Link: https://zenodo.org/record/3470250#.XZVW7kZKhPY
    Description: Data to replicate our results are hosted online (https://zenodo.org/record/3470250#.XZVW7kZKhPY) and code to run the analysis can be found in an online repository (https://github.com/jonathanventura/canopy)
Back to TopTop