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Remote Sens. 2019, 11(1), 69; https://doi.org/10.3390/rs11010069

Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

1
Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA
2
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
3
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
4
Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA
5
International Arctic Research Center, University of Alaska, Fairbanks, AK 99775, USA
*
Author to whom correspondence should be addressed.
Received: 1 November 2018 / Revised: 15 December 2018 / Accepted: 25 December 2018 / Published: 2 January 2019
(This article belongs to the Special Issue Machine Learning Applications in Earth Science Big Data Analysis)
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Abstract

Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ∼343 km2 area, and a high-resolution (5 m) vegetation classification map was generated. An unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters. We employed a quantitative method to add supervision to the unlabeled clusters, producing a fully labeled vegetation map. We then developed convolutional neural networks (CNNs) using the multi-sensor fusion datasets to map vegetation distributions using the original classes and the classes produced by the unsupervised classification method. To validate the resulting CNN maps, vegetation observations were collected at 30 field plots during the summer of 2016, and the resulting vegetation products developed were evaluated against them for accuracy. Our analysis indicates the CNN models based on the labels produced by the unsupervised classification method provided the most accurate mapping of vegetation types, increasing the validation score (i.e., precision) from 0.53 to 0.83 when evaluated against field vegetation observations. View Full-Text
Keywords: hyperspectral; field-scale mapping; arctic; vegetation classification; convolutional neural network hyperspectral; field-scale mapping; arctic; vegetation classification; convolutional neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Langford, Z.L.; Kumar, J.; Hoffman, F.M.; Breen, A.L.; Iversen, C.M. Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks. Remote Sens. 2019, 11, 69.

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