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Article

High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests

1
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
2
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
3
Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 351; https://doi.org/10.3390/rs11030351
Received: 14 December 2018 / Revised: 2 February 2019 / Accepted: 4 February 2019 / Published: 10 February 2019
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods over large areas and with high confidence. We used airborne imaging spectroscopy data collected over three redwood forests by the Carnegie Airborne Observatory, in combination with field training data and application of a gradient boosted regression tree (GBRT) machine learning algorithm, to map the distribution of redwoods at 2-m spatial resolution. Training data collected from the three sites showed that redwoods have spectral signatures distinct from the other common tree species found in redwood forests. We optimized a gradient boosted regression model for high performance and computational efficiency, and the resulting model was demonstrably accurate (81–98% true positive rate and 90–98% overall accuracy) in mapping redwoods in each of the study sites. The resulting maps showed marked variation in redwood abundance (0–70%) within a 1 square kilometer aggregation block, which match the spatial resolution of currently-available redwood distribution maps. Our resulting high-resolution mapping approach will facilitate improved research, conservation, and management of redwood trees in California. View Full-Text
Keywords: Carnegie Airborne Observatory; tree species classification; coastal redwood; gradient boosted regression; CART; hyperspectral imagery Carnegie Airborne Observatory; tree species classification; coastal redwood; gradient boosted regression; CART; hyperspectral imagery
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MDPI and ACS Style

Francis, E.J.; Asner, G.P. High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests. Remote Sens. 2019, 11, 351. https://doi.org/10.3390/rs11030351

AMA Style

Francis EJ, Asner GP. High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests. Remote Sensing. 2019; 11(3):351. https://doi.org/10.3390/rs11030351

Chicago/Turabian Style

Francis, Emily J., and Gregory P. Asner. 2019. "High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests" Remote Sensing 11, no. 3: 351. https://doi.org/10.3390/rs11030351

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