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Article

Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

1
Earth Lab, University of Colorado Boulder, 4001 Discovery Drive, Suite S348 611 UCB, Boulder, CO 80303, USA
2
Department of Geography, University of Colorado Boulder, GUGG 110, 260 UCB, Boulder, CO 80309, USA
3
Human-Environment Systems, ERB 4153, 1215 W University Drive, Boise State University, Boise, ID 83706, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(9), 1414; https://doi.org/10.3390/rs12091414
Received: 30 March 2020 / Revised: 24 April 2020 / Accepted: 28 April 2020 / Published: 30 April 2020
(This article belongs to the Special Issue She Maps)
Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters. View Full-Text
Keywords: airborne remote sensing; tree species classification; National Ecological Observatory Network; machine learning; hyperspectral; multispectral; LiDAR; training data preparation airborne remote sensing; tree species classification; National Ecological Observatory Network; machine learning; hyperspectral; multispectral; LiDAR; training data preparation
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MDPI and ACS Style

Scholl, V.M.; Cattau, M.E.; Joseph, M.B.; Balch, J.K. Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification. Remote Sens. 2020, 12, 1414. https://doi.org/10.3390/rs12091414

AMA Style

Scholl VM, Cattau ME, Joseph MB, Balch JK. Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification. Remote Sensing. 2020; 12(9):1414. https://doi.org/10.3390/rs12091414

Chicago/Turabian Style

Scholl, Victoria M., Megan E. Cattau, Maxwell B. Joseph, and Jennifer K. Balch 2020. "Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification" Remote Sensing 12, no. 9: 1414. https://doi.org/10.3390/rs12091414

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