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

Victoria Scholl,Jennifer Balch,Megan Cattau,Maxwell Joseph

doi:10.3390/rs12091414

Victoria Scholl, Jennifer Balch + Show 2 more

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https://doi.org/10.3390/rs12091414

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Abstract

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.

Highlights

Mapping and monitoring the tree species composition of Earth’s forests using remote sensing technologies is a challenging task that has motivated an active research community over the past three decades [1,2,3,4,5,6,7]
The Airborne Observation Platform (AOP) hyperspectral reflectance curves appear very similar across all four tree species, including the characteristic green peak (550 nm) within the visible wavelength region and the steep slope at the edge between the red and near-infrared regions (750 nm) and the shoulder or flattening off into the near-infrared region (800 nm) (Figure 6)
We trained Random Forest (RF) models to predict species using each training set paired with AOP remote sensing features, and determined which remote sensing data products were most important for species identification

Summary

Introduction

Mapping and monitoring the tree species composition of Earth’s forests using remote sensing technologies is a challenging task that has motivated an active research community over the past three decades [1,2,3,4,5,6,7]. The combination of hyperspectral and LiDAR data has the ability to differentiate between species with similar reflectance properties but different mean heights [3]. These remotely sensed spectral and structural characteristics of trees are used to predict species using a variety of pixel-based and object-based classification approaches [6,23,24,25]. Neural networks are increasingly used in ecological remote sensing studies for their ability to identify trends and patterns from data, model complex relationships, accept a wide variety of input predictor data, and produce high accuracies, at the expense of requiring large amounts of training data [13,32,33,34]. Other factors that impact tree species classification accuracy include tree species complexity [38], and the time of acquisition within a year or season, especially for deciduous trees with distinctive phenological patterns [27]

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