National Ecological Observatory Network Airborne Research Articles

Integrating airborne remote sensing and field campaigns for ecology and Earth system science

Abstract In recent years, the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality and foliar trait distributions. However, we have barely begun to unlock the potential of these data to use direct mapping of vegetation characteristics to infer subsurface properties of the critical zone. To assess their utility for Earth systems research, imaging spectroscopy data acquisitions require integration with large, coincident ground‐based datasets collected by experts in ecology and environmental and Earth science. Without coordinated, well‐planned field campaigns, potential knowledge leveraged from advanced airborne data collections could be lost. Despite the growing importance of this field, documented methods to couple such a wide variety of disciplines remain sparse. We coordinated the first National Ecological Observatory Network Airborne Observation Platform (AOP) survey performed outside of their core sites, which took place in the Upper East River watershed, Colorado. Extensive planning for sample tracking and organization allowed field and flight teams to update the ground‐based sampling strategy daily. This enabled collection of an extensive set of physical samples to support a wide range of ecological, microbiological, biogeochemical and hydrological studies. We present a framework for integrating airborne and field campaigns to obtain high‐quality data for foliar trait prediction and document an archive of coincident physical samples collected to support a systems approach to ecological research in the critical zone. This detailed methodological account provides an example of how a multi‐disciplinary and multi‐institutional team can coordinate to maximize knowledge gained from an airborne survey, an approach that could be extended to other studies. The coordination of imaging spectroscopy surveys with appropriately timed and extensive field surveys, along with high‐quality processing of these data, presents a unique opportunity to reveal new insights into the structure and dynamics of the critical zone. To our knowledge, this level of co‐aligned sampling has never been undertaken in tandem with AOP surveys and subsequent studies utilizing this archive will shed considerable light on the breadth of applications for which imaging spectroscopy data can be leveraged.

Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest

Imaging spectroscopy is a valuable tool for mapping canopy foliar traits in forested ecosystems at landscape and larger scales. Most efforts to date have involved two-dimensional mapping of traits, typically representing top-of-canopy conditions. However, traits and their associated biological functions vary through the canopy vertical profile, such that incorporating information about vertical patterns may improve modeling of ecosystem processes like primary productivity. In 2016 and 2017, we collected extensive field data in forests in Domain 5 (Great Lakes) of the National Ecological Observatory Network (NEON) to characterize the vertical variation in leaf mass per area (LMA), an important foliar trait related to plant growth and defense. Fieldwork was coincident with NEON Airborne Observation Platform (AOP) overflights which collected imaging spectroscopy and lidar data. Using imaging spectroscopy to map top-of-canopy LMA and lidar to model vertical gradients of transmittance, we developed a method to map three-dimensional patterns in LMA in temperate broadleaf forests. Partial least squares regression (PLSR) was used to estimate top-of-canopy LMA (R2: 0.57, RMSE 10.8 g m−2), which, along with lidar-derived metrics of light transmittance and height, was used in a multilevel regression to model within-canopy LMA (R2: 0.78, RMSE 8.3 g m−2). The coupled models accurately estimated LMA throughout the canopy without taking into account species composition (R2 = 0.82, RMSE: 8.5 g m−2).

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

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.