The soil and plant biogeochemistry sampling design for The National Ecological Observatory Network

Human impacts on biogeochemical cycles are evident around the world, from changes to forest structure and function due to atmospheric deposition, to eutrophication of surface waters from agricultural effluent, and increasing concentrations of carbon dioxide (CO2) in the atmosphere. The National Ecological Observatory Network (NEON) will contribute to understanding human effects on biogeochemical cycles from local to continental scales. The broad NEON biogeochemistry measurement design focuses on measuring atmospheric deposition of reactive mineral compounds and CO2 fluxes, ecosystem carbon (C) and nutrient stocks, and surface water chemistry across 20 eco‐climatic domains within the United States for 30 yr. Herein, we present the rationale and plan for the ground‐based measurements of C and nutrients in soils and plants based on overarching or “high‐level” requirements agreed upon by the National Science Foundation and NEON. The resulting design incorporates early recommendations by expert review teams, as well as recent input from the larger natural sciences community that went into the formation and interpretation of the requirements, respectively. NEON\u27s efforts will focus on a suite of data streams that will enable end‐users to study and predict changes to biogeochemical cycling and transfers within and across air, land, and water systems at regional to continental scales. At each NEON site, there will be an initial, one‐time effort to survey soil properties to 1 m (including soil texture, bulk density, pH, baseline chemistry) and vegetation community structure and diversity. A sampling program will follow, focused on capturing long‐term trends in soil C, nitrogen (N), and sulfur stocks, isotopic composition (of C and N), soil N transformation rates, phosphorus pools, and plant tissue chemistry and isotopic composition (of C and N). To this end, NEON will conduct extensive measurements of soils and plants within stratified random plots distributed across each site. The resulting data will be a new resource for members of the scientific community interested in addressing questions about long‐term changes in continental‐scale biogeochemical cycles, and is predicted to inspire further process‐based research

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Aim: Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).Location: Eastern USA.Time period: 2017– 2018.Taxa studied: Trees.Methods: With a fusion of hyperspectral and lidar data from the NEON AOP, we as-sess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.Results: Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.Main conclusions: We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine-resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30-yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human-centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long-term costs. In this manuscript, we first review the current status of instrument-based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound-producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground-based measurements for plant biodiversity, and laboratory-based imaging for physical specimens and samples in the NEON biorepository. Through its data science-literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human-driven surveys

Investigating the impact of spatially-explicit sub-pixel structural variation on the assessment of vegetation structure from imaging spectroscopy data

Consistent and scalable estimation of vegetation structural parameters from imaging spectroscopy is essential to remote sensing for ecosystem studies, with applications to a wide range of biophysical assessments. NASA has proposed the Hyperspectral Infrared Imager (HyspIRI) imaging spectrometer, which measures the radiance between 380-2500 nm in 10 nm contiguous bands with 60 m ground sample distance (GSD), in support of global vegetation assessment. However, because of the large pixel size on the ground, there is uncertainty as to the effects of sub-pixel vegetation structure on observed radiance. The purpose of this research was to evaluate the link between vegetation structure and imaging spectroscopy spectra. Specifically, the goal was to assess the impact of sub-pixel vegetation density and position, i.e., structural variability, on large-footprint spectral radiances. To achieve this objective, three virtual forest scenes were constructed, corresponding to the actual vegetation structure of the National Ecological Observatory Network (NEON) Pacific Southwest domain (PSW; D17; Fresno, CA). These scenes were used to simulate anticipated HyspIRI data (60 m GSD) using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, a physics-driven synthetic image generation model developed by the Rochester Institute of Technology (RIT). Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) and NEON\u27s high-resolution imaging spectrometer (NIS) data were used to verify the geometric parameters and physical models. Multiple simulated HyspIRI data sets were generated by varying within-pixel structural variables, such as forest density, tree position, and distribution of trees, in order to assess the impact of sub-pixel structural variation on the observed HyspIRI data. As part of the effort, a partial least squares (PLS) regression model, along with narrow-band vegetation indices (VIs), were used to characterize the sub-pixel vegetation structure from simulated HyspIRI-like spectroscopy data-like. These simulations were extended to quantitative assessments of within-pixel impact on pixel-level spectral response. The correlation coefficients (R^2) of leaf area index-to-normalized difference vegetation index (LAI-NDVI), canopy cover-to-vegetation index (VI), and PLS models were 0.92, 0.98, and 0.99, respectively. Results of the research have shown that HyspIRI is sensitive to sub-pixel vegetation density variation in the visible to short-wavelength infrared spectrum, due to vegetation structural changes, and associated pigment and water content variation. These findings have implications for improving the system\u27s suitability for consistent global vegetation structural assessments by adapting calibration strategies to account for this sub-pixel variation

Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data

Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, green, blue (RGB) light, hyperspectral imaging spectrometer spanning the visible to shortwave infrared wavelengths, and lidar systems to capture the spectral and structural properties of vegetation. As participants in the IDTReeS competition, we developed a two-stage deep learning approach to integrate NEON remote sensing data from all three sensors and classify individual plant species and genera. The first stage was a convolutional neural network that generates taxon probabilities from RGB images, and the second stage was a fusion neural network that &ldquo;learns&rdquo; how to combine these probabilities with hyperspectral and lidar data. Our two-stage approach leverages the ability of neural networks to flexibly and automatically extract descriptive features from complex image data with high dimensionality. Our method achieved an overall classification accuracy of 0.51 based on the training set, and 0.32 based on the test set which contained data from an unseen site with unknown taxa classes. Although transferability of classification algorithms to unseen sites with unknown species and genus classes proved to be a challenging task, developing methods with openly available NEON data that will be collected in a standardized format for 30 years allows for continual improvements and major gains for members of the computational ecology community. We outline promising directions related to data preparation and processing techniques for further investigation, and provide our code to contribute to open reproducible science efforts. &nbsp;</p

Towards global data products of Essential Biodiversity Variables on species traits

Essential Biodiversity Variables (EBVs) allow observation and reporting of global biodiversity change, but a detailed framework for the empirical derivation of specific EBVs has yet to be developed. Here, we re-examine and refine the previous candidate set of species traits EBVs and show how traits related to phenology, morphology, reproduction, physiology and movement can contribute to EBV operationalization. The selected EBVs express intra-specific trait variation and allow monitoring of how organisms respond to global change. We evaluate the societal relevance of species traits EBVs for policy targets and demonstrate how open, interoperable and machine-readable trait data enable the building of EBV data products. We outline collection methods, meta(data) standardization, reproducible workflows, semantic tools and licence requirements for producing species traits EBVs. An operationalization is critical for assessing progress towards biodiversity conservation and sustainable development goals and has wide implications for data-intensive science in ecology, biogeography, conservation and Earth observation

Synergies Among Environmental Science Research and Monitoring Networks: A Research Agenda

Many research and monitoring networks in recent decades have provided publicly available data documenting environmental and ecological change, but little is known about the status of efforts to synthesize this information across networks. We convened a working group to assess ongoing and potential cross-network synthesis research and outline opportunities and challenges for the future, focusing on the US-based research network (the US Long-Term Ecological Research network, LTER) and monitoring network (the National Ecological Observatory Network, NEON). LTER-NEON cross-network research synergies arise from the potentials for LTER measurements, experiments, models, and observational studies to provide context and mechanisms for interpreting NEON data, and for NEON measurements to provide standardization and broad scale coverage that complement LTER studies. Initial cross-network syntheses at co-located sites in the LTER and NEON networks are addressing six broad topics: how long-term vegetation change influences C fluxes; how detailed remotely sensed data reveal vegetation structure and function; aquatic-terrestrial connections of nutrient cycling; ecosystem response to soil biogeochemistry and microbial processes; population and species responses to environmental change; and disturbance, stability and resilience. This initial study offers exciting potentials for expanded cross-network syntheses involving multiple long-term ecosystem processes at regional or continental scales. These potential syntheses could provide a pathway for the broader scientific community, beyond LTER and NEON, to engage in cross-network science. These examples also apply to many other research and monitoring networks in the US and globally, and can guide scientists and research administrators in promoting broad-scale research that supports resource management and environmental policy

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on \u3e100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on \u3e100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Assessing the Integration and Pre-Processing of Neon Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

Accurately mapping tree species composition is a critical step towards understanding post-disturbance forest recovery. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. NEON data include in-situ tree measurements along with hyperspectral, multispectral, and light detection and ranging (LiDAR) airborne remote sensing imagery. By linking these NEON data, this study explores the impact of training set preparation and preprocessing on coniferous tree species classification at the subalpine forest NEON site in Colorado. Pixel-based random forest machine learning models were trained using various reference sets with remote sensing raster data as descriptive features. The highest classification accuracy (73%) was achieved using polygons created with half the maximum crown diameter per tree. LiDAR features were found to be the most important, followed by vegetation indices. This work contributes to reproducible forest composition mapping efforts and the open ecological science community