Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research

Ordway, Elsa M.; Elmore, Andrew J.; Kolstoe, Sonja; Quinn, John E.; Swanwick, Rachel; Cattau, Megan E.; Taillie, Dylan; Guinn, Steven M.; Chadwick, Kristi; Atkins, Jeff W.; Blake, Rachael E.; Chapman, Melissa; Cobourn, Kelly M.; Goulden, Tristan; Helmus, Matthew R.; Hondula, Kelly L.; Hritz, Carrie; Jensen, Jennifer; Julian, Jason P.; Kuwayama, Yusuke; Lulla, Vijay; O’Leary, Donal S.; Nelson, Donald R.; Ocón, Jonathan P.; Pau, Stéphanie; Ponce‐Campos, Guillermo E.; Portillo-Quintero, Carlos; Pricope, Narcisa G.; Rivero, Rosanna G.; Schneider, Laura; Steele, Meredith K.; Tulbure, Mirela G.; Williamson, Matthew A.; Wilson, Cyril O.

doi:10.1002/ecs2.3640

Cited by 8 publications

(1 citation statement)

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“…AOP remote sensing data have contributed to more than 78 peer-reviewed publications covering a wide range of ecological applications, including explorations into the relationships between vegetation structure and heterogeneity, plant foliar traits and processes such as carbon assimilation and habitat diversity (Chadwick et al, 2020;Kamoske et al, 2021;Marconi et al, 2021;Wang et al, 2020); improved forest inventories, tree-crown delineation and species identification (Ayrey & Hayes, 2018;Dalponte et al, 2019;Fricker et al, 2019;McMahon, 2019;Sumsion et al, 2019;Weinstein et al, 2021;Zou et al, 2019); refinements in canopy height modelling and biomass estimation (Khati et al, 2020;Liu et al, 2021); methods for measuring biodiversity (Carrasco et al, 2019;Kamoske et al, 2022;Scholl et al, 2021;Schweiger & Laliberté, 2022); geology and critical zone mapping (Brogan et al, 2019;Hermes et al, 2020;Wainwright et al, 2022); socio-environmental systems research (Ordway et al, 2021); and new remote sensing methods (Babadi et al, 2019;MacLean, 2017;Queally et al, 2021) among others (a complete list is available at neon.dimen sions.ai/disco ver/publi cation). This paper describes the spatial and temporal sampling design for the AOP that aims to provide standardized, high-quality remote sensing data capable of meeting the diverse range of research needs of the ecological science community, within the operational and environmental requirements and constraints affecting airborne data collection.…”

Section: Introductionmentioning

confidence: 99%

Spanning scales: The airborne spatial and temporal sampling design of the National Ecological Observatory Network

Musinsky

Goulden

Wirth³

et al. 2022

Methods Ecol Evol

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1. Each year, the National Ecological Observatory Network's (NEON) Airborne Observation Platform (AOP) collects high-resolution hyperspectral imagery, discrete and waveform lidar, and digital photography at a subset of 81 terrestrial and aquatic research sites throughout the United States. These open remote sensing data, together with NEON in situ sensor measurements and field observations, enable researchers to characterize ecological processes at multiple spatial and temporal scales.2. Here we describe the sampling design for the AOP that aims to meet the diverse research needs of the ecological science community within the operational constraints affecting airborne data collection. Our spatial sampling protocol captures NEON instrumented systems, field plots and environmental gradients around each site while considering the context of airspace restrictions and remote sensing instrument capabilities. We use time series of moderate resolution imaging spectroradiometer (MODIS) satellite and PhenoCam near-surface observations to define temporal sampling windows based on vegetation peak foliar greenness. We developed a probabilistic model based on MODIS reflectance imagery and Monte Carlo simulation to estimate sampling durations for cloud-free data collection at each site. Agreement in the estimated phenophase transition dates between MODIS EnhancedVegetation Index and PhenoCam Green Chromatic Coordinate varied by vegetation class. Results from both sensors show that some vegetation classes have relatively consistent interannual peak greenness start-and end-dates, while others experience high year-to-year variability in green-up and senescence. In addition to phenological variability among sites, certain vegetation forms demonstrate distinct, asynchronous responses to climate, resulting in non-overlapping peak greenness periods within a single site. Results from flight campaigns showed that the cloud-likelihood model underestimated actual cloud conditions by 13%-26%, depending on the probability used.4. Where interannual or intra-site phenology is highly variable or clouds are a persistent problem, it becomes challenging to schedule domain deployments so that all sites are flown in cloud-free conditions while their vegetation communities are in peak greenness. Despite limitations, application of cloud and peak greennessThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Spanning scales: The airborne spatial and temporal sampling design of the National Ecological Observatory Network

Musinsky

Goulden

Wirth³

et al. 2022

Methods Ecol Evol

Self Cite

View full text Add to dashboard Cite

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Anthromes

Quinn,

Ellis

2023

Handbook of the Anthropocene

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Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

Senf

2022

Ecosystems

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Remote sensing techniques are increasingly used for studying ecosystem dynamics, delivering spatially explicit information on the properties of Earth over large spatial and multi-decadal temporal extents. Yet, there is still a gap between the more technology-driven development of novel remote sensing techniques and their applications for studying ecosystem dynamics. Here, I review the existing literature to explore how addressing these gaps might enable recent methods to overcome longstanding challenges in ecological research. First, I trace the emergence of remote sensing as a major tool for understanding ecosystem dynamics. Second, I examine recent developments in the field of remote sensing that are of particular importance for studying ecosystem dynamics. Third, I consider opportunities and challenges for emerging open data and software policies and suggest that remote sensing is at its most powerful when it is theoretically motivated and rigorously ground-truthed. I close with an outlook on four exciting new research frontiers that will define remote sensing ecology in the upcoming decade.

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Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research

Abstract: v Volume 12(6) v Article e03640 Leveraging the NEON Airborne Observation Platform for socio-environmental systems research. Ecosphere 12(6): e03640.

Cited by 8 publications

References 86 publications

Spanning scales: The airborne spatial and temporal sampling design of the National Ecological Observatory Network

Spanning scales: The airborne spatial and temporal sampling design of the National Ecological Observatory Network

Anthromes

Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

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