Evaluation of In Situ FAPAR Measurement Protocols Using 3D Radiative Transfer Simulations DOI Creative Commons
Christian Lanconelli, Fabrizio Cappucci, Jennifer Adams

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4552 - 4552

Published: Dec. 4, 2024

The fraction of absorbed photosynthetically active radiation (FAPAR) is one the bio-geophysical Essential Climate Variables assessed through remote sensing observations and distributed globally by space environmental agencies. Any reliable product should be benchmarked against a reference, which normally determined means ground-based measurements. They generally aggregated spatially to compared with products at different resolutions. In this work, effectiveness various in situ sampling methods proposed assess FAPAR from flux measurements was evaluated using three-dimensional radiative transfer framework over eight virtual vegetated landscapes, including dense forests (leaf-on leaf-off models), open canopies, sparse vegetation, agricultural fields nominal extension 1 hectare. reference value summing PAR-equivalent photons either all canopy components, both branches leaves, or only leaves. incoming upwelling PAR fluxes were simulated illumination conditions high spatial resolution (50 cm). served replicate measurements, carried out stationary sensor networks transects. focus on examining inherent advantages drawbacks measurement protocols GCOS requirements. Consequently, proficiency each technique reflecting distribution incident reflected fluxes—essential for calculating FAPAR—was assessed. This study aims support activities related validation assessing potential uncertainty associated determination values. Among schemes considered our cross shaped showed particular efficiency properly representing pixel scale most scenario considered.

Language: Английский

Enhancing ecosystem productivity and stability with increasing canopy structural complexity in global forests DOI Creative Commons
Xiaoqiang Liu, Yuhao Feng, Tianyu Hu

et al.

Science Advances, Journal Year: 2024, Volume and Issue: 10(20)

Published: May 15, 2024

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped global distribution CSC revealed factors influencing its using worldwide light detection ranging data. We find that predominantly demonstrates significant positive with stability globally, although substantial variations exist among ecoregions. The effects on are balanced results biodiversity resource availability, providing valuable insights for comprehending functions. Managed forests found to have lower more potent enhancing than intact forests, highlighting urgent need integrate into development management plans effective climate change mitigation.

Language: Английский

Citations

20
Recommendations for developing, documenting, and distributing data products derived from NEON data DOI Creative Commons
Jeff W. Atkins, Kelly S. Aho,

Xuan Chen

et al.

Ecosphere, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 1, 2025

Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States Puerto Rico. These include both field remote sensing collected using standardized protocols sampling schema, with centralized quality assurance control (QA/QC) provided by NEON staff. Such breadth of creates opportunities for research community to extend basic applied while also extending impact reach through creation derived products—higher level user data. Derived are curated, documented, reproducibly‐generated datasets created applying various processing steps one or more lower products—including interpolation, extrapolation, integration, statistical analysis, modeling, transformations. directly benefit increase broadening size diversity base, decreasing time effort needed working data, providing primary foci development via derivation process, helping users address multidisciplinary questions. Creating promotes personal career advancement those involved publications, citations, future grant proposals. However, is a nontrivial task. Here we provide an overview process creating outlining advantages, challenges, major considerations.

Language: Английский

Citations

0
GROUNDED EO: Data-driven Sentinel-2 LAI and FAPAR retrieval using Gaussian processes trained with extensive fiducial reference measurements DOI Creative Commons
Luke A. Brown, Richard Fernandes, Jochem Verrelst

et al.

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 326, P. 114797 - 114797

Published: May 16, 2025

Language: Английский

Citations

0
The US National Ecological Observatory Network and the Global Biodiversity Framework: national research infrastructure with a global reach DOI Open Access
Katherine M. Thibault, Christine Laney, Kelsey M. Yule

et al.

Journal of Ecology and Environment, Journal Year: 2023, Volume and Issue: 47

Published: Dec. 14, 2023

Ecological Observatory Network (NEON) is a continental-scale program intended to provide open data, samples, and infrastructure understand changing ecosystems for period of 30 years.NEON collects co-located measurements drivers environmental change biological responses, using standardized methods at 81 field sites systematically sample variability trends enable inferences regional continental scales.Alongside key atmospheric variables, NEON measures the biodiversity many taxa, including microbes, plants, animals, samples from these organisms long-term archiving research use.Here we review composition use resources date as whole specific an exemplar potential national contribute globally relevant outcomes.Since initiated full operations in 2019, has produced, on average, 1.4 M records over 32 TB data per year across more than 180 products, with 85 products that include taxonomic or other organismal information science.NEON also collected curated 503,000 specimens spanning all domains life, up 100,000 be added annually.Various metrics use, web portal visitation, download requests, scientific publications, reveal substantial interest global community NEON.More 47,000 unique IP addresses around world visit NEON's portals each month, requesting average 1.8 200 researchers have engaged requests Biorepository.Through its partnerships, particularly Global Biodiversity Information Facility, been used 900 publications date, samples.These outcomes demonstrate provided by NEON, situated broader network infrastructures, are critical scientists, conservation practitioners, policy makers.They effective approaches meeting targets, such those captured Kunming-Montreal Framework.

Language: Английский

Citations

6
neonPlantEcology: An R package for preparing NEON plant data for use in ecological research DOI
Adam L. Mahood, Ranjan Muthukrishnan, Jacob A. Macdonald

et al.

Ecological Modelling, Journal Year: 2024, Volume and Issue: 493, P. 110750 - 110750

Published: May 30, 2024

Language: Английский

Citations

1
Lessons from constructing and operating the national ecological observatory network DOI Open Access
Christopher McKay

Journal of Ecology and Environment, Journal Year: 2023, Volume and Issue: 47

Published: Dec. 5, 2023

Language: Английский

Citations

1
Data science competition for cross-site individual tree species identification from airborne remote sensing data DOI Creative Commons
Sarah Graves, Sergio Marconi, Dylan Stewart

et al.

PeerJ, Journal Year: 2023, Volume and Issue: 11, P. e16578 - e16578

Published: Dec. 21, 2023

Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about composition and structure with a continuous spatial coverage over large extents. Classifying trees their taxonomic species regions data is challenging. Methods classify are often accurate for common species, but perform poorly less when applied new sites. We ran science competition help identify effective methods task of classification identity. The included three sites assess each methods’ ability generalize patterns across two simultaneously apply an untrained site. Three different metrics were used compare model performance. Six teams participated, representing four countries nine individuals. highest performing method previous in 2017 was as baseline understand advancements changes successful methods. best based two-stage fully connected neural network that significantly outperformed random gradient boosting ensemble All generalized well showing relatively strong performance trained (accuracy = 0.46–0.55, macro F1 0.09–0.32, cross entropy loss 2.4–9.2), generally failed transfer effectively site 0.07–0.32, 0.02–0.18, 2.8–16.3). Classification influenced number samples labels available training, most predicting at training (maximum score 0.86) relative uncommon where none predicted. errors between same genus occur habitat. Most performed better than detecting if not mixed-species class, especially This work has highlighted competitions can encourage advancement methods, particularly bringing people outside focal discipline, open dataset evaluation criteria which participants learn.

Language: Английский

Citations

1
neonPlantEcology: an R package for preparing NEON plant data for use in ecological research. DOI Creative Commons
Adam L. Mahood, Jacob L. Macdonald, Ranjan Muthukrishnan

et al.

Published: Jan. 17, 2024

The National Ecological Observatory Network (NEON) is a continental-scale endeavor of ecological data collection for 30 years. We created software package, neonPlantEcology that automatically arranges the raw from plant presence and percent cover (DP1.10058.001) product NEON into tables familiar to ecologists. Because broad scale observatory, it necessary tailor idiosyncrasies each 47 different ecosystems. Furthermore, practices are occasionally modified various reasons. These complexities, along with volume multiscalar nature data, need be understood accounted in order correctly process data. This particularly true diversity product. present three case studies using centered around primary functions neonPlantEcology. By automating preparing NEON’s makes more accessible wide range users.

Language: Английский

Citations

0
Evaluation of In Situ FAPAR Measurement Protocols Using 3D Radiative Transfer Simulations DOI Creative Commons
Christian Lanconelli, Fabrizio Cappucci, Jennifer Adams

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4552 - 4552

Published: Dec. 4, 2024

The fraction of absorbed photosynthetically active radiation (FAPAR) is one the bio-geophysical Essential Climate Variables assessed through remote sensing observations and distributed globally by space environmental agencies. Any reliable product should be benchmarked against a reference, which normally determined means ground-based measurements. They generally aggregated spatially to compared with products at different resolutions. In this work, effectiveness various in situ sampling methods proposed assess FAPAR from flux measurements was evaluated using three-dimensional radiative transfer framework over eight virtual vegetated landscapes, including dense forests (leaf-on leaf-off models), open canopies, sparse vegetation, agricultural fields nominal extension 1 hectare. reference value summing PAR-equivalent photons either all canopy components, both branches leaves, or only leaves. incoming upwelling PAR fluxes were simulated illumination conditions high spatial resolution (50 cm). served replicate measurements, carried out stationary sensor networks transects. focus on examining inherent advantages drawbacks measurement protocols GCOS requirements. Consequently, proficiency each technique reflecting distribution incident reflected fluxes—essential for calculating FAPAR—was assessed. This study aims support activities related validation assessing potential uncertainty associated determination values. Among schemes considered our cross shaped showed particular efficiency properly representing pixel scale most scenario considered.

Language: Английский

Citations

0