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1. Quantifying mangrove canopy regrowth and recovery after Hurricane Irma with large-scale repeat airborne lidar in the Florida Everglades

Author

Lin Xiong, David Lagomasino, Sean P. Charles, Edward Castañeda-Moya, Bruce D. Cook, Jed Redwine, and Lola Fatoyinbo

Subjects
Lidar, Mangrove forests, Hurricanes, Canopy height model, Recovery, South Florida, Physical geography, GB3-5030, Environmental sciences, GE1-350
Abstract

Hurricane Irma caused significant damages to mangrove forested wetlands in south Florida, including defoliation, tree snapping, and uprooting. Previous studies have used optical satellite imagery to estimate large-scale forest disturbance and resilience patterns. However, satellite images alone cannot provide measurements of vertical mangrove structure. In this study, we used dense point cloud data collected by NASA Goddard’s LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager before (March 2017) and after (December 2017 and March 2020) Hurricane Irma to quantify the recovery, or lack thereof, of the three-dimensional (3D) mangrove forest structure. Recent resilience and vulnerability models developed from Landsat time series following the storm were used to group the lidar data into distinct disturbance-recovery classes. We then analyzed lidar-based forest canopy within each of the recovery classes to test a suite of forest structural characteristics. Our results indicate that 77.0 % of the survey area experienced canopy height loss three months after Hurricane Irma, whereby the majority of canopy height loss occurred in areas with the tallest mangrove forests (i.e., 15–25 m tall). Our analysis shows that the mangrove canopy height in South Florida increased by an average 0.26 m from December 2017 to March 2020, with most of the forest (84.7 % of the survey area) experiencing canopy height regrowth. However, only 38.1 % of the survey area has recovered to pre-storm canopy height. The distribution of canopy height was significantly altered by Hurricane Irma in the low and intermediate resilience classes, but were not significantly different 2.5 years later. Indeed, in areas of low resilience, little to no vertical change has occurred suggesting the absence of canopy regrowth and natural regeneration. Conversely, mangroves in high resilience class, which are dominated by shorter canopies (

Published
2022
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2. Storm surge and ponding explain mangrove dieback in southwest Florida following Hurricane Irma

Author

David Lagomasino, Temilola Fatoyinbo, Edward Castañeda-Moya, Bruce D. Cook, Paul M. Montesano, Christopher S. R. Neigh, Lawrence A. Corp, Lesley E. Ott, Selena Chavez, and Douglas C. Morton

Subjects
Science
Abstract

Mangroves are adapted to cope with tropical storms, but might be threatened by rising frequency and intensity of these events. Here the authors document one of the largest mangrove diebacks on record following Hurricane Irma in Florida, and show a greater role of storm surge and ponding rather than wind as a mechanism for mangrove dieback.

Published
2021
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3. Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Author

Elias Ayrey, Daniel J. Hayes, John B. Kilbride, Shawn Fraver, John A. Kershaw, Bruce D. Cook, and Aaron R. Weiskittel

Subjects
LiDAR, airborne laser scanning, enhanced forest inventory, aboveground biomass, forest carbon, deep learning, Science
Abstract

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Published
2021
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4. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Gilberto Pastorello, Carlo Trotta, Eleonora Canfora, Housen Chu, Danielle Christianson, You-Wei Cheah, Cristina Poindexter, Jiquan Chen, Abdelrahman Elbashandy, Marty Humphrey, Peter Isaac, Diego Polidori, Markus Reichstein, Alessio Ribeca, Catharine van Ingen, Nicolas Vuichard, Leiming Zhang, Brian Amiro, Christof Ammann, M. Altaf Arain, Jonas Ardö, Timothy Arkebauer, Stefan K. Arndt, Nicola Arriga, Marc Aubinet, Mika Aurela, Dennis Baldocchi, Alan Barr, Eric Beamesderfer, Luca Belelli Marchesini, Onil Bergeron, Jason Beringer, Christian Bernhofer, Daniel Berveiller, Dave Billesbach, Thomas Andrew Black, Peter D. Blanken, Gil Bohrer, Julia Boike, Paul V. Bolstad, Damien Bonal, Jean-Marc Bonnefond, David R. Bowling, Rosvel Bracho, Jason Brodeur, Christian Brümmer, Nina Buchmann, Benoit Burban, Sean P. Burns, Pauline Buysse, Peter Cale, Mauro Cavagna, Pierre Cellier, Shiping Chen, Isaac Chini, Torben R. Christensen, James Cleverly, Alessio Collalti, Claudia Consalvo, Bruce D. Cook, David Cook, Carole Coursolle, Edoardo Cremonese, Peter S. Curtis, Ettore D’Andrea, Humberto da Rocha, Xiaoqin Dai, Kenneth J. Davis, Bruno De Cinti, Agnes de Grandcourt, Anne De Ligne, Raimundo C. De Oliveira, Nicolas Delpierre, Ankur R. Desai, Carlos Marcelo Di Bella, Paul di Tommasi, Han Dolman, Francisco Domingo, Gang Dong, Sabina Dore, Pierpaolo Duce, Eric Dufrêne, Allison Dunn, Jiří Dušek, Derek Eamus, Uwe Eichelmann, Hatim Abdalla M. ElKhidir, Werner Eugster, Cacilia M. Ewenz, Brent Ewers, Daniela Famulari, Silvano Fares, Iris Feigenwinter, Andrew Feitz, Rasmus Fensholt, Gianluca Filippa, Marc Fischer, John Frank, Marta Galvagno, Mana Gharun, Damiano Gianelle, Bert Gielen, Beniamino Gioli, Anatoly Gitelson, Ignacio Goded, Mathias Goeckede, Allen H. Goldstein, Christopher M. Gough, Michael L. Goulden, Alexander Graf, Anne Griebel, Carsten Gruening, Thomas Grünwald, Albin Hammerle, Shijie Han, Xingguo Han, Birger Ulf Hansen, Chad Hanson, Juha Hatakka, Yongtao He, Markus Hehn, Bernard Heinesch, Nina Hinko-Najera, Lukas Hörtnagl, Lindsay Hutley, Andreas Ibrom, Hiroki Ikawa, Marcin Jackowicz-Korczynski, Dalibor Janouš, Wilma Jans, Rachhpal Jassal, Shicheng Jiang, Tomomichi Kato, Myroslava Khomik, Janina Klatt, Alexander Knohl, Sara Knox, Hideki Kobayashi, Georgia Koerber, Olaf Kolle, Yoshiko Kosugi, Ayumi Kotani, Andrew Kowalski, Bart Kruijt, Julia Kurbatova, Werner L. Kutsch, Hyojung Kwon, Samuli Launiainen, Tuomas Laurila, Bev Law, Ray Leuning, Yingnian Li, Michael Liddell, Jean-Marc Limousin, Marryanna Lion, Adam J. Liska, Annalea Lohila, Ana López-Ballesteros, Efrén López-Blanco, Benjamin Loubet, Denis Loustau, Antje Lucas-Moffat, Johannes Lüers, Siyan Ma, Craig Macfarlane, Vincenzo Magliulo, Regine Maier, Ivan Mammarella, Giovanni Manca, Barbara Marcolla, Hank A. Margolis, Serena Marras, William Massman, Mikhail Mastepanov, Roser Matamala, Jaclyn Hatala Matthes, Francesco Mazzenga, Harry McCaughey, Ian McHugh, Andrew M. S. McMillan, Lutz Merbold, Wayne Meyer, Tilden Meyers, Scott D. Miller, Stefano Minerbi, Uta Moderow, Russell K. Monson, Leonardo Montagnani, Caitlin E. Moore, Eddy Moors, Virginie Moreaux, Christine Moureaux, J. William Munger, Taro Nakai, Johan Neirynck, Zoran Nesic, Giacomo Nicolini, Asko Noormets, Matthew Northwood, Marcelo Nosetto, Yann Nouvellon, Kimberly Novick, Walter Oechel, Jørgen Eivind Olesen, Jean-Marc Ourcival, Shirley A. Papuga, Frans-Jan Parmentier, Eugenie Paul-Limoges, Marian Pavelka, Matthias Peichl, Elise Pendall, Richard P. Phillips, Kim Pilegaard, Norbert Pirk, Gabriela Posse, Thomas Powell, Heiko Prasse, Suzanne M. Prober, Serge Rambal, Üllar Rannik, Naama Raz-Yaseef, Corinna Rebmann, David Reed, Victor Resco de Dios, Natalia Restrepo-Coupe, Borja R. Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Scott R. Saleska, Enrique P. Sánchez-Cañete, Zulia M. Sanchez-Mejia, Hans Peter Schmid, Marius Schmidt, Karl Schneider, Frederik Schrader, Ivan Schroder, Russell L. Scott, Pavel Sedlák, Penélope Serrano-Ortíz, Changliang Shao, Peili Shi, Ivan Shironya, Lukas Siebicke, Ladislav Šigut, Richard Silberstein, Costantino Sirca, Donatella Spano, Rainer Steinbrecher, Robert M. Stevens, Cove Sturtevant, Andy Suyker, Torbern Tagesson, Satoru Takanashi, Yanhong Tang, Nigel Tapper, Jonathan Thom, Michele Tomassucci, Juha-Pekka Tuovinen, Shawn Urbanski, Riccardo Valentini, Michiel van der Molen, Eva van Gorsel, Ko van Huissteden, Andrej Varlagin, Joseph Verfaillie, Timo Vesala, Caroline Vincke, Domenico Vitale, Natalia Vygodskaya, Jeffrey P. Walker, Elizabeth Walter-Shea, Huimin Wang, Robin Weber, Sebastian Westermann, Christian Wille, Steven Wofsy, Georg Wohlfahrt, Sebastian Wolf, William Woodgate, Yuelin Li, Roberto Zampedri, Junhui Zhang, Guoyi Zhou, Donatella Zona, Deb Agarwal, Sebastien Biraud, Margaret Torn, and Dario Papale

Subjects
Science
Abstract

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

Published
2021
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5. Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

Author

Caileigh Shoot, Hans-Erik Andersen, L. Monika Moskal, Chad Babcock, Bruce D. Cook, and Douglas C. Morton

Subjects
random forest, machine learning, classification, forest type, LiDAR, hyperspectral, Science
Abstract

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

Published
2021
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6. Integrating Solar Induced Fluorescence and the Photochemical Reflectance Index for Estimating Gross Primary Production in a Cornfield

Author

Yen-Ben Cheng, Elizabeth M. Middleton, Qingyuan Zhang, Karl F. Huemmrich, Petya K. E. Campbell, Lawrence A. Corp, Bruce D. Cook, William P. Kustas, and Craig S. Daughtry

Subjects
gross primary production, light use efficiency, photochemical reflectance index, solar induced fluorescence, cornfield, Science
Abstract

The utilization of remotely sensed observations for light use efficiency (LUE) and tower-based gross primary production (GPP) estimates was studied in a USDA cornfield. Nadir hyperspectral reflectance measurements were acquired at canopy level during a collaborative field campaign conducted in four growing seasons. The Photochemical Reflectance Index (PRI) and solar induced chlorophyll fluorescence (SIF), were derived. SIF retrievals were accomplished in the two telluric atmospheric oxygen absorption features centered at 688 nm (O2-B) and 760 nm (O2-A). The PRI and SIF were examined in conjunction with GPP and LUE determined by flux tower-based measurements. All of these fluxes, environmental variables, and the PRI and SIF exhibited diurnal as well as day-to-day dynamics across the four growing seasons. Consistent with previous studies, the PRI was shown to be related to LUE (r2 = 0.54 with a logarithm fit), but the relationship varied each year. By combining the PRI and SIF in a linear regression model, stronger performances for GPP estimation were obtained. The strongest relationship (r2 = 0.80, RMSE = 0.186 mg CO2/m2/s) was achieved when using the PRI and SIF retrievals at 688 nm. Cross-validation approaches were utilized to demonstrate the robustness and consistency of the performance. This study highlights a GPP retrieval method based entirely on hyperspectral remote sensing observations.

Published
2013
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7. NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

Author

Vuong Ly, Paul M. Montesano, Kenneth J. Ranson, Jeffrey G. Masek, Joel T. McCorkel, Douglas C. Morton, Elizabeth M. Middleton, Ross F. Nelson, Lawrence A. Corp, and Bruce D. Cook

Subjects
remote sensing, airborne scanning LiDAR, imaging spectroscopy, surface temperature, sensor fusion, data fusion, ecosystem structure, forest disturbance, forest health, primary production, Science
Abstract

The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (~1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT’s data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA’s Data and Information policy.

Published
2013
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8. Why To Model Remote Sensing Measurements In 3d? Recent Advances In Dart: Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence And Satellite Image Inversion.

Author

Jean-Philippe Gastellu-Etchegorry, Yingjie Wang, Omar Regaieg, Tiangang Yin, Zbynek Malenovský, Zhijun Zhen, Xuebo Yang, Zhu Tao, Lucas Landier, Ahmad Al Bitar, Adrien Deschamps, Nicolas Lauret, Jordan Guilleux, Eric Chavanon, Biao Cao, Jianbo Qi, Abdelaziz Kallel, Zina Mitraka, Nektarios Chrysoulakis, Bruce D. Cook, and Douglas C. Morton

Published
2020
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10. Recent Improvements in the Dart Model for Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence, Satellite Image Inversion.

Author

Jean-Philippe Gastellu-Etchegorry, Yingjie Wang, Omar Regaieg, Tiangang Yin, Zbynek Malenovský, Zhijun Zhen, Xuebo Yang, Zhu Tao, Lucas Landier, Ahmad Al Bitar, Adrien Deschamps, Nicolas Lauret, Jordan Guilleux, Eric Chavanon, Biao Cao, Jianbo Qi, Abdelaziz Kallel, Zina Mitraka, Nektarios Chrysoulakis, Bruce D. Cook, and Douglas C. Morton

Published
2020
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11. Patterns and Controls on Island-Wide Aboveground Biomass Accumulation in Second-Growth Forests of Puerto Rico

Author

Sebastián Martinuzzi, Bruce D Cook, Eileen H Helmer, Michael Keller, Dexter H Locke, Humfredo Marcano-Vega, María Uriarte, and Douglas C Morton

Subjects
Earth Resources and Remote Sensing
Abstract

Understanding the heterogeneity of biomass accumulation in second-growth tropical forests following land use abandonment is important for informing ecosystem carbon models and forest restoration efforts. There is an urgent need for a broad sample of second-growth forests to enhance our knowledge of carbon accumulation in human-dominated landscapes, especially for older forests. Puerto Rico has predominantly second-growth forests, ranging in age from approximately 25 to more than 80 years. We used an island-wide sample of airborne lidar from the NASA Goddard Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager collected on March 2017, forest inventory data, and data on forest age, precipitation, soils, and land use to estimate aboveground biomass stocks in moist and wet, second-growth tropical forests. Biomass accumulation rates in Puerto Rico were lower, on average, than in other Neotropical forests. Median biomass across >16,700 ha of older second-growth forests was 105 Mg ha−1, and sampled biomass rarely surpassed 250 Mg ha−1. Differences in biomass by age were large and persistent across different substrates and land uses, with a plateau in the pattern of island-wide biomass accumulation after about 33 years. A spatial regression model showed that multiple factors were related to biomass accumulation, including time since abandonment, geologic substrate, past land use as coffee or pasture, precipitation, topographic wetness index, and slope. Our findings have important consequences for the total carbon storage and expected climate mitigation benefits of large-scale reforestation efforts, and highlight the value of airborne lidar for quantifying biomass variability in complex tropical landscapes.

Published
2022
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15. Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data

Author

Tiangang Yin, Bruce D. Cook, and Douglas C. Morton

Subjects
Meteorology And Climatology
Abstract

Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias and for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with and for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ . Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

Published
2021
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16. Tracking the Rates and Mechanisms of Canopy Damage and Recovery Following Hurricane Maria Using Multitemporal Lidar Data

Author

Veronika Leitold, Douglas C. Morton, Sebastián Martinuzzi, Ian Paynter, María Uriarte, Michael Keller, António Ferraz, Bruce D. Cook, Lawrence A. Corp, and Grizelle González

Subjects
Earth Resources And Remote Sensing, Meteorology And Climatology
Abstract

Hurricane Maria, a Category 4 storm, snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne Lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100–800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥ 1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13.5% of the study area had canopy heights ≤ 2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss > 1 m (2017–2018) followed by rapid height gain > 1 m (2018–2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no height changes larger than ± 1 m in either period. Canopy layers < 10 m tall accounted for most increases in canopy height and fractional cover between 2018 and 2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage and highlight the diversity of ecosystem impacts from heterogeneous spatial patterns and vertical stratification of forest regrowth following a major disturbance event.

Published
2021
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17. Assessing impacts of canopy 3D structure on chlorophyll fluorescence radiance and radiative budget of deciduous forest stands using DART

Author

Omar Regaieg, Tiangang Yin, Zbynek Malenovsky, Bruce D. Cook, Douglas C. Morton, and Jean-Philippe Gasteallu-Etchegorry

Subjects
Earth Resources And Remote Sensing
Abstract

Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly used as a valuable source of information about vegetation photosynthetic activity, the RS SIF observations are significantly influenced by canopy-specific structural features (i.e., canopy architecture including leaf area index and presence of woody components), atmospheric conditions during their acquisition (e.g., proportion of direct and diffuse irradiance) and observational geometric configurations (e.g., sun and viewing directions). Radiative transfer (RT) models have the potential to provide a better understanding of the canopy structural effects on the SIF emission and RS signals. Here, we used the DART model to assess the daily influence, from morning to evening, of forest 3D architecture on SIF nadir radiance, emission, escape factor and nadir yield of eight 100 m × 100 m forest study plots established in a temperate deciduous forest of the Smithsonian Environmental Research Center (Edgewater, MD, USA). The 3D architecture of each plot was derived from airborne LiDAR. DART simulations of these 3D forest plots and their 1D (i.e., vertical profile of sun-adapted and shade-adapted leaves) and 0D (i.e., homogeneous layer of sun-adapted leaves above an homogeneous layer of shade-adapted leaves) abstractions were compared to assess the relative errors (ε1D−3D and ε0D−3D) associated with horizontal and vertical structural heterogeneity, respectively. Forest 3D structure, especially horizontal heterogeneity, had a great influence on forest nadir SIF radiance, resulting in ε1D−3D up to 55% at 8:00 and 18:00 (i.e., for oblique sun directions). The key indicators of this impact, in the descending order of importance, were the SIF escape factor (ε1D−3D up to 40%), the attenuation of incident photosynthetically active radiation (ε1D−3D less than 5%), and the SIF emission yield (ε1D−3D less than 2%). The influence of forest architecture on the nadir SIF escape factor and SIF yield (ε1D−3D up to 40%) varied over time, with differences in forest stand structure, and per spectral domain, being always larger between 640 and 700 nm than between 700 and 850 nm. In addition, woody elements demonstrated a large influence on forest SIF radiance due to their “shading” effect (ε up to 17%) and their “blocking” effect (ε ≈ 10%), both of them higher for far-red than for red SIF. These results underline the importance of 3D forest canopy architecture, especially 2D heterogeneity, and inclusion of woody elements in RT modeling used for interpretation of the RS SIF signal, and subsequently for the estimation of gross primary production and detection of vegetation stress.

Published
2021
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18. Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

Author

Janice Cessna, Michael G. Alonzo, Adrianna Foster, and Bruce D. Cook

Subjects
Earth Resources And Remote Sensing
Abstract

The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts per sq. m); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts per sq.m). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

Published
2021
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19. Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar

Author

Michael Alonzo, Roman J.Dial, Bethany K. Schulz, Hans Erik Andersen, Eric Lewis-Clark, Bruce D. Cook, and Douglas C. Morton

Subjects
Earth Resources And Remote Sensing
Abstract

Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R^2 = 0.81, RMSE = 1.09 kg/sq. m) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R^2 = 0.74, RMSE = 1.26 kg/sq. m), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts./sq. m) to the G-LiHT SfM point cloud (500–2000 pts./sq. m) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.

Published
2020
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21. Boreal Canopy Surfaces from Spaceborne Stereogrammetry

Author

Paul M Montesano, Christopher S R Neigh, William Wagner, Margaret Wooten, and Bruce D Cook

Subjects
Earth Resources And Remote Sensing
Abstract

Surface elevation estimates from high resolution spaceborne image (HRSI) stereogrammetry are used to examine fine-scaled structure of boreal forest canopies. These data can depict detailed spatial patterns of vertical forest structure at remote sites across the circumpolar domain where these estimates would otherwise be unavailable. This work examines where these estimates are most effective at describing vertical forest structure to explain which canopy surfaces they represent. We evaluated the variation in canopy surface estimates captured from four general types of HRSI digital surface models (DSMs) across the full range of boreal canopy cover.

Published
2019
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22. Ancient Maya Rural Settlement Patterns, Household Cooperation, and Regional Subsistence Interdependency in the Río Bec Area: Contributions from G-LiHT

Author

Bruce D. Cook, Nicholas P. Dunning, Scott R. Hutson, Nicolas C. Barth, Daniel Conley, and Thomas Ruhl

Subjects
business.industry, media_common.quotation_subject, Subsistence agriculture, Social relation, Interdependence, Agricultural intensification, Geography, Arts and Humanities (miscellaneous), Agriculture, Anthropology, Economic anthropology, Economic geography, business, Rural settlement, Ancient maya, media_common
Abstract

Research on intensive agricultural features contributes to the social relations of farming, including the means by which farmers mobilize labor and the possible destination of surplus. Lidar provid...

Published
2021
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37. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations.

Author

Faith Ann Heinsch, Maosheng Zhao, Steven W. Running, John S. Kimball, Ramakrishna R. Nemani, Kenneth J. Davis, Paul V. Bolstad, Bruce D. Cook, Ankur R. Desai, Daniel M. Ricciuto, Beverly E. Law, Walt C. Oechel, Hyojung Kwon, Hongyan Luo, Steven C. Wofsy, Allison L. Dunn, J. William Munger, Dennis D. Baldocchi, Liukang Xu, David Y. Hollinger, Andrew D. Richardson, Paul C. Stoy, Mario B. S. Siqueira, Russell K. Monson, Sean P. Burns, and Lawrence B. Flanagan

Published
2006
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38. Boreal canopy surfaces from spaceborne stereogrammetry

Author

Christopher S.R. Neigh, W. Wagner, Bruce D. Cook, Paul Montesano, and Margaret Wooten

Subjects
Canopy, Percentile, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, Taiga, Elevation, Soil Science, Geology, 02 engineering and technology, Snow, 01 natural sciences, 020801 environmental engineering, Lidar, Boreal, Spatial ecology, Environmental science, Computers in Earth Sciences, 0105 earth and related environmental sciences, Remote sensing
Abstract

Surface elevation estimates from high resolution spaceborne image (HRSI) stereogrammetry are used to examine fine-scaled structure of boreal forest canopies. These data can depict detailed spatial patterns of vertical forest structure at remote sites across the circumpolar domain where these estimates would otherwise be unavailable. This work examines where these estimates are most effective at describing vertical forest structure to explain which canopy surfaces they represent. We evaluated the variation in canopy surface estimates captured from four general types of HRSI digital surface models (DSMs) across the full range of boreal canopy cover. These DSMs, classified into 4 types by grouping them according to the acquisition's (1) sun elevation angle (low or high) and (2) seasonality-driven ground surface condition (snow presence/absence), vary with acquisition characteristics and the details of this variation continues to be studied. We explored some of this variation by comparing the distributions of differences in boreal canopy percentile heights derived from reference small footprint lidar in Tanana Valley, Alaska with canopy surface elevations derived from these 4 types of HRSI DSMs. We examined how canopy surface estimates from HRSI DSMs differ according to acquisition characteristics and canopy cover, and ultimately which canopy surfaces are represented in these DSMs. Our results help clarify which boreal canopy surfaces are representative of those captured with HRSI DSMs. They show that in the Tanana Valley (1) DSMs grouped by sun elevation angle and ground surface condition provide different surface estimates of boreal canopies; (2) the two DSM types that appear to most differently capture boreal forest canopy surfaces are DSMs from snow-free images acquired at sun elevation angles 60% cover, 70th–80th percentile heights); and (5) in the most dense forests (>80% cover) where canopy gaps are least likely to bias downward the average surface estimates, the snow-free DSM types are representative of 70th - 80th percentile heights (“Low sun elev. & snow-free”) and 60th–70th percentile heights (“High sun elev. & snow-free”). The combination of horizontal structure (canopy cover) and acquisition characteristics affect the boreal vertical structure (canopy surface height) estimates from spaceborne stereogrammetry. These effects should be considered when analyzing products derived from HRSI DSMs, and as part of a comprehensive approach to spaceborne remote sensing of circumpolar boreal forests.

Published
2019
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39. Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes

Author

Bruce D. Cook, Hans E. Andersen, Sudipto Banerjee, Andrew O. Finley, Abhirup Datta, and Douglas C. Morton

Subjects
FOS: Computer and information sciences, Statistics and Probability, Computer science, Bayesian probability, Statistics - Computation, Statistics - Applications, 01 natural sciences, Article, k-nearest neighbors algorithm, 010104 statistics & probability, symbols.namesake, 0502 economics and business, Convergence (routing), Computational statistics, Discrete Mathematics and Combinatorics, Applications (stat.AP), 0101 mathematics, Gaussian process, Computation (stat.CO), 050205 econometrics, Stochastic process, Efficient algorithm, 05 social sciences, 15. Life on land, symbols, Statistics, Probability and Uncertainty, Algorithm
Abstract

We consider alternate formulations of recently proposed hierarchical Nearest Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing high-performance numerical linear algebra libraries. Computational and inferential benefits are assessed for alternate NNGP specifications using simulated datasets and remotely sensed light detection and ranging (LiDAR) data collected over the US Forest Service Tanana Inventory Unit (TIU) in a remote portion of Interior Alaska. The resulting data product is the first statistically robust map of forest canopy for the TIU.

Published
2019
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40. Storm surge and ponding explain mangrove dieback in southwest Florida following Hurricane Irma

Author

Selena Chavez, Bruce D. Cook, Christopher S.R. Neigh, Lawrence A. Corp, Temilola Fatoyinbo, Paul Montesano, Lesley Ott, Douglas C. Morton, David Lagomasino, and Edward Castañeda-Moya

Subjects
0106 biological sciences, Canopy, Satellite Imagery, Conservation of Natural Resources, 010504 meteorology & atmospheric sciences, Science, Ecosystem ecology, General Physics and Astronomy, Storm surge, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, Article, Water Cycle, Ecosystem, Ponds, Ponding, 0105 earth and related environmental sciences, Hydrology, Multidisciplinary, Ecology, Cyclonic Storms, 010604 marine biology & hydrobiology, Storm, General Chemistry, Wetlands ecology, Tropical ecology, Wetlands, Florida, Cyclone, Environmental science, Avicennia, Tropical cyclone, Mangrove, Forest ecology
Abstract

Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (>1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones., Mangroves are adapted to cope with tropical storms, but might be threatened by rising frequency and intensity of these events. Here the authors document one of the largest mangrove diebacks on record following Hurricane Irma in Florida, and show a greater role of storm surge and ponding rather than wind as a mechanism for mangrove dieback.

Published
2021

43. Tracking the rates and mechanisms of canopy damage and recovery following Hurricane Maria using multitemporal lidar data

Author

Ian Paynter, María Uriarte, Michael Keller, Grizelle González, V. Leitold, Antonio Ferraz, Bruce D. Cook, Sebastián Martinuzzi, Lawrence A. Corp, and Douglas C. Morton

Subjects
Canopy, Ecology, Crown (botany), Storm, Forestry, Experimental forest, Stratification (vegetation), Vegetation, Height loss, Disturbance (ecology), Spatial ecology, Environmental Chemistry, Forest structure, Environmental science, Ecosystem, Lidar data, Ecology, Evolution, Behavior and Systematics
Abstract

Hurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes >|1| m in either period. Canopy layers MANUSCRIPT HIGHLIGHTSHurricane Maria gave forests a haircut by toppling trees and shearing branches.Regrowth after Maria was patchy, with equal areas of height gain and no change.3-D measures of forest recovery after hurricanes can improve ecosystem models.

Published
2021
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45. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Eddy Moors, Uwe Eichelmann, Christian Brümmer, Stefano Minerbi, Barbara Marcolla, Gil Bohrer, Leonardo Montagnani, Üllar Rannik, Han Dolman, Janina Klatt, Samuli Launiainen, Elizabeth A. Walter-Shea, Nina Buchmann, Hank A. Margolis, Beniamino Gioli, Peter S. Curtis, Margaret S. Torn, Gabriela Posse, Luca Belelli Marchesini, Gianluca Filippa, Kenneth J. Davis, Leiming Zhang, Alexander Graf, Ray Leuning, Andrew Feitz, Simone Sabbatini, Harry McCaughey, Werner Eugster, Juha Pekka Tuovinen, Timothy J. Arkebauer, N. N. Vygodskaya, Adam J. Liska, Rosvel Bracho, Sebastian Wolf, Marc Aubinet, Jiří Dušek, Eugénie Paul-Limoges, Christof Ammann, Daniel Berveiller, Zoran Nesic, Giacomo Nicolini, Jaclyn Hatala Matthes, Russell L. Scott, David E. Reed, Frans-Jan W. Parmentier, Changliang Shao, Penélope Serrano-Ortiz, Yingnian Li, Jason Beringer, Marc Fischer, Deb Agarwal, Rasmus Fensholt, Russell K. Monson, Agnès de Grandcourt, Stefan K. Arndt, Timo Vesala, Uta Moderow, Joseph Verfaillie, Mika Aurela, Bev Law, Nina Hinko-Najera, Taro Nakai, Richard P. Phillips, Lindsay B. Hutley, Benjamin Loubet, Michele Tomassucci, Ayumi Kotani, Hans Peter Schmid, Raimundo Cosme de Oliveira, Anatoly A. Gitelson, Domenico Vitale, Regine Maier, Caitlin E. Moore, Xiaoqin Dai, Damien Bonal, John M. Frank, Yuelin Li, Christopher M. Gough, Shijie Han, Shirley A. Papuga, Edoardo Cremonese, Shawn Urbanski, Sébastien C. Biraud, Scott D. Miller, Mana Gharun, Annalea Lohila, Ian McHugh, Giovanni Manca, Bert Gielen, Wayne S. Meyer, Pierpaolo Duce, Bruce D. Cook, Carsten Gruening, Hiroki Ikawa, B.R. Reverter, Marian Pavelka, Andrew M. S. McMillan, Gang Dong, Isaac Chini, Kimberly A. Novick, Dalibor Janouš, Anne De Ligne, E. Beamesderfer, Marty Humphrey, Virginie Moreaux, Christian Wille, Markus Hehn, Hideki Kobayashi, Allen H. Goldstein, Walter C. Oechel, Richard Silberstein, Francisco Domingo, Francesco Mazzenga, Elise Pendall, Juha Hatakka, Lutz Merbold, Xingguo Han, Daniela Famulari, Carlo Trotta, Naama Raz-Yaseef, Dario Papale, Jean Marc Ourcival, Benoit Burban, Pavel Sedlák, Diego Polidori, Asko Noormets, Huimin Wang, Birger Ulf Hansen, Thomas Grünwald, Caroline Vincke, Robert M. Stevens, Carole Coursolle, D. P. Billesbach, Karl Schneider, Guoyi Zhou, Marcin Jackowicz-Korczynski, Paul V. Bolstad, Iris Feigenwinter, Shiping Chen, Julia Boike, Ivan Schroder, D. S. Christianson, Junhui Zhang, Pierre Cellier, Catharine van Ingen, Andrej Varlagin, A. Ribeca, Claudia Consalvo, Derek Eamus, Jason Brodeur, Alan G. Barr, Denis Loustau, Andreas Ibrom, Ankur R. Desai, Andrew E. Suyker, Efrén López-Blanco, Peter Cale, Nicola Arriga, William J. Massman, Abdelrahman Elbashandy, Yoshiko Kosugi, Pauline Buysse, Cove Sturtevant, T. A. Black, Housen Chu, David R. Bowling, Sabina Dore, Albin Hammerle, Tilden P. Meyers, M. Altaf Arain, Hatim Abdalla M. ElKhidir, Ignacio Goded, Roberto Zampedri, Alessio Collalti, Torsten Sachs, Tuomas Laurila, Cristina Poindexter, E. Canfora, Alexander Knohl, Donatella Spano, Silvano Fares, Scott R. Saleska, Michiel K. van der Molen, Suzanne M. Prober, Marryanna Lion, Steven C. Wofsy, Michael L. Goulden, Matthew Northwood, Antje Lucas-Moffat, Christine Moureaux, Jean-Marc Limousin, Sara H. Knox, Damiano Gianelle, Olaf Kolle, Jørgen E. Olesen, Mikhail Mastepanov, Bernard Heinesch, Christian Bernhofer, Peter D. Blanken, Hyojung Kwon, Georg Wohlfahrt, Peili Shi, Yann Nouvellon, Allison L. Dunn, Onil Bergeron, Mauro Cavagna, Heiko Prasse, Natalia Restrepo-Coupe, Yanhong Tang, Donatella Zona, Andrew S. Kowalski, Eric Dufrêne, Kim Pilegaard, Serena Marras, Yongtao He, Brent E. Ewers, Siyan Ma, Jean Marc Bonnefond, Jonas Ardö, Ko van Huissteden, Roser Matamala, Robin Weber, Nigel J. Tapper, Humberto Ribeiro da Rocha, Eva van Gorsel, Torbern Tagesson, Frederik Schrader, Frank Tiedemann, Myroslava Khomik, Torben R. Christensen, Jonathan E. Thom, James Cleverly, Víctor Resco de Dios, Ivan Shironya, Jeffrey P. Walker, You Wei Cheah, Ana López-Ballesteros, Georgia R. Koerber, J. William Munger, Shicheng Jiang, Johannes Lüers, Bruno De Cinti, Gilberto Pastorello, David R. Cook, Werner L. Kutsch, Paul Di Tommasi, Nicolas Delpierre, Peter Isaac, Carlos Marcelo Di Bella, Jiquan Chen, Craig Macfarlane, Dennis D. Baldocchi, William Woodgate, Riccardo Valentini, Marilyn Roland, Ladislav Šigut, Tomomichi Kato, Sebastian Westermann, Ivan Mammarella, Bart Kruijt, Marta Galvagno, Marius Schmidt, Serge Rambal, J. Kurbatova, Sean P. Burns, Ettore D'Andrea, Chad Hanson, Vincenzo Magliulo, Anne Griebel, Brian D. Amiro, M. Goeckede, Enrique P. Sánchez-Cañete, Thomas L. Powell, Marcelo D. Nosetto, Cacilia Ewenz, Michael J. Liddell, Satoru Takanashi, Lukas Hörtnagl, Zulia Mayari Sanchez-Mejia, W.W.P. Jans, N. Pirk, Johan Neirynck, Rainer Steinbrecher, Lukas Siebicke, Matthias Peichl, Rachhpal S. Jassal, Costantino Sirca, Earth and Climate, Earth Sciences, Institute for Atmospheric and Earth System Research (INAR), INAR Physics, Micrometeorology and biogeochemical cycles, Viikki Plant Science Centre (ViPS), Ecosystem processes (INAR Forest Sciences), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Università degli studi della Tuscia [Viterbo], California State University [Sacramento], Michigan State University System, University of Virginia, Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Chinese Academy of Sciences [Beijing] (CAS), University of Manitoba [Winnipeg], Agroscope, McMaster University [Hamilton, Ontario], Lund University [Lund], University of Nebraska–Lincoln, University of Nebraska System, University of Melbourne, University of Antwerp (UA), Université de Liège, Finnish Meteorological Institute (FMI), University of California [Berkeley] (UC Berkeley), University of California (UC), University of Saskatchewan [Saskatoon] (U of S), Peoples Friendship University of Russia [RUDN University] (RUDN), The University of Western Australia (UWA), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Ecologie Systématique et Evolution (ESE), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of British Columbia (UBC), University of Colorado [Colorado Springs] (UCCS), Ohio State University [Columbus] (OSU), Humboldt University Of Berlin, University of Minnesota System, SILVA (SILVA), AgroParisTech-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Utah, University of Central Florida [Orlando] (UCF), Thunen Institute of Climate-Smart Agriculture, Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Ecologie des forêts de Guyane (UMR ECOFOG), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Université de Guyane (UG)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Fondazione Edmund Mach - Edmund Mach Foundation [Italie] (FEM), Aarhus University [Aarhus], University of Technology Sydney (UTS), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), NASA Goddard Space Flight Center (GSFC), Argonne National Laboratory [Lemont] (ANL), Université Laval [Québec] (ULaval), Universidade de São Paulo = University of São Paulo (USP), Pennsylvania State University (Penn State), Penn State System, Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Wisconsin-Madison, Vrije Universiteit Amsterdam [Amsterdam] (VU), Centro de Investigaciones Biológicas (CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Shanxi University (SXU), Worcester State University [Worcester], Czech Academy of Sciences [Prague] (CAS), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), UCL - SST/ELI/ELIE - Environmental Sciences, GILBERTO PASTORELLO, Lawrence Berkeley National Laboratory, THOMAS ANDREW BLACK, University of British Columbia, PETER D. BLANKEN, University of Colorado, GIL BOHRER, Ohio State University, JULIA BOIKE, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research / Humboldt-Universität zu Berlin, PAUL V. BOLSTAD, University of Minnesota, JEAN-MARC BONNEFOND, ISPA Bordeaux Sciences Agro, DAVID R. BOWLING, University of Utah, ROSVEL BRACHO, University of Florida, JASON BRODEUR, McMaster University, CHRISTIAN BRÜMMER, Thünen Institute of Climate-Smart Agriculture, NINA BUCHMANN, ETH Zurich, BENOIT BURBAN, INRAE UMR ECOFOG, AGNES DE GRANDCOURT, UMR Eco&Sols, CIRAD, ANNE DE LIGNE, University of Liege, RAIMUNDO COSME DE OLIVEIRA JUNIOR, CPATU, HAN DOLMAN, Universiteit Amsterdam, FRANCISCO DOMINGO, CSIC, GANG DONG, Shanxi University, SABINA DORE, HydroFocus, PIERPAOLO DUCE, National Research Council of Italy, MARTA GALVAGNO, Environmental Protection Agency of Aosta Valley, MANA GHARUN, ETH Zurich, DAMIANO GIANELLE, Fondazione Edmund Mach, MARCIN JACKOWICZ-KORCZYNSKI, Lund University / Aarhus University, DALIBOR JANOUS, Global Change Research Institute of the Czech Academy of Sciences, WILMA JANS, Wageningen University and Research, RACHHPAL JASSAL, University of British Columbia, SHICHENG JIANG, Northeast Normal University, ANA LÓPEZ-BALLESTEROS, Trinity College Dublin, EFRÉN LÓPEZ-BLANCO, Aarhus University, BENJAMIN LOUBET, Université Paris-Saclay, DENIS LOUSTAU, ISPA - INRA, JOHANNES LÜERS, University of Bayreuth, JOHAN NEIRYNCK, Research Institute for Nature and Forest, ZORAN NESIC, University of British Columbia, GIACOMO NICOLINI, University of Tuscia / CMCC, ASKO NOORMETS, Texas A&M University, MATTHEW NORTHWOOD, Charles Darwin University, KIMBERLY NOVICK, Indiana University Bloomington, MARILYN ROLAND, University of Antwerp, SIMONE SABBATINI, University of Tuscia, TORSTEN SACHS, GFZ German Research Centre for Geosciences, SCOTT R. SALESKA, University of Arizona, ENRIQUE P. SÁNCHEZ-CAÑETE, University of Granada / CEAMA-IISTA, ZULIA M. SANCHEZ-MEJIA, Instituto Tecnológico de Sonora, RAINER STEINBRECHER, Karlsruhe Institute of Technology, ROBERT M. STEVENS, Sentek Pty Ltd, COVE STURTEVANT, National Ecological Observatory Network Program, ANDY SUYKER, University of Nebraska-Lincoln, TORBERN TAGESSON, Lund University / University of Copenhagen, SATORU TAKANASHI, Forestry and Forest Products Research Institute, DOMENICO VITALE, University of Tuscia / CMCC, NATALIA VYGODSKAYA, Russian Academy of Sciences, JEFFREY P. WALKER, Monash University, ELIZABETH WALTER-SHEA, University of Nebraska-Lincoln, HUIMIN WANG, Chinese Academy of Sciences, ROBIN WEBER, University of California Berkeley, SEBASTIAN WESTERMANN, Instituto Nacional de Tecnologia Agropecuaria (INTA), CHRISTIAN WILLE, GFZ German Research Centre for Geosciences, STEVEN WOFSY, Harvard University, GEORG WOHLFAHRT, University of Innsbruck, SEBASTIAN WOLF, ETH Zurich, WILLIAM WOODGATE, CSIRO Land and Water, YUELIN LI, Chinese Academy of Sciences, DONATELLA ZONA, San Diego State University / University of Sheffield, DEB AGARWAL, Lawrence Berkeley National Laboratory, SEBASTIEN BIRAUD, Lawrence Berkeley National Laboratory, MARGARET TORN, Lawrence Berkeley National Laboratory, DARIO PAPALE, University of Tuscia / CMCC., ALLISON DUNN, Worcester State University, JIRÍ DUSEK, Global Change Research Institute of the Czech Academy of Sciences, DEREK EAMUS, University of Technology Sydney, UWE EICHELMANN, Technische Universität Dresden, HOUSEN CHU, Lawrence Berkeley National Laboratory, DANIELLE CHRISTIANSON, Lawrence Berkeley National Laboratory, YOU-WEI CHEAH, Lawrence Berkeley National Laboratory, CRISTINA POINDEXTER, California State University, JIQUAN CHEN, Michigan State University, ABDELRAHMAN ELBASHANDY, Lawrence Berkeley National Laboratory, MARTY HUMPHREY, University of Virginia, PETER ISAAC, TERN Ecosystrem Processes, DIEGO POLIDORI, University of Tuscia / CMCC, ALESSIO RIBECA, University of Tuscia / CMCC, CATHARINE VAN INGEN, Lawrence Berkeley National Laboratory, LEIMINGZ HANG, Chinese Academy of Sciences, BRIAN AMIRO, University of Manitoba, CHRISTOF AMMANN, Agroscope Research Institute, M. ALTAF ARAIN, McMaster University, JONAS ARDÖ, Lund University, TIMOTHY ARKEBAUER, University of Nebraska-Lincoln, STEFAN K. ARNDT, The University of Melbourne, NICOLA ARRIGA, University of Antwerp / Joint Research Centre, MARC AUBINET, University of Liege, MIKA AURELA, Finnish Meteorological Institute, DENNIS BALDOCCHI, University of California Berkeley, ALAN BARR, University of Saskatchewan / Environment and Climate Change Canada, DAMIEN BONAL, Université de Lorraine, SEAN P. BURNS, University of Colorado / National Center for Atmospheric Research, PAULINE BUYSSE, Université Paris-Saclay, PETER CALE, Australian Landscape Trust, MAURO CAVAGNA, Fondazione Edmund Mach, PIERRE CELLIER, Université Paris-Saclay, SHIPING CHEN, Chinese Academy of Sciences, ISAAC CHINI, Fondazione Edmund Mach, TORBEN R . CHRISTENSEN, Aarhus University, JAMES CLEVERLY, University of Technology Sydney, ALESSIO COLLALTI, University of Tuscia / National Research Council of Italy, CLAUDIA CONSALVO, University of Tuscia / National Research Council of Italy, BRUCE D. COOK, NASA Goddard Space Flight Center, DAVID COOK, Argonne National Laboratory, CAROLE COURSOLLE, Natural Resources Canada / Université Laval, EDOARDO CREMONESE, Climate Change Unit, PETER S. CURTIS, Ohio State University, ETTORE DANDREA, National Research Council of Italy, HUMBERTO DA ROCHA, USP, XIAOQIN DAI, Chinese Academy of Sciences, KENNETH J. DAVIS, The Pennsylvania State University, BRUNO DE CINTI, National Research Council of Italy, NICOLAS DELPIERRE, Université Paris-Saclay, ANKUR R . DESAI, University of Wisconsin-Madison, CARLOS MARCELO DI BELLA, Facultad de Agronomía, UBA, Buenos Aires., PAUL DI TOMMASI, National Research Council of Italy, ERIC DUFRÊNE, Université Paris-Saclay, MARIUS SCHMIDT, Agrosphere (IBG3), HATIM ABDALLA M. ELKHIDIR, ElObeid Research Station, WERNER EUGSTER, ETH Zurich, CACILIA M. EWENZ, TERN Ecosystem Processes Central Node, BRENT EWERS, University of Wyoming, DANIELA FAMULARI, National Research Council of Italy, SILVANO FARES, National Research Council of Italy / Research Centre for Forestry and Wood, IRIS FEIGENWINTER, ETH Zurich, ANDREW FEITZ, Geoscience Australia, RASMUS FENSHOLT, University of Copenhagen, GIANLUCA FILIPPA, Environmental Protection Agency of Aosta Valley, MARC FISCHER, Lawrence Berkeley National Laboratory, JOHN FRANK, USDA Forest Service, BERT GIELEN, University of Antwerp, BENIAMINO GIOLI, National Research Council of Italy, ANATOLY GITELSON, University of Nebraska-Lincoln, IGNACIO BALLARIN GODED, Joint Research Centre, MATHIAS GOECKEDE, University of Nebraska-Lincoln, ALLEN H. GOLDSTEIN, University of California Berkeley, CHRISTOPHER M. GOUGH, Virginia Commonwealth University, MICHAEL L. GOULDEN, University of California, ALEXANDER GRAF, Forschungszentrum Jülich, ANNE GRIEBEL, The University of Melbourne, CARSTEN GRUENING, Joint Research Centre, THOMAS GRÜNWALD, Technische Universität Dresden, ALBIN HAMMERLE, University of Innsbruck, SHIJIE HAN, Henan University / Chinese Academy of Sciences, XINGGUO HAN, Chinese Academy of Sciences, BIRGER ULF HANSEN, University of Copenhagen, CHAD HANSON, Oregon State University, JUHA HATAKKA, Finnish Meteorological Institute, YONGTAO HE, Chinese Academy of Sciences / University of Chinese Academy of Sciences, MARKUS HEHN, Technische Universität Dresden, BERNARD HEINESCH, University of Liege, NINA HINKO-NAJERA, The University of Melbourne, LUKAS HÖRTNAGL, ETH Zurich, LINDSAY HUTLEY, Charles Darwin University, ANDREAS IBROM, Technical University of Denmark, HIROKI IKAWA, National Agriculture and Food Research Organization, TOMOMICHI KATO, Hokkaido University, MYROSLAVA KHOMIK, McMaster University / Geography and Environmental Management, JANINA KLATT, Karlsruhe Institute of Technology, ALEXANDER KNOHL, University of Goettingen, SARA KNOX, The University of British Columbia, HIDEKI KOBAYASHI, Institute of Arctic Climate and Environment Research, GEORGIA KOERBER, University of Adelaide, OLAF KOLLE, Max Planck Institute for Biogeochemistry, YOSHIKO KOSUGI, Kyoto University, AYUMI KOTANI, Nagoya University, ANDREW KOWALSKI, University of Granada, BART KRUIJT, Wageningen University, JULIA KURBATOVA, Russian Academy of Sciences, WERNER L. KUTSCH, ICOS ERIC, HYOJUNG KWON, Oregon State University, SAMULI LAUNIAINEN, Natural Resources Institute Finland, TUOMAS LAURILA, Finnish Meteorological Institute, BEV LAW, Oregon State University, RAY LEUNING, In memoriam, YINGNIAN LI, Chinese Academy of Sciences, MICHAEL LIDDELL, James Cook University, JEAN-MARC LIMOUSIN, Univ Montpellier, KARL SCHNEIDER, University of Cologne, MARRYANNA LION, Forest Research Institute Malaysia, ADAM J. LISKA, University of Nebraska-Lincoln, ANNALEA LOHILA, Finnish Meteorological Institute / University of Helsinki, ANTJE LUCAS-MOFFAT, Thünen Institute of Climate-Smart Agriculture / Centre for Agrometeorological Research, SIYAN MA, University of California Berkeley, CRAIG MACFARLANE, CSIRO Land and Water, VINCENZO MAGLIULO, National Research Council of Italy, REGINE MAIER, ETH Zurich, IVAN MAMMARELLA, University of Helsinki, GIOVANNI MANCA, Joint Research Centre, BARBARA MARCOLLA, Fondazione Edmund Mach, HANK A . MARGOLIS, Université Laval, SERENA MARRAS, CMCC / University of Sassari, WILLIAM MASSMAN, USDA Forest Service, MIKHAIL MASTEPANOV, Aarhus University / University of Oulu, ROSER MATAMALA, Argonne National Laboratory, JACLYN HATALA MATTHES, Wellesley College, FRANCESCO MAZZENGA, National Research Council of Italy, HARRY MCCAUGHEY, Queen’s University, IAN MCHUGH, The University of Melbourne, ANDREW M. S. MCMILLAN, Environmental Analytics NZ, LUTZ MERBOLD, International Livestock Research Institute, WAYNE MEYER, University of Adelaide, TILDEN MEYERS, NOAA/OAR/Air Resources Laboratory, SCOTT D. MILLER, State University of New York at Albany, STEFANO MINERBI, Forest Department of South Tyrol, UTA MODEROW, Technische Universität Dresden, RUSSELL K. MONSON, University of Arizona, LEONARDO MONTAGNANI, Forest Department of South Tyrol / Free University of Bolzano, CAITLIN E. MOORE, University of Illinois at Urbana-Champaign, EDDY MOORS, IHE Delft / VU Amsterdam, VIRGINIE MOREAUX, ISPA / University Grenoble Alpes, CHRISTINE MOUREAUX, University of Liege, J. WILLIAM MUNGER, Harvard University, TARO NAKAI, National Taiwan University / University of Alaska Fairbanks, MARCELO NOSETTO, Instituto de Matemática Aplicada San Luis / UNER, YANN NOUVELLON, Univ Montpellier-CIRAD-INRA-IRD-Montpellier SupAgro, WALTER OECHEL, San Diego State University / University of Exeter, JORGEN EIVIND OLESEN, Aarhus University, JEAN-MARC OURCIVAL, Univ Montpellier, SHIRLEY A. PAPUGA, Wayne State University, FRANS-JAN PARMENTIER, Lund University / University of Oslo, EUGENIE PAUL-LIMOGES, University of Zurich, MARIAN PAVELKA, Global Change Research Institute of the Czech Academy of Sciences, MATTHIAS PEICHL, Swedish University of Agricultural Sciences, ELISE PENDALL, Western Sydney University, RICHARD P. PHILLIPS, Indiana University Bloomington, KIM PILEGAARD, Technical University of Denmark, NORBERT PIRK, Lund University / CSIRO Land and Water, GABRIELA POSSE, Instituto Nacional de Tecnologia Agropecuaria (INTA), THOMAS POWELL, Lawrence Berkeley National Laboratory, HEIKO PRASSE, Technische Universität Dresden, SUZANNE M. PROBER, CSIRO Land and Water, SERGE RAMBAL, Univ Montpellier, ÜLLAR RANNIK, University of Helsinki, DAVID REED, Michigan State University, VICTOR RESCO DE DIOS, Western Sydney University / Southwest University of Science and Technology, NATALIA RESTREPO-COUPE, University of Arizona, BORJA R. REVERTER, Universidade Federal da Paraiba, HANS PETER SCHMID, Karlsruhe Institute of Technology, FREDERIK SCHRADER, Federal Research Institute of Rural Areas, IVAN SCHRODER, Geoscience Australia, RUSSELL L. SCOTT, Southwest Watershed Research Center, PAVEL SEDLÁK, Global Change Research Institute of the Czech Academy of Sciences / Institute of Atmospheric Physics of the Czech Academy of Sciences, PENÉLOPE SERRANO-ORTÍZ, CEAMA-IISTA / University of Granada, CHANGLIANG SHAO, Chinese Academy of Agricultural Sciences, PEILI SHI, Chinese Academy of Sciences, IVAN SHIRONYA, Russian Academy of Sciences, LUKAS SIEBICKE, Bioclimatology, University of Goettingen, LADISLAV SIGUT, Global Change Research Institute of the Czech Academy of Sciences, RICHARD SILBERSTEIN, University of Western Australia / Edith Cowan University, COSTANTINO SIRCA, CMCC / University of Sassari, DONATELLA SPANO, CMCC / University of Sassari, YANHONG TANG, Peking University, NIGEL TAPPER, Monash University, JONATHAN THOM, University of Wisconsin-Madison, FRANK TIEDEMANN, University of Goettingen, MICHELE TOMASSUCCI, University of Tuscia / Terrasystem srl, JUHA-PEKKA TUOVINEN, Finnish Meteorological Institute, SHAWN URBANSKI, Rocky Mountain Research Station, RICCARDO VALENTINI, University of Tuscia / CMCC, MICHIEL VAN DER MOLEN, Wageningen University, EVA VAN GORSEL, Australian National University Canberra, KO VAN HUISSTEDEN, Vrije Universiteit Amsterdam, ANDREJ VARLAGIN, Russian Academy of Sciences, JOSEPH VERFAILLIE, University of California Berkeley, TIMO VESALA, University of Helsinki, CAROLINE VINCKE, Chinese Academy of Sciences, ROBERTO ZAMPEDRI, Fondazione Edmund Mach, JUNHUI ZHANG, Chinese Academy of Sciences, GUOYI ZHOU, Nanjing University of Information Science & Technology, NAAMA RAZ-YASEEF, Lawrence Berkeley National Laboratory, ERIC BEAMESDERFER, McMaster University, CARLO TROTTA, University of Tuscia, ELEONORA CANFORA, University of Tuscia / CMCC, LUCA BELELLI MARCHESINI, Fondazione Edmund Mach / RUDN University, ONIL BERGERON, Ministère du Développement durable de l’Environnement et de la Lutte contre les changements climatiques, JASON BERINGER, University of Western Australia, CHRISTIAN BERNHOFER, Technische Universität Dresden, DANIEL BERVEILLER, Université Paris-Saclay, and DAVE BILLESBACH, University of Nebraska-Lincoln

Subjects
Meteorologie en Luchtkwaliteit, Data Descriptor, 010504 meteorology & atmospheric sciences, Settore AGR/05 - ASSESTAMENTO FORESTALE E SELVICOLTURA, dataset provides ecosystem, UNCERTAINTY, Eddy covariance, Observation météorologique, 01 natural sciences, ecosystem-scale data, lcsh:Science, SITES, Energy, Respiration, Statistics, Uncertainty, Carbon cycle, Biological measurements, Terrestrial biome, RESPIRATION, gapfilling, [SDE]Environmental Sciences, Assimilation, Anhídrid carbònic, ddc:500, Net ecosystem exchange, Écosystème, STORAGE, Information Systems, Statistics and Probability, ecosystem approaches [EN], Meteorology and Air Quality, ASSIMILATION, Library and Information Sciences, Education, collection [EN], Donnée climatique, Data collection, Water, 15. Life on land, Earth system science, Climate Resilience, Klimaatbestendigheid, lcsh:Q, processing, Climate sciences, Ecophysiology, Storage, Oceanography, Hydrology, Water Resources, 010501 environmental sciences, CARBON-DIOXIDE, ENERGY-BALANCE CLOSURE, ddc:550, Échange d'énergie, FLUXNET2015, Biosphere, Energy balance closure, fluxnet, Computer Science Applications, Collecte de données, Energia, P01 - Conservation de la nature et ressources foncières, Statistics, Probability and Uncertainty, INTERANNUAL VARIABILITY, Eddy Covariance, SDG 6 - Clean Water and Sanitation, Engineering sciences. Technology, Sensoriamento Remoto, FLUX, 1171 Geosciences, Consistency (database systems), eau, Life Science, Time series, Remote sensing studies, Measurement device, 0105 earth and related environmental sciences, Remote sensing, Ecosystem respiration and photosynthetic, WIMEK, NET ECOSYSTEM EXCHANGE, Pipeline (software), Environmental sciences, Metadata, Earth sciences, Carbon dioxide, 13. Climate action, Environmental science, Probability and Uncertainty, Water Systems and Global Change, Dioxyde de carbone
Abstract

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-flled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the frst time in this paper. In addition, 206 of these sites are for the frst time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible., European Union (EU), United States Department of Energy (DOE)

Published
2020
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46. Recent Improvements in the Dart Model for Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence, Satellite Image Inversion

Author

Ahmad Al Bitar, Jean-Philippe Gastellu-Etchegorry, Z. Tao, Biao Cao, Tiangang Yin, E. Chavanon, Lucas Landier, O. Regaieg, Deschamps, Nektarios Chrysoulakis, Zhijun Zhen, Bruce D. Cook, Zina Mitraka, Yingjie Wang, Z. Malenovsky, Jianbo Qi, J. Guilleux, Xue Yang, Abdelaziz Kallel, Douglas C. Morton, and Nicolas Lauret

Subjects
Dart, Physical model, 010504 meteorology & atmospheric sciences, Spectrometer, 0208 environmental biotechnology, Monte Carlo method, 02 engineering and technology, Atmospheric model, Laser, 01 natural sciences, 020801 environmental engineering, law.invention, Atmosphere, law, Radiative transfer, Environmental science, computer, 0105 earth and related environmental sciences, Remote sensing, computer.programming_language
Abstract

Physical models simulating the radiative budget (RB) and remote sensing (RS) observation of three-dimensional (3D) landscapes are critical to better understand human and natural components of the Earth system and further develop RS technology. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (https://dart.omp.eu). Here, five recent advances are presented. 1) Atmosphere RT. 2) RT in non repetitive topography. 3) Monte Carlo modelling for fast RS image simulation of large landscapes. 4) SIF modelling for vegetation simulated as facets and turbid cells. 5) RS image inversion for mapping the optical properties of urban material and the urban radiative budget.

Published
2020
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47. Why To Model Remote Sensing Measurements In 3d? Recent Advances In Dart: Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence And Satellite Image Inversion

Author

Z. Mitraka, Ahmad Al Bitar, Bruce D. Cook, B. Cao, Douglas C. Morton, Tiangang Yin, N. Chrysoulakis, Z. Tao, Abdelaziz Kallel, Jianbo Qi, Z. Malenovsky, Jean-Philippe Gastellu-Etchegorry, O. Regaieg, Yingjie Wang, Zhijun Zhen, X. Yang, Lucas Landier, Nicolas Lauret, J. Guilleux, E. Chavanon, and Deschamps

Subjects
Dart, Physical model, Spectrometer, Inversion (meteorology), Laser, law.invention, law, Satellite image, Radiative transfer, Environmental science, computer, Chlorophyll fluorescence, computer.programming_language, Remote sensing
Abstract

Remote sensing (RS) dedicated to the study of land surfaces benefits from more and more advanced sensors. However, the interpretation of RS data is often is often inaccurate due to the complexity of the observed land surfaces. Therefore, RS models, in particular physical models, that simulate RS observations of the three-dimensional (3D) landscapes are critical to correctly interpret RS data. DART is one of the most comprehensive 3D models of Earthatmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar-induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (dart.omp.eu). After illustrating three significant sources of inaccuracy in RS interpretation, five recent DART advances are presented: RT in the atmosphere and topography, fast RS image simulation of large landscapes, SIF modelling, and satellite image inversion.

Published
2020
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49. Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data

Author

Bruce D. Cook, Ross Nelson, Feng Zhao, Chengquan Huang, Matthew E. Fagan, Jeffery G. Masek, and Douglas C. Morton

Subjects
Forest inventory, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Soil Science, Hyperspectral imaging, Geology, 02 engineering and technology, Land cover, 01 natural sciences, Lidar, Remote sensing (archaeology), Spatial ecology, Environmental science, Satellite, Computers in Earth Sciences, Scale (map), 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest cover in the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations. Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat-based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.

Published
2018
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50. Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements

Author

Iurii Shendryk, Feng Zhao, Shawn P. Serbin, Philip E. Dennison, Ran Meng, Ryan P. Hanavan, Bruce D. Cook, and Amanda Rickert

Subjects
Canopy, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Soil Science, Hyperspectral imaging, Red edge, Geology, 02 engineering and technology, Understory, 01 natural sciences, Imaging spectroscopy, Lidar, Disturbance (ecology), Environmental science, Computers in Earth Sciences, Temporal scales, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

Defoliation by herbivorous insects is a widespread forest disturbance driver, affecting global forest health and ecosystem dynamics. Compared with time- and labor-intensive field surveys, remote sensing provides the only realistic approach to mapping canopy defoliation by herbivorous insects over large spatial and temporal scales. However, the spectral and structural signatures of defoliation by insects at the individual tree level have not been well studied. Additionally, the predictive power of spectral and structural metrics for mapping canopy defoliation has seldom been compared. These critical knowledge gaps prevent us from consistently detecting and mapping canopy defoliation by herbivorous insects across multiple scales. During the peak of a gypsy moth outbreak in Long Island, New York in summer 2016, we leveraged bi-temporal airborne imaging spectroscopy (IS, i.e., hyperspectral imaging) and LiDAR measurements at 1 m spatial resolution to explore the spectral and structural signatures of canopy defoliation in a mixed oak-pine forest. We determined that red edge and near-infrared spectral regions within the IS data were most sensitive to crown-scale defoliation severity. LiDAR measurements including B70 (i.e., 70th bincentile height), intensity skewness, and kurtosis were effectively able to detect structural changes caused by herbivorous insects. In addition to canopy leaf loss, increased exposure of understory and non-photosynthetic materials contributed to the detected spectral and structural signatures. Comparing the ability of individual sensors to map canopy defoliation, the LiDAR-only Ordinary Least-Square (OLS) model performed better than the IS-only model (Adj. R-squared = 0.77, RMSE = 15.37% vs. Adj. R-squared = 0.63, RMSE = 19.11%). The IS + LiDAR model improved on performance of the individual sensors (Adj. R-squared = 0.81, RMSE = 14.46%). Our study improves our understanding of spectral and structural signatures of defoliation by herbivorous insects and presents a novel approach for mapping insect defoliation at the individual tree level. Additionally, with the current and next generation of spaceborne sensors (e.g., WorldView-3, Landsat, Sentinel-2, HyspIRI, and GEDI), higher accuracy and frequent monitoring of insect defoliation may become more feasible across a range of spatial scales, which are critical for ecological research and management of forest resources including the economic consequences of forest insect infestations (e.g., reduced growth and increased mortality), as well as for informing and testing of carbon cycle models.

Published
2018
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