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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Photosynethic Performance Measurements in Biomass Sorghum Varietals in Central Illinois during Four Growing Seasons.

In a set of field studies across four years, the effect of self-shading on photosynthetic performance in lower canopy sorghum leaves was studied at sites in Champaign County, IL. Photosynthetic parameters in upper and lower canopy leaves, carbon assimilation, electron transport, stomatal conductance, and activity of three C4-specific photosynthetic enzymes, were compared within a genetically diverse range of accessions varying widely in canopy architecture and thereby in the degree of self-shading. Accessions with erect leaves and high light transmission through the canopy are henceforth referred to as ‘erectophile’ and those with low leaf erectness, ‘planophile’. In the final year of the study, bundle sheath leakiness in erectophile and planophile accessions was also compared.

Leaf Inclination↗
DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Panama tropical forest digital camera imagery for vegetation phenology, Dec2016-May2019, PA-SLZ, PA-PNM, PA-BCI

Tropical forest phenology monitoring undertaken by use of Wingscapes timelapse cameras (phenocams) at three tropical evergreen forests in Panama: San Lorenzo, Parque Natural Metropolitano, and Barro Colorado Island. Following installation in December 2016, images were acquired every 10 minutes from 10 am to 2 pm daily, ending in May 2019. A total of 37 cameras were mounted on towers and tree branches, and view sunlit canopy or understory forest as follows: PA-SLZ, 11 canopy, 14 understory; PA-PNM, 6 canopy, 2 understory, PA-BCI, 4 canopy (trees also monitored for sap flow). This data package contains a full data description, metadata, species identification guides and summary files showing example fields of view from each camera location as csv and pdf files. The data description provides instructions for download of the full resolution image files, and other summary data products stored on external servers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Leaf spectral reflectance and transmittance, San Lorenzo, Panama, 2020

This data package contains full-range (350 – 2500 nm) leaf reflectance and transmittance spectra of leaves collected at the San Lorenzo forest canopy crane site in Panama (PA-SLZ) from January to March 2020. Around 1000 leaves were measured from 50 species and various leaf phenological stages. Leaves were sampled from both the top of the canopy and multiple heights within the canopy from 10 vertical profiles and for six core species, from the top of the canopy. This data package includes processed leaf spectra (*.csv), and raw data from the spectroradiometer (compressed as .zip). Metadata files include data descriptions (_dd.csv) for tabular data and sample information and a description of the field campaign protocol (PA-SLZ_2020_Protocol.pdf). In addition to the leaf spectral measurements included here, these samples were also used for measurement of leaf gas exchange, carbon and nitrogen content and leaf mass per unit leaf area (LMA) contained in related data packages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Leaf structural and chemical traits, and BNL field campaign sample details, San Lorenzo, Panama, 2020

This data package includes leaf traits, canopy traits and sample details for leaves from 71 species sampled from the San Lorenzo forest canopy crane site, Panama (PA-SLZ) during the BNL field campaign in January to March 2020. Each leaf sample is described with species, phenological stage and location within vertical canopy profiles. Leaf area index (LAI) and height is presented for each canopy profile location. Leaf mass per area (LMA), leaf water content (LWC) and leaf carbon and nitrogen content are included for a subset of the samples. This data package includes sample details, processed data for leaf traits and LAI (*.csv), LAI raw data (compressed as *.zip) and digital camera images (*.jpg, compressed as *.zip) of the leaf samples. Metadata files include data descriptions (_dd.csv) for tabular data, a list of all species sampled during the campaign (*.csv) and a detailed description of the field campaign protocol and methods (*.pdf). See related datasets for leaf gas exchange, leaf water potential and leaf spectral measurements made on the samples described here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Dynamic subcanopy leaf traits drive resistance of net primary production across a disturbance severity gradient

Across the globe, the forest carbon sink is increasingly vulnerable to an expanding array of low- to moderate-severity disturbances. However, some forest ecosystems exhibit functional resistance (i.e., the capacity of ecosystems to continue functioning as usual) following disturbances such as extreme weather events and insect or fungal pathogen outbreaks. Unlike severe disturbances (e.g., stand-replacing wildfires), moderate severity disturbances do not always result in near-term declines in forest production because of the potential for compensatory growth, including enhanced subcanopy production. Community-wide shifts in subcanopy plant functional traits, prompted by disturbance-driven environmental change, may play a key mechanistic role in resisting declines in net primary production (NPP) up to thresholds of canopy loss. However, the temporal dynamics of these shifts, as well as the upper limits of disturbance for which subcanopy production can compensate, remain poorly characterized. In this study, we leverage a 4-year dataset from an experimental forest disturbance in northern Michigan to assess subcanopy community trait shifts as well as their utility in predicting ecosystem NPP resistance across a wide range of implemented disturbance severities. Through mechanical girdling of stems, we achieved a gradient of severity from 0% (i.e., control) to 45, 65, and 85% targeted gross canopy defoliation, replicated across four landscape ecosystems broadly representative of the Upper Great Lakes ecoregion. We found that three of four examined subcanopy community weighted mean (CWM) traits including leaf photosynthetic rate (p = 0.04), stomatal conductance (p = 0.07), and the red edge normalized difference vegetation index (p < 0.0001) shifted rapidly following disturbance but before widespread changes in subcanopy light environment triggered by canopy tree mortality. Surprisingly, stimulated subcanopy production fully compensated for upper canopy losses across our gradient of experimental severities, achieving complete resistance (i.e., no significant interannual differences from control) of whole ecosystem NPP even in the 85% disturbance treatment. Additionally, we identified a probable mechanistic switch from nutrient-driven to light-driven trait shifts as disturbance progressed. Our findings suggest that remotely sensed traits such as the red edge normalized difference vegetation index (reNDVI) could be particularly sensitive and robust predictors of production response to disturbance, even across compositionally diverse forests. The potential of leaf spectral indices to predict post-disturbance functional resistance is promising given the capabilities of airborne to satellite remote sensing. We conclude that dynamic functional trait shifts following disturbance can be used to predict production response across a wide range of disturbance severities.

resistance↗
DOE OSTI2021

Assessing Forest Level Response to the Death of a Dominant Tree within a Premontane Tropical Rainforest

Small-scale treefall gaps are among the most important forms of forest disturbance in tropical forests. These gaps expose surrounding trees to more light, promoting rapid growth of understory plants. However, the effects of such small-scale disturbances on the distribution of plant water use across tree canopy levels are less known. To address this, we explored plant transpiration response to the death of a large emergent tree, Mortoniodendron anisophyllum Standl. & Steyerm (DBH > 220 cm; height ~40 m). Three suppressed, four mid-story, and two subdominant trees were selected within a 50 × 44 m premontane tropical forest plot at the Texas A&M Soltis Center for Research and Education located in Costa Rica. We compared water use rates of the selected trees before (2015) and after (2019) the tree gap using thermal dissipation sap flow sensors. Hemispherical photography indicated a 40% increase in gap fraction as a result of changes in canopy structure after the treefall gap. Micrometeorological differences (e.g., air temperature, relative humidity, and vapor pressure deficit (VPD)) could not explain the observed trends. Rather, light penetration, as measured by sensors within the canopy, increased significantly in 2019. One year after the tree fell, the water usage of trees across all canopy levels increased modestly (15%). Moreover, average water usage by understory trees increased by 36%, possibly as a result of the treefall gap, exceeding even that of overstory trees. These observations suggest the possible reallocation of water use between overstory and understory trees in response to the emergent tree death. With increasing global temperatures and shifting rainfall patterns increasing the likelihood of tree mortality in tropical forests, there is a greater need to enhance our understanding of treefall disturbances that have the potential to redistribute resources within forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

3D reconstruction↗
DOE OSTI2024

Nitrogen fertilization effects on aged Miscanthus × giganteus stands: Exploring biomass yield, yield components, and biomass prediction using in–season morphological traits

For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha –1 ) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index, and leaf chlorophyll content [LCC]). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018 to 2021 in Urbana and 2020 to 2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha –1 . Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2–3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management history.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data from: Biomass yield, yield components and growing season phenotypic measurements of Miscanthus

For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha-1) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index (LAI), and leaf chlorophyll content (LCC)). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018-2021 in Urbana and 2020-2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha-1. Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2-3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management.

Aging↗
DOE OSTI2026

Nitrogen dynamics and physiological N use efficiency in high‐biomass sorghum

Improving nitrogen (N) efficiency is essential for sustainable high-biomass sorghum ( Sorghum bicolor L. Moench) production. This study evaluated leaf and stem N dynamics, canopy N remobilization, and physiological nitrogen use efficiency (pNUE) in two photoperiod-sensitive sorghum hybrids under two N rates (0 and 168 kg-N ha −1 ) across multiple environments in Texas and Illinois. Leaf N concentrations increased with plant height in the canopy with steeper gradients under low-N conditions, indicating enhanced N remobilization when N is limited. Stem tissue showed less variation in N concentration across canopy nodal positions, with within-plant differences ranging from 1.2 to 7.6 g kg −1 , compared to 3.1 to 16.3 g kg −1 in leaves. While pNUE was generally higher under unfertilized conditions, it varied largely by site; however, genotypic differences were minimal within the given year. These results highlight the importance of integrating environmental and management factors into breeding and fertilization strategies to enhance N efficiency in high-biomass sorghum.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Forest Structural Complexity and Biomass Predict First-Year Carbon Cycling Responses to Disturbance

The pre-disturbance vegetation characteristics that predict carbon (C) cycling responses to disturbance are not well known. To address this gap, we initiated the Forest Resilience Threshold Experiment, a manipulative study in which more than 3600 trees were stem girdled to achieve replicated factorial combinations of four levels (control, 45, 65, and 85% gross defoliation) of disturbance severity and two disturbance types (targeting upper or lower canopy strata). Applying a standardized stability framework in which initial C cycling resistance to disturbance was calculated as the first-year natural log response ratio of disturbance and control treatments, we investigated to what extent pre-disturbance levels of species diversity, aboveground woody biomass, leaf area index, and canopy rugosity—a measure of structural complexity—predict the initial responses of subcanopy light-saturated leaf CO 2 assimilation (A sat ), aboveground wood NPP (ANPP w ), and soil respiration (R s ) to phloem-disrupting disturbance. In the year following stem girdling, we found that above-ground C cycling processes, A sat and ANPP w , were highly resistant to increases in disturbance severity, while Rs resistance declined as severity increased. Disturbance type had no effect on first-year resistance. Pre-disturbance aboveground woody biomass, and canopy rugosity were positive predictors of ANPP w resistance and, conversely, negatively related to R s resistance. Subcanopy A sat resistance was not related to pre-disturbance vegetation characteristics. Stability of C uptake processes along with R s declines suggest the net C sink was sustained in the initial months following disturbance. Therefore, we conclude that biomass and complexity are significant, but not universal, predictors of initial C cycling resistance to disturbance. Moreover, our findings highlight the utility of standardized stability measures when comparing functional responses to disturbance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Attributing differences of solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationships between two C4 crops: corn and miscanthus

There remains limited information to characterize the solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationship in C4 cropping systems. The annual C4 crop corn and perennial C4 crop miscanthus differ in phenology, canopy structure and leaf physiology. Investigating the SIF-GPP relationships in these species could deepen our understanding of SIF-GPP relationships within C4 crops. Using in situ canopy SIF and GPP measurements for both species along with leaf-level measurements, we found considerable differences in the SIF-GPP relationships between corn and miscanthus, with a stronger SIF-GPP relationship and higher slope of SIF-GPP observed in corn compared to miscanthus. These differences were mainly caused by leaf physiology. For miscanthus, high non-photochemical quenching (NPQ) under high light, temperature and water vapor deficit (VPD) conditions caused a large decline of fluorescence yield (Φ F ), which further led to a SIF midday depression and weakened the SIF-GPP relationship. The larger slope in corn than miscanthus was mainly due to its higher GPP in mid-summer, largely attributed to the higher leaf photosynthesis and less NPQ. Furthermore, our results demonstrated variation of the SIF-GPP relationship within C4 crops and highlighted the importance of leaf physiology in determining canopy SIF behaviors and SIF-GPP relationships.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R 2 ) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance.

09 BIOMASS FUELS↗
DOE OSTI2021

Is a Prescribed Fire Sufficient to Slow the Spread of Woody Plants in an Infrequently Burned Grassland? A Case Study in Tallgrass Prairie

In many mesic grasslands, such as the central Great Plains in North America, frequent fire is a key regulator of ecological processes. Long periods of infrequent fire facilitate the conversion of herbaceous-dominated grassland to woody-dominated shrubland or woodland. At the Konza Prairie Biological Station in northeast Kansas, one infrequently burned portion of the landscape has undergone transformation from grassland to woodland after nearly 30 yr without fire. In Spring 2017, a prescribed burn was implemented to assess fire effectiveness on woody plant mortality. A postfire census of 3 000+ individual woody plants identified the distribution of species by size (height), topographic position, and slope on the landscape. Mortality and canopy fire damage were calculated for each individual. In lowland locations with near-continuous shrub cover (30.7% of the landscape), woody plants were unaffected by fire. However, in upland and slope locations, where shrubs and trees were sparser, survival probability varied by topographic position and species. In these locations 68% of all woody individuals experienced 90% or greater fire damage to the canopy, with 56% of these individuals exhibiting new canopy regrowth within 2 mo after the fire. Here, the two most abundant woody shrubs, Cornus drummondii and Rhus aromatica, showed high survival at all height classes and landscape positions. The two abundant tree species, Gleditsia triacanthos and Juniperus virginiana, showed increased survival probability with tree height that varied by landscape position. Survival of J. virginiana also varied according to proximity and size of neighboring clonal shrubs, providing a mechanism for persistence of this fire-sensitive tree species even at small height classes. The probability survival curves developed here are useful for managers assessing when to prescribe fire to maximize mortality for J. virginiana and provide insight relevant for broader ecological understanding of woody encroachment within grasslands throughout the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year -1 ). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r 2 =0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r 2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Localized and Collective Dynamics in Liquid-like Polyethylenimine-Based Nanoparticle Organic Hybrid Materials

In this work, broadband dielectric spectroscopy, rheology, and nuclear magnetic resonance spectroscopy are employed to study molecular dynamics in a nanoparticle organic hybrid materials(NOHMs)system comprising 20 wt% silica nanoparticles ionically-bonded to a polyethylenimine canopy. By comparing the neat polymer (used as a canopy) to the derivative NOHMs, we find that timescales characterizing segmental dynamics in the NOHM are identical to those for the neat polymer. Detailed analysis of the carbon-spin lattice relaxation times yields mechanistic insights into localized and collective dynamics, in quantitative agreement with dielectric results. Interestingly, the NOHMs retain liquid-like characteristics unlike conventional polymer nanocomposites but exhibit higher viscosity due to additional contributions from tethered polymer chains, and mesoscopic structuring. These findings demonstrate the potential of achieving unique and desired material properties via NOHMs by an informed choice of the canopy material.

36 MATERIALS SCIENCE↗