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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Belowground plant carbon and nitrogen exchange: plant-derived carbon inputs and pore structure formation

Belowground plant transfer of carbon (C) and nitrogen (N) can benefit soil ecosystems, increasing soil C gains and plant N availability, while improving soil pore structure. We explored such transfers among three plant species of North American prairie, where C and N were transferred from a grass (Panicum virgatum L., switchgrass (Sgrass)) to either a legume (Lespedeza capitata Michx., bush clover (Bclover)), a forb (Rudbeckia hirta L., black-eyed Susan (BSforb)), or a mixture of the two. The plants were grown either with/out direct root contact, thus allowing assessment of the relative contributions of fungal- and root-based transfer pathways. The Sgrass was labeled with 13 C and 15 N, and C and N transfers were assessed by measuring isotope enrichment of roots and aboveground biomass of neighboring plants. Soil inputs of plant-derived C and N were assessed by isotope analyses of the rhizosphere soil. X-ray computed tomography was used for pore structure analyses. Carbon transfer was much higher in the presence of direct/close root contact between source and recipient plants, yet N transfers appeared to be mainly fungal driven. Here, while C and N were readily transferred from Sgrass to other Sgrass and Bclover neighbors, transfers to BSforb were negligible. However, in a three species system, the presence of the legume enhanced C and N transfers to BSforb, suggesting non-additive influences of diverse plant community composition. The more plant-derived C and N was found in the rhizosphere of recipient plants, the greater C and N transfers through roots. Greater C and N transfers were associated with increases in 8–30 μm diameter pores and decreases in >150 μm pores. Summarily, diverse plant communities, especially those with legumes, increase C and N transfers, which then benefit soil C inputs and its protection via changes in pore structure.

13 CO 2 pulse labeling↗
DOE OSTI2021

Interactions among rooting traits for deep water and nitrogen uptake in upland and lowland ecotypes of switchgrass ( Panicum virgatum L.)

Abstract The response of plant growth and development to nutrient and water availability is an important adaptation for abiotic stress tolerance. Roots need to intercept both passing nutrients and water while foraging into new soil layers for further resources. Substantial amounts of nitrate can be lost in the field when leaching into groundwater, yet very little is known about how deep rooting affects this process. Here, we phenotyped root system traits and deep 15N nitrate capture across 1.5 m vertical profiles of solid media using tall mesocosms in switchgrass (Panicum virgatum L.), a promising cellulosic bioenergy feedstock. Root and shoot biomass traits, photosynthesis and respiration measures, and nutrient uptake and accumulation traits were quantified in response to a water and nitrate stress factorial experiment for switchgrass upland (VS16) and lowland (AP13) ecotypes. The two switchgrass ecotypes shared common plastic abiotic responses to nitrogen (N) and water availability, and yet had substantial genotypic variation for root and shoot traits. A significant interaction between N and water stress combination treatments for axial and lateral root traits represents a complex and shared root development strategy for stress mitigation. Deep root growth and 15N capture were found to be closely linked to aboveground growth. Together, these results represent the wide genetic pool of switchgrass and show that deep rooting promotes nitrate capture, plant productivity, and sustainability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Directing Traffic in the Rhizosphere: How Phage and Fauna Shape the Flow and Fate of Root Carbon through Microbial Pathways

The rhizosphere, the portion of soil directly influenced by plant roots, is an area of great biological activity and intense carbon (C) cycling. Interactions among the residents of the rhizosphere, such as bacteria, fungi, viruses, and fauna, result in transformation and transfer C from root exudates and root debris to biomass and developing soil organic matter. The primary object of this research project was to explore the complex interactions controlling C flow in the rhizosphere and the response of these interactions to soil water conditions. We used stable isotope techniques combined with molecular analyses to observe the flow of C as it is fixed by plants from the atmosphere, deposited into the soil through plant roots, and exchanged through multiple pathways of the soil food web. This technical report summarizes the achievements of this project from field, greenhouse, and lab experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wavelet Analysis of GPR Data for Belowground Mass Assessment of Sorghum Hybrid for Soil Carbon Sequestration

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical.

Wolfe, Matthew↗
DOE OSTI2024

Changes in microbial community and network structure precede shrub degradation in a desert ecosystem

Large-scale restoration is intended to promote ecological recovery. Improvements in plant and microbial conditions, however, may slow or even reverse in late succession. To better understand long-term restoration outcomes and underlying drivers of successional pathways, we tracked plant, bacterial and fungal, and soil conditions across a 40-year shrub plantation that was intended to stabilize desertified land in northern China. Here, we found that planted Haloxylon ammodendron shrubs developed and then subsequently became degraded after 30–40 years. Bacterial abundance and α-diversity were much higher than those of fungi, but no significant differences in composition and structure were found in different plantation ages. In contrast, the dominant taxa of fungal communities shifted from symbiotroph and saprotroph species towards pathotroph species with increased soil nutrients in the plantation chronosequence after two decades. The changes in fungal dominant species led to a transition in microbial network structure and function, with an increase in negative linkages among taxa that began in the middle stages of succession. Changes in fungal community structure had direct and indirect negative effects on shrub leaf physiology, root activity, and biomass. Our results highlight the preceding role of a breakdown in soil microbial community composition and network structure on the degradation of shrub performance in long-term desert succession. Our study emphasizes the importance of understanding soil-microbial-plant linkages on restoration outcomes, and mechanisms that can slow or reverse the recovery of ecosystems.

Bacterial and fungal community composition↗
DOE OSTI2020

Nonlinear drought plasticity reveals intraspecific diversity in a dominant grass species

Abstract Intraspecific diversity of dominant species in native plant communities can modulate ecosystem function under both optimal and stressful conditions. Yet, few genotype by environment interaction studies quantify differences in the shape of plasticity functions or phenotypic breakpoints across genotypes in natural populations. Using three genotypes with a history of drought selection, we performed a greenhouse study on the dominant tallgrass prairie species Andropogon gerardii . We investigated phenotypic plasticity and recovery differences among genotypes across a water availability gradient, measuring growth‐related, instantaneous and cumulative phenotypes. To further understand genotype by environment effects, we quantified plasticity functions and breakpoints among genotypes. Like other studies, we found strong evidence for phenotypic and plasticity differences among genotypes. However, we also found nonlinear plasticity functions and breakpoints were common across phenotypes, especially relative growth rates, biomass allocation and root architecture. Drought selected genotypes were also more likely to flower during recovery, but all genotypes were resilient to drought across treatments. We demonstrate that plasticity functions may help explain intraspecific diversity, patterns of selection and nonlinear community responses to more variable rainfall within an experimental population. In particular, plasticity functions can help disentangle drought/variability tolerance versus acquisitive strategies. A better understanding of intraspecific diversity in this grass species will provide more mechanistic insight into its ability to buffer ecosystem changes and provide resiliency in the tallgrass prairie under future droughts. A free Plain Language Summary can be found within the Supporting Information of this article.

Hoffman, Ava M.↗
DOE OSTI2021

Integrating Arctic Plant Functional Types in a Land Surface Model Using Above‐ and Belowground Field Observations

Abstract Accurate simulations of high‐latitude ecosystems are critical for confident Earth system model (ESM) projections of carbon cycle feedbacks to global climate change. Land surface model components of ESMs, including the E3SM Land Model (ELM), simulate vegetation growth and ecosystem responses to changing climate and atmospheric CO 2 concentrations by grouping heterogeneous vegetation into like sets of plant functional types (PFTs). Many such models represent high‐latitude vegetation using only two PFTs (shrub and grass), thereby missing the diversity of vegetation growth forms and functional traits in the Arctic. Here, we use field observations of biomass and leaf traits across a gradient of plant communities on the Seward Peninsula in northwest Alaska to replace the original ELM configuration for the first time with nine Arctic‐specific PFTs. The newly developed PFTs include: (1) nonvascular mosses and lichens, (2) deciduous and evergreen shrubs of various height classes, including an alder PFT, (3) graminoids, and (4) forbs. Improvements relative to the original model configuration included greater belowground biomass allocation, persistent fine roots and rhizomes of nonwoody plants, and better representation of variability in total plant biomass across sites with varying plant communities and depth to bedrock. Simulations through 2100 using the RCP8.5 climate scenario and constant PFT fractional areas showed alder‐dominated plant communities gaining more biomass and lichen‐dominated communities gaining less biomass compared to default PFTs. Our results highlight how representing the diversity of arctic vegetation and confronting models with measurements from varied plant communities improves the representation of arctic vegetation in terrestrial ecosystem models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Statistical analysis of environmental variability within the CELSS breadboard project's biomass production chamber

Variability in the aerial and root environments of NASA's Breadboard Project's Biomass Production Chamber (BPC) was determined. Data from two lettuce and two potato growouts were utilized. One growout of each crop was conducted prior to separating the upper and lower chambers; the other was subsequent to separation. There were little or no differences in pH, EC, or solution temperature between the upper and lower chamber or within a chamber. Variation in the aerial environment within a chamber was two to three times greater than variation between chambers for air temperature, relative humidity, and PPF. High variability in air velocity, relative to tray position, was observed. Separating the BPC had no effect on PPF, air velocity, solution temperature, pH, or EC. Separation reduced the gradient in air temperature and relative humidity between the upper and lower chambers, but increased the variability within a chamber. Variation between upper and lower chambers was within 5 percent of environmental set-points and of little or no physiological significance. In contrast, the variability within a chamber limits the capability of the BPC to generate statistically reliable data from individual tray treatments at this time.

Stutte, G. W.↗
DOE OSTI2020

Serendipita bescii promotes winter wheat growth and modulates the host root transcriptome under phosphorus and nitrogen starvation

Summary Serendipita vermifera ssp. bescii , hereafter referred to as S. bescii , is a root‐associated fungus that promotes plant growth in both its native switchgrass host and a variety of monocots and dicots. Winter wheat ( Triticum aestivum L.), a dual‐purpose crop, used for both forage and grain production, significantly contributes to the agricultural economies of the Southern Great Plains, USA. In this study, we investigated the influence of S. bescii on growth and transcriptome regulation of nitrogen (N) and phosphorus (P) metabolism in winter wheat. Serendipita bescii significantly improved lateral root growth and forage biomass under a limited N or P regime. Further, S. bescii activated sets of host genes regulating N and P starvation responses. These genes include, root‐specific auxin transport, strigolactone and gibberellin biosynthesis, degradation of phospholipids and biosynthesis of glycerolipid, downregulation of ammonium transport and nitrate assimilation, restriction of protein degradation by autophagy and subsequent N remobilization. All these genes are hypothesized to regulate acquisition, assimilation and remobilization of N and P. Based on transcriptional level gene regulation and physiological responses to N or P limitation, we suggest S. bescii plays a critical role in modulating stress imposed by limitation of these two critical nutrients in winter wheat.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1989

Sweet potato growth parameters, yield components and nutritive value for CELSS applications

Sweet potatoes have been grown hydroponically using the nutrient film technique (NFT) to provide a potential food source for long-term manned space missions. Experiments in both sand and NFT cultivars have produced up to 1790 g/plant of fresh storage root with an edible biomass index ranging from 60-89 percent and edible biomass linear growth rates of 39-66 g/sq m day in 105 to 130 days. Experiments with different cultivars, nutrient solution compositions, application rates, air and root temperatures, photoperiods, and light intensities indicate good potential for sweet potatoes in CELSS.

Loretan, P. A.↗
DOE OSTI2022

Agroecosystem model simulations reveal spatial variability in relative productivity in biomass sorghum and maize in Iowa, USA

Biomass sorghum (Sorghum bicolor L. Moench) is an annual C 4 grass that has emerged as a candidate bioenergy crop but has not been widely grown in the United States. Corn (Zea mays L.), another annual C 4 grass, has been produced on a large scale in the United States for biofuels. Iowa leads the nation in both corn and ethanol production. The high productivity of corn in Iowa creates the research question: could biomass sorghum be as or more productive than corn in the state in terms of bioenergy? Efforts to use crop models to fill the gaps left by field experimentation on biomass sorghum have also been limited thus far. To address our research question, we collected biophysical data on biomass sorghum grown in Iowa for two growing seasons, used them to develop a biomass sorghum module in an agroecosystem model (Agro-IBIS), and predict the potential performance of biomass sorghum across Iowa compared to maize. Despite dry conditions in 2019 and 2020, average biomass sorghum yields were 17.20 Mg ha –1 . By comparison, average corn aboveground biomass was higher in 2020 (22.02 Mg ha –1 ). Soil cores indicated average belowground biomass of 1.46 Mg ha –1 , with roots concentrated near the surface (73% of biomass above 50 cm in a 1 m core). When biomass sorghum model parameters were calibrated with measured values, model output was in close agreement with measured biomass (slope = 0.932, R 2 = 0.91) and evapotranspiration (slope = 0.757, R 2 = 0.64). Subsequent regional simulations revealed a notable latitudinal gradient in biomass sorghum yield, with a strong linear relationship between yield and seasonal growing degree-days (R 2 = 0.89). When these yields were compared to simulated corn aboveground biomass yields, only 3.4% of the state had biomass sorghum yields that were significantly higher than corn. This number was reduced to 0.3% when comparing the crops in terms of energy ethanol yield. Thus, we conclude biomass sorghum must be improved to be competitive with corn as an annual biofuel crop in Iowa. This study provides a baseline against which to compare advancements in sorghum breeding for biomass and stress tolerance in Iowa.

Agro-IBIS↗
NASA NTRS2012

A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha

soil moisture↗
DOE OSTI2022

Whole plant traits of coastal dune vegetation and implications for interactions with dune dynamics

Abstract Coastal dunes are important protective features against sea level rise and coastal storms. Interactions between dune plant aboveground structures and sediment trapping that allow for dune building and maintenance are well established. More recently, studies documenting belowground biomass for promoting erosion resistance in dominant dune species have been conducted, yet a knowledge gap remains regarding species‐specific characterization of whole plants, specifically with respect to roots, rhizomes, and belowground stems. Our objective was to quantify above‐ and belowground traits of four dominant dune grasses to document the potential for species‐specific effects on dune growth, maintenance, and erosion resistance. We examined above‐ and belowground traits among four prominent dune grasses of the Atlantic and Gulf Coasts of North America: Ammophila breviligulata , Panicum amarum , Spartina patens , and Uniola paniculata . Whole plant samples of each species were collected from the foredune at the US Army Engineer Research and Development Center's Field Research Facility in Duck, North Carolina, USA, and quantified for several above‐ and belowground traits (e.g., stem height, rhizome number and length, root surface area by diameter class, root tensile strength, and mycorrhizal percent infection). Belowground factors known to impact important dune processes, such as rhizome length, mycorrhizal percent infection, and root traits, differed substantially among species. When visualized in multivariate space, all species significantly differed in suites of above‐ and belowground traits. When considering belowground only, Ammophila and Spartina were similar, despite differences in biomass allocation. Species separated along axes related to mycorrhizal association, biomass allocation, and root construction. The four co‐occurring dune grass species were dissimilar in suites of plant traits. Belowground trait differences were driven by those describing root construction, biomass allocation, and mycorrhizal infection. Dissimilarity in above‐ and belowground suites of traits may demonstrate different approaches for surviving the dune environment. Incorporating belowground traits into modeling will enhance predictions of dune response to climate change through interactions between vegetation and dune dynamics that facilitate coastal resistance and resilience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Rhizosphere bacteria containing ACC deaminase decrease root ethylene emission and improve maize root growth with localized nutrient supply

Abstract Localized nutrient supply can enhance maize root proliferation, but also increase root ethylene production. Whether engineering ethylene signalling in the rhizosphere can further enhance root growth and nutrient uptake remains unknown. Here, field and column experiments for maize ( Zea mays . L) were designed as different nutrient treatments (broadcast or localized nutrient supply containing ammonium and phosphorus) with or without inoculation with rhizobacterium Variovorax paradoxus 5C‐2 containing the 1‐aminocyclopropane‐1‐carboxylate (ACC) deaminase. Rhizobacterial inoculation increased shoot biomass by 12% and root length density by 50% with localized nutrient supply. Meanwhile, localized nutrient supply increased root ethylene production by 54% compared with broadcast, and rhizobacterial inoculation prevented the increase in root ethylene. Reduced root ethylene production following V. paradoxus 5C‐2 inoculation was highly associated with a greater proportion of fine root proliferation under localized nutrient supply, which may account for the increased nitrogen and phosphorus uptake. Our work sheds light on the understanding of the interactions between root and microbe through taking hormone into consideration to dissect the relationship between below ground and above ground. It is useful to explore the strategy of soil–crop management by introducing rhizosphere microorganisms to regulate plant ethylene signal and then benefit sustainable agriculture.

Jin, Kemo↗
DOE OSTI2024

Model code and data: biomass allocation adjustments induced by elevated CO2 and warming in a C3 brackish marsh, 2017-2022, Maryland

This dataset and R script accompany the published paper Bruns et al. (2024) in Geophysical Research Letters. The data are from the first six years of a field manipulation of whole-ecosystem warming and elevated CO2 experiment (Salt Marsh Accretion Response to Temperature eXperiment, or SMARTX) in the Smithsonian's Global Change Research Wetland (GCReW), a brackish, microtidal wetland site on a subestuary of the Chesapeake Bay. These data were generated to understand how warming and elevated CO2 interact to structure ecosystem-level responses to global change, particularly in terms of carbon sequestration. The dataset covers 2017-2022 and includes peak annual above ground biomass, annual belowground fine root productivity, and porewater NH4 for each experimental plot. The overall experiment is replicated in two locations on the marsh, a lower elevation zone dominated the C3 sedge S. Americanus and a higher elevation plot dominated by the C4 species. This paper and its data release is only for the C3 plot. Variable descriptions for data file is available in variable_descriptions.pdf. The R script Bruns_et_al_2024_GRL_make_figures.Rmd contains model code and other scripts used to generate all paper figures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling the Effects of Global Change on Ecosystem Processes in a Tropical Rainforest

Ongoing land-use change and climate change in wet tropical forests can potentially drive shifts in tree species composition, representing a change in individual species within a functional group, tropical evergreen trees. The impacts on the global carbon cycle are potentially large, but unclear. We explored the differential effects of species within this functional group, in comparison with the effects of climate change, using the Century model as a research tool. Simulating effects of individual tree species on biome-level biogeochemical cycles constituted a novel application for Century. Background and Objectives: A unique, long-term, replicated field experiment containing five evergreen tree species in monodominant stands under similar environmental conditions in a Costa Rican wet forest provided data for model evaluation. Our objectives were to gain insights about this forest’s biogeochemical cycles and effects of tree species within this functional group, in comparison with climate change. Materials and Methods: We calibrated Century, using long-term meteorological, soil, and plant data from the field-based experiment. In modeling experiments, we evaluated effects on forest biogeochemistry of eight plant traits that were both observed and modeled. Climate-change simulation experiments represented two climate-change aspects observed in this region. Results: Model calibration revealed that unmodeled soil processes would be required to sustain observed P budgets. In species-traits experiments, three separate plant traits (leaf death rate, leaf C:N, and allocation to fine roots) resulted in modeled biomass C stock changes of >50%, compared with a maximum 21% change in the climate-change experiments. Conclusions: Modeled ecosystem properties and processes in Century were sensitive to changes in plant traits and nutrient limitations to productivity. Realistic model output was attainable for some species, but unusual plant traits thwarted predictions for one species. Including more plant traits and soil processes could increase realism, but less-complex models provide an accessible means for exploring plant-soil-atmosphere interactions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Effects of Disinfectants in Water on Mir- and Earth-Grown Wheat

Iodine and silver fluoride are used to purify water onboard U. S. Shuttles and the Russian Space Station, Mir, respectively. In 1995, iodine-treated water, which ranged from 1.0-4.0 mg x kg(exp -1) with a mean of 2.9 mg x kg(exp -1), was applied to Super Dwarf wheat (Triticum aestivum L.) plants when Mir water (grey or tech grade) became scarce. The potential phytotoxicity of iodine on Super Dwarf wheat is an unknown. Since use of iodine-treated water was not part of the experiment, we sought to determine whether it accounted for the subsequent poor wheat seedling growth and floral development onboard the Mir. Super Dwarf wheat seeds were imbibed in iodine or silver fluoride concentrations of 0.0, 1.0, 2.0, 4.0, 8.0 or 16.0 mg x kg(exp -1) for 96 h at 4 C. Five seeds were then planted per 13.3 cm x 13.3 cm pots containing a granular clinoptilolite (Cp) zeolite (1 -2 mm dia.) and placed in Percival(TM) growth chambers programmed for 20/15 C and 18/6 h d/n regime. Plants were irrigated with distilled water, and Iodine- or silver fluoride-treated distilled water. In separate experiments, seeds were treated as above and germination and early seedling growth were determined by examining seedling responses to disinfectants in rolled paper towels. Silver fluoride had very little effect on wheat seed germination. By contrast, iodine reduced germination at all treatment levels. Seedlings exposed to 1.0, 2.0, and 4.0 mg x kg(exp -1) of iodine or silver fluoride levels exhibited a slight stimulation in shoot and root growth. Both disinfectants at 8 and 16 mg x kg(exp -1) showed significantly (p is less than or equal to 0.01) reduced seedling shoot and root lengths and fresh biomasses compared to the control and lower disinfectant levels. The number of spikelets per spike, florets per spikelet, seeds per spike and seed weight were also significantly reduced at the 8 and 16 mg x kg(exp -1) compared to the control and lower levels of disinfectant. Based on these ground-based post-flight analyses, the levels of iodine- and/or silver fluoride-treated water used on Mir-grown plants onboard the Mir did not cause the poor growth and development of the wheat plants.

Campbell, William .F.↗
NASA NTRS2021

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

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.

Elias Ayrey↗