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NASA NTRS2016

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

Babcock, Chad↗

NASA NTRS2023

Tree Growth Enhancement Drives a Persistent Biomass Gain in Unmanaged Temperate Forests

While enhanced tree growth over the last decades has been reported in forests across the globe, it remains unclear whether it drives persistent biomass increases of forest stands, particularly in mature forests. Enhanced tree growth and stand-level biomass are often linked with a simultaneous increase in density-driven mortality and a reduction in tree longevity. Identifying empirical evidence regarding the balance between these processes is challenging due to the confounding effects of stand history, management, and environmental changes. Here, we investigate the link between growth and biomass via the negative relationship between average tree size and stand density (tree number per area). We find increasing stand density for a given mean tree size in unmanaged closed-canopy forests in Switzerland over the past six decades and a positive relationship between tree growth and stand density across forest plots—qualitatively consistent with our simulations using a mechanistic, cohort-resolving ecosystem model (BiomeE). Model simulations show that, in the absence of other disturbances, enhanced tree growth persistently increases biomass stocks despite simultaneous decreases in carbon residence time and tree longevity. However, the magnitude of simulated biomass changes for a given growth enhancement critically depends on the shape of the mortality functions. Our analyses reconcile reports of growth-induced reductions of tree longevity with model predictions of persistent biomass increases, and with our finding of trends toward denser forests in response to growth—also in mature stands.

biomass stocks↗

NASA NTRS1996

Biomass accumulation in hydroponically grown sweetpotato in a controlled environment: a preliminary study

In the development of a plant growth model, the assumptions made and the general equations representing an understanding of plant growth are gradually refined as more information is acquired through experimentation. One such experiment that contributed to sweetpotato model development consisted of measuring biomass accumulation of sweetpotato grown in hydroponic culture in a plant growth chamber. Plants were started from fifteen centimeter long 'TU-82-155' sweetpotato vine cuttings spaced 25 cm apart in each of 18 rectangular growing channels (0.15 by 0.15 by 1.2m) in a system designed to use the nutrient film technique (NFT). Each channel contained four plants. The 3.5m by 5.2m plant growth chamber environmental parameters included an 18h photoperiod, 500 micromoles m-2 s-1 of photosynthetic photon flux (PPF), and a diurnal light/dark temperature of 28 degrees C/22 degrees C. The relative humidity was 80 +/- 5% and the CO2 partial pressure was ambient (350 ppm). The nutrient solution contained in 30L reservoirs was a modified half Hoagland's solution with a 1:2.4 N:K ratio and a pH of 6.2. Solution replenishment occurred when the electrical conductivity (EC) level dropped below 1050. Plants were harvested at 15 days after planting (DAP) and weekly thereafter until day 134. By 57 DAP, stems and fibrous roots had acquired 90% of their total dry biomass, while leaves had reached 84% of their maximum dry biomass. Beginning at 64 DAP dry biomass accumulation in the storage roots dominated the increase in dry biomass for the plants. Dry weight of storage roots at 120 DAP was 165 g/plant or 1.1 kg/m2. Resulting growth curves were consistent with the physiological processes occurring in the plant. Results from this study will be incorporated in a plant growth model for use in conjunction with controlled life support systems for long-term manned space missions.

Biomass↗

NASA NTRS2022

An Evaluation of Biomass Burning Aerosol Mass, Extinction, and Size Distribution in Geos Using Observations From Camp2EX

Biomass burning aerosol impacts aspects of the atmosphere and Earth system through radiative forcing, serving as cloud condensation nuclei, and air quality. Despite its importance, the representation of biomass burning aerosol is not always accurate in numerical weather prediction and climate models or reanalysis products. Using observations collected as part of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) in August through October of 2019, aerosol concentration and optical properties are evaluated within the Goddard Earth Observing System (GEOS) and its underlying aerosol module, GOCART. In the operational configuration, GEOS assimilates aerosol optical depth observations at 550 nm to constrain aerosol fields. Particularly for biomass burning aerosol, without the assimilation of aerosol optical depth, aerosol extinction is underestimated compared to observations collected in the Philippines region during the CAMP2Ex campaign. The assimilation process adds excessive amounts of carbon to account for the underestimated extinction, resulting in positive biases in the mass of black and organic carbon, especially within the boundary layer, relative to in situ observations from the Langley Aerosol Research Group Experiment. Counteracting this, GEOS is deficient in sulphate and nitrate aerosol just above the boundary layer. Aside from aerosol mass, extinction within GEOS is a function of ambient relative humidity and an assumed particle size distribution. The relationship between dry and ambient extinction in GEOS reveals that hygroscopic growth is too aggressive within the model for biomass burning aerosol. An additional concern lies in the assumed particle size distribution for GEOS, which has a mode radius that is too small for organic carbon. Variability in the observed particle size distribution for biomass burning aerosol within a single flight also illuminates the fact that a single assumed particle size distribution is not sufficient and that for a proper representation, a more advanced aerosol module with GEOS may be necessary.

Aerosol Mass↗

NASA NTRS2015

Regional Rates of Young US Forest Growth Estimated From Annual Landsat Disturbance History and IKONOS Stereo Imagery

Forests of the Contiguous United States (CONUS) have been found to be a large contributor to the global atmospheric carbon sink. The magnitude and nature of this sink is still uncertain and recent studies have sought to define the dynamics that control its strength and longevity. The Landsat series of satellites has been a vital resource to understand the long-term changes in land cover that can impact ecosystem function and terrestrial carbonstock. We combine annual Landsat forest disturbance history from 1985 to 2011 with single date IKONOS stereoimagery to estimate the change in young forest canopy height and above ground live dry biomass accumulation for selected sites in the CONUS. Our approach follows an approximately linear growth rate following clearing over short intervals and does not estimate the distinct non-linear growth rate over longer intervals.We produced canopy height models by differencing digital surface models estimated from IKONOS stereo pairs with national elevation data (NED). Correlations between height and biomass were established independently using airborne LiDAR, and then applied to the IKONOS-estimated canopy height models. Graphing current biomass against time since disturbance provided biomass accumulation rates. For 20 study sites distributed across five regions of the CONUS, 19 showed statistically significant recovery trends (p is less than 0.001) with canopy growth from 0.26 m yr-1to 0.73 m yr-1. Above ground live dry biomass (AGB) density accumulation ranged from 1.31 t/ha yr-1 to 12.47 t/ha yr-1. Mean forest AGB accumulationwas 6.31 t/ha yr-1 among all sites with significant growth trends. We evaluated the accuracy of our estimates by comparing to field estimated site index curves of growth, airborne LiDAR data, and independent model predictions of C accumulation. Growth estimates found with this approach are consistent with site index curves and total biomass estimates fall within the range of field estimates. This is aviable approach to estimate forest biomass accumulation in regions with clear-cut harvest disturbances.

Neigh, Christopher S. R.↗

NASA NTRSDate not supplied

Evaluation of GEOS Aerosols Using CAMP2Ex Observations

Biomass burning aerosol impacts aspects of the atmosphere and Earth system through direct and semi-direct effects, as well as influencing air quality. Despite its importance, the representation of biomass burning aerosol is not always accurate in numerical weather prediction and climate models or reanalysis products. Using observations collected as part of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) in August through October of 2019, aerosol concentration and optical properties are evaluated within the Goddard Earth Observing System (GEOS) and its underlying aerosol module, GOCART. In the operational configuration, GEOS assimilates aerosol optical depth observations at 550 nm from AERONET and MODIS to constrain aerosol fields. Particularly for biomass burning aerosol, without the assimilation of aerosol optical depth, aerosol extinction is underestimated compared to observations collected in the Philippines region during the CAMP2Ex campaign. The assimilation process adds excessive amounts of carbon to account for the underestimated extinction, resulting in positive biases in the mass of black and organic carbon, especially within the boundary layer, relative to in situ observations from the Langley Aerosol Research Group Experiment. Counteracting this, GEOS is deficient in sulfate and nitrate aerosol just above the boundary layer. Aerosol extinction within GEOS is a function of the mass of different aerosol species, the ambient relative humidity, the assumed spectral optical properties, and particle size distribution per species. The relationship between dry and ambient extinction in GEOS reveals that hygroscopic growth is too high within the model for biomass burning aerosol. An additional concern lies in the assumed particle size distribution for GEOS, which has a single mode radius that is too small for organic carbon. Variability in the observed particle size distribution for biomass burning aerosol within a single flight also illuminates the fact that a single assumed particle size distribution is not sufficient and that for a proper representation, a more advanced aerosol module within GEOS may be necessary.

Allison Collow↗

NASA NTRS1997

Characterization of Proteins in Filtrate from Biodegradation of Crop Residue

Biodegradation of plant biomass is a feasible path for transformation of crop residue and recycling of nutrients for crop growth. The need to model the effects of factors associated with recycling of plant biomass resulting from hydroponic sweet potato production has led to investigation of natural soil isolates with the capacity for starch hydrolysis. This study sought to use nondenaturing gel electrophoresis to characterize the proteins present in filtered effluent from bioreactors seeded with starch hydrolyzing bacterial culture used in the biodegradation of senesced sweet potato biomass. The study determined the relative molecular weight of proteins in sampled effluent and the protein banding pattern was characterized. The protein profiles of effluent were similar for samples taken from independent runs under similar conditions of starch hydrolysis. The method can be used as a quality control tool for confirmation of starch hydrolysis of crop biomass. In addition, this method will allow monitoring for presence of contaminants within the system-protein profiles indicative of new enzymes in the bioreactors.

Horton, Wileatha↗

NASA NTRS1983

A mathematical model for crop spectral-temporal trajectories based on a plant growth model

The Kubelka-Munk radiative transfer model is combined with an approximation of Kauth-Thomas greeness and brightness transforms to derive approximate closed form expressions for crop greeness and brightness surrogates in terms of canopy biomass. The greeness relation derived resembles an existing empirical relation between leaf area index and greeness. A simple growth model based on interception and utilization of photosynthetically active radiation is developed and used to describe the time evolution of greeness and brightness. The model developed does not yet yield definitive profile calculations but suggests a conceptual framework which may be found useful for further profile analysis.

Woolford, T. L.↗

NASA NTRS1995

Interactions of CO2, temperature and management practices: simulations with a modified version of CERES-Wheat

A new growth subroutine was developed for CERES-Wheat, a computer model of wheat (Triticum aestivum) growth and development. The new subroutine simulates canopy photosynthetic response to CO2 concentrations and light levels, and includes the effects of temperature on canopy light-use efficiency. Its performance was compared to the original CERES-Wheat V-2 10 in 30 different cases. Biomass and yield predictions of the two models were well correlated (correlation coefficient r > 0.95). As an application, summer growth of spring wheat was simulated at one site. Modeled crop responses to higher mean temperatures, different amounts of minimum and maximum warming, and doubled CO2 concentrations were compared to observations. The importance of irrigation and nitrogen fertilization in modulating the wheat crop climatic responses were also analyzed. Specifically, in agreement with observations, rainfed crops were found to be more sensitive to CO2 increases than irrigated ones. On the other hand, low nitrogen applications depressed the ability of the wheat crop to respond positively to CO2 increases. In general, the positive effects of high CO2 on grain yield were found to be almost completely counterbalanced by the negative effects of high temperatures. Depending on how temperature minima and maxima were increased, yield changes averaged across management practices ranged from -4% to 8%.

NASA Discipline Life Support Systems↗

NASA NTRS2019

A Multidisciplinary Modeling Approach of Plant Gas Exchange in Reduced Gravity Environments

In-situ food production is a necessary step for human exploration of the solar system and requires a deep understanding of plant growth in reduced gravity environments. In particular, the lack of buoyancy-driven convection changes the gas exchange at the leaf surface, which decreases photosynthesis and transpiration rates, and ultimately biomass production. To understand the intricate relations between physical, chemical, and biochemical processes, the following methodology combines the development of a mechanistic model of plant growth in reduced gravity environments, computational fluid dynamics (CFD) simulations, and experiments in different time frames.The model presented here is a coupled mass and energy balance using the single round leaf assumption, including gravity as an entry parameter, and the leaf surface temperature as an output variable. Measures of the leaf surface temperature using infra-red cameras allow for a computation of the transpiration rate. This approach was followed to design a parabolic flight experiment, which performed 7 flights, and enabled data collection for model validation in different gravity and ventilation settings on a short time frame. Current measures of carbon assimilation and transpiration rate at the leaf and canopy level using an infra-red gas analyzer (Li-6800) in 1g lab conditions on several species will enable a validation on longer time frames and further calibration of the model. CFD studies both on the parabolic flight and on the lab experimental set-up allow the precise assessment of ventilation above the canopy and plants' leaves.Ultimately, this work will provide recommendations for the design of future plant growth hardware, especially on the lowest adequate ventilation for optimal plant growth in reduced gravity environments, as well as assessing biomass and oxygen production rates on planetary surfaces and space stations. This work was funded by CNES, CNRS, Clermont Auvergne Metropole, and NASA Space Biology through NASA postdoctoral program / USRA.

Poulet, Lucie↗

NASA NTRS2022

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

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.

Sebastián Martinuzzi↗

NASA NTRS1989

The use of models to predict potential contamination aboard orbital vehicles

A model of fungal growth on air-exposed, nonnutritive solid surfaces, developed for utilization aboard orbital vehicles is presented. A unique feature of this testable model is that the development of a fungal mycelium can facilitate its own growth by condensation of water vapor from its environment directly onto fungal hyphae. The fungal growth rate is limited by the rate of supply of volatile nutrients and fungal biomass is limited by either the supply of nonvolatile nutrients or by metabolic loss processes. The model discussed is structurally simple, but its dynamics can be quite complex. Biofilm accumulation can vary from a simple linear increase to sustained exponential growth, depending on the values of the environmental variable and model parameters. The results of the model are consistent with data from aquatic biofilm studies, insofar as the two types of systems are comparable. It is shown that the model presented is experimentally testable and provides a platform for the interpretation of observational data that may be directly relevant to the question of growth of organisms aboard the proposed Space Station.

Boraas, Martin E.↗

NASA NTRS2003

Adaptation of SUBSTOR for controlled-environment potato production with elevated carbon dioxide

The SUBSTOR crop growth model was adapted for controlled-environment hydroponic production of potato (Solanum tuberosum L. cv. Norland) under elevated atmospheric carbon dioxide concentration. Adaptations included adjustment of input files to account for cultural differences between the field and controlled environments, calibration of genetic coefficients, and adjustment of crop parameters including radiation use efficiency. Source code modifications were also performed to account for the absorption of light reflected from the surface below the crop canopy, an increased leaf senescence rate, a carbon (mass) balance to the model, and to modify the response of crop growth rate to elevated atmospheric carbon dioxide concentration. Adaptations were primarily based on growth and phenological data obtained from growth chamber experiments at Rutgers University (New Brunswick, N.J.) and from the modeling literature. Modified-SUBSTOR predictions were compared with data from Kennedy Space Center's Biomass Production Chamber for verification. Results show that, with further development, modified-SUBSTOR will be a useful tool for analysis and optimization of potato growth in controlled environments.

NASA Discipline Life Support Systems↗

NASA NTRS2016

Crop Model Improvement Reduces the Uncertainty of the Response to Temperature of Multi-Model Ensembles

To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures greater than 24 C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

damage assessment↗

NASA NTRS2024

Evaluating Differences Among Crop Models in Simulating Soybean in-Season Growth

Crop models are useful tools for simulating agricultural systems that require continued model development and testing to increase their robustness and improve how they describe our current understanding of processes. Coordinated and “blind” evaluation of multiple models using same protocols and experimental datasets provides unique opportunities to further improve models and enhance their reliability. For soybean [Glycine max (L.) Merr.], there has been limited coordinated multi-model evaluations for the simulation of in-season plant growth dynamics. We evaluated ten dynamic soybean crop models for their simulation of in-season plant growth using data from five experiments conducted in Argentina, Brazil, France, and USA. We evaluated models after a Blind (using only phenology data) and a Full calibration (with in-season and end-of-season variables). Calibration reduced model uncertainty by reducing standard bias for the simulation of in-season variables (biomass, leaf, pod, and stem weights, and leaf area index, LAI). However, we found that most models had difficulty in reproducing leaf growth dynamics, with normalized root mean squared error (nRMSE) of 56% for leaf weight and 43% for LAI (across locations and models after Full calibration). Models with different levels of complexity and experience were capable of simulating final seed yield at maturity with reasonable accuracy (nRMSE of 8–31% after Full calibration). However, the nRMSE for pod weight (of 17–64% after Full calibration) was two-fold larger than that of seed yield. Moreover, the models differed in how they simulated timing from sowing to beginning seed growth (47–93 days) and effective seed filling period (18–54 days), owing to model structural differences in defining the reproductive developmental stages. Overall, we identified the following processes that can benefit from further model improvement: leaf expansion and senescence, reproductive phenology, and partitioning to reproductive growth. Simulation of pod wall tissue and individual seed cohorts is another aspect that many models currently lack. Model improvement can benefit from high-temporal resolution experimental datasets that concurrently account for phenology, plant growth, and partitioning. Further, we recommend collecting reproductive phenology in the field consistent with actual dry matter allocation to organs in the models and collecting multiple observations of seed and pod weight to aid model improvement for simulation of seed growth and yield formation.

Agricultural Model Intercomparison and Improvement↗

NASA NTRS2006

An Energy Balance Model to Predict Chemical Partitioning in a Photosynthetic Microbial Mat

Studies of biosignature formation in photosynthetic microbial mat communities offer potentially useful insights with regards to both solar and extrasolar astrobiology. Biosignature formation in such systems results from the chemical transformation of photosynthetically fixed carbon by accessory microorganisms. This fixed carbon represents a source not only of reducing power, but also energy, to these organisms, so that chemical and energy budgets should be coupled. We tested this hypothesis by applying an energy balance model to predict the fate of photosynthetic productivity under dark, anoxic conditions. Fermentation of photosynthetically fixed carbon is taken to be the only source of energy available to cyanobacteria in the absence of light and oxygen, and nitrogen fixation is the principal energy demand. The alternate fate for fixed carbon is to build cyanobacterial biomass with Redfield C:N ratio. The model predicts that, under completely nitrogen-limited conditions, growth is optimized when 78% of fixed carbon stores are directed into fermentative energy generation, with the remainder allocated to growth. These predictions were compared to measurements made on microbial mats that are known to be both nitrogen-limited and populated by actively nitrogen-fixing cyanobacteria. In these mats, under dark, anoxic conditions, 82% of fixed carbon stores were diverted into fermentation. The close agreement between these independent approaches suggests that energy balance models may provide a quantitative means of predicting chemical partitioning within such systems - an important step towards understanding how biological productivity is ultimately partitioned into biosignature compounds.

Hoehler, Tori M.↗

NASA NTRS1989

Transpiration during life cycle in controlled wheat growth

A previously-developed model of wheat growth, designed for convenient incorporation into system-level models of advanced space life support systems is described. The model is applied to data from an experiment that grew wheat under controlled conditions and measured fresh biomass and cumulated transpiration as a function of time. The adequacy of modeling the transpiration as proportional to the inedible biomass, and an age factor which varies during the life cycle, are examined. Results indicate that during the main phase of vegetative growth in the first half of the life cycle, the rate of transpiration per unit mass of inedible biomass is more than double the rate during the phase of grain development and maturation during latter half of the life cycle.

Volk, Tyler↗

NASA NTRS2017

Energy in a Planetary Context

The potential present day habitability of solar system bodies beyond Earth is limited to subsurface environments, where the availability of energy in biologically useful form is a paramount consideration. Energy availability is commonly quantified in terms of molar Gibbs energy changes for metabolisms of interest, but this can provide an incomplete and even misleading picture. A second aspect of life's requirement for energy - the rate of delivery, or power - strongly influences habitability, biomass abundance, growth rates, and, ultimately, rates of evolution. We are developing an approach to quantify metabolic power, using a cell-scale reactive transport model in which physical and chemical environmental parameters are varied. Simultaneously, we evaluate cell-specific energy flux requirements and their dependence on environmental "extremes". Comparison of metabolic power supply and demand provides a constraint on how biomass abundance varies across a range of environmental parameters, and thereby a prediction of the relative habitability of different environments. We are evaluating the predictive capability of this approach through comparison to observed distributions of microbial abundance in a range of subsurface (predominantly serpentinizing) systems.

MARINE GEOMICROBIOLOGY↗