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

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

Michael Alonzo↗

Simple radiative transfer model for relationships between canopy biomass and reflectance

A modified Kubelka-Munk model has been utilized to derive useful equations for the analysis of apparent canopy reflectance. Based on the solution to the model simple working equations were formulated by employing reflectance characteristic parameters. The relationships derived show the asymptotic nature of reflectance data that is typically observed in remote sensing studies of plant biomass. They also establish the range of expected apparent canopy reflectance values for specific plant canopy types. The usefulness of the simplified equations was demonstrated by the exceptionally close fit of the theoretical curves to two separately acquired data sets for alfalfa and shortgrass prairie canopies.

Park, J. K.↗

Recent advances in radar remote sensing of forest

On a global scale, forests represent most of the terrestrial standing biomass (80 to 90 percent). Thus, natural and anthropogenic change in forest covers can have major impacts not only on local ecosystems but also on global hydrologic, climatic, and biogeochemical cycles that involve exchange of energy, water, carbon, and other elements between the earth and atmosphere. Quantitative information on the state and dynamics of forest ecosystems and their interactions with the global cycles appear necessary to understand how the earth works as a natural system. The information required includes the lateral and vertical distribution of forest cover, the estimates of standing biomass (woody and foliar volume), the phenological and environmental variations and disturbances (clearcutting, fires, flood), and the longer term variations following deforestation (regeneration, successional stages). To this end, seasonal, annual, and decadal information is necessary in order to separate the long term effects in the global ecosystem from short term seasonal and interannual variations. Optical remote sensing has been used until now to study the forest cover at local, regional, and global scales. Radar remote sensing, which provides recent SAR data from space on a regular basis, represents an unique means of consistently monitoring different time scales, at all latitudes and in any atmospheric conditions. Also, SAR data have shown the potential to detect several forest parameters that cannot be inferred from optical data. The differences--and complementarity--lie in the penetration capabilities of SAR data and their sensitivity to dielectric and geometric properties of the canopy volume, whereas optical data are sensitive to the chemical composition of the external foliar layer of the vegetation canopy.

Letoan, Thuy↗

From Surface Chlorophyll a to Phytoplankton Community Composition in Oceanic Waters

The objective of the present study is to examine the potential of using the near-surface total chlorophyll a concentration (C(sub surf)), as it can be derived from ocean color observation, to infer the column-integrated and the vertical distribution of the phytoplanktonic biomass, both in a quantitative way and in a qualitative way (z.e., in terms of community structure). Within this context, a large HPLC (High Performance Liquid Chromatography) pigment database has been analyzed. It includes 2419 vertical pigment profiles, all sampled in Case-1 waters with various trophic states. The relationshps between C(sub surf) and the total chlorophyll alpha vertical distribution, as previously derived by Morel and Berthon, are fully confirmed, as the present results coincide with the previous ones. This agreement allows to go further, namely to examine the possibility of extracting relationships between C(sub surf) and the vertical composition of the algal assemblages. Thanks to the detailed pigment composition available from HPLC measurements, the contribution of three size classes (micro-, nano-, and pico-phytoplankton) to the local total chlorophyll a concentration can be assessed. Corroborating previous findings (e.g., large species dominate in eutrophc environments, whereas tiny phytoplankton prevail in oligotrophic zones), the results lead to a statistically based parameterization. The predictive skill of this parameterization is successfully tested on a separate data set. With such a tool, the vertical total chlorophyll a profiles associated with each size class can be inferred from the sole knowledge of C(sub surf). By combining this tool with satellite ocean color data, it becomes conceivable to quantify on a global scale the phytoplankton biomass associated with each of the three size classes.

Uitz, Julia↗

Satellites May Underestimate Rice Residue and Associated Burning Emissions in Vietnam

In this study, we estimate rice residue, associated burning emissions, and compare results with existing emissions inventories employing a bottom-up approach. We first estimated field-level post-harvest rice residues, including separate fuel-loading factors for rice straw and rice stubble. Results suggested fuel-loading factors of 0.27 kg/sq m (+/-0.033), 0.61 kg/sq m (+/-0.076), and 0.88 kg/sq m (+/-0.083) for rice straw, stubble, and total post-harvest biomass, respectively. Using these factors, we quantified potential emissions from rice residue burning and compared our estimates with other studies. Our results suggest total rice residue burning emissions as 2.24 Gg PM2.5, 36.54 Gg CO and 567.79 Gg CO2 for Hanoi Province, which are significantly higher than earlier studies. We attribute our higher emission estimates to improved fuel-loading factors; moreover, we infer that some earlier studies relying on residue-to-product ratios could be underestimating rice residue emissions by more than a factor of 2.3 for Hanoi, Vietnam. Using the rice planted area data from the Vietnamese government, and combining our fuel-loading factors, we also estimated rice residue PM2.5 emissions for the entirety of Vietnam and compared these estimates with an existing all-sources emissions inventory, and the Global Fire Emissions Database (GFED). Results suggest 75.98 Gg of PM2.5 released from rice residue burning accounting for 12.8% of total emissions for Vietnam. The GFED database suggests 42.56 Gg PM2.5 from biomass burning with 5.62 Gg attributed to agricultural waste burning indicating satellite-based methods may be significantly underestimating emissions. Our results not only provide improved residue and emission estimates, but also highlight the need for emissions mitigation from rice residue burning.

Air pollution↗

Connecting Indonesian Fires and Drought With the Type of El Niño and Phase of the Indian Ocean Dipole During 1979-2016

This study advances the previous understanding of the role of climate variability on Indonesian fire activity, by considering (i) the presence of different types of El Niño and (ii) the interaction between El Niño and the Indian Ocean Dipole (IOD). We classify 12 El Niño events during 1979-2016 into Eastern Pacific (EP) and Central Pacific (CP) types (four and eight El Niño events, respectively) and analyze observational data of sea surface temperature, precipitation, drought code, biomass burning carbon emission, visibility, and aerosol optical depth accordingly. We find that more intense and prolonged Indonesian drought and fires occur in the EP type, during which the emitted carbon amounts almost double those in the CP type. By further separating the CP type El Niño according to the phase of the IOD, that is, positive and negative, we show that fire seasons with less burning intensity and shorter duration are predominantly associated with weakly positive or even negative phase of the IOD phenomena. Moreover, fire intensity exhibits geographic diversity: fires are always more intensive in southern Kalimantan than in southern Sumatra in all El Niño events, although it is less dry in the former region. The outcome of this study can be applied to drought early warning, fire management, and air quality forecast in Indonesia and adjacent areas by identifying the type of El Niño and the phase of the IOD in advance.

IOD phenomena↗

Integrated Active Fire Retrievals and Biomass Burning Emissions Using Complementary Near-Coincident Ground, Airborne and Spaceborne Sensor Data

Ground, airborne and spaceborne data were collected for a 450 ha prescribed fire implemented on 18 October 2011 at the Henry W. Coe State Park in California. The integration of various data elements allowed near coincident active fire retrievals to be estimated. The Autonomous Modular Sensor-Wildfire (AMS) airborne multispectral imaging system was used as a bridge between ground and spaceborne data sets providing high quality reference information to support satellite fire retrieval error analyses and fire emissions estimates. We found excellent agreement between peak fire radiant heat flux data (less than 1% error) derived from near-coincident ground radiometers and AMS. Both MODIS and GOES imager active fire products were negatively influenced by the presence of thick smoke, which was misclassified as cloud by their algorithms, leading to the omission of fire pixels beneath the smoke, and resulting in the underestimation of their retrieved fire radiative power (FRP) values for the burn plot, compared to the reference airborne data. Agreement between airborne and spaceborne FRP data improved significantly after correction for omission errors and atmospheric attenuation, resulting in as low as 5 difference between AquaMODIS and AMS. Use of in situ fuel and fire energy estimates in combination with a collection of AMS, MODIS, and GOES FRP retrievals provided a fuel consumption factor of 0.261 kg per MJ, total energy release of 14.5 x 10(exp 6) MJ, and total fuel consumption of 3.8 x 10(exp 6) kg. Fire emissions were calculated using two separate techniques, resulting in as low as 15 difference for various species

airborne scanner↗

Mapping of taiga forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

A maximum a posteriori Bayesian classifier for multifrequency polarimetric SAR data is used to perform a supervised classification of forest types in the floodplains of Alaska. The image classes include white spruce, balsam poplar, black spruce, alder, non-forests, and open water. The authors investigate the effect on classification accuracy of changing environmental conditions, and of frequency and polarization of the signal. The highest classification accuracy (86 percent correctly classified forest pixels, and 91 percent overall) is obtained combining L- and C-band frequencies fully polarimetric on a date where the forest is just recovering from flooding. The forest map compares favorably with a vegetation map assembled from digitized aerial photos which took five years for completion, and address the state of the forest in 1978, ignoring subsequent fires, changes in the course of the river, clear-cutting of trees, and tree growth. HV-polarization is the most useful polarization at L- and C-band for classification. C-band VV (ERS-1 mode) and L-band HH (J-ERS-1 mode) alone or combined yield unsatisfactory classification accuracies. Additional data acquired in the winter season during thawed and frozen days yield classification accuracies respectively 20 percent and 30 percent lower due to a greater confusion between conifers and deciduous trees. Data acquired at the peak of flooding in May 1991 also yield classification accuracies 10 percent lower because of dominant trunk-ground interactions which mask out finer differences in radar backscatter between tree species. Combination of several of these dates does not improve classification accuracy. For comparison, panchromatic optical data acquired by SPOT in the summer season of 1991 are used to classify the same area. The classification accuracy (78 percent for the forest types and 90 percent if open water is included) is lower than that obtained with AIRSAR although conifers and deciduous trees are better separated due to the presence of leaves on the deciduous trees. Optical data do not separate black spruce and white spruce as well as SAR data, cannot separate alder from balsam poplar, and are of course limited by the frequent cloud cover in the polar regions. Yet, combining SPOT and AIRSAR offers better chances to identify vegetation types independent of ground truth information using a combination of NDVI indexes from SPOT, biomass numbers from AIRSAR, and a segmentation map from either one.

Rignot, Eric↗

Spectral Range Within Global aCDOM(440) Algorithms for Oceanic, Coastal, and Inland Waters with Application to Airborne Measurements

The optically active component of dissolved organic material in aquatic ecosystems, or colored dissolved organic matter (CDOM), is represented by the coefficient of absorption due to the dissolved aquatic constituents at 440 nm, aCDOM(440). Remote sensing of aCDOM(440) enables characterization of ecosystem processes and aids in retrieval of chlorophyll a, a proxy for phytoplankton biomass. Spectrally adjacent band-ratio domains, e.g., blue to green, have previously been applied for remote sensing of aCDOM(440) in coastal and oceanic waters with similar results compared to more complex semi-analytical algorithms. Estimation of aCDOM(440) from ratios of the most spectrally separated ocean color wavebands (end members), e.g., ultraviolet (UV) to near-infrared (NIR), termed end-member analysis (EMA), has previously been shown to increase the accuracy of global aCDOM(440) retrievals from in-water observations of diffuse attenuation and to enable a unified algorithmic perspective without requiring regional adjustment of internal bio-optical parameters. EMA of above-water observations is evaluated herein, with a focus on coastal and inland waters in which increasing optical complexity and likelihood of bottom reflectance challenge the oceanic algorithms developed for deep and optically simple (case-1) waters. Analysis herein of three independent, in situ, bio-optical datasets indicates significant correlation between aCDOM(440) and end-member band ratios (next-generation 320 and 780 nm or legacy 412 and 670 nm ratios) with a coefficient of determination, R2, of 0.87 (log-scale) or higher based on a dataset spanning the dynamic range of global, conservative water bodies. For applicable wavelengths, EMA algorithms are shown to agree with case-1 relationships and to produce consistent log-scale uncertainties across more than three orders of magnitude in aCDOM(440) values (0.001–2.305 m􀀀1). EMA using UV and NIR wavelengths (320 and 780 nm) is applied to low-altitude airborne observations and satisfies 25% uncertainty based on unbiased percent differences (UPDs) within each of three dissimilar match-up sites ranging in aCDOM(440) from 0.02–0.57 m􀀀1. Results demonstrate that EMA is a useful and robust approach for the remote sensing of aCDOM(440) in coastal and inland waters, which are generally shallower, contain more optically complex environments, and span a greater range in aCDOM(440) than oceanic waters.

CDOM↗

Develop Recovery Systems for Separations of Salts from Process Streams for use in Advanced Life Support System

The main objectives of this project were the development of a four-compartment electrolytic cell using high selective membranes to remove nitrate from crop residue leachate and convert it to nitric acid, and the development of an six compartment electrodialysis cell to remove selectively sodium from urine wastes. The recovery of both plant inedible biomass and human wastes nutrients to sustain a biomass production system are important aspects in the development of a controlled ecological life support system (CELSS) to provide the basic human needs required for life support during long term space missions. A four-compartment electrolytic cell has been proposed to remove selectively nitrate from crop residue and to convert it to nitric acid, which is actually used in the NASA-KSC Controlled Ecological Life Support System to control the pH of the aerobic bioreactors and biomass production chamber. Human activities in a closed system require large amount of air, water and minerals to sustain life and also generate wastes. Before using human wastes as nutrients, these must be treated to reduce organic content and to remove some minerals which have adverse effects on plant growth. Of all the minerals present in human urine, sodium chloride (NACl) is the only one that can not be used as nutrient for most plants. Human activities also requires sodium chloride as part of the diet. Therefore, technology to remove and recover sodium chloride from wastes is highly desirable. A six-compartment electrodialysis cell using high selective membranes has been proposed to remove and recover NaCl from human urine.

Colon, Guillermo↗

Simulation Studies of Forest Structure using 3D Lidar and Radar Models

The use of lidars and radars to measure forest structure attributes such as height and biomass are being considered for future Earth Observation missions. Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield information about the vertical profile of the canopy. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and is useful for estimating biomass. Interferometric SAR (InSAR) has been shown to yield forest canopy height information. For example, the height of scattering phase retrieved from InSAR data is considered to be correlated with the three height and the spatial structure of the forest stand. There is much interest in exploiting these technologies separately and together to get important information for carbon cycle and ecosystem science. More detailed information of the electromagnetic radiation interactions within forest canopies is needed. And backscattering models can be of much utility here. As part of a NASA funded project to explore data fusion, a three-dimensional (3D) coherent radar backscattering model and a 3D lidar backscatter models were used to investigate the use of large footprint lidar, SAR and InSAR for characterizing realistic forest scenes. For this paper, we use stem maps and other forest measurements to develop a realistic spatial structure of a spruce-hemlock forest canopy found in Maine, USA. The radar and lidar models used measurements of the 3D forest scene as input and simulated the coherent radar backscattering signature and 1064nm energy backscatter, respectively. The relationships of backscatter derived forest structure were compared with field measurements. In addition, we also had detailed airborne lidar (Laser Imaging Vegetation Sensor, LVIS) data available over the stem map sites that was used to study the accuracies of tree height derived from modeled SAR backscatter and the scattering phase center retrieved from the simulated InSAR data will be compared with the height indices, or other structure parameters derived from the lidar data. These results will address the possible synergies between lidar and radar in data in terms of forest structural information.

Sun, Guoqing↗

Aircraft-Measured Indirect Cloud Effects from Biomass Burning Smoke in the Arctic and Subarctic

The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200-300% over the next 50-100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were approx. 40- 60% smaller than in background clouds. Based on the relationship between cloud droplet number (N(liq)/ and various biomass burning tracers (BBt/ across the multi-campaign data set, we calculated the magnitude of subarctic and Arctic smoke aerosol-cloud interactions (ACIs, where ACI = (1/3) x dln(N(liq))/dln(BBt)) to be approx. 0.16 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (0.02 gm/cu m and very high aerosol concentrations (2000- 3000/ cu cm in the most polluted clouds, the estimated ACI value was only 0.05. In this case, competition for water vapor by the high concentration of cloud condensation nuclei (CCN) strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease local summertime short-wave radiative flux by between 2 and 4 W/sq m or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic.We lastly explore evidence suggesting that numerous northern-latitude background Aitken particles can interact with combustion particles, perhaps impacting their properties as cloud condensation and ice nuclei.

cloud microphysics↗

Interannual Variability and Trends of Combustion Aerosol and Dust in Major Continental Outflows Revealed by MODIS Retrievals and CAM5 Simulations During 2003-2017

Emissions and long-range transport of mineral dust and combustion-related aerosol from burning fossil fuels and biomass vary from year to year, driven by the evolution of the economy and changes in meteorological conditions and environmental regulations. This study offers both satellite and model perspectives of interannual variability and possible trend of combustion aerosol and dust in major continental outflow regions over the past 15 years (2003-2017). The decade-long record of aerosol optical depth (AOD, denoted as t), separately for combustion aerosol (τ(sub c)) and dust (τ(sub d)), over global oceans is derived from the Collection 6 aerosol products of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard both Terra and Aqua. These MODIS/Aqua datasets, complemented by aerosol source-tagged simulations using the Community Atmospheric Model Version 5 (CAM5), are then analyzed to understand the interannual variability and potential trend of τ(sub c) and τ(sub d) in the major continental outflows. Both MODIS and CAM5 consistently yield a similar decreasing trend of -0.017 to - 0.020 decade(exp -1) for τ(sub c) over the North Atlantic Ocean and the Mediterranean Sea that is attributable to reduced emissions from North America and Europe, respectively. On the contrary, both MODIS and CAM5 display an increasing trend of +0.017 to +0.036 decade(exp -1) for τ(sub c) over the tropical Indian Ocean, the Bay of Bengal, and the Arabian Sea, which reflects the influence of increased anthropogenic emissions from South Asia and Middle East in the last two decades. Over the northwestern Pacific Ocean that is often affected by East Asian emissions of pollution and dust, the MODIS retrievals show a decreasing trend of - 0.021 decade(exp -1) for τ(sub c) and -0.012 decade(exp -1) for τ(sub d), which is however not reproduced by the CAM5 model. In other outflow regions strongly influenced by biomass burning smoke or dust, both MODIS retrievals and CAM5 simulations show no statistically significant trends; and the MODIS observed interannual variability is usually larger than that of the CAM5 simulation.

Hongbin Yu↗

Effect of channel size on sweet potato storage root enlargement in the Tuskegee University hydroponic nutrient film system

The potential of the sweet potato as a food source for future long term manned space missions is being evaluated for NASA's Controlled Ecological Life Support Systems (CELSS) program. Sweet potatoes have been successfully grown in a specially designed Tuskegee University nutrient film technique (TU NFT) system. This hydroponic system yielded storage roots as high as 1790 g/plant fresh weight. In order to determine the effect of channel size on the yield of sweet potatoes, the width and depth of the growing channels were varied in two separate experiments. Widths were studied using the rectangular TU NFT channels with widths of 15 cm (6 in), 30 cm (12 in) and 45 cm (18 in). Channel depths of 5 cm (2 in), 10 cm (4 in), and 15 cm (6 in) were studied using a standard NASA fan shaped Biomass Production Chamber (BPC) channel. A comparison of preliminary results indicated that, except for storage root number, the growth and yield of sweet potatoes were not affected by channel width. Storage root yield was affected by channel depth although storage root number and foliage growth were not. Both experiments are being repeated.

Morris, Carlton E.↗

Forest Attributes from Radar Interferometric Structure and its Fusion with Optical Remote Sensing

The possibility of global, three-dimensional remote sensing of forest structure with interferometric synthetic aperture radar (InSAR) bears on important forest ecological processes, particularly the carbon cycle. InSAR supplements two-dimensional remote sensing with information in the vertical dimension. Its strengths in potential for global coverage complement those of lidar (light detecting and ranging), which has the potential for high-accuracy vertical profiles over small areas. InSAR derives its sensitivity to forest vertical structure from the differences in signals received by two, spatially separate radar receivers. Estimation of parameters describing vertical structure requires multiple-polarization, multiple-frequency, or multiple-baseline InSAR. Combining InSAR with complementary remote sensing techniques, such as hyperspectral optical imaging and lidar, can enhance vertical-structure estimates and consequent biophysical quantities of importance to ecologists, such as biomass. Future InSAR experiments will supplement recent airborne and spaceborne demonstrations, and together with inputs from ecologists regarding structure, they will suggest designs for future spaceborne strategies for measuring global vegetation structure.

InSAR↗

Influences of Solar Irradiation and Aerosol Surface Area on Nitrous Acid Production in Western U.S. Wildfire Smoke

Biomass burning has been identified as a primary emission source for a host of gas and aerosol phase compounds which can damage environmental and human health. During FIREX-AQ collaborators from UNH, NASA Langley Aerosol Research Group Experiment (LARGE), and Brown University conducted measurements out of NASA Langley’s mobile laboratory during July and August of 2019. Nitrous acid (HONO) and nitric acid (HNO3) concentrations were measured at nominal 5-minute resolution using a dual mist chamber-ion chromatograph system resulting in approximately 1,200 total samples from five separate fires in the western US. Enhancement ratios between PM2.5 and carbon monoxide were used to discriminate between young and aged smoke plumes. Average HONO concentrations were significantly higher (P << 0.01) in young daytime compared to nighttime smoke while no statistical differences (P > 0.05) were observed between young and aged smoke during day or night. In daytime smoke from the Williams Flats fire (75 km SE of Spokane, WA on the Columbia River) UV-A irradiation was highly correlated with HONO to nitrogen dioxide (NO2) ratios; indicating that heterogenous NO2 to HONO conversion more than compensated for rapid photolytic loss of HONO. Black carbon (BC) may serve as a stable proxy for total available aerosol surface area in biomass burning plumes when total aerosol size distributions are unavailable. During the day HONO/NO2 is weakly correlated to BC when BC abundance exceeds 1 ug/m3 but at lower BC concentrations the relationship breaks down. At night this relationship was stronger, partly due to consistently high BC concentrations. These observations suggest that heterogenous conversion of NO2 to HONO on aerosol is a significant source of HONO in dense smoke plumes. In more diffuse smoke conversion on other surfaces (soil, foliage) becomes more important.

J. H. Kaspari↗

Excess nutrients in hydroponic solutions alter nutrient content of rice, wheat, and potato

Environment has significant effects on the nutrient content of field-grown crop plants. Little is known, however, about compositional changes caused by controlled environments in which plants receive only artificial radiation and soilless, hydroponic culture. This knowledge is essential for developing a safe, nutritious diet in a Controlled Ecological Life-Support System (CELSS). Three crops that are candidates for inclusion in a CELSS (rice, wheat, and white potato) were grown both in the field and in controlled environments where the hydroponic nutrient solution, photosynthetic photon flux (PPF), and CO2 level were manipulated to achieve rapid growth rates. Plants were harvested at maturity, separated into discrete parts, and dried prior to analysis. Plant materials were analyzed for proximate composition (protein, fat, ash, and carbohydrate), total nitrogen (N), nitrate, minerals, and amino-acid composition. The effect of environment on nutrient content varied by crop and plant part. Total N and nonprotein N (NPN) contents of plant biomass generally increased under controlled-environment conditions compared to field conditions, especially for leafy plant parts and roots. Nitrate levels were increased in hydroponically-grown vegetative tissues, but nitrate was excluded from grains and tubers. Mineral content changes in plant tissue included increased phosphorus and decreased levels of certain micronutrient elements under controlled-environment conditions. These findings suggest that cultivar selection, genetic manipulation, and environmental control could be important to obtain highly nutritious biomass in a CELSS.

NASA Discipline Number 61-20↗

Aircraft-Measured Indirect Cloud Effects from Biomass Burning Smoke in the Arctic and Subarctic

The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200-300 over the next 50-100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were 50 smaller than in background clouds. Based on the relationship between cloud droplet number (N(liq))/ and various biomass burning tracers (BBt/ across the multi-campaign dataset, we calculated the magnitude of subarctic and Arctic smoke aerosol-cloud interactions (ACI, where ACI = (1/3) x dln(N(liq))/dln(BBt)) to be 0.12 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (0.02 gm/ cu m) and very high aerosol concentrations (2000-3000 cu m) in the most polluted clouds, the estimated ACI value was only 0.06. In this case, competition for water vapor by the high concentration of CCN strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease shortwave radiative flux by 2 and 4 W/sq or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic. We lastly show evidence to suggest that numerous northern latitude background Aitken particles can interact with combustion particles, perhaps impacting their properties as cloud condensation and ice nuclei. However, the influence of background particles on smoke-driven indirect effects is currently unclear.

Arctic↗