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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Land carbon models underestimate the severity and duration of drought’s impact on plant productivity

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0–6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO2.

carbon cycle↗
NASA NTRS2013

Global Ocean Phytoplankton

Phytoplankton are free-floating algae that grow in the euphotic zone of the upper ocean, converting carbon dioxide, sunlight, and available nutrients into organic carbon through photosynthesis. Despite their microscopic size, these photoautotrophs are responsible for roughly half the net primary production on Earth (NPP; gross primary production minus respiration), fixing atmospheric CO2 into food that fuels our global ocean ecosystems. Phytoplankton thus play a critical role in the global carbon cycle, and their growth patterns are highly sensitive to environmental changes such as increased ocean temperatures that stratify the water column and prohibit the transfer of cold, nutrient richwaters to the upper ocean euphotic zone.

Franz, B. A.↗
NASA NTRS2014

Impacts of Light Use Efficiency and fPAR Parameterization on Gross Primary Production Modeling

This study examines the impact of parameterization of two variables, light use efficiency (LUE) and the fraction of absorbed photosynthetically active radiation (fPAR or fAPAR), on gross primary production(GPP) modeling. Carbon sequestration by terrestrial plants is a key factor to a comprehensive under-standing of the carbon budget at global scale. In this context, accurate measurements and estimates of GPP will allow us to achieve improved carbon monitoring and to quantitatively assess impacts from cli-mate changes and human activities. Spaceborne remote sensing observations can provide a variety of land surface parameterizations for modeling photosynthetic activities at various spatial and temporal scales. This study utilizes a simple GPP model based on LUE concept and different land surface parameterizations to evaluate the model and monitor GPP. Two maize-soybean rotation fields in Nebraska, USA and the Bartlett Experimental Forest in New Hampshire, USA were selected for study. Tower-based eddy-covariance carbon exchange and PAR measurements were collected from the FLUXNET Synthesis Dataset. For the model parameterization, we utilized different values of LUE and the fPAR derived from various algorithms. We adapted the approach and parameters from the MODIS MOD17 Biome Properties Look-Up Table (BPLUT) to derive LUE. We also used a site-specific analytic approach with tower-based Net Ecosystem Exchange (NEE) and PAR to estimate maximum potential LUE (LUEmax) to derive LUE. For the fPAR parameter, the MODIS MOD15A2 fPAR product was used. We also utilized fAPAR chl, a parameter accounting for the fAPAR linked to the chlorophyll-containing canopy fraction. fAPAR chl was obtained by inversion of a radiative transfer model, which used the MODIS-based reflectances in bands 1-7 produced by Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. fAPAR chl exhibited seasonal dynamics more similar with the flux tower based GPP than MOD15A2 fPAR, especially in the spring and fall at the agricultural sites. When using the MODIS MOD17-based parameters to estimate LUE, fAPAR chl generated better agreements with GPP (r2= 0.79-0.91) than MOD15A2 fPAR (r2= 0.57-0.84).However, underestimations of GPP were also observed, especially for the crop fields. When applying the site-specific LUE max value to estimate in situ LUE, the magnitude of estimated GPP was closer to in situ GPP; this method produced a slight overestimation for the MOD15A2 fPAR at the Bartlett forest. This study highlights the importance of accurate land surface parameterizations to achieve reliable carbon monitoring capabilities from remote sensing information.

grossprimary↗
NASA NTRS1997

Regional Application of an Ecosystem Production Model for Studies of Biogeochemistry in the...

The degree to which primary production, soil carbon, and trace gas fluxes in tropical forests of the Amazon are limited by moisture availability and other environmental factors was examined using an ecosystem modeling application for the country of Brazil. A regional geographic information system (GIS) serves as the data source of climate drivers, satellite images, land cover, and soil properties for input to the NASA Ames-CASA (Carnegie-Ames-Stanford Approach) model over a 8-km grid resolution. Simulation results supports the hypothesis that net primary production (NPP) is limited by cloud interception of solar radiation over the humid northwestern portion of the region. Peak annual rates for NPP of nearly 1.4 kg C m-2yr -1are localized in the seasonally dry eastern Amazon in areas that we assume are primarily deep-rooted evergreen forest cover. Regional effects of forest conversion on NPP and soil carbon content are indicated in the model results, especially in seasonally dry areas. Comparison of model flux predictions along selected eco-climatic transects reveal moisture, soil, and land use controls on gradients of ecosystem production and soil trace gas emissions (CO2, N2O, and NO). These results are used to formulate a series of research hypotheses for testing in the next phase of regional modeling, which includes recalibration of the light-use efficiency term in CASA using field measurements of NPP, and refinements of vegetation index and soil property (texture and potential rooting depth) maps for the region.

Potter, C. S.↗
NASA NTRS2000

BOREAS TE-17 Production Efficiency Model Images

A Boreal Ecosystem-Atmospheric Study (BOREAS) version of the Global Production Efficiency Model (http://www.inform.umd.edu/glopem/) was developed by TE-17 (Terrestrial Ecology) to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region. This document provides basic information on the model and how the maps were generated. The data generated by the model are stored in binary image-format files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS1994

CO2 exchange in the Hudson Bay lowlands: Community characteristics and multispectral reflectance properties

Net ecosystem CO2 exchange was measured during the 1990 growing season (June to August) along a transect starting 10 km inland from James Bay and extending 100 km interior to Kinosheo Lake, Ontario. Sites were chosen in three distinct areas: a coastal fen, an interior fen, and a bog. For the most productive sites in the bog, net daily uptake rates reached a maximum of 2.5 g C-CO2 m(exp -2)/d with an area-weighted exchange of 0.3 g C-CO2 m(exp -2)/d near midsummer. This site was estimated to be a net carbon source of 9 g C-CO2 m(exp -2) to the atmosphere over a 153-day growing season. The interior fen was less productive on a daily basis with a net maximum uptake of 0.5 g C-CO2 m(exp -2)/d and with corresponding area-weighted uptake of 0.1 g C-CO2 m(exp -2)/d during midsummer. Early and late season release of carbon to the atmosphere resulted in a net loss of 21 g C-CO2 m(exp -2) over the growing season from this site. The coastal fen was the most productive site with uptake rates peaking near 1.7 g C-CO2 m(exp -2)/d which corresponded to an area-weighted uptake of 0.8 g C-CO2 m(exp -2)/d during midsummer and an estimated net uptake of 6 g C-CO2 m(exp -2) for the growing season. Associated with net CO2 exchange measurements, multispectral reflectance properties of the sites were measured over the growing season using portable radiometers. These properties were related to exchange rates with the goal of examining the potential for satellite remote sensing to monitor biosphere/atmosphere CO2 exchange in this biome. The normalized difference vegetation index (NDVI) computed from surface reflectance was correlated with net CO2 exchange for all sites with the exception of areas with large proportions of Sphagnum moss cover. These mosses have greater near-infrared reflectance than typical surrounding vegetation and may require special adjustment for regional exchange/remote sensing applications.

Whiting, Gary J.↗
NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
NASA NTRS2021

The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska

The increase in wildfire occurrence and severity seen over the past decades in the boreal and Arctic biomes is expected to continue in the future in response to rapid climate change in this region. Recent studies documented positive trends in gross primary productivity (GPP) for Arctic boreal biomes driven by warming, but it is unclear how GPP trends are affected by wildfires. Here, we used satellite vegetation observations and environmental data with a diagnostic GPP model to analyze recovery from large fires in Alaska over the period 2000‐2019. We confirmed earlier findings that warmer‐than‐average years provide favorable climate conditions for vegetation growth, leading to a GPP increase of 1 Tg C/yr, contributed mainly from enhanced productivity in the early growing season. However, higher temperatures increase the risk of wildfire occurrence leading to direct carbon loss over a period of 1‐3 years. While mortality related to severe wildfires reduce ecosystem productivity, post‐fire productivity in moderately burned areas shows a significant positive trend. The rapid GPP recovery following fires reported here might be favorable for maintaining the region’s net carbon sink, but wildfires can indirectly promote the release of long‐term stored carbon in the permafrost. With the projected increase in severity and frequency of wildfires in the future, we expect a reduction of GPP and therefore amplification of climate warming in this region.

Nima Madani↗
NASA NTRSDate not supplied

Hourly Carbon Fluxes Estimation Using the GOES Advanced Baseline Imager (ABI) Data Over the Conterminous USA

Tremendous efforts by Fluxnet scientists over the past few decades have made thousands of site-years of carbon flux observations available for advancing our understanding of carbon cycling in terrestrial ecosystems. One of key Fluxnet measurements is net ecosystem exchange (NEE) as it is directly related to carbon budget of terrestrial ecosystems. However, carbon flux estimation studies using satellite remote sensing have focused mainly on daily Gross Primary Production (GPP). The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. Because daily NEE is close to zero value, the carbon flux models using the polar orbiting satellite data have not been well used for NEE estimation. The new generation of geostationary satellite sensors (e.g., GOES Advanced Baesline Imager (ABI) and Himawari Advanced Himawari Imager (AHI)) provide frequent observations, often less than every 10 minutes. Here, we use GOES ABI data to estimate hourly NEE over the conterminous USA. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and, in particular, solar radiation that is directly derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Fluxnet data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

Geostationary satellite↗
NASA NTRS2022

A Process-Model Perspective on Recent Changes in the Carbon Cycle of North America

Continental North America has been found to be a carbon (C) sink over recent decades by multiple studies employing a variety of estimation approaches. However, several key questions and uncertainties remain with these assessments. Here we used results from an ensemble of 19 state-of-the-art dynamic global vegetation models from the TRENDYv9 project to improve these estimates and study the drivers of its interannual variability. Our results show that North America has been a C sink with a magnitude of 0.37 ± 0.38 (mean and one standard deviation) PgC year−1 for the period 2000–2019 (0.31 and 0.44 PgC year−1 in each decade); split into 0.18 ± 0.12 PgC year−1 in Canada (0.15 and 0.20), 0.16 ± 0.17 in the United States (0.14 and 0.17), 0.02 ± 0.05 PgC year−1 in Mexico (0.02 and 0.02) and 0.01 ± 0.02 in Central America and the Caribbean (0.01 and 0.01). About 57% of the new C assimilated by terrestrial ecosystems is allocated into vegetation, 30% into soils, and 13% into litter. Losses of C due to fire account for 41% of the interannual variability of the mean net biome productivity for all North America in the model ensemble. Finally, we show that drought years (e.g., 2002) have the potential to shift the region to a small net C source in the simulations (−0.02 ± 0.46 PgC year−1). Our results highlight the importance of identifying the major drivers of the interannual variability of the continental-scale land C cycle along with the spatial distribution of local sink-source dynamics.

Guillermo Murray-Tortarolo↗
NASA NTRS1996

Modelling Temporal Variability in the Carbon Balance of a Spruce/Moss Boreal Forest

A model of the daily carbon balance of a black spruce/feathermoss boreal forest ecosystem was developed and results compared to preliminary data from the 1994 BOREAS field campaign in northern Manitoba, Canada. The model, driven by daily weather conditions, simulated daily soil climate status (temperature and moisture profiles), spruce photosynthesis and respiration, moss photosynthesis and respiration, and litter decomposition. Model agreement with preliminary field data was good for net ecosystem exchange (NEE), capturing both the asymmetrical seasonality and short-term variability. During the growing season simulated daily NEE ranged from -4 g C m(exp -2) d(exp -1) (carbon uptake by ecosystem) to + 2 g C m(exp -2) d(exp -1) (carbon flux to atmosphere), with fluctuations from day to day. In the early winter simulated NEE values were + 0.5 g C m(exp -2) d(exp -1), dropping to + 0.2 g C m(exp -2) d(exp -1) in mid-winter. Simulated soil respiration during the growing season (+ 1 to + 5 g C m(exp -2) d(exp -1)) was dominated by metabolic respiration of the live moss, with litter decomposition usually contributing less than 30% and live spruce root respiration less than 10% of the total. Both spruce and moss net primary productivity (NPP) rates were higher in early summer than late summer. Simulated annual NEE for 1994 was -51 g C m(exp -2) y(exp -1), with 83% going into tree growth and 17% into the soil carbon accumulation. Moss NPP (58 g C m(exp -2) d(exp -1)) was considered to be litter (i.e. soil carbon input; no net increase in live moss biomass). Ecosystem respiration during the snow-covered season (84 g Cm(exp -2)) was 58% of the growing season net carbon uptake. A simulation of the same site for 1968-1989 showed about 10-20% year-to-year variability in heterotrophic respiration (mean of + 113 g C m-2 y@1). Moss NPP ranged from 19 to 114 g C m(exp -2) y(exp -1); spruce NPP from 81 to 150 g C nt-2 y,@l; spruce growth (NPP minus litterfall) from 34 to 103 g C m(exp -2) y(exp -1); NEE ranged from +37 to -142 g C m(exp -2) y(exp -1). Values for these carbon balance terms in 1994 were slightly smaller than the 1969 - 89 means. Higher ecosystem productivity years (more negative NEE) generally had early springs and relatively wet summers; lower productivity years had late springs and relatively dry summers.

Frolking, S.↗
NASA NTRS2021

An Operational Overview of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) Northeast Pacific Field Deployment

The goal of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign is to develop a predictive understanding of the export, fate, and carbon cycle impacts of global ocean net primary production. To accomplish this goal, observations of export flux pathways, plankton community composition, food web processes, and optical, physical, and biogeochemical (BGC) properties are needed over a range of ecosystem states. Here we introduce the first EXPORTS field deployment to Ocean Station Papa in the Northeast Pacific Ocean during summer of 2018, providing context for other papers in this special collection. The experiment was conducted with two ships: a Process Ship, focused on ecological rates, BGC fluxes, temporal changes in food web, and BGC and optical properties, that followed an instrumented Lagrangian float; and a Survey Ship that sampled BGC and optical properties in spatial patterns around the Process Ship. An array of autonomous underwater assets provided measurements over a range of spatial and temporal scales, and partnering programs and remote sensing observations provided additional observational context. The oceanographic setting was typical of late-summer conditions at Ocean Station Papa: a shallow mixed layer, strong vertical and weak horizontal gradients in hydrographic properties, sluggish sub-inertial currents, elevated macronutrient concentrations and low phytoplankton abundances. Although nutrient concentrations were consistent with previous observations, mixed layer chlorophyll was lower than typically observed, resulting in a deeper euphotic zone. Analyses of surface layer temperature and salinity found three distinct surface water types, allowing for diagnosis of whether observed changes were spatial or temporal.The 2018 EXPORTS field deployment is among the most comprehensive biological pump studies ever conducted. A second deployment to the North Atlantic Ocean occurred in spring 2021, which will be followed by focused work on data synthesis and modeling using the entire EXPORTS data set.

Biological pump↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS2021

Reconciling Carbon-cycle Processes from Ecosystem to Global Scales

Understanding carbon (C) dynamics from ecosystem to global scales remains a challenge. Although expansion of global carbon dioxide (CO2) observatories makes it possible to estimate C-cycle processes from ecosystem to global scales, these estimates do not necessarily agree. At the continental US scale, only 5% of C fixed through photosynthesis remains as net ecosystem exchange (NEE), but ecosystem measurements indicate that only 2% of fixed C remains in grasslands, whereas as much as 30% remains in needleleaf forests. The wet and warm Southeast has the highest gross primary productivity and the relatively wet and cool Midwest has the highest NEE, indicating important spatial mismatches. Newly available satellite and atmospheric data can be combined in innovative ways to identify potential C loss pathways to reconcile these spatial mismatches. Independent datasets compiled from terrestrial and aquatic environments can now be combined to advance C-cycle science across the land–water interface.

carbon cycle↗
NASA NTRS1993

Net exchange of CO2 in a mid-latitude forest

The eddy correlation method was used to measure the net ecosystem exchange of carbon dioxide continuously from April 1990 to December 1991 in a deciduous forest in central Massachusetts. The annual net uptake was 3.7 +/- 0.7 metric tons of carbon per hectare per year. Ecosystem respiration calculated from the relation between nighttime exchange and soil temperature, was 7.4 metric tons of carbon per hectare per year, implying gross ecosystem production of 11.1 metric tons of carbon per hectare per year. The observed rate of accumulation of carbon reflects recovery from agricultural development in the 1800s. Carbon uptake rates were notably larger than those assumed for temperate forests in global carbon studies. Carbon storage in temperate forests can play an important role in determining future concentrations of atmospheric carbon dioxide.

Wofsy, S. C.↗
NASA NTRS2002

The Effects of Low Sulfate Concentrations on Modern Microbial Mat Communities: A Long Term Manipulation

Microbial mats were widespread during the first ca. 2 Ga. of our biosphere's history. To better understand microbial ecosystems and their biomarkers under the low sulfate levels present in early oceans, we attempted a long-term (ca. 1 year) manipulation of sulfate in modem mats. Mats collected from salt ponds at Guerrero Negro, Baja Calif. Sur were incubated in a Greenhouse "Collaboratory" at Ames. Mats were maintained in artificial seawater brine containing either: 1) sulfate levels normal for these mats (70 mM), or 2) brine in which sulfate was replaced by chloride. Sulfate concentrations in the "low sulfate" brine gradually approached their lowest (to date) value of 0. 1 mM as sulfate was consumed and/or diffused out of the mat over a period of ca. 4 months. During that period of time, a number of differences between the treatments emerged. Relative to the "low sulfate" mats, "normal sulfate" mats had: 1) lower consumption of oxygen in the lower levels of the mat, 2) higher efficiencies of oxygenic photosynthesis, and 3) higher rates of nitrogen fixation. Rates of methane production by the mats increased greatly as sulfate concentrations fell below ca. 0.2 mM. In contrast, "low" and "normal" sulfate mats had similar net rates of exchange of O2 and dissolved inorganic C between the mats and overlying water. Reduced sulfate levels have diverse impacts upon these ecosystems.

Bebout, Brad↗
NASA NTRS1986

Error analysis of leaf area estimates made from allometric regression models

Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).

Feiveson, A. H.↗