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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Fire-induced Carbon Emissions and Regrowth Uptake in Western U.S. Forests: Documenting Variation Across Forest Types, Fire Severity, and Climate Regions

The forest area in the western United States that burns annually is increasing with warmer temperatures, more frequent droughts, and higher fuel densities. Studies that examine fire effects for regional carbon balances have tended to either focus on individual fires as examples or adopt generalizations without considering how forest type, fire severity, and regional climate influence carbon legacies. This study provides a more detailed characterization of fire effects and quantifies the full carbon impacts in relation to direct emissions, slow release of fire-killed biomass, and net carbon uptake from forest regrowth. We find important variations in fire-induced mortality and combustion across carbon pools (leaf, live wood, dead wood, litter, and duff) and across low- to high-severity classes. This corresponds to fire-induced direct emissions from 1984 to 2008 averaging 4 TgC/yr and biomass killed averaging 10.5 TgC/yr, with average burn area of 2723 sq km/yr across the western United States. These direct emission and biomass killed rates were 1.4 and 3.7 times higher, respectively, for high-severity fires than those for low-severity fires. The results show that forest regrowth varies greatly by forest type and with severity and that these factors impose a sustained carbon uptake legacy. The western U.S. fires between 1984 and 2008 imposed a net source of 12.3 TgC/yr in 2008, accounting for both direct fire emissions (9.5 TgC/yr) and heterotrophic decomposition of fire-killed biomass (6.1 TgC yr1) as well as contemporary regrowth sinks (3.3 TgC/yr). A sizeable trend exists toward increasing emissions as a larger area burns annually.

Forest↗
NASA NTRS2000

Passive Microwave Measurements Over Conifer Forests at L-Band and C-Band

Measurements have been made at L-band and C-band over conifer forests in Virginia to study the response of passive microwave instruments to biomass and soil moisture. A series of aircraft measurements were made in July, August and November, 1999 over relatively homogenous conifer forests of varying biomass. Three radiometers participated in these measurements. These were: 1) the L-band radiometer ESTAR, a horizontally polarized synthetic aperture radiometer which has been used extensively in past measurements of soil moisture; 2) the L-band radiometer SLFMR, a vertically polarized cross-track scanner which has been used successfully in the past for mapping sea surface salinity; and 3) The ACMR, a new C-band radiometer which operates at V- and H-polarization and in the configuration for these experiments did not scan. All three radiometers were flown on the NASA P-3 aircraft based at the Goddard Space Flight Center's Wallops Flight Facility. The ESTAR and SLFMR were mounted in the bomb bay of the P-3 and imaged across track whereas the ACMR was mounted to look aft at 54 degrees up from nadir. Data was collected at altitudes of 915 meters and 457 meters. The forests consisted of relatively homogeneous "managed" stands of conifer located near Waverly, Virginia. This is a relatively flat area about 30 miles southeast of Richmond, VA with numerous stands of trees being grown for the forestry industry. The stands selected for study consisted of areas of regrowth and mature stands of pine. In addition, a small stand of very large trees was observed. Soil moisture sampling was done in each stand during the aircraft over flights. Data was collected on July 7, August 27, November 15 and November 30, 1999. Measurements were made with ESTAR on all days. The ACMR flew on the summer missions and the SLFMR was present only on the August 27 flight. Soil moisture varied from quite dry on July 7 to quite moist on November 30 (which was shortly after a period of rain). The microwave images clearly distinguish between the different forest stands. Research is continuing to seek a quantitative correlation with biomass and surface soil moisture.

LeVine, D. M.↗
NASA NTRS2011

Soil Moisture Limitations on Monitoring Boreal Forest Regrowth Using Spaceborne L-Band SAR Data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha-1) and (L-HH) and (L-HV) for the data collected on each of the three dates, with the highest correlation found using the LHV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur

Kasischke, Eric S.↗
NASA NTRS2015

Airborne Lidar-Based Estimates of Tropical Forest Structure in Complex Terrain: Opportunities and Trade-Offs for REDD+

Background: Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. Results: We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (approx. 20 returns/sq m) data was highly accurate (mean signed error of 0.19 +/-0.97 m), while those derived from reduced-density datasets (8/sq m, 4/sq m, 2/sq m and 1/sq m) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4/sq m, the bias in height estimates translated into errors of 80-125 Mg/ha in predicted aboveground biomass. Conclusions: Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

Airborne lidar↗
NASA NTRS2020

High productivity in hybrid-poplar plantations without isoprene emission to the atmosphere

Hybrid-poplar tree plantations provide a source for biofuel and biomass, but they also increase forest isoprene emissions. The consequences of increased isoprene emissions include higher rates of tropospheric ozone production, increases in the lifetime of methane, and increases in atmospheric aerosol production, all of which affect the global energy budget and/or lead to the degradation of air quality. Using RNA interference (RNAi) to suppress isoprene emission, we show that this trait, which is thought to be required for the tolerance of abiotic stress, is not required for high rates of photosynthesis and woody biomass production in the agroforest plantation environment, even in areas with high levels of climatic stress. Biomass production over 4 y in plantations in Arizona and Oregon was similar among genetic lines that emitted or did not emit significant amounts of isoprene. Lines that had substantially reduced isoprene emission rates also showed decreases in flavonol pigments, which reduce oxidative damage during extremes of abiotic stress, a pattern that would be expected to amplify metabolic dysfunction in the absence of isoprene production in stress-prone climate regimes. However, compensatory increases in the expression of other proteomic components, especially those associated with the production of protective compounds, such as carotenoids and terpenoids, and the fact that most biomass is produced prior to the hottest and driest part of the growing season explain the observed pattern of high biomass production with low isoprene emission. Our results show that it is possible to reduce the deleterious influences of isoprene on the atmosphere, while sustaining woody biomass production in temperate agroforest plantations.

oxidative stress↗
NASA NTRS1989

Radar modeling of tropical mangal forest stands

Radar modeling of mangal forest stands in the Sundarbans area of southern Bangladesh is discussed. The modeling uses radar system parameters with forest data on tree height, spacing, biomass, species combinations, and water content (including slightly conductive water) in leaves and trunks of the mangal. For Sundri and Gewa tropical mangal forests, six model components are proposed; the models are required to explain the contributions of various combinations of forest species in the attenuation and scattering of mangal-vegetated nonflooded or flooded surfaces. Statistical data of simulated images have been compared with those of SIR-B images both to refine the modeling procedures and to characterize the model output appropriately. The possibility of delineation of flooded or nonflooded boundaries is discussed.

Wang, Yong↗
NASA NTRS1989

Improvement and extension of a radar forest backscattering model

Radar modeling of mangal forest stands, in the Sundarbans area of Southern Bangladesh, was developed. The modeling employs radar system parameters with forest data on tree height, spacing, biomass, species combinations, and water (including slightly conductive water), content both in leaves and trunks of the mangal. For Sundri and Gewa tropical mangal forests, six model components are proposed, which are required to explain the contributions of various forest species combinations in the attenuation and scattering of mangal vegetated nonflooded or flooded surfaces. Statistical data of simulated images were compared with those of SIR-B images both to refine the modeling procedures and to appropriately characterize the model output. The possibility of delineation of flooded or nonflooded boundaries is discussed.

Simonett, David S.↗
NASA NTRS1989

Improvement and extension of a radar forest backscattering model

Radar modeling of mangal forest stands, in the Sundarbans area of Southern Bangladesh, was developed. The modeling employs radar system parameters such as wavelength, polarization, and incidence angle, with forest data on tree height, spacing, biomass, species combinations, and water content (including slightly conductive water) both in leaves and trunks of the mangal. For Sundri and Gewa tropical mangal forests, five model components are proposed, which are required to explain the contributions of various forest species combinations in the attenuation and scattering of mangal vegetated nonflooded or flooded surfaces. Statistical data of simulated images (HH components only) were compared with those of SIR-B images both to refine the modeling procedures and to appropriately characterize the model output. The possibility of delineation of flooded or non-flooded boundaries is discussed.

Simonett, David S.↗
NASA NTRS2019

Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of -4.14 +/- 0.76 MgC/hay. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.

Pinage, Ekena Rangel↗
NASA NTRS1977

Remote sensing and today's forestry issues

The actual and the desirable roles of remote sensing in dealing with current forestry issues, such as national forest policy, supply and demand for forest products and competing demands for forest land are discussed. Topics covered include wood shortage, regional timber inventories, forests in tropical and temperate zones, Skylab photography, forest management and protection, available biomass studies, and monitoring.

Sayn-Wittgenstein, L.↗
NASA NTRS2011

Radar and Lidar Radar DEM

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

remote sensing↗
NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide (NO 2 ), formaldehyde (CH2O), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ), and aerosol optical depth (AOD). Value-added products [e.g., Particulate matter with diameters less than 2.5 micrometers (PM2.5)] are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Air Quality↗
NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide, formaldehyde, sulfur dioxide, carbon monoxide, and ozone, and aerosol optical depth. Value-added products, such as level 4 PM2.5 products, are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Forecasting↗
NASA NTRS1995

The Global Impact of Biomass Burning: An Interview with EPA's Robert Huggett

The extent of biomass burning has increased significantly over the past 100 years because of human activities, and such burning is much more frequent and widespread than was previously believed. Biomass burning is now recognized as a significant global source of emissions, contributing as much as 40% of gross carbon dioxide and 38% of tropospheric ozone. Most of the world's burned biomass matter is from the savannas, and because two-thirds of the Earth's savannas are located in Africa, that continent is now recognized as the "burn center" of the planet. In the past few years the international scientific community has conducted field experiments using ground-based and airborne measurements in Africa, South America. and Siberia to better assess the global production of gases and particulates by biomass burning. Researchers are gathering this month in Williamsburg, VA, to discuss the results of these and other investigations at the Second Chapman Conference on Biomass Burning and Global Change, sponsored by the American Geophysical Union. The first international biomass burning conference, held in 1990, was attended by atmospheric chemists, climatologists, ecologists, forest and soil scientists, fire researchers, remote- sensins specialists, and environmental planners and managers from more than 25 countries.When we hear about biomass burning, we usually think of the burning of the worlds tropical forests for permanent land clearing. However, biomass burning serves a variety of land use changes, including the clearing of forests and savannas for agricultural and grazing use; shifting agriculture practices; the control of grass, weeds, and litter on agricultural and grazing lands; the elimination of stubble and waste on agricultural lands after the harvest; and the domestic use of biomass matter.

Sevine, Joel S.↗
NASA NTRS2000

BOREAS TE-18 Biomass Density Image of the SSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. This biomass density image covers almost the entire BOREAS SSA. The pixels for which biomass density is computed include areas, that are in conifer land cover classes only. The biomass density values represent the amount of overstory biomass (i.e., tree biomass only) per unit area. It is derived from a Landsat-5 TM image collected on 02-Sep-1994. The technique that was used to create this image is very similar to the technique that was as used to create the physical classification of the SSA. The data are provided in a binary image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Hall, Forrest G.↗