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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

High-Resolution Forest Canopy Height Estimation in an African Blue Carbon Ecosystem

Mangrove forests are one of the most productive and carbon dense ecosystems that are only found at tidally inundated coastal areas. Forest canopy height is an important measure for modeling carbon and biomass dynamics, as well as land cover change. By taking advantage of the flat terrain and dense canopy cover, the present study derived digital surface models (DSMs) using stereophotogrammetric techniques on high-resolution spaceborne imagery (HRSI) for southern Mozambique. A mean-weighted ground surface elevation factor was subtracted from the HRSI DSM to accurately estimate the canopy height in mangrove forests in southern Mozambique. The mean and H100 tree height measured in both the field and with the digital canopy model provided the most accurate results with a vertical error of 1.18-1.84 m, respectively. Distinct patterns were identified in the HRSI canopy height map that could not be discerned from coarse shuttle radar topography mission canopy maps even though the mode and distribution of canopy heights were similar over the same area. Through further investigation, HRSI DSMs have the potential of providing a new type of three-dimensional dataset that could serve as calibration/validation data for other DSMs generated from spaceborne datasets with much larger global coverage. HSRI DSMs could be used in lieu of Lidar acquisitions for canopy height and forest biomass estimation, and be combined with passive optical data to improve land cover classifications.

HRSI↗
NASA NTRS2004

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↗
NASA NTRS1994

Lick optical spectra of quasar HS 1946+7658 at 10 kilometers per second resolution Lyman-alpha forest and metal absorption systems

We present optical spectra of the most luminous known quasi stellar object (QSO) HS 1946+7658 (z(sub em) = 3.051). Our spectra have both full wavelength coverage, 3240-10570 A, and in selected regions, either high signal-to-noise ratio, SNR approximately equals 40-100, or unusually high approximately 10 km/sec resolution, and in parts of the Ly alpha forest and to the red of Ly alpha emission they are among the best published. We find 113 Ly alpha systems and six metal-line systems, three of which are new. The metal systems at z(sub abs) = 2.844 and 3.050 have complex velocity structure with four and three prominent components, respectively. We find that the system at z(sub abs) = 2.844 is a damped Ly alpha absorption (DLA) system, with a neutral hydrogen column density of log N(H I) = 20.2 +/- 0.4, and it is the cause of the Lyman limit break at lambda approximately equals 3520 A. We believe that most of the H I column density in this system is in z(sub abs) = 2.8443 component which shows the strongest low-ionization absorption lines. The metal abundance in the gas phase of the system is (M/H) approximately equals -2.6 +/- 0.3, with a best estimate of (M/H) = -2.8, with ionizaion parameter log gamma = -2.75, from a photoionization model. The ratios of the logarithmic abundances of C, O, Al, and Si are all within a factor of 2 of solar, which is important for two reasons. First, we believe that the gas abundances which we measure are close to the total abundances, because the ratio of aluminum to other elements is near cosmic, and Al is a refractory element which depletes very readily like chromium, in the interstellar medium. Second, we do not see the enhancement of O with respect to C of (O/C) approximately equals 0.5-0.9 reported in three partial Lyman limit systems by Reimers et al. (1992) and Vogel & Reimers (1993); we measure (O/C) = -0.06 for observed ions and (O/C) approximately equals 0.2 after ionization corrections, which is consistent with solar abundances. We see C II*(lambda 1335) offset by 15 km/sec with respect to C II(lambda 1334), presumably because the gas density varies from 2 to 8 cm(exp -3) with changing velocity in the DLA system. These densities imply that the damped component is 6-25 pc thick, which is reasonable for a single cloud in a cold spiral disk. They also imply that the cloud is relatively highly ionized with more C III than C II, more O III than O I, and log N(H I) = 20.72, which is 3 times the H I column. The system at z(abs) = 1.7382 is also believed to be damped with N(H I) approximately equals 10(exp 21) cm(exp -2), because we see Cr II, but its Ly alpha line will never be seen because it is below the Lyman limit of the other DLA system. We see a 2.6 sigma lack of Ly alpha forest lines well away from the QSO redshift, which may be a chance fluctuation. We also see a correlation between column density N(H I) and Doppler parameter b for 96 unsaturated Ly Alpha forest absorption lines, and although this correlation persists in the 36 Ly alpha lines which lie in regions where the SNR approximately equals 8-16, we agree with Rauch et al. (1993) that it is probably a bogus effect of low supernova remnant (SNR). The same applies to lines with very low b values: in regions where SNR less than or equal to 8 we see many Ly alpha lines which appear to have 10 less than or equal to b less than or equal to 20, but when 8 less than or equal to SNR less than or equal to 16 we see only one line with b less than or equal to 15 km/sec, and two others which we believe have b less than or equal to 20, with values of 20 and 16 km/sec. Traditional Ly alpha line samples which include all lines which have W/sigma(W) greater than or equal to 4 are not adequate to explore the distribution of the properties of individual clouds, because we need much higher (W/sigma(W)) and SNR to avoid the strong biases.

Fan, Xiao-Ming↗
NASA NTRS1994

Amazon Rain Forest Classification Using J-ERS-1 SAR Data

The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.

J-ERS-1↗
DOE OSTI2023

Attaining freshwater and estuarine-water soil saturation in an ecosystem-scale coastal flooding experiment

Abstract Coastal upland forests are facing widespread mortality as sea-level rise accelerates and precipitation and storm regimes change. The loss of coastal forests has significant implications for the coastal carbon cycle; yet, predicting mortality likelihood is difficult due to our limited understanding of disturbance impacts on coastal forests. The manipulative, ecosystem-scale Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m 2 ), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Transient saturation (5 h) of the entire soil rooting zone (0–30 cm) across a 2000 m 2 coastal forest was attained by delivering 300 m 3 of water through a spatially distributed irrigation network at a rate just above the soil infiltration rate. Our water delivery approach also elevated the water table (typically ~ 2 m belowground) and achieved extensive, low-level inundation (~ 8 cm standing water). A TEMPEST simulation approximated a 15-cm rainfall event and based on historic records, was of comparable intensity to a 10-year storm for the area. This characterization was supported by showing that Hurricane Ida’s (~ 5 cm rainfall) hydrologic impacts were shorter (40% lower duration) and less expansive (80% less coverage) than those generated through experimental manipulation. Future work will apply TEMPEST treatments to evaluate coastal forest resilience to changing hydrologic disturbance regimes and identify conditions that initiate ecosystem state transitions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Radiative Effects of Aerosols Generated from Biomass Burning, Dust Storms, and Forest Fires

Atmospheric aerosol particles, both natural and anthropogenic, are important to the earth's radiative balance. They scatter the incoming solar radiation and modify the shortwave reflective properties of clouds by acting as Cloud Condensation Nuclei (CCN). Although it has been recognized that aerosols exert a net cooling influence on climate (Twomey et al. 1984), this effect has received much less attention than the radiative forcings due to clouds and greenhouse gases. The radiative forcing due to aerosols is comparable in magnitude to current anthropogenic greenhouse gas forcing but opposite in sign (Houghton et al. 1990). Atmospheric aerosol particles generated from biomass burning, dust storms and forest fires are important regional climatic variables. A recent study by Penner et al. (1992) proposed that smoke particles from biomass burning may have a significant impact on the global radiation balance. They estimate that about 114 Tg of smoke is produced per year in the tropics through biomass burning. The direct and indirect effects of smoke aerosol due to biomass burning could add up globally to a cooling effect as large as 2 W/sq m. Ackerman and Chung (1992) used model calculations and the Earth Radiation Budget Experiment (ERBE) data to show that in comparison to clear days, the heavy dust loading over the Saudi Arabian peninsula can change the Top of the Atmosphere (TOA) clear sky shortwave and longwave radiant exitance by 40-90 W/sq m and 5-20 W/sq m, respectively. Large particle concentrations produced from these types of events often are found with optical thicknesses greater than one. These aerosol particles are transported across considerable distances from the source (Fraser et al. 1984). and they could perturb the radiative balance significantly. In this study, the regional radiative effects of aerosols produced from biomass burning, dust storms and forest fires are examined using the Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) data and the instantaneous scanner ERBE data from the NOAA-9 and NOAA-10 satellites.

Christopher Sundar A.↗
DOE OSTI2022

Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activity

Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales. Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed (n = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry (n = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development.

16S rRNA↗
NASA NTRS2001

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

LeMoigne, Jacqueline↗
NASA NTRS2022

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision-makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods either rely on chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time-series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product – NASA’s Goddard Earth Observing System “Composition Forecasting” (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next five days at a 1-km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next two days had overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following three forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with validation normalized mean bias close to zero, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

PM2.5↗
DOE OSTI2022

Forest Canopy Density Effects on Snowpack Across the Climate Gradients of the Western United States Mountain Ranges

Limitations inherent to field studies such as limited spatial and temporal coverage has made it very challenging, if not impossible, to explicitly disentangle the effect of canopy density from climate on snowpack dynamics. Hence, our understanding of how variations in canopy density can affect snow processes under diverse climate conditions has been limited. To address this knowledge gap, this study uses a physics-based modeling approach to represent most key processes that drive snow accumulation and melt in the open and forest with varied forest density, across the climate gradients of the Western U.S. mountain ranges, as represented by 228 Snow Telemetry (SNOTEL) locations. Simulations suggest that, under most winter climate conditions, canopy density exerts a strong influence on under-canopy snowpack dynamics and their controlling processes. Changing canopy density can shift the direction of the modeled differential peak snow accumulation and snow disappearance date (SDD) between the open and forest. For example, while simulated snowpack lasts longer in the open than the high-density forest at most wet/warm and dry/cold locations by up to 55 days, about 90% of all locations show a longer snow duration in the low-density forest by up to 27 days as compared to the open. Interannual variability in climate can also change the magnitude and direction of canopy impact on snow processes. By comparing model simulations to published field observations, and we also identify conditions that require additional field observations to improve process-scale understanding of forest-snow interaction.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

On-ground Live Fuel Moisture Content Sampling Along California’s Central Coast Santa Lucia Preserve - Carmel Valley, CA

The ratio of water to dry mass within live plants is an important factor when determining the severity and intensity of potential wildfires. Currently, live fuel moisture content (LFMC) samples support wildland fire management, but are limited in number and spatial extent. In Monterey County, the only LFMC data to analyze spatial and temporal trends is collected by CalFire and the Los Padres National Forest Unit of the United States Forest Service across five long-term sampling sites. Our project aims to enhance the spatial coverage of current LFMC data being collected across Monterey County. Here, we share results from the ongoing work to monitor LFMC across Carmel Valley, CA. We evaluate LFMC data by environmental factors such as slope, aspect, topography, and species composition. We also place our LFMC samples in relation to nearby long-term sampling locations. We conclude by interpreting spatial and temporal trends in the preliminary data and discuss logistical challenges of establishing more long-term LFMC sampling sites to better represent the complex topography and vegetation composition of Central California.

On-ground↗
DOE OSTI2026

Comprehensive framework for assessing and optimizing existing research networks

Conservation, monitoring, and research networks, or collections of ecological research sites unified under a common mission of data collection or a research mission, are essential infrastructure for understanding large landscapes. However, most networks developed opportunistically over decades rather than through systematic design, creating potential limitations in the ability to address conservation challenges across entire regions. We developed a framework to evaluate how well an existing research network represents the environmental conditions its members study and devised an approach to rank sites of priority for strategic expansion. Our approach measures performance through environmental representativeness, geographic coverage, and adequacy for scientific inference and thus optimizes limited monitoring resources to maximize scientific impact. We demonstrated this approach with the U.S. Department of Agriculture (USDA) Forest Service Experimental Forests and Ranges Network (EFRN), a 79‐site network across the United States that grew opportunistically over a century. At the national scale, the network effectively captured high‐biomass forests important for carbon cycle research; 82% of forest biomass was in well‐represented areas. Some areas in Texas, Florida, the Rocky Mountains, and the West Coast had no relevant EFRN sites, which limits the ability to make regional inferences. A fundamental challenge for the EFRN was that sites improving regional extent coverage sometimes provided minimal national benefits, which can create conflicts between local and global priorities. Adding the highest‐ranked candidate site provided a relevant site for 17% of currently poorly represented 1‐km pixel cells nationally, but regional and national site rankings varied considerably due to nested spatial inference. This framework provides quantitative tools for strategic infrastructure decision‐making, ensures that limited monitoring resources maximize conservation impact, and can be applied broadly to address the widespread challenge of optimizing conservation and monitoring networks worldwide.

additional site↗
NASA NTRS2004

The GOES-R Lightning Mapper Sensor

The Lightning Mapper Sensor on GOES-R builds on previous measurements of lightning from low earth orbit by the OTD (Optical Transient Detector) and LIS (Lightning Imaging Sensor) sensors. Unlike observations from low earth orbit, the GOES-R platform will allow continuous monitoring of lightning activity over the Continental United States and southern Canada, Central and South America, and portions of the Atlantic and Pacific Oceans. The LMS will detect total (cloud-to-ground and intracloud) lightning at storm scale resolution (approx. 8 km) using a highly sensitive Charge Coupled Device (CCD) detector array. Discrimination between lightning optical transients and a bright sunlit background scene is accomplished by employing spectral, spatial, and temporal filtering along with a background subtraction technique. The result is 24 hour detection capability of total lightning. These total lightning observations can be made available to users within about 20 seconds. Research indicates a number of ways that total lightning observations from LMS could benefit operational activities, including 1) potential increases in lead times and reduced false alarms for severe thunderstorm and tornado Warnings, 2) improved routing of &rail around thunderstorms, 3) support for spacecraft launches and landings, 4) improved ability to monitor tropical cyclone intensity, 5) ability to monitor thunderstorm intensification/weakening during radar outages or where radar coverage is poor, 6) better identification of deep convection for the initialization of numerical prediction models, 7) improved forest fire forecasts, 8) identification of convective initiation, 9) identification of heavy convective snowfall, and 10) enhanced temporal resolution of storm evolution (1 minute) than is available from radar observations. Total lightning data has been used in an operational environment since July 2003 at the Huntsville, Alabama National Weather Service office. Total lightning measurements are obtained by the North Alabama Lightning Mapping Array (LMA) and have successfully been used in warning decisions. Every 2 minutes, total lightning counts in 2 km by 2 km horizontal, 1 km vertical grids are available to forecasters on an AWIPS (Advanced Weather Interactive Processing System) workstation. Experience with the LMA total lightning data is used to illustrate the potential use of LMS data that would be available to forecasters across the US. This abstract is for submission as a presentation to the National Weather Association Annual Meeting to be held 16-21 October 2004 in Portland, OR. This abstract will be published in the conference proceedings.

Buechler, Dennis↗
DOE OSTI2024

Perturbation of soil organic carbon induced by land-use change from primary forest

Abstract The impact of land-use change (LUC) on soil organic carbon (SOC) has been a wide concern of land management policymakers because CO 2 emissions induced by LUC have been the second largest carbon source worldwide. However, due to insufficient data quality and limited biome coverage, a global big picture of the impact of LUC on SOC is still not clear. This study conducted a meta-analysis on 288 independent observations sourced from 62 peer-reviewed papers to provide a global summary of the change in SOC after the conversion of primary forests into other land-use types. The conversion of primary forest to cropland resulted in the most severe SOC loss (−33.2%), followed by conversion into plantation forests (−22.3%) and secondary forests (−19.1%). Nonetheless, SOC increased by 9.1% after a conversion from primary forests into pasture. More SOC loss was found at sites with lower precipitation for primary forests converted to cropland and plantation forests. The SOC loss decreased consistently with increasing mean annual temperature (MAT) for all four types of LUC. Moreover, the loss of SOC tended to worsen over time when primary forests are converted to cropland or plantation forests. In contrast, SOC loss recovered over time following conversion to secondary forests. The gain of SOC gradually increased over time after conversion to pastures. To conclude, the changes in SOC are related not only to the land-use type but also to precipitation, temperature and turn years after LUC. Due to limited data, this study focuses on soil profiles within 30 cm depth, and future research should explore SOC dynamics induced by LUC at greater depths. Overall, cases of SOC loss of approximately 30% following deforestation were very common (except for conversion to pasture), and the results of this study show that the loss of SOC following LUC should be carefully considered and monitored in land management.

Zhang, Zhiyuan (ORCID:0000000223407001)↗
NASA NTRS1980

The application of remotely sensed data in support of emergency rehabilitation of wildfire-damage areas

The depth, texture, and water holding capacity of the soil before the fire in the Bridge Creek area of Deschutes National Forest (1979) were determined from available aerial photography and LANDSAT MSS digital data. Three days after the fire was out, complete coverage of the burned area was acquired on 35 mm color infrared film from a near vertical or low oblique perspective. These photographs were used in assessing the condition of vegetation, and in predicting the likelihood of survival. Negatives from vertical natural photography obtained during the same flight were used to produce 3R prints from which large scale mosaics of the entire burned area were obtained. LANDSAT MSS data obtained on the day the fire was under control were used to evaluate vegetative vigor (by calculating a band 7/band 5 ratio value for each spectral class) and to determine the boundary between altered and unaltered land.

Isaacson, D. L.↗
NASA NTRS1992

Evaluation of a technique for satellite-derived area estimation of forest fires

The advanced very high resolution radiometer (AVHRR), has been found useful for the location and monitoring of both smoke and fires because of the daily observations, the large geographical coverage of the imagery, the spectral characteristics of the instrument, and the spatial resolution of the instrument. This paper will discuss the application of AVHRR data to assess the geographical extent of burning. Methods have been developed to estimate the surface area of burning by analyzing the surface area effected by fire with AVHRR imagery. Characteristics of the AVHRR instrument, its orbit, field of view, and archived data sets are discussed relative to the unique surface area of each pixel. The errors associated with this surface area estimation technique are determined using AVHRR-derived area estimates of target regions with known sizes. This technique is used to evaluate the area burned during the Yellowstone fires of 1988.

Cahoon, Donald R., Jr.↗
NASA NTRS2013

A Comparison of Foliage Profiles in the Sierra National Forest Obtained with a Full-Waveform Under-Canopy EVI Lidar System with the Foliage Profiles Obtained with an Airborne Full-Waveform LVIS Lidar System

Foliage profiles retrieved froma scanning, terrestrial, near-infrared (1064 nm), full-waveformlidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r = 0.987 at 100 mhorizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Alsowe noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions.

Full-waveform↗
NASA NTRS1996

Modeling Global Biogenic Emission of Isoprene: Exploration of Model Drivers

Vegetation provides the major source of isoprene emission to the atmosphere. We present a modeling approach to estimate global biogenic isoprene emission. The isoprene flux model is linked to a process-based computer simulation model of biogenic trace-gas fluxes that operates on scales that link regional and global data sets and ecosystem nutrient transformations Isoprene emission estimates are determined from estimates of ecosystem specific biomass, emission factors, and algorithms based on light and temperature. Our approach differs from an existing modeling framework by including the process-based global model for terrestrial ecosystem production, satellite derived ecosystem classification, and isoprene emission measurements from a tropical deciduous forest. We explore the sensitivity of model estimates to input parameters. The resulting emission products from the global 1 degree x 1 degree coverage provided by the satellite datasets and the process model allow flux estimations across large spatial scales and enable direct linkage to atmospheric models of trace-gas transport and transformation.

Alexander, Susan E.↗