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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
DOE OSTI2023

EVI-Pro for Bogota Columbia [Slides]

The transportation sector is one of the largest producers of greenhouse gas emissions globally, with 37% of global CO 2 emissions in 2021 according to the International Energy Agency. Transitioning from internal combustion engine vehicles powered by fossil fuels to electrified transportation is a critical component in reducing those emissions significantly over the coming decades and avoiding the worst consequences of climate change. With governments and automakers increasingly announcing electrification targets and a progression away from the production and sale of internal combustion engine vehicles, a robust network of charging infrastructure must be built to enable the widespread use of electric vehicles (EVs). Some of the greatest hurdles to EV deployment are linked to the buildout of the required charging infrastructure including where it should be built, what types and how much charging would be optimal given high costs of installation, and the impact on the electrical grid. To this end, the National Renewable Energy Laboratory (NREL) conducts extensive research on EV deployment, including the development of the EVI-Pro model used to plan the future buildout of charging infrastructure given the travel patterns in a region. This document summarizes work done by NREL in the Bogotá, Colombia region to help Colombia plan for its EV deployment goals.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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↗
DOE OSTI2023

EVI-X Updates and National Charging Assessment Report

With the support of DOE's Vehicle Technologies Office and the DOE/DOT Joint Office (JO), NREL has applied the EVI-X modeling suite to conduct a National Electric Vehicle Infrastructure Needs Assessment. This report considers a 2030 scenario in which 30-42 million light-duty electric vehicles are on the road (including plug-in hybrids). We consider the needs of vehicles used for typical daily driving, drivers without access to residential charging, corridor charging supporting long-distance travel, and ride-hailing electrification. We find that a national network of 28 million charging ports (including public and private infrastructure) will be necessary in our mid adoption scenario. This result is framed as a conservative estimate as the assumed costs include charging equipment and installation but exclude the cost of grid upgrades and distributed energy resources.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2020

DC Fast Charging Infrastructure for Electrified Road Trips

To assess DC fast charging station network required for electrified road trips by 2030 in California, a new charging infrastructure simulation tool/model, EVI-Pro (Electric Vehicle Infrastructure Projection) RoadTrip, has been developed. In contrast to the existing EVI-Pro model that is primarily for short-distance travels, EVI-Pro RoadTrip is exclusively focused on road trips (long-distance travels, 100 or miles per day per vehicle). Also, the charging paradigm or strategy is different. EVI-Pro RoadTrip is built upon waypoint charging, in which vehicles are forced to stop to charge or replenish the on-board batteries, along the routes between origins and destinations. On the other hand, EVI-Pro is based on destination charging, in which charging is conducted when vehicles are parked in destinations (e.g., work, home). EVI-Pro RoadTrip takes coordinate-level origin and destination data for road trips (intra-state as well as domestic or international out-of-state) and estimates energy consumption and charging needs along the routes between origins and destinations on a minute-by-minute resolution. Based on charging demands for electrified road trips across the state, the optimal locations of charging stations are determined accounting for preferred land use types (e.g., commercial areas) and station service area (e.g., 5 or less miles). Based on station-by-station charging load profiles, the required number of plugs/connectors is estimated for each station and entire state. By comparing hosting capacity of the electric grid (circuit-level) and the charging load output from EVI-Pro RoadTrip, capacity deficit is also evaluated.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2023

Combining field and remote sensing data to estimate forest canopy damage and recovery following tropical cyclones across tropical regions

Abstract As tropical forests cycle the most water and carbon, it is crucial to understand the short- and long-term effects of intensifying cyclones on these ecosystems. Soil nutrient status has been shown to moderate forest cyclone responses using field litterfall measurements, but litterfall is one of the multiple cyclone impact metrtics, which may or may not be correlated with one another or with site nutrients. We used remotely sensed vegetation indices to quantify immediate damage and two-year recovery for 42 cases across nine tropical forests in Hawaii, Puerto Rico, Mexico, Australia, and Taiwan affected by 12 cyclones between 2004 and 2017. We tested whether changes in leaf area index (LAI) and enhanced vegetation index (EVI) correlated with changes in litterfall observations and how changes varied with total soil phosphorus (P) concentrations across regions. We compared cyclone-induced changes and recovery of LAI and EVI to litterfall observations compiled in a pantropical meta-analysis. We found large variation in changes in LAI and EVI across forests, with the greatest reductions in LAI (−77%) and EVI (−77%) in Mexico (Jalisco) and Puerto Rico, respectively. LAI ( r = −0.52) and EVI ( r = −0.60) changes correlated with those in litterfall across cases. Post-cyclone data showed recovery of LAI by four months, EVI by two months, and litterfall by ten months. We detected larger changes in LAI and EVI in forests with higher soil P, but these relationships were not significant when accounting for cyclone and site as random effects. Principal component analyses indicated a regional clustering of cases related to their contrasting cyclone regimes, with the frequency and intensity of cyclone events negatively correlated. Overall, remote sensing observations complement but do not substitute for ground observations that reveal cyclone damage and post-cyclone recovery in tropical forests, and soil phosphorus moderates some but not all metrics of stability in response to cyclones.

Bloom, Dellena E. (ORCID:0000000205981747)↗
NASA NTRS2012

Interpretation of Variations in Modis-Measured Greenness Levels of Amazon Forests During 2000 to 2009

This work investigates variations in satellite-measured greenness of Amazon forests using ten years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) data. Corruption of optical remote sensing data with clouds and aerosols is prevalent in this region; filtering corrupted data causes spatial sampling constraints, as well as reducing the record length, which introduces large biases in estimates of greenness anomalies. The EVI data, analyzed in multiple ways and taking into account EVI accuracy, consistently show a pattern of negligible changes in the greenness levels of forests both in the area affected by drought in 2005 and outside it. Small random patches of anomalous greening and browning-especially prominent in 2009-appear in all ten years, irrespective of contemporaneous variations in precipitation, but with no persistence over time. The fact that over 90% of the EVI anomalies are insignificantly small-within the envelope of error (95% confidence interval) in EVI-warrants cautious interpretation of these results: there were no changes in the greenness of these forests, or if there were changes, the EVI data failed to capture these either because the constituent reflectances were saturated or the moderate resolution precluded viewing small-scale variations. This suggests a need for more accurate and spatially resolved synoptic views from satellite data and corroborating comprehensive ground sampling to understand the greenness dynamics of these forests.

MODIS↗
NASA NTRS2020

Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiaġvik tundra ecosystem in Alaska with the LVS3 model

Satellite observations for the Arctic and boreal region may contain information of vegetation, soil, snow, snowmelt, and/or other surface water bodies. We investigated the impacts of vegetation, soil, snow and surface water on empirical vegetation/snow indices on a tundra ecosystem area located around Utqiaġvik (formerly Barrow) of Alaska with the Moderate Resolution Imaging Spectrometer (MODIS) images in 2001–2014. Empirical vegetation indices, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), the index of near infrared of vegetation (NIRv), and modified EVI (EVI2), have been used to monitor vegetation. Normalized difference snow index (NDSI) has been widely applied to monitor snow. The vegetation cover fraction (VGCF), the soil cover fraction (SOILCF), the snow cover fraction (SNOWCF), the surface water body cover fraction (WaterBodyCF), the fractional absorption of photosynthetically active radiation (PAR) by vegetation chlorophyll (fAPARchl), the fractional absorption of PAR by non-chlorophyll components of the vegetation (fAPARnon-chl), and the fractional absorption of PAR by the entire canopy (fAPARcanopy) are retrieved with the MODIS images and a coupled Leaf-Vegetation-Soil-Snow-Surface water body radiative transfer model, LVS3. The vegetation indices (NDVI, EVI, EVI2 and NIRv) differ from VGCF, fAPARchl, fAPARnon-chl, and fAPARcanopy. In addition to vegetation, we find that soil, snow and surface water also have impacts on vegetation indices NDVI, EVI (EVI2), and NIRv. Presence of snow makes lower the observed values of NDVI, EVI2 and NIRv. After snowmelt is gone, the vegetation indices (NDVI, EVI, EVI2 and NIRv) linearly decrease with SOILCF and WaterBodyCF, and WaterBodyCF has stronger impacts on these vegetation indices than SOILCF. The relationship between EVI and snow is complicated. NDSI non-linearly increases with SNOWCF, but linearly increases with sum of SNOWCF and WaterBodyCF (sum = 0.5893 × NDSI +0.4342, R2 = 0.976). NDSI linearly decreases with VGCF, and the relationship between NDSI and SOILCF is complex. Retrievals of VGCF, fAPARchl, fAPARnon-chl and fAPARcanopy with the LVS3 model provide alternatives for vegetation monitoring and ecological modeling.

Qingyuan Zhang↗
DOE OSTI2025

Examining the Impact of Local Constraint Violations on Energy Computations in DFT

ABSTRACT This work examines the impact of locally imposed constraints in Density Functional Theory (DFT). Using a metric referred to as the extent of violation index (EVI), we quantify how well exchange‐correlation functionals adhere to local constraints. Applying EVIs to a diverse set of molecules for GGA functionals reveals constraint violations, particularly for semi‐empirical functionals. We leverage EVIs to explore potential connections between these violations and errors in chemical properties. While no correlation is observed for atomization energies, a significant statistical correlation emerges between EVIs and total energies. Similarly, the analysis of reaction energies suggests weak positive correlations for specific constraints. However, definitive conclusions about error cancellation mechanisms cannot be made at this time. These observations revealed by EVIs may be useful for consideration when designing future generations of semilocal functionals.

Khanna, Vaibhav [Department of Chemistry Universit↗
DOE OSTI2020

An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Measuring Gap Fraction, Element Clumping Index and LAI in Sierra Forest Stands Using a Full-Waveform Ground-Based Lidar

The Echidna Validation Instrument (EVI), a ground-based, near-infrared (1064 nm) scanning lidar, provides gap fraction measurements, element clumping index measurements, effective leaf area index (LAIe) and leaf area index (LAI) measurements that are statistically similar to those from hemispherical photos. In this research, a new method integrating the range dimension is presented for retrieving element clumping index using a unique series of images of gap probability (Pgap) with range from EVI. From these images, we identified connected gap components and found the approximate physical, rather than angular, size of connected gap component. We conducted trials at 30 plots within six conifer stands of varying height and stocking densities in the Sierra National Forest, CA, in August 2008. The element clumping index measurements retrieved from EVI Pgap image series for the hinge angle region are highly consistent (R2=0.866) with those of hemispherical photos. Furthermore, the information contained in connected gap component size profiles does account for the difference between our method and gap-size distribution theory based method, suggesting a new perspective to measure element clumping index with EVI Pgap image series and also a potential advantage of three dimensional Lidar data for element clumping index retrieval. Therefore further exploration is required for better characterization of clumped condition from EVI Pgap image series.

Zhao, Feng↗
NASA NTRS2013

Management Approach for Earth Venture Instrument

The Earth Venture Instrument (EVI) element of the Earth Venture Program calls for developing instruments for participation on a NASA-arranged spaceflight mission of opportunity to conduct innovative, integrated, hypothesis or scientific question-driven approaches to pressing Earth system science issues. This paper discusses the EVI element and the management approach being used to manage both an instrument development activity as well as the host accommodations activity. In particular the focus will be on the approach being used for the first EVI (EVI-1) selected instrument, Tropospheric Emissions: Monitoring of Pollution (TEMPO), which will be hosted on a commercial GEO satellite and some of the challenges encountered to date and corresponding mitigations that are associated with the management structure for the TEMPO Mission and the architecture of EVI.

Hope, Diane L.↗
NASA NTRS2015

Estimation of Crop Gross Primary Production (GPP): Do Scaled (MODIS) Vegetation Indices Improve Performance? - 2

Satellite remote sensing estimates of Gross Primary Production (GPP) have routinely been made using spectral Vegetation Indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVIgreen), and the green band Chlorophyll Index (CIgreen) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVIgreen, or CIgreen). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X-Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPARchl) and the VIs, and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPARchl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R2), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions. The scaled CIgreen did not improve results, compared to the original CIgreen. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the scaled EVI or NDVI in estimating crop daily GPP at these agricultural fields. The scaled VIs are more physiologically meaningful than original un-scaled VIs, but scaling factors and offsets may vary across crop types and surface conditions.

Vegetation Index↗
NASA NTRS2014

Estimation of Crop Gross Primary Production (GPP): I. Impact of MODIS Observation Footprint and Impact of Vegetation BRDF Characteristics

Accurate estimation of gross primary production (GPP) is essential for carbon cycle and climate change studies. Three AmeriFlux crop sites of maize and soybean were selected for this study. Two of the sites were irrigated and the other one was rainfed. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green band chlorophyll index (CIgreen), and the green band wide dynamic range vegetation index (WDRVIgreen) were computed from the moderate resolution imaging spectroradiometer (MODIS) surface reflectance data. We examined the impacts of the MODIS observation footprint and the vegetation bidirectional reflectance distribution function (BRDF) on crop daily GPP estimation with the four spectral vegetation indices (VIs - NDVI, EVI, WDRVIgreen and CIgreen) where GPP was predicted with two linear models, with and without offset: GPP = a × VI × PAR and GPP = a × VI × PAR + b. Model performance was evaluated with coefficient of determination (R2), root mean square error (RMSE), and coefficient of variation (CV). The MODIS data were filtered into four categories and four experiments were conducted to assess the impacts. The first experiment included all observations. The second experiment only included observations with view zenith angle (VZA) ≤ 35◦ to constrain growth of the footprint size,which achieved a better grid cell match with the agricultural fields. The third experiment included only forward scatter observations with VZA ≤ 35◦. The fourth experiment included only backscatter observations with VZA ≤ 35◦. Overall, the EVI yielded the most consistently strong relationships to daily GPP under all examined conditions. The model GPP = a × VI × PAR + b had better performance than the model GPP = a × VI × PAR, and the offset was significant for most cases. Better performance was obtained for the irrigated field than its counterpart rainfed field. Comparison of experiment 2 vs. experiment 1 was used to examine the observation footprint impact whereas comparison of experiment 4 vs. experiment 3 was used to examine the BRDF impact. Changes in R2, RMSE,CV and changes in model coefficients "a" and "b" (experiment 2 vs. experiment 1; and experiment 4 vs. experiment 3) were indicators of the impacts. The second experiment produced better performance than the first experiment, increasing R2 (↑0.13) and reducing RMSE (↓0.68 g C m−2 d−1) and CV (↓9%). For each VI, the slope of GPP = a × VI × PAR in the second experiment for each crop type changed little while the slope and intercept of GPP = a × VI × PAR + b varied field by field. The CIgreen was least affected by the MODIS observation footprint in estimating crop daily GPP (R2, ↑0.08; RMSE, ↓0.42 g C m−2 d−1; and CV, ↓7%). Footprint most affected the NDVI (R2, ↑0.15; CV, ↓10%) and the EVI (RMSE, ↓0.84 g C m−2 d−1). The vegetation BRDF impact also caused variation of model performance and change of model coefficients. Significantly different slopes were obtained for forward vs. backscatter observations, especially for the CIgreen and the NDVI. Both the footprint impact and the BRDF impact varied with crop types, irrigation options, model options and VI options.

Chlorophyll↗