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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantifying cover crop effects on soil health and productivity

The dataset presented here supports the research paper entitled “A calculator to quantify cover crop effects on soil health and productivity”. Soil health (sometimes used synonymously with soil quality) is a concept that describes soil as a living system to sustain plants, animals, and human. Soil physical, chemical, and biological properties, along with their interactions, are required to quantify soil health. The use of cover crops in agricultural rotations may enhance soil health, yet there has been little progress in understanding how external factors such as climate, soil type, and agronomic practices affect soil and cash crop responses. In response, this dataset compiles measurements from 281 studies and provides an analysis of field-measured changes in 38 soil health indicators due to cover crop usage. Environmental and background indicators were also compiled to assess how climatic and management practices affect soil and cash crop responses to cover crops, with specific categories including climate type (tropical, arid, temperate, and continental), soil texture (coarse, medium, and fine), cover crop type (legume, grass, multi-species mixture, and other), and cash crop type (corn, soybean, wheat, vegetable, corn-soybean rotation, corn-soybean-wheat rotation, and other). An unbalanced analysis of variation was used to determine the hierarchy of most to least important factors that affected responsiveness of each soil health indicator. Based on the hierarchy structure, a soil health calculator was then developed to quantify the response of 13 parameters – erosion, runoff, weed suppression, soil aggregate stability, leaching, infiltration, microbial biomass carbon, soil bulk density, soil organic carbon, soil nitrogen, microbial biomass nitrogen, cash crop yield, and saturated hydraulic conductivity – to cover crops. The presented data in the calculator report the mean change in parameter values based on all combinations of climate, soil texture, cover crop type, and cash crop type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessment of Screen-Covered Grooved Sodium Heat Pipes for Microreactor Applications

New nuclear reactor designs that incorporate heat pipes are being investigated for possible near-term deployment in terrestrial applications. Here, this study explores the use of screen-covered axially grooved sodium heat pipes and their applicability for providing heat removal for microreactors. A sodium working fluid is appropriate for microreactors operating in the 5 to 20 MW(thermal) range at approximately 650°C. HTPIPE, a legacy software code, was validated for the case of screen-covered grooves and used to perform steady-state analyses to determine the performance limits of a proposed heat pipe design. The performance limits of a sodium heat pipe with a screen-covered square grooved wick structure is compared to that of an equivalent heat pipe with an annular wick. In a horizontal orientation at an operating temperature of 650°C,the performance limits for the heat pipe with an annular wick configuration are 15% higher than for the screen-covered grooved wick. At operating temperatures below 777°C, the annular wick outperforms the screen-covered grooved wick, and at temperatures above 777°C, the screen-covered grooved wick outperforms the annular wick. However, the marginal performance gain at higher temperatures may not justify the use of heat pipes with a screen-covered grooved wick structure due to increased manufacturing costs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data

Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not be available, due to cost, data storage, or bandwidth issues. Instead, users may only have RGB and near infrared (NIR) bands available for land cover classification. Sometimes, light detection and ranging (LiDAR) data may also be available to assist land cover classification. A natural research problem is to investigate how well land cover classification can be achieved under the aforementioned data constraints. In this paper, we investigate the performance of land cover classification while only using four bands (RGB+NIR) or five bands (RGB+NIR+LiDAR). A number of algorithms have been applied to a well-known dataset (2013 IEEE Geoscience and Remote Sensing Society Data Fusion Contest). One key observation is that some algorithms can achieve better land cover classification performance by using only four bands as compared to that of using all 144 bands in the original hyperspectral data with the help of synthetic bands generated by Extended Multi-attribute Profiles (EMAP). Moreover, LiDAR data do improve the land cover classification performance even further.

58 GEOSCIENCES↗
DOE OSTI2023

A functional trait framework for integrating nitrogen‐fixing cover crops into short‐rotation woody crop systems

Abstract Developing approaches to simultaneously maximize short‐rotation woody crop (SRWC) productivity while minimizing footprints associated with intensive management is imperative to profitable and sustainable bioenergy production systems. Intercropping nitrogen (N)‐fixing cover crops in SRWC systems is an overlooked approach to sustainably intensify SRWC production by increasing N availability using less environmentally costly inputs. Here, we discuss how functional traits (e.g., seasonal activity, lifespan, leaf habit, soil exploration) of cover crops and SRWCs may interact through space and time influencing access to light, water, and nutrients to provide a framework for successful integration of cover crops into SRWCs. Next, we summarize the literature on intercropping forest plantations with N‐fixing cover crops to identity research gaps and outline future research needs and opportunities. And then, using empirical N demand and productivity data from SRWCs and cover crop N inputs from the literature, we illustrate how SRWC leaf habit (conifer evergreens and deciduous hardwoods) would influence successful integration of cover crops and potential N fixation. We estimate that integrating cover crops into SRWCs could supply 27% and 72% of the N demand across a 10‐year rotation for an evergreen and a deciduous hardwood, respectively. These figures suggest these integrated SRWC systems may approach a virtual minimal external N input when other biogeochemical cycles are considered. The guiding principles presented here are grounded in ecological theory and provide a framework for sustainable intensification of forest production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Quantifying the Impacts of Land-Cover Change on the Hydrologic Response to Hurricane Ida in the Lower Mississippi River Basin

Abstract The Lower Mississippi River basin (LMRB) has experienced significant changes in land cover and is one of the most vulnerable regions to hurricanes in the United States. Here, we study the impacts of land-cover change on the hydrologic response to Hurricane Ida in LMRB. By using an integrated surface–subsurface hydrologic model, Energy Exascale Earth System Model (E3SM) Land Model coupled with the three-dimensional ParFlow subsurface flow model (ELM-ParFlow), we simulate the effects of land-cover change on the flood volume and peak timing induced by rainfall from Hurricane Ida. The results show that land-cover changes from 1850 to 2015, which resulted in a smoother surface and less vegetation, exacerbated both flood peak time and volume induced by Hurricane Ida. The effects of land-cover changes can be decomposed into two mechanisms: a smoother surface routes more water faster to a watershed outlet and less vegetation allows more water to contribute to surface runoff. By comparing scenarios in which the two mechanisms were isolated, we found that changes in soil moisture due to vegetation cover change have more dominant effects on floods in the southern part and changes in Manning’s coefficient have the largest effect on floods in the northern part of the LMRB. The study provides important insights into the complex relationship between land-use, land-cover, and hydrologic processes in coastal regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

How Can Floor Covering Influence Buildings’ Demand Flexibility?

Although the thermal mass of floors in buildings has been demonstrated to help shift cooling load, there is still a lack of information about how floor covering can influence the floor’s load shifting capability and buildings’ demand flexibility. To fill this gap, we estimated demand flexibility based on the daily peak cooling load reduction for different floor configurations and regions, using EnergyPlus simulations. As a demand response strategy, we used precooling and global temperature adjustment. The result demonstrated an adverse impact of floor covering on the building’s demand flexibility. Specifically, under the same demand response strategy, the daily peak cooling load reductions were up to 20–34% for a concrete floor whereas they were only 17–29% for a carpet-covered concrete floor. This is because floor covering hinders convective coupling between the concrete floor surface and the zone air and reduces radiative heat transfer between the concrete floor surface and the surrounding environment. In hot climates such as Phoenix, floor covering almost negated the concrete floor’s load shifting capability and yielded low demand flexibility as a wood floor, representing low thermal mass. Sensitivity analyses showed that floor covering’s effects can be more profound with a larger carpet-covered area, a greater temperature adjustment depth, or a higher radiant heat gain. With this effect ignored for a given building, its demand flexibility would be overestimated, which could prevent grid operators from obtaining sufficient demand flexibility to maintain a grid. Our findings also imply that for more efficient grid-interactive buildings, a traditional standard for floor design could be modified with increasing renewable penetration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images

Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reduced Erosion Augments Soil Carbon Storage Under Cover Crops

ABSTRACT Cover crops, a promising strategy to increase soil organic carbon (SOC) storage in croplands and mitigate climate change, have typically been shown to benefit soil carbon (C) storage from increased plant C inputs. However, input‐driven C benefits may be augmented by the reduction of C outputs induced by cover crops, a process that has been tested by individual studies but has not yet been synthesized. Here we quantified the impact of cover crops on organic C loss via soil erosion (SOC erosion) and revealed the geographical variability at the global scale. We analyzed the field data from 152 paired control and cover crop treatments from 57 published studies worldwide using meta‐analysis and machine learning. The meta‐analysis results showed that cover crops widely reduced SOC erosion by an average of 68% on an annual basis, while they increased SOC stock by 14% (0–15 cm). The absolute SOC erosion reduction ranged from 0 to 18.0 Mg C −1 ha −1 year −1 and showed no correlation with the SOC stock change that varied from −8.07 to 22.6 Mg C −1 ha −1 year −1 at 0–15 cm depth, indicating the latter more likely related to plant C inputs. The magnitude of SOC erosion reduction was dominantly determined by topographic slope. The global map generated by machine learning showed the relative effectiveness of SOC erosion reduction mainly occurred in temperate regions, including central Europe, central‐east China, and Southern South America. Our results highlight that cover crop‐induced erosion reduction can augment SOC stock to provide additive C benefits, especially in sloping and temperate croplands, for mitigating climate change.

Huang, Wenjuan [Department of Ecology, Evolution, ↗
DOE OSTI2024

Metatranscriptomic analysis reveals dissimilarity in viral community activity between an ice-free and ice-covered winter in Lake Erie

Winter is a relatively under-studied season in freshwater ecology. The paucity of wintertime surveys has led to a lack of knowledge regarding microbial community activity during the winter in Lake Erie, a North American Great Lake. Viruses shape microbial communities and regulate biogeochemical cycles by acting as top-down controls, yet very few efforts have been made to examine active virus populations during the winter in Lake Erie. Furthermore, climate change-driven declines in seasonal ice cover have been shown to influence microbial community structure, but no studies have compared viral community activity between different ice cover conditions. We surveyed surface water metatranscriptomes for viral hallmark genes as a proxy for active virus populations and compared activity metrics between ice-covered and ice-free conditions from two sampled winters. Transcriptionally active viral communities were detected in both winters, spanning diverse phylogenetic clades of putative bacteriophage (Caudoviricetes), giant viruses (Nucleocytoviricota, or NCLDV), and RNA viruses (Orthornavirae). However, viral community activity metrics revealed pronounced differences between the ice-covered and ice-free winters. Viral community composition was distinct between winters and viral hallmark gene richness was reduced in the ice-covered relative to the ice-free conditions. In addition, the observed differences in viral communities correlated with microbial community activity metrics. Overall, these findings contribute to our understanding of the viral populations that are active during the winter in Lake Erie and suggest that viral community activity may be associated with ice cover extent.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

E-PiPEline: Quick to Market Face Coverings and Face Shields Using Commonly Available Materials

The Center for Disease Control has recommended that the public should wear cloth face coverings in public settings. Face coverings and face shields can be made by using Commonly Available Materials (CAMs). As part of the Sandia COVID-19 LDRD effort (funded under the Materials Science Investment Area), the Sandia E-PiPEline task evaluated design options for face coverings and face shields considering their effectiveness, durability, build difficulty, build cost, and comfort. Observations from this investigation are presented here to provide guidelines for home construction of face coverings and face shields. This executive summary includes a brief roadmap of the analysis methodology, two one-page handouts geared to be distributed to the public at large (one for face coverings and one for face shields), and additional observations regarding potential solutions for face coverings and face shields included to further support the one-page handouts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths: Supporting Data

The extent and duration of snow cover is predicted to be altered as the climate changes. Developing high-resolution estimates of snow cover change is crucial for estimating changes in snow cover and the effects of these changes on watershed and ecosystems processes. Remote sensing tools have been a common method for rapidly mapping snow covered area (SCA) across a landscape. The most common remote sensing method for estimating SCA uses satellite-based calculations of the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI is effective at catchment- to regional-scale estimates of SCA, but due to spatial resolution limitations of SWIR measurements, NDSI cannot be used to assess fine-scale SCA. In this work, we develop a new algorithm, called the Blue Snow Threshold (BST) algorithm, that maps high-resolution SCA by calculating a threshold on the blue wavelengths from high-resolution satellite imagery. This data package includes Orthorectified IKONOS-2 imagery (IkonosTestImage.tif) from August 14, 2004 at 1.00 meters Ground Sample Distance for Cook Inlet, Alaska (59.966414 , -152.982975). The Blue Snow Threshold algorithm (BST.py) was then used to produce a snow cover estimate (IkonosTestImage_BST.tif) for this study area. Additional imagery metadata is included in the ImageInfo.txt file. See Thaler et al., 2023 (https://doi.org/10.1016/j.rse.2022.113403) for more information about the BST algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantifying direct yield benefits of soil carbon increases from cover cropping

Cropland management practices that restore soil organic carbon (SOC) are increasingly presented as climate solutions that also enhance yields. But how often these benefits align at the farm level—the scale of farmers’ decision making—remains uncertain. Here we examined concurrent SOC and yield responses to cover cropping, including their direct connection, with a global meta-analysis. Cover cropping simultaneously increased yields and SOC in 59.7% of 434 paired observations. Increases in SOC directly increased crop yields in soils with initial SOC concentrations below 11.6 g kg-1; for example, a change from 5 g kg -1 to 6 g kg -1 increased yields by +2.4%. These yield benefits of SOC did not decline as nitrogen inputs increased or when legume cover crops were used, suggesting fertility inputs cannot substitute for SOC effects. Regardless of direct effects of SOC increases on yields, integrating legume cover crops into systems with simplified rotations or with nitrogen inputs < 157 kg ha -1 season -1 N led to the largest yield increases (up to +24.3%), with legumes also increasing SOC more than non-legumes (up to +1.5 g kg -1 ). By simultaneously increasing yields and SOC, cover cropping provides an opportunity to benefit both food security and climate, including via direct yield benefits from SOC increases on low carbon soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effect of Quartz Aperture Covers on the Fluid Dynamics and Thermal Efficiency of Falling Particle Receivers

Falling particle receivers are an emerging technology for use in concentrating solar power systems. In this paper, quartz half-shells are investigated for use as full or partial aperture covers to reduce receiver thermal losses. Quartz half-shell aperture covers offer the ability to minimally interfere with incoming solar radiation from the heliostat field while obstructing thermal radiation and advection from leaving the receiver cavity. The fluid dynamics and heat transfer of a receiver subdomain and surrounding air are modeled using ANSYS®FLUENT We compare the percentage of total incident solar power lost due to conduction through the receiver walls, advective losses through the aperture, and radiation exiting the aperture. Contrary to expected outcomes, results show that quartz aperture covers can increase radiative losses and result in modest to nonexistent reductions in advective losses. The increased radiative losses are driven by elevated quartz half-shell temperatures and have the potential to be mitigated by active cooling and/or material selection. Quartz half-shell total transmissivity was measured experimentally using a radiometer and the National Solar Thermal Test Facility heliostat field with values up to 0.97 ± 0.01. Quartz half-shell aperture covers did not yield expected efficiency gains in numerical results due to increased radiative losses, but efficiency improvement in some numerical results and the performance of quartz half-shells subject to concentrated solar radiation suggest that quartz half-shell aperture covers should be investigated further.

14 SOLAR ENERGY↗
DOE OSTI2025

G-Mapper: Learning a Cover in the Mapper Construction

The Mapper algorithm is a visualization technique in topological data analysis (TDA) that outputs a graph reflecting the structure of a given dataset. However, the Mapper algorithm requires tuning several parameters in order to generate a “nice” Mapper graph. This paper focuses on selecting the cover parameter. We present an algorithm that optimizes the cover of a Mapper graph by splitting a cover repeatedly according to a statistical test for normality. Our algorithm is based on G-means clustering, which searches for the optimal number of clusters in 𝑘-means by iteratively applying the Anderson–Darling test. Our splitting procedure employs a Gaussian mixture model to carefully choose the cover according to the distribution of the given data. In conclusion, experiments for synthetic and real-world datasets demonstrate that our algorithm generates covers so that the Mapper graphs retain the essence of the datasets, while also running significantly faster than a previous iterative method.

G-means clustering↗
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↗
DOE OSTI2025

Phosphorus and cover crop management practices affect phosphorus speciation in soils and eroded sediments

Abstract Agricultural runoff often contains P in dissolved and sediment‐bound forms, decreasing surface water quality. No‐till and cover cropping conservation practices have been recommended for reducing erosion and nutrient loss from cropping systems. The overall aims of this study were to characterize and evaluate the effects of fertilizer (placement and source) and cover crop management on P speciation in surface runoff sediments and source soil. In 2014, a field‐scale experiment was established in a no‐till, corn (Zea maysL.)–soybean (Glycine maxL.) cropping system with two cover crop treatments (with and without a winter crop; winter wheat [Triticum aestivumL.], rapeseed [Brassica napusL.], hairy vetch [Vicia villosaRoth], winter triticale [×Triticosecale Wittm.], and cereal rye [Secale cerealeL.]) and three P fertilizer management treatments (no P, fall broadcast diammonium phosphate, and spring subsurface injected ammonium polyphosphate). Phosphorus fractionation in the source soil collected in the fall of 2019 and sediment samples collected throughout 2020 were analyzed using a modified sequential P extraction method to evaluate the cumulative effects of imposing the treatment factors over 5 years. The direct P speciation was done using X‐ray absorption near edge structure spectroscopy. The indirect P speciation (fractionation) results showed that the management practices affected the exchangeable, organic matter‐associated, and Fe‐bound P fractions in sediments and the exchangeable and residual fractions in source soil. Direct P speciation results showed a depletion of Fe‐associated P in soil and sediment from cover crop treatment, suggesting that Fe‐associated P species were affected by cover crops. Changes in soil and runoff sediment P speciation would change the proportions and forms of soluble and particulate P in runoff sediments and may influence P bioavailability in aquatic ecosystems. Developing P fertilizer and cropping system management options with an understanding of soil P transformations helps maintain environmental sustainability.

Environmental Sciences & Ecology↗
DOE OSTI2023

Raccoon densities across four land cover types in the southeastern United States

Raccoons (Procyon lotor) are the primary reservoir for rabies virus in eastern North America. Management of rabies in raccoons is achieved primarily with the use of oral rabies vaccination (ORV) and effective ORV bait densities are determined in part by the densities of raccoons. Decisions regarding ORV bait densities, however, are limited by an incomplete understanding of raccoon densities across the spectrum of landscapes they occupy. We carried out a mark-recapture study of raccoons on the Savannah River Site in South Carolina, USA, from 2017–2019, to develop sex- and landscape-specific raccoon density estimates across 4 rural land cover types in the southeastern United States: bottomland hardwood, riparian forest, isolated wetland, and upland pine (Pinus spp.). We captured 404 unique raccoons 773 times over the 3-year trapping period. Estimated densities were 5.44 ± 0.37 (SE) animals/km 2 in bottomland hardwood forest, 2.62 ± 0.32 animals/km 2 in riparian forest, 2.19 ± 0.29 animals/km 2 in isolated wetlands, and 2.14 ± 0.23 animals/km 2 in upland pine. Densities were significantly higher in bottomland hardwood than all other land cover types, whereas densities among the remaining cover types were similar. These patterns are likely the result of landscape fragmentation and configuration, with riparian forests typically embedded in a matrix of less suitable cover types, leading to low densities despite presumably high resource availability. There were higher densities of males than females in every cover type except upland pine, where the sex ratio was balanced. Densities on our site were low compared to other rural areas, which likely results from the lack of human influence in terms of agriculture or development. The financial cost of baiting for ORV distribution may be reduced by considering the comparatively low densities of raccoons in these rural landscapes in the southeastern United States.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗