Research Awards and Grants (December 2022)

Each month College of Natural Resources faculty receive awards and grants from various federal, state, and nongovernmental agencies in support of their research. This report recognizes the faculty who received funding in December 2022.

Workforce and Skills Development for Enhancing Diversity and Multicultural Representation in USDA Forest Service-related Research Priority Areas

• PI: Nelson, Stacy A.
• Direct Sponsor Name: US Forest Service
• Awarded Amount: $50,000.00 

Abstract: This program aims to attract, recruit, retain, and successfully graduate highly-skilled, career-ready candidates to fill USDA Forest Service critical job series within the Southern Research Station (SRS), the Forest Service nation-wide, and/or complementary supporting agencies and industries.  Researchers and the SRS will help ensure the development of these skills and their utmost importance to the agency by serving on each of the student’s graduate research committees. This role is important as it helps to set research direction, provides mentorship and relationship-building with the students and faculty involved. Where possible, faculty from the students’ former HBCU/MSI institution will also be included as a part of the research guidance committee. Students will also meet with SRS leadership for further relationship development and exposure to the USDA Forest Service as an employer of choice. 

US-UK Collab: Long-Distance Dispersal and Disease Spread Under Increased Ecological Complexity

• PI: Jones, Christopher Michael
• Direct Sponsor Name: US Dept. of Agriculture – National Institute of Food and Agriculture (USDA NIFA)
• Awarded Amount: $248,162.00 

Abstract: Epidemic invasions have substantial impacts on both ecosystem function and human welfare (1,16,31,67,91), and may become more frequent owing to globalization (116). Understanding the establishment and spread of such diseases can contribute significantly to identifying appropriate disease control strategies (35,96,115). Pathogens demonstrating long-distance dispersal (LDD) are of particular concern, owing to their potential to rapidly spread over large spatial scales. This includes pathogens with propagules that have the potential for long-distance transport through air, such as foot-and-mouth disease (FMD) (60), West Nile Virus (90), avian influenza (62), white-nose syndrome of bats (4) and many diseases of plants (8), through water, such as Aspergillosis of coral (134), and perhaps also pathogens spread through human transport systems, such as influenza (66) and Ebola virus (40). Bird migration can result in fat-tailed, LDD dispersal patterns, with dispersal over hundreds or thousands of kilometers (95,130). Similarly, ‘anomalous diffusion’ has been suggested to result in fat-tailed distributions and superdiffusive spread of a range of organisms (6,131). Developing effective models for such large-scale processes remains a challenge, and will likely require a range of approaches and comparative studies encompassing a diversity of pathogens and hosts. 

Recreation Resources Service (RRS)

• PI: Larson, Lincoln Ray
• Direct Sponsor Name: NC Dept. of Natural & Cultural Resources formerly NC Dept of Env. & Natural Resources (DENR)
• Amount Awarded: $5,538,530.00 

Abstract: The Recreation Resources Service (RRS) is established for the specific purpose of providing assistance to public and private segments of the leisure service industry within North Carolina. Clientele of the program include: municipal and county park and recreation departments, nonprofit agencies, private recreation agencies, recreation consumer groups, and recreation and park board and commission members. RRS provides timely,cutting edge technical assistance to improve community park and recreation opportunities, sponsors a variety of continuing education opportunities addressing current issues facing park and recreation professionals, conducts applied research studies, and assist communities with state and federal park and recreation grants.

Mapping Playspace Inequity in Three Locally-focused Colorado Communities

• PI: Hipp, James A.
• Direct Sponsor Name: The Colorado Health Foundation
• Awarded Amount: $70,050.00 

Abstract: KABOOM! and NC State propose to conduct a comprehensive, cross-system map of playspace inequities that exist within three locally-focused communities to illuminate gaps in access to safe, quality places to play. This data-driven, community-informed approach will ultimately help catalyze equitable and inclusive playspace investments that help address the gaps identified through this project.

Fiber Modification to Improve Tissue Sheet Properties

• PI: Pal, Lokendra 
• Direct Sponsor Name: Kemira Chemicals
• Awarded Amount: $220,000.00 

Abstract: There is an immediate and pressing need for low-cost sustainable tissue and hygienic products for a variety of commercial applications. The next phase plans include studies leading to the generation of unbleached tissue and towel with different hardwood fiber types, various treatment chemistries, creping, and assessment of the pathways for delivery of low cost-sustainable products.  

A New Generation of Flexible Food Packaging from Agro-Based Fibers and Biopolymers

• PI: Pal, Lokendra 
• Direct Sponsor Name: Wm Wrigley Jr. Company
• Amount Awarded: $475,000.00 

Abstract: Consumer demands for sustainability and recent changes in government policies and regulations, such as the ban on single-use plastic products, are forcing companies to consider new alternatives to plastic-based packaging. There has been tremendous growth in the development and production of paper and bio-coating technology. This project will focus on the development of alternative fiber-based packaging and barrier coatings to deliver high functionality while maintaining compostability and biodegradability profile. 

Bio-based Barrier Layer for Multilayer Packaging

• PI: Lavoine, Nathalie Marie
• Direct Sponsor Name: Pepsico, Inc.
• Awarded Amount: $28,778.00 

Abstract: This research collaboration aims to develop a bio-based moisture barrier layer for multilayer packaging. More especially, two bio-sourced components of hydrophobic characteristic and chemical compatibility will be combined as a coating formulation for metal-based and polyester-based substrates. The barrier properties of this layer will be fully characterized. The NCSU team will elaborate the formulation and test it on model substrates; some provided by the sensor Pepsico. Pepsico will test the formulation in their facility and schedule pilot-scale trials to test the lab-made formulation under required conditions.

Resistance is Futile

• PI: Lucia, Lucian 
• Direct Sponsor Name: Eastman Chemical Company
• Awarded Amount: $358,048.00 

Abstract: We will perform an investigative review of the abrasion phenomenon in Tritan polymers as part of providing a potential solution. Based on the mechanistic information obtained, we will circumvent surface (abrasion) damage by adopting an innovative surface chemistry approach: we will incorporate friction-dissipating macromolecular dendron assemblies (we will refer to them as tribophores) either on the surface or within the bulk of the polyester backbone.

Consortium on Sustainable and Alternative Fibers Initiative (SAFI)   Membership Pool Agreement

• PI: Gonzalez, Ronalds Wilfredo
• Direct Sponsor Name: NCSU Consortium Sustainable and Alternative Fibers Initiative (SAFI)
• Awarded Amount: $0

Abstract: No abstract on record.

Shells to Sleeves (StS)

• PI: Pal, Lokendra 
• Direct Sponsor Name: Eastman Chemical Company
• Awarded Amount: $150,000.00 

Abstract: Testing services agreement to produce and evaluate dissolving pulp from old corrugated containers

Eastman TSA Production and Evaluation of Dissolving Pulp from Old Corrugated Containers (OCC)

• PI: Jameel, Hasan 
• Direct Sponsor Name: Eastman Chemical Company
• Awarded Amount: $150,000.00 

Abstract: Testing services agreement to produce and evaluate dissolving pulp from old corrugated containers

BASF Testing Agreement

• PI: Gonzalez, Ronalds Wilfredo
• Direct Sponsor Name: BASF Corporation
• Awarded Amount: $10,000.00 

Abstract: NC State University (NCSU) will test the encapsulation of antimicrobial and pesticidal molecules (active ingredients) using lignin for the purpose of controlling the particle size and release of the active compound. 

Membership in Consortium on Sustainable and Alternative Fibers Initiative (SAFI), Full Member

• PI: Gonzalez, Ronalds Wilfredo
• Direct Sponsor Name: Unilever, Inc.
• Awarded Amount: $60,000.00 

Abstract: The purpose of the Consortium on Sustainable and Alternative Fibers Initiative (SAFI) is to develop fundamental and applied research on the use of alternative and sustainable fibers for the manufacturing of market pulp, hygiene products and nonwovens. The idea for SAFI has grown out of societal needs for alternative yet sustainable materials. SAFI will study the potential of alternative fibers based on technical (performance), sustainable and economic principles.

A Grid that’s Risk-Aware for Clean Electricity – GRACE

• PI: Kern, Jordan 
• Direct Sponsor Name: US Dept. of Energy (DOE) – Energy Efficiency & Renewable Energy (EERE)
• Awarded Amount: $345,444.00 

Abstract: We will develop a framework for characterizing the uncertainty on the performance of electric power system’s assets and for using that uncertainty characterization in the operations of electricity markets (including scheduling, dispatch, pricing,and settlement). We will focus on the uncertainty of bulk renewables (wind farms, solar PV farms, and hydropower) w/o energy storage systems, but will also consider smaller scale renewables in the system that are either directly participating in wholesale markets, or behind-the meter, impacting load.

Towards Global Flooding Dynamics in Near Real-time: A Multi-sensor Fusion Approach Based on Public Domain Time-series of Optical and Radar Data

• PI: Tulbure, Mirela Gabriela
• Direct Sponsor Name: National Aeronautics & Space Administration (NASA)
• Awarded Amount: $423,728.00 

Abstract: Spatiotemporal quantification of surface water and flooding is essential for research on hydrological cycles. Satellite remote sensing is the only means of monitoring these dynamics across vast areas and over time. Several regional to global surface water data sets have been developed using optical time-series, either from MODIS-type sensors with coarse spatial resolution but daily frequency, or based on the entire Landsat archive. Despite its high spatial resolution, the 16-day repeat frequency of Landsat means that short-lived hazardous flooding and the maximum extent of large floods are likely missed. Meanwhile, spatially coarser MODIS-type sensors may miss small water bodies and floods entirely. In addition, two limitations when mapping inundation with optical data have been detecting water under vegetation and cloud obscuration, which often coincides with floods. Both issues can be overcome by fusing multiple optics with synthetic aperture radar (SAR) data, taking advantage of complementary observation properties including SAR’s ability to penetrate through clouds. Thus, combining observations and spectral properties of the newly available Sentinel 1 SAR (S1) and Sentinel 2 (S2) series of satellites with Landsat 8 (L8) holds promise for global surface water and flood mapping with  improved spatial and temporal resolution and accuracy. To accurately capture maximum extent of all floods in near real time, our key objectives are to (1) map flooding dynamics globally,  using machine learning applied to time-series of multi-sensor optical (L8, S2) and radar (S1) time series data, (2) assess the accuracy of  the mapped flood extent, and (3) test the ability of our algorithms to map (a) ephemeral floods in a dynamic dryland river system (b)  a complex delta including inundated vegetation in Western Canada (leveraging field validation data on extent of inundated vegetation  collected during  NASA’s Arctic Boreal Vulnerability Experiment), (c) extreme flooding in North Carolina (during hurricanes in 2016,  2018 and 2019), and (d) small water bodies (< 5ha) in irrigated areas (i.e. Arkansas, the U.S. state with the 3rd largest irrigated area,  where hundreds of small reservoirs have been constructed since 2015). We will use NASA’s 30m Harmonized L8/S2 (HLS) Products that seamlessly combine L8 and S2 observations, and S1 as input to  machine learning-based mapping of surface water and flooding. As training data, we will use the freely available USGS Spatial Procedures for Automated Removal of Cloud and Shadow dataset, which contains water and flooded masks. We will further augment flood labels via active learning, by evaluating initial model results and adding labels on misclassified areas. To assess the  accuracy of our flood maps we will use a stratified sampling design, with flooding and water as the rare classes used as strata to improve precision of the accuracy estimates. We will assess whether the increased temporal frequency resulting from multiple/fused data streams will result in improved detections  of small and short-lived flooding events, and maximum extent of large floods compared to the use of L8, S2 or S1 alone over a dynamic  dryland basin (i.e., Australia’s Murray-Darling Basin), and over small farm dams of Arkansas. To test the improved capacity of flood  mapping when adding SAR to HLS during cloudy conditions we will focus on 3 hazardous floods in North Carolina. We will assess  the ability of C-band S1 combined with optical image time series to detect water under vegetation in Canada’s Peace-Athabasca Delta,  where detailed validation data will be available. This proposal is significant to this NASA solicitation as it will enable improved quantification of flood extent dynamics and water quantity. The algorithms and maps produced can be used for better mapping of floods during hazardous conditions and assessment of  how changes in land cover and land use and climate impact surface water and flood dynamics.

Equipping Cooperative Extension Professionals to Better Meet Community Wildland Fire Needs

• PI: Fawcett, Jennifer L
• Direct Sponsor Name: US Dept. of Agriculture – National Institute of Food and Agriculture (USDA NIFA)
• Awarded Amount: $24,436.00 

Abstract: The Southeast leads the nation in applying prescribed fire to millions of acres each year, much of which is on private lands. The Southeast also has the most land area in the Wildland Urban Interface, putting numerous communities at risk from wildfire. Prescribed fire helps lower the risk of catastrophic wildfires and promotes ecosystem services in fire-dependent landscapes. However, climate change, growing populations, and development are increasingly complicating the use of prescribed fire to accomplish these objectives. Surveys of Extension professionals indicate wildland fire information is a pressing need. Given these circumstances, Extension professionals must be prepared to serve constituents’ needs related to wildland fire. This can be challenging, particularly when Extension professionals’ primary responsibilities and expertise lie elsewhere. Moreover, Extension professionals’ needs relative to wildland fire can vary greatly according to the objectives and needs of constituents. This project will equip Southern Extension professionals to understand and serve constituents needs related to wildland fire and connect them to local professionals that can offer more expertise. An online course will be developed that will address needs identified in a previously conducted needs assessment and based on the expertise of the authors. In addition, training programs will be held in North Carolina, South Carolina, and Florida, at a minimum, to facilitate networking between Extension professionals and local wildland fire experts and reinforce concepts related to wildland fire, and Extension professionals will be enabled to assist with the creation of community-based Prescribed Burn Associations. Since the need for Extension to address wildland fire issues spans the nation, this project will be replicable in other states and regions.

CNH2-l: The Coupled, Co-Evolving Roles of Drought and Electricity Systems in Humans’ Exposure to Air Pollution

• PI: Kern, Jordan 
• Direct Sponsor Name: National Science Foundation (NSF)
• Awarded Amount: $345,456.00 

Abstract: This project will develop an improved understanding of the coupled dynamics among the natural processes that underpin drought and poor air quality, the human systems that manage water resources and electricity supply, and localized human exposure to fine particulate matter and ozone pollution, all under the influence of two anthropogenic drivers: technology adoption and climate change.  

Development of Near Real-Time Land Surface Phenology Product by Fusing Geostationary Satellite and VIIRS Observations in Support of Agriculture and Land Management

• PI: Gray, Joshua Michael
• Direct Sponsor Name: National Aeronautics & Space Administration (NASA)
• Awarded Amount: $33,751.00 

Abstract: This proposal responds to ROSES2019 A.33 (Earth Science Research from Operational Geostationary Satellite Systems), focusing on the development of high-spatial-and-temporal environmental data products from geostationary satellite observations. Specifically, we propose to develop and implement an operational near real-time land surface phenology (NRT-LSP) product in support of agriculture and forest management. The NRT-LSP product has the capability of providing near real-time monitoring and short-term prediction of vegetation phenology development. In this proposal, near real-time monitoring is referred to as the detection of phenological events occurring before the date of latest available satellite observation, and short-term prediction as the estimation of phenological events within next 10 days (10 days ahead) during a vegetation growing season. This product will be implemented at 500 m pixels, biweekly, and with a latency less than one week. LSP reflects an interaction between a plant’s life-cycle events and environmental variables and is one of the simplest and most effective indicators of environmental change. Documenting LSP change supports efforts to reconstruct the historical environmental data record and to make predictions about biological responses to future environmental scenarios. To date, various LSP products have been produced annually and with latency greater than a year using polar-orbiting satellite datasets including AVHRR, MODIS, and VIIRS during the past several decades. Because of frequent cloud contaminations, it is infeasible to utilize these same techniques to detect LPS in near real time, or to forecast near-term phenological development at regional or global scales. Existing LSP data have been invaluable for retrospective analyses, but there would be great social, cultural, and economic value in a reduced latency LSP product. Such an NRT-LSP product would be particularly important in assisting farmers and agricultural agencies for predicting the optimum timing of cultivation practices, monitoring crop growth, and estimating crop yield; foresters for detecting disturbances related to forest pests, disease outbreaks, and species invasion; weather modelers for estimating surface energy balance in the numerical weather prediction models; and tourists for seeing spring wildflowers and fall foliage colors. Indeed, there is an urgent and substantial need to produce a near real-time LSP product. The high frequency of diurnal observations from geostationary satellites (GOES-16/17, Himawari-8/9, GEO-KOMPSAT-2A, and the upcoming EUMETSAT Meteosat Third Generation) maximizes the number of cloud-free views for creating a daily cloud-free trajectory of vegetation greenness. This offers a unique opportunity to generate an NRT-LSP product for quantifying the timing of key phenological transitions and vegetation growth conditions. This proposal describes a project that will operationally produce an NRT-LSP product at 500 m spatial resolution, with a biweekly update over the ABI full disk (North American and South America). The specific goals of this project are: Establish climatological greenness trajectories from historical MODIS satellite data, Calculate daily angularly-corrected vegetation greenness from diurnal ABI observations, Generate synthetic time series of daily vegetation greenness by fusing ABI and VIIRS data to improve spatiotemporal resolution, Simulate potential trajectories of LSP development from available ABI-VIIRS synthetic time series and MODIS climatological vegetation greenness trajectories •     Produce phenometrics (phenological timing and greenness magnitude) during a vegetation growing season in near real time and 10 days ahead, Validate the proposed phenometrics using in-situ and Phenocam observations, Evaluate the the NRT-LSP for monitoring crop and forest growth and invasive species 

Expanding Forest Management and Promoting Ecosystem Services through access to  Environmental Markets: Modeling landscape changes using Landis-II

• PI: Scheller, Robert 
• Direct Sponsor Name: US Forest Service
• Awarded Amount: $59,895.00 

Abstract: There are two major objectives of this project. The first is to reparametrize and re-run LANDIS-II for the Tahoe Central Sierra Initiative landscape. The output of these model runs will then be used as input to a hydrologic model. The second objective is to couple the output of a FORSYS model run, which will provide an optimized portfolio of management activities, to LANDIS-II, and to run LANDIS-II over a 40 year time horizon in order to verify that the management portfolio provides adequate levels of ecosystem services. A third objective is to lead or assist with at least one peer reviewed publication or report. 

Diversity in Citizen Science

• PI: Cooper, Caren Beth
• Direct Sponsor Name: Tides
• Amount Awarded: $75,000.00 

Abstract: Most participants in citizen science projects are white, affluent, and highly educated. Consequently, the scientific conclusions about biodiversity and ecological health contain sampling bias and inequities in participation itself is a type of distributional environmental injustice that hampers sustainability efforts. The Inclusive, Diverse, Equitable, Accessible, Large-scale (IDEAL) Working Group, led by Cooper, is completing guidance to help citizen science leadership teams (re-)design inclusive projects. This proposal is to carry out the IDEAL guidance with two citizen science projects that use digital technologies (AI and 3-D graphics) and assess the effectiveness of the guidance on project team leadership and project planning. 

Mutated-Modeling and Understanding Using Temporal Analysis of Transient Earth Data Phase II

• PI: Gray, Joshua Michael
• Direct Sponsor Name: Office of the Director of National Intelligence
• Amount Awarded: $92,744.00 

Abstract: We propose to build a system producing near-realtime SMART data cubes for broad area search to identify candidate areas of change. The system can, then, isolate these areas of interest and create an enhanced SMART datacube around them, maximizing spatial resolution and temporal completeness as needed. This architecture can be used for forensic analysis and change monitoring of current events with the possibility to tip-and-cue complementary future imagery products (e.g. through new tasking) to build such enhanced SMART datacube in operational settings. From this proposed research we envision an outcome that shall consist of a product that analyzes and screens over a broad area and is used to task and/or collect as much high resolution commercial imagery available or complementary imagery to do change detection, attribution and characterization (DAC) that can generate alerts for current events, as well as produce comprehensive forensic reports from completed activities or events. The broad area search will identify hotspots and will provide context on what data sources should be used to build the enhanced SMART datacube. We will conduct research to determine the optimal configuration of both near-realtime and enhanced SMART datacubes (e.g. optimal GSD for near-realtime and enhanced cubes, data sources to satisfy temporal requirements, number of spectral bands needed to detect change in large areas, etc.). Our data fusion framework shall support fusion of very diverse data sources (e.g. WorldView-1,2,3, Sentinel-2, Landsat, and other commercial providers) to conduct these experiments, as well as supporting consumption of new tasking.