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Dr. Narcisa Pricope is an Associate Professor of applied geography in the Department of Earth and Ocean Sciences at the University of North Carolina Wilmington. She is also the Program Director of UNCW’s externally-accredited (through the United States Geospatial Intelligence Foundation) Geospatial Intelligence (GEOINT) Certificate and the Director of the Socio-Environmental Analysis Lab at UNCW. While at UNCW, Dr. Pricope was the first advocate for the inception of a drone program aimed at training students for a burgeoning industry and utilizing the emerging technology on various research applications. Since then, she became the first FAA certified Part 107 Small UAS PIC at UNCW, offered one of the first graduate courses in aerial drone applications in geosciences in the UNC system and has garnered over half a million dollars in research funding that leverages the power of UAS and drone technology for a wide variety of applications. She received a PhD from the University of Florida in Geography and Environmental Engineering in 2011, a Master’s of Science degree in Geoscience from Western Kentucky University in 2006 and a double Bachelors’ degree in Geography and English from Romania’s flagship research university, Babes-Bolyai Cluj-Napoca. To date, Dr. Pricope has published over 34 peer-reviewed articles on the various aspects of integrating earth observation, remote sensing and drone photogrammetry and spatio-temporal modeling to advance human-well being.
During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi-resolution information for analysis. Climate change, land-use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high-density, and high-resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio-Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross-site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio-environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data.
Elsa M. Ordway; Andrew J. Elmore; Sonja Kolstoe; John E. Quinn; Rachel Swanwick; Megan Cattau; Dylan Taillie; Steven M. Guinn; K. Dana Chadwick; Jeff W. Atkins; Rachael E. Blake; Melissa Chapman; Kelly Cobourn; Tristan Goulden; Matthew R. Helmus; Kelly Hondula; Carrie Hritz; Jennifer Jensen; Jason P. Julian; Yusuke Kuwayama; Vijay Lulla; Donal O’Leary; Donald R. Nelson; Jonathan P. Ocón; Stephanie Pau; Guillermo E. Ponce‐Campos; Carlos Portillo‐Quintero; Narcisa G. Pricope; Rosanna G. Rivero; Laura Schneider; Meredith Steele; Mirela G. Tulbure; Matthew A. Williamson; Cyril Wilson. Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research. Ecosphere 2021, 12, e03640 .
AMA StyleElsa M. Ordway, Andrew J. Elmore, Sonja Kolstoe, John E. Quinn, Rachel Swanwick, Megan Cattau, Dylan Taillie, Steven M. Guinn, K. Dana Chadwick, Jeff W. Atkins, Rachael E. Blake, Melissa Chapman, Kelly Cobourn, Tristan Goulden, Matthew R. Helmus, Kelly Hondula, Carrie Hritz, Jennifer Jensen, Jason P. Julian, Yusuke Kuwayama, Vijay Lulla, Donal O’Leary, Donald R. Nelson, Jonathan P. Ocón, Stephanie Pau, Guillermo E. Ponce‐Campos, Carlos Portillo‐Quintero, Narcisa G. Pricope, Rosanna G. Rivero, Laura Schneider, Meredith Steele, Mirela G. Tulbure, Matthew A. Williamson, Cyril Wilson. Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research. Ecosphere. 2021; 12 (6):e03640.
Chicago/Turabian StyleElsa M. Ordway; Andrew J. Elmore; Sonja Kolstoe; John E. Quinn; Rachel Swanwick; Megan Cattau; Dylan Taillie; Steven M. Guinn; K. Dana Chadwick; Jeff W. Atkins; Rachael E. Blake; Melissa Chapman; Kelly Cobourn; Tristan Goulden; Matthew R. Helmus; Kelly Hondula; Carrie Hritz; Jennifer Jensen; Jason P. Julian; Yusuke Kuwayama; Vijay Lulla; Donal O’Leary; Donald R. Nelson; Jonathan P. Ocón; Stephanie Pau; Guillermo E. Ponce‐Campos; Carlos Portillo‐Quintero; Narcisa G. Pricope; Rosanna G. Rivero; Laura Schneider; Meredith Steele; Mirela G. Tulbure; Matthew A. Williamson; Cyril Wilson. 2021. "Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research." Ecosphere 12, no. 6: e03640.
Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.
Kyle Woodward; Narcisa Pricope; Forrest Stevens; Andrea Gaughan; Nicholas Kolarik; Michael Drake; Jonathan Salerno; Lin Cassidy; Joel Hartter; Karen Bailey; Henry Luwaya. Modeling Community-Scale Natural Resource Use in aTransboundary Southern African Landscape: IntegratingRemote Sensing and Participatory Mapping. Remote Sensing 2021, 13, 631 .
AMA StyleKyle Woodward, Narcisa Pricope, Forrest Stevens, Andrea Gaughan, Nicholas Kolarik, Michael Drake, Jonathan Salerno, Lin Cassidy, Joel Hartter, Karen Bailey, Henry Luwaya. Modeling Community-Scale Natural Resource Use in aTransboundary Southern African Landscape: IntegratingRemote Sensing and Participatory Mapping. Remote Sensing. 2021; 13 (4):631.
Chicago/Turabian StyleKyle Woodward; Narcisa Pricope; Forrest Stevens; Andrea Gaughan; Nicholas Kolarik; Michael Drake; Jonathan Salerno; Lin Cassidy; Joel Hartter; Karen Bailey; Henry Luwaya. 2021. "Modeling Community-Scale Natural Resource Use in aTransboundary Southern African Landscape: IntegratingRemote Sensing and Participatory Mapping." Remote Sensing 13, no. 4: 631.
The Kazavango‐Zambezi Transfrontier Conservation Area is home to the largest remaining elephant population in Africa but is also the site of high levels of human‐elephant conflict through crop depredation. Offsetting the costs of coexisting with elephants in this area is critical to incentivizing elephant conservation within community‐based conservation (CBC) areas, and trophy hunting has long been touted as a method for generating revenue for communities from wildlife. However, the idea that sustainable elephant hunting can offset the costs of crop depredation remains largely untested. We combined household survey data, financial records, and elephant population data to compare the potential benefits of sustainable hunting with the costs of crop depredation in a CBC area in northeastern Namibia. We determined that sustainable trophy hunting only returns ~30% of the value of crops lost to the community and cannot alone offset the current costs of coexistence with elephants. As core institutions supporting the practice of conservation, CBC efforts must promote community management capacity to combine multiple wildlife‐based income streams and build partnerships at multiple scales of governance to address the challenges of elephant management.
Michael D. Drake; Jonathan Salerno; Ryan E. Langendorf; Lin Cassidy; Andrea E. Gaughan; Forrest R. Stevens; Narcisa G. Pricope; Joel Hartter. Costs of elephant crop depredation exceed the benefits of trophy hunting in a community‐based conservation area of Namibia. Conservation Science and Practice 2020, 3, 1 .
AMA StyleMichael D. Drake, Jonathan Salerno, Ryan E. Langendorf, Lin Cassidy, Andrea E. Gaughan, Forrest R. Stevens, Narcisa G. Pricope, Joel Hartter. Costs of elephant crop depredation exceed the benefits of trophy hunting in a community‐based conservation area of Namibia. Conservation Science and Practice. 2020; 3 (1):1.
Chicago/Turabian StyleMichael D. Drake; Jonathan Salerno; Ryan E. Langendorf; Lin Cassidy; Andrea E. Gaughan; Forrest R. Stevens; Narcisa G. Pricope; Joel Hartter. 2020. "Costs of elephant crop depredation exceed the benefits of trophy hunting in a community‐based conservation area of Namibia." Conservation Science and Practice 3, no. 1: 1.
Wetlands provide critical ecosystem services across a range of environmental gradients and are at heightened risk of degradation from anthropogenic pressures and continued development, especially in coastal regions. There is a growing need for high-resolution (spatially and temporally) habitat identification and precise delineation of wetlands across a variety of stakeholder groups, including wetlands loss mitigation programs. Traditional wetland delineations are costly, time-intensive and can physically degrade the systems that are being surveyed, while aerial surveys are relatively fast and relatively unobtrusive. To assess the efficacy and feasibility of using two variable-cost LiDAR sensors mounted on a commercial hexacopter unmanned aerial system (UAS) in deriving high resolution topography, we conducted nearly concomitant flights over a site located in the Atlantic Coastal plain that contains a mix of palustrine forested wetlands, upland coniferous forest, upland grass and bare ground/dirt roads. We compared point clouds and derived topographic metrics acquired using the Quanergy M8 and the Velodyne HDL-32E LiDAR sensors with airborne LiDAR and results showed that the less expensive and lighter payload sensor outperforms the more expensive one in deriving high resolution, high accuracy ground elevation measurements under a range of canopy cover densities and for metrics of point cloud density and digital terrain computed both globally and locally using variable size tessellations. The mean point cloud density was not significantly different between wetland and non-wetland areas, but the two sensors were significantly different by wetland/non-wetland type. Ultra-high-resolution LiDAR-derived topography models can fill evolving wetlands mapping needs and increase accuracy and efficiency of detection and prediction of sensitive wetland ecosystems, especially for heavily forested coastal wetland systems.
Narcisa Gabriela Pricope; Joanne Nancie Halls; Kerry Lynn Mapes; Joseph Britton Baxley; James JyunYueh Wu. Quantitative Comparison of UAS-Borne LiDAR Systems for High-Resolution Forested Wetland Mapping. Sensors 2020, 20, 4453 .
AMA StyleNarcisa Gabriela Pricope, Joanne Nancie Halls, Kerry Lynn Mapes, Joseph Britton Baxley, James JyunYueh Wu. Quantitative Comparison of UAS-Borne LiDAR Systems for High-Resolution Forested Wetland Mapping. Sensors. 2020; 20 (16):4453.
Chicago/Turabian StyleNarcisa Gabriela Pricope; Joanne Nancie Halls; Kerry Lynn Mapes; Joseph Britton Baxley; James JyunYueh Wu. 2020. "Quantitative Comparison of UAS-Borne LiDAR Systems for High-Resolution Forested Wetland Mapping." Sensors 20, no. 16: 4453.
Knowledge of temperature variation within and across beach-nesting bird habitat, and how such variation may affect the nesting success and survival of these species, is currently lacking. This type of data is furthermore needed to refine predictions of population changes due to climate change, identify important breeding habitat, and guide habitat restoration efforts. Thermal imagery collected with unmanned aerial vehicles (UAVs) provides a potential approach to fill current knowledge gaps and accomplish these goals. Our research outlines a novel methodology for collecting and implementing active thermal ground control points (GCPs) and assess the accuracy of the resulting imagery using an off-the-shelf commercial fixed-wing UAV that allows for the reconstruction of thermal landscapes at high spatial, temporal, and radiometric resolutions. Additionally, we observed and documented the behavioral responses of beach-nesting birds to UAV flights and modifications made to flight plans or the physical appearance of the UAV to minimize disturbance. We found strong evidence that flying on cloudless days and using sky-blue camouflage greatly reduced disturbance to nesting birds. The incorporation of the novel active thermal GCPs into the processing workflow increased image spatial accuracy an average of 12 m horizontally (mean root mean square error of checkpoints in imagery with and without GCPs was 0.59 m and 23.75 m, respectively). The final thermal indices generated had a ground sampling distance of 25.10 cm and a thermal accuracy of less than 1 °C. This practical approach to collecting highly accurate thermal data for beach-nesting bird habitat while avoiding disturbance is a crucial step towards the continued monitoring and modeling of beach-nesting birds and their habitat.
Kerry L. Mapes; Narcisa G. Pricope; J. Britton Baxley; Lauren E. Schaale; Raymond M. Danner. Thermal Imaging of Beach-Nesting Bird Habitat with Unmanned Aerial Vehicles: Considerations for Reducing Disturbance and Enhanced Image Accuracy. Drones 2020, 4, 12 .
AMA StyleKerry L. Mapes, Narcisa G. Pricope, J. Britton Baxley, Lauren E. Schaale, Raymond M. Danner. Thermal Imaging of Beach-Nesting Bird Habitat with Unmanned Aerial Vehicles: Considerations for Reducing Disturbance and Enhanced Image Accuracy. Drones. 2020; 4 (2):12.
Chicago/Turabian StyleKerry L. Mapes; Narcisa G. Pricope; J. Britton Baxley; Lauren E. Schaale; Raymond M. Danner. 2020. "Thermal Imaging of Beach-Nesting Bird Habitat with Unmanned Aerial Vehicles: Considerations for Reducing Disturbance and Enhanced Image Accuracy." Drones 4, no. 2: 12.
With predicted alterations in climate and land use, managing water resources is of the utmost importance, especially in areas such as the United States (U.S.) Coastal Plain where extensive connections exist between surface and groundwater systems. These changes create the need for models that effectively assess shifting hydrologic regimes and, in that context, we examine the performance of the Soil and Water Assessment Tool (SWAT) in a low-gradient, shallow-aquifer-dominated watershed of the U.S. Coastal Plain using a gridded reanalysis dataset. We evaluate accuracy, uncertainty, and parameter sensitivity by comparing observed and predicted streamflow at two gaging stations and assess model predictions for yearly average runoff (SURQ), percolation (PERC), and sediment loss (SYLD). Streamflow performance was acceptable during calibration (NSE = 0.67 and 0.60) and very good during validation (NSE = 0.84 and 0.91). Model predictions for SURQ, PERC, and SYLD coincided with expected ranges for this region. Parameters related to shallow aquifer properties or groundwater were highly sensitive, which indicates the need for continued study of spatial and temporal variability within the sub-surface components of these hydrologic systems. Our findings highlight the applicability of this reanalysis dataset for modeling hydrologic processes in poorly gaged watersheds and adds to the body of research that seeks to develop effective assessment tools for shallow-aquifer-dominated systems. Our methodology can effectively assist watershed managers in establishing baseline rates of hydrologic processes as is crucial with future predicted shifts in hydrologic regimes due to land-use alteration and climate change.
Kerry L. Mapes; Narcisa G. Pricope. Evaluating SWAT Model Performance for Runoff, Percolation, and Sediment Loss Estimation in Low-Gradient Watersheds of the Atlantic Coastal Plain. Hydrology 2020, 7, 21 .
AMA StyleKerry L. Mapes, Narcisa G. Pricope. Evaluating SWAT Model Performance for Runoff, Percolation, and Sediment Loss Estimation in Low-Gradient Watersheds of the Atlantic Coastal Plain. Hydrology. 2020; 7 (2):21.
Chicago/Turabian StyleKerry L. Mapes; Narcisa G. Pricope. 2020. "Evaluating SWAT Model Performance for Runoff, Percolation, and Sediment Loss Estimation in Low-Gradient Watersheds of the Atlantic Coastal Plain." Hydrology 7, no. 2: 21.
The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding the human dimensions of global change (HDGC), the need for a synchronization of agendas is evident. We conduct a bibliometric assessment and review of the last two decades of peer-reviewed literature to ascertain what the trends and current directions of integrating remote sensing into HEI research have been and discuss emerging themes, challenges, and opportunities. Despite advances in applying remote sensing to understanding ever more complex HEI fields such as land use/land cover change and landscape degradation, agricultural dynamics, urban geography and ecology, natural hazards, water resources, epidemiology, or paleo HEIs, challenges remain in acquiring and leveraging accurately georeferenced social data and establishing transferable protocols for data integration. However, recent advances in micro-satellite, unmanned aerial systems (UASs), and sensor technology are opening new avenues of integration of remotely sensed data into HEI research at scales relevant for decision-making purposes that simultaneously catalyze developments in HDGC research. Emerging or underutilized methodologies and technologies such as thermal sensing, digital soil mapping, citizen science, UASs, cloud computing, mobile mapping, or the use of “humans as sensors” will continue to enhance the relevance of HEI research in achieving sustainable development goals and driving the science of HDGC further.
Narcisa G. Pricope; Kerry L. Mapes; Kyle D. Woodward. Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions. Remote Sensing 2019, 11, 2783 .
AMA StyleNarcisa G. Pricope, Kerry L. Mapes, Kyle D. Woodward. Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions. Remote Sensing. 2019; 11 (23):2783.
Chicago/Turabian StyleNarcisa G. Pricope; Kerry L. Mapes; Kyle D. Woodward. 2019. "Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions." Remote Sensing 11, no. 23: 2783.
Conducting research on coupled social-ecological systems (SESs) presents inherent challenges, such as coordination across disparate disciplines or integrating across multiple scales and levels of governance. To overcome these common challenges, we propose that structuring the research design itself according to SES principles provides for integrative execution of SES science. First, starting with pilot work, human and natural science researchers should work as a team to identify and access multi-level entry points (i.e. points of direct engagement) within the system, relative to the spatiotemporal scales under investigation. Second, teams should implement an adaptive process that begins with the proposed research design and uses shared experiences from pilot work to refine protocols prior to subsequent data collection. We provide examples of multi-level and multi-scale entry points, and show that adaptive management of research design through coordinated iteration allows for better research integration and applicable outcomes.
Narcisa Gabriela Pricope; Lin Cassidy; Andrea Elizabeth Gaughan; Jonathan David Salerno; Forrest Robert Stevens; Joel Hartter; Michael Drake; Patricia Mupeta-Muyamwa. Addressing Integration Challenges of Interdisciplinary Research in Social-Ecological Systems. Society & Natural Resources 2019, 33, 418 -431.
AMA StyleNarcisa Gabriela Pricope, Lin Cassidy, Andrea Elizabeth Gaughan, Jonathan David Salerno, Forrest Robert Stevens, Joel Hartter, Michael Drake, Patricia Mupeta-Muyamwa. Addressing Integration Challenges of Interdisciplinary Research in Social-Ecological Systems. Society & Natural Resources. 2019; 33 (3):418-431.
Chicago/Turabian StyleNarcisa Gabriela Pricope; Lin Cassidy; Andrea Elizabeth Gaughan; Jonathan David Salerno; Forrest Robert Stevens; Joel Hartter; Michael Drake; Patricia Mupeta-Muyamwa. 2019. "Addressing Integration Challenges of Interdisciplinary Research in Social-Ecological Systems." Society & Natural Resources 33, no. 3: 418-431.
There is a growing demand for the collection of ultra-high spatial resolution imagery using unmanned aerial systems (UASs). UASs are a cost-effective solution for data collection on small scales and can fly at much lower altitudes, thus yielding spatial resolutions not previously achievable with manned aircraft or satellites. The use of commercially available software for image processing has also become commonplace due to the relative ease at which imagery can be processed and the minimal knowledge of traditional photogrammetric processes required by users. Commercially available software such as AgiSoft Photoscan and Pix4Dmapper Pro are capable of generating the high-quality data that are in demand for environmental remote sensing applications. We quantitatively assess the implications of processing parameter decision-making on UAS product accuracy and quality for orthomosaic and digital surface models for RGB and multispectral imagery. We iterated 40 processing workflows by incrementally varying two key processing parameters in Pix4Dmapper Pro, and conclude that maximizing for the highest intermediate parameters may not always translate into effective final products. We also show that multispectral imagery can effectively be leveraged to derive three-dimensional models of higher quality despite the lower resolution of sensors when compared to RGB imagery, reducing time in the field and the need for multiple flights over the same area when collecting multispectral data is a priority. We conclude that when users plan to use the highest processing parameter values, to ensure quality end-products it is important to increase initial flight coverage in advance.
Narcisa G. Pricope; Kerry L. Mapes; Kyle D. Woodward; Steele F. Olsen; J. Britton Baxley. Multi-Sensor Assessment of the Effects of Varying Processing Parameters on UAS Product Accuracy and Quality. Drones 2019, 3, 63 .
AMA StyleNarcisa G. Pricope, Kerry L. Mapes, Kyle D. Woodward, Steele F. Olsen, J. Britton Baxley. Multi-Sensor Assessment of the Effects of Varying Processing Parameters on UAS Product Accuracy and Quality. Drones. 2019; 3 (3):63.
Chicago/Turabian StyleNarcisa G. Pricope; Kerry L. Mapes; Kyle D. Woodward; Steele F. Olsen; J. Britton Baxley. 2019. "Multi-Sensor Assessment of the Effects of Varying Processing Parameters on UAS Product Accuracy and Quality." Drones 3, no. 3: 63.
This article presents an ArcGIS geodatabase of socio-demographic and physical characteristics derived from recent high resolution data sources to construct measures of population vulnerability to inundation in the 28 counties of coastal North Carolina, U.S.A. as presented in Pricope et al., 2019. The region is simultaneously densely populated, low-lying and exposed to recurrent inundation related to storms and incremental sea level rise. The data presented here can be used as a decision support tool in coastal planning, emergency management preparedness, designing adaptation strategies and developing strategies for coastal resilience. The socio-demographic data (population and housing) was derived from 228 tables at the block-group level of geography from the 2010 U.S. Census Bureau. These data were statistically analyzed, using Principal Component Analysis, to identify key factors and then used to construct a Social Vulnerability Index (SOVI) at the block-group level of geography which highlighted regions where socio-demographic characteristics such as family structure, race, housing (primarily owner vs. renter-occupied), special needs populations (e.g. elderly and group living), and household/family size play an overwhelmingly important role in determining community vulnerability from a social perspective. An index of physical exposure was developed using the National Flood Hazards Maps (available from North Carolina's Flood Risk Information System and FEMA) along with a novel building inventory dataset available from the North Carolina Department of Public Safety that contains the Finished-Floor Elevation of every structure in the state. We took advantage of the unprecedented high spatial resolution nature of the building inventory dataset to calculate an index of physical vulnerability to inundation of every block group in the 28 coastal counties relative to Base Flood elevations and identified hotspots where this intersection predisposes people to an increased risk of flooding. Here, we present the final derived dataset containing the social, physical and an integrative measure of vulnerability to flooding that can be used at multiple scales of analysis, starting with the regional, county, local, and neighborhood to identify areas of priority intervention for risk-reduction in coastal planning and emergency management preparedness as well as forward-looking adaptation strategies.
Narcisa G. Pricope; Joanne N. Halls; Lauren M. Rosul; Christopher Hidalgo. Residential flood vulnerability along the developed North Carolina, USA coast: High resolution social and physical data for decision support. Data in Brief 2019, 24, 103975 .
AMA StyleNarcisa G. Pricope, Joanne N. Halls, Lauren M. Rosul, Christopher Hidalgo. Residential flood vulnerability along the developed North Carolina, USA coast: High resolution social and physical data for decision support. Data in Brief. 2019; 24 ():103975.
Chicago/Turabian StyleNarcisa G. Pricope; Joanne N. Halls; Lauren M. Rosul; Christopher Hidalgo. 2019. "Residential flood vulnerability along the developed North Carolina, USA coast: High resolution social and physical data for decision support." Data in Brief 24, no. : 103975.
Densely populated coastal regions are vulnerable to threats associated with climate change and variability, especially storms. In the United States, millions of people are repeatedly at risk of flooding and because this number will only continue to grow, the identification of the intersection of social vulnerability and physical risk to flood inundation is essential for both coastal planning and adaptation purposes. Although a key tool to identify vulnerable populations, most vulnerability models are built at the county or coarser scales, thereby hindering the effectiveness of mitigation and adaptation planning at community scales, which are more socially and physically diverse than what county-scale analyses can reveal. We present an integrated social and physical model of vulnerability at the block-group level of geography using census data to measure social variability based population and housing data and physical exposure based on the intersection of finished floor elevation of all buildings in coastal North Carolina, USA with flood hazards maps. We identify, in a spatially-explicit manner and at multiple levels of governance, areas of high social vulnerability and their intersection with areas of high physical exposure to inundation. We found that in the 28 coastal counties of North Carolina, 45.3% of the structures within the 100-year floodplain were structurally exposed to potential damage from inundation. Supporting our hypothesized patterns of vulnerability to inundation, a significant clustering of highly vulnerable block-groups were located in Albemarle and Eastern Carolina coastal regions, yet high vulnerability outliers were also located at significant distance away from the highly physically-exposed coastline. Our findings suggest that the high-resolution block-group level analysis identified multiple levels of vulnerability to inundation at the sub-county scale and provide essential information for effective hazard mitigation within scales ranging from the community to transboundary governing bodies.
Narcisa G. Pricope; Joanne N. Halls; Lauren M. Rosul. Modeling residential coastal flood vulnerability using finished-floor elevations and socio-economic characteristics. Journal of Environmental Management 2019, 237, 387 -398.
AMA StyleNarcisa G. Pricope, Joanne N. Halls, Lauren M. Rosul. Modeling residential coastal flood vulnerability using finished-floor elevations and socio-economic characteristics. Journal of Environmental Management. 2019; 237 ():387-398.
Chicago/Turabian StyleNarcisa G. Pricope; Joanne N. Halls; Lauren M. Rosul. 2019. "Modeling residential coastal flood vulnerability using finished-floor elevations and socio-economic characteristics." Journal of Environmental Management 237, no. : 387-398.
Janardan Mainali; Narcisa G. Pricope. Mapping the need for adaptation: assessing drought vulnerability using the livelihood vulnerability index approach in a mid-hill region of Nepal. Climate and Development 2018, 11, 607 -622.
AMA StyleJanardan Mainali, Narcisa G. Pricope. Mapping the need for adaptation: assessing drought vulnerability using the livelihood vulnerability index approach in a mid-hill region of Nepal. Climate and Development. 2018; 11 (7):607-622.
Chicago/Turabian StyleJanardan Mainali; Narcisa G. Pricope. 2018. "Mapping the need for adaptation: assessing drought vulnerability using the livelihood vulnerability index approach in a mid-hill region of Nepal." Climate and Development 11, no. 7: 607-622.
Janardan Mainali; Narcisa G. Pricope. High-resolution spatial assessment of population vulnerability to climate change in Nepal. Applied Geography 2017, 82, 66 -82.
AMA StyleJanardan Mainali, Narcisa G. Pricope. High-resolution spatial assessment of population vulnerability to climate change in Nepal. Applied Geography. 2017; 82 ():66-82.
Chicago/Turabian StyleJanardan Mainali; Narcisa G. Pricope. 2017. "High-resolution spatial assessment of population vulnerability to climate change in Nepal." Applied Geography 82, no. : 66-82.
We present a geographic information system (GIS) dataset with a nominal spatial resolution of one-kilometer composed of grid polygons originally derived and utilized in a high-resolution climate vulnerability model for Nepal. The different data sets described and shared in this article are processed and tailored to the specific objectives of our research paper entitled "High-resolution Spatial Assessment of Population Vulnerability to Climate Change in Nepal" (Mainali and Pricope, In press) [1]. We share these data recognizing that there is a significant gap in regards to data availability, the spatial patterns of different biophysical and socioeconomic variables, and the overall population vulnerability to climatic variability and disasters in Nepal. Individual variables, as well as the entire set presented in this dataset, can be used to better understand the spatial pattern of different physical, biological, climatic, and vulnerability characteristics in Nepal. The datasets presented in this article are sourced from different national and global databases and have been statistically treated to meet the needs of the article. The data are in GIS-ready ESRI shapefile file format of one-kilometer grid polygon with various fields (columns) for each dataset.
Janardan Mainali; Narcisa G. Pricope. Geospatial datasets in support of high-resolution spatial assessment of population vulnerability to climate change in Nepal. Data in Brief 2017, 12, 459 -462.
AMA StyleJanardan Mainali, Narcisa G. Pricope. Geospatial datasets in support of high-resolution spatial assessment of population vulnerability to climate change in Nepal. Data in Brief. 2017; 12 ():459-462.
Chicago/Turabian StyleJanardan Mainali; Narcisa G. Pricope. 2017. "Geospatial datasets in support of high-resolution spatial assessment of population vulnerability to climate change in Nepal." Data in Brief 12, no. : 459-462.
Hydrologic models will be an increasingly important tool for water resource managers as water availability dwindles and water security concerns become more pertinent in data-scarce regions. Fortunately, newly available satellite remote sensing technology provides an opportunity for improving the spatial resolution and quality of input data to hydrologic models in such regions. In particular, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset provides quasi-global high resolution precipitation information derived from a blend of in situ and active and passive remote sensing data sources. We piloted the incorporation of the CHIRPS dataset into the Soil and Water Assessment Tool (SWAT), a hydrologic model. Comparisons of results between estimation of streamflow using in situ rainfall gauge station data, the Climate Forecast System Reanalysis (CFSR) dataset, and the CHIRPS dataset in the data-scarce Nzoia Basin in western Kenya over the temporal range 1990–2000 were reported. Simulated streamflow estimates were poor with rainfall gauge station data but improved significantly with the CFSR and CHIRPS datasets. However, the use of the CHIRPS dataset in comparison with the CFSR dataset provided an improved statistical performance following model calibration with the exception of one streamflow gauge station in higher elevation regions. Overall, the use of the CHIRPS dataset had the greatest linear correlation, relative variability, and normalized bias despite overall average Nash-Sutcliffe Efficiency (NSE) and R2 values.
Alyssa M. Le; Narcisa G. Pricope. Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data. Water 2017, 9, 114 .
AMA StyleAlyssa M. Le, Narcisa G. Pricope. Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data. Water. 2017; 9 (2):114.
Chicago/Turabian StyleAlyssa M. Le; Narcisa G. Pricope. 2017. "Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data." Water 9, no. 2: 114.
Humans and the ecosystem services they depend on are threatened by climate change. Places with high or growing human population as well as increasing climate variability, have a reduced ability to provide ecosystem services just as the need for these services is most critical. A spiral of vulnerability and ecosystem degradation often ensues in such places. We apply different global conservation schemes as proxies to examine the spatial relation between wet season precipitation, population change over three decades, and natural resource conservation. We pose two research questions: 1) Where are biodiversity and ecosystem services vulnerable to the combined effects of climate change and population growth? 2) Where are human populations vulnerable to degraded ecosystem services? Results suggest that globally only about 20% of the area between 50 degrees latitude North and South has experienced significant change–largely wetting–in wet season precipitation. Approximately 40% of rangelands and 30% of rainfed agriculture lands have experienced significant precipitation changes, with important implications for food security. Over recent decades a number of critical conservation areas experienced high population growth concurrent with significant wetting or drying (e.g. the Horn of Africa, Himalaya, Western Ghats, and Sri Lanka), posing challenges not only for human adaptation but also to the protection and sustenance of biodiversity and ecosystem services. Identifying areas of climate and population risk and their overlap with conservation priorities can help to target activities and resources that promote biodiversity and ecosystem services while improving human well-being.
Juliann E. Aukema; Narcisa G. Pricope; Gregory J. Husak; David López-Carr. Biodiversity Areas under Threat: Overlap of Climate Change and Population Pressures on the World’s Biodiversity Priorities. PLOS ONE 2017, 12, e0170615 .
AMA StyleJuliann E. Aukema, Narcisa G. Pricope, Gregory J. Husak, David López-Carr. Biodiversity Areas under Threat: Overlap of Climate Change and Population Pressures on the World’s Biodiversity Priorities. PLOS ONE. 2017; 12 (1):e0170615.
Chicago/Turabian StyleJuliann E. Aukema; Narcisa G. Pricope; Gregory J. Husak; David López-Carr. 2017. "Biodiversity Areas under Threat: Overlap of Climate Change and Population Pressures on the World’s Biodiversity Priorities." PLOS ONE 12, no. 1: e0170615.
The Chobe River Basin (CRB), a sub-basin of the Upper Zambezi Basin shared by Namibia and Botswana, is a complex hydrologic system that lies at the center of the world’s largest transfrontier conservation area. Despite its regional importance for livelihoods and biodiversity, its hydrology, controlled by the timing and relative contributions of water from two regional rivers, remains poorly understood. An increase in the magnitude of flooding in this region since 2009 has resulted in significant displacements of rural communities. We use an innovative approach that employs time-series of thermal imagery and station discharge data to model seasonal flooding patterns, identify the driving forces that control the magnitude of flooding and the high population density areas that are most at risk of high magnitude floods throughout the watershed. Spatio-temporal changes in surface inundation determined using NASA Moderate-resolution Imaging Spectroradiometer (MODIS) thermal imagery (2000–2015) revealed that flooding extent in the CRB is extremely variable, ranging from 401 km2 to 5779 km2 over the last 15 years. A multiple regression model of lagged discharge of surface contributor basins and flooding extent in the CRB indicated that the best predictor of flooding in this region is the discharge of the Zambezi River 64 days prior to flooding. The seasonal floods have increased drastically in magnitude since 2008 causing large populations to be displaced. Over 46,000 people (53% of Zambezi Region population) are living in high magnitude flood risk areas, making the need for resettlement planning and mitigation strategies increasingly important.
Jeri J. Burke; Narcisa G. Pricope; James Blum. Thermal Imagery-Derived Surface Inundation Modeling to Assess Flood Risk in a Flood-Pulsed Savannah Watershed in Botswana and Namibia. Remote Sensing 2016, 8, 676 .
AMA StyleJeri J. Burke, Narcisa G. Pricope, James Blum. Thermal Imagery-Derived Surface Inundation Modeling to Assess Flood Risk in a Flood-Pulsed Savannah Watershed in Botswana and Namibia. Remote Sensing. 2016; 8 (8):676.
Chicago/Turabian StyleJeri J. Burke; Narcisa G. Pricope; James Blum. 2016. "Thermal Imagery-Derived Surface Inundation Modeling to Assess Flood Risk in a Flood-Pulsed Savannah Watershed in Botswana and Namibia." Remote Sensing 8, no. 8: 676.
Despite growing research into the socio-economic aspects of vulnerability [1]-[4], relatively little work has linked population dynamics with climate change beyond the complex relationship between migration and climate change [5]. It is likely, however, that most people experience climate change in situ, so understanding the role of population dynamics remains critical. How a given number of people, in a given location and with varying population characteristics may exacerbate or mitigate the impacts of climate change or how, conversely, they may be vulnerable to climate change impacts are basic questions that remain largely unresolved [6]. This paper explores where and to what extent population dynamics intersect with high exposure to climate change. Specifically, in Eastern Africa's Lake Victoria Basin (LVB), a climate change/health vulnerability hotspot we have identified in prior research [7], we model child undernutrition vulnerability indices based on climate variables, including proxy measures (NDVI) derived from satellite imagery, at a 5-km spatial resolution. Results suggest that vegetation changes associated with precipitation decline in rural areas of sub-Saharan Africa can help predict deteriorating child health.
David López-Carr; Kevin M. Mwenda; Narcisa G. Pricope; Phaedon C. Kyriakidis; Marta M. Jankowska; John Weeks; Chris Funk; Gregory Husak; Joel Michaelsen. Climate-Related Child Undernutrition in the Lake Victoria Basin: An Integrated Spatial Analysis of Health Surveys, NDVI, and Precipitation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016, 9, 2830 -2835.
AMA StyleDavid López-Carr, Kevin M. Mwenda, Narcisa G. Pricope, Phaedon C. Kyriakidis, Marta M. Jankowska, John Weeks, Chris Funk, Gregory Husak, Joel Michaelsen. Climate-Related Child Undernutrition in the Lake Victoria Basin: An Integrated Spatial Analysis of Health Surveys, NDVI, and Precipitation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016; 9 (6):2830-2835.
Chicago/Turabian StyleDavid López-Carr; Kevin M. Mwenda; Narcisa G. Pricope; Phaedon C. Kyriakidis; Marta M. Jankowska; John Weeks; Chris Funk; Gregory Husak; Joel Michaelsen. 2016. "Climate-Related Child Undernutrition in the Lake Victoria Basin: An Integrated Spatial Analysis of Health Surveys, NDVI, and Precipitation Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 6: 2830-2835.
Human-environment interactions in the Nepali Himalaya are a topic of increased interest given the importance of the region from a biophysical, hydro-climatic, and socio-economic point of view. In this paper, we discuss a range of anthropogenic and environmental disturbance factors affecting one of the best-known conservation areas in Nepal: the Annapurna region. Similar to other mountainous environments, this region has been experiencing heightened human and natural pressures resulting in environmental degradation from a variety of multiple causal factors, such as deforestation, over-grazing, improper cultivation techniques on poor soils and slopes and haphazard policy and management decisions regarding conservation and tourism. Mountain ranges are very sensitive to environmental changes and even slight alterations and imbalances can result in exponentially detrimental effects not only for the livelihoods of local and regional communities, but also biodiversity and ecosystem functioning. We utilize a combination of field-collected data such as ground control points and remotely-sensed imagery and datasets and, acknowledging the variability of the constantly changing landscape, we provide a preliminary quantitative analysis of environmental and socio-economic impacts in the Annapurna Conservation Area to highlight the extent of anthropogenically-induced changes in the region over the last decades.
N. G. Pricope; J. D. All; L. Miles. Anthropogenic and Environmental Disturbance Factors in the Annapurna Conservation Area of Nepal. COVID-19 Pandemic Trajectory in the Developing World 2016, 271 -285.
AMA StyleN. G. Pricope, J. D. All, L. Miles. Anthropogenic and Environmental Disturbance Factors in the Annapurna Conservation Area of Nepal. COVID-19 Pandemic Trajectory in the Developing World. 2016; ():271-285.
Chicago/Turabian StyleN. G. Pricope; J. D. All; L. Miles. 2016. "Anthropogenic and Environmental Disturbance Factors in the Annapurna Conservation Area of Nepal." COVID-19 Pandemic Trajectory in the Developing World , no. : 271-285.
Increasing temperatures and wildfire incidence and decreasing precipitation and river runoff in southern Africa are predicted to have a variety of impacts on the ecology, structure, and function of semi-arid savannas, which provide innumerable livelihood resources for millions of people. This paper builds on previous research that documents change in inundation and fire regimes in the Chobe River Basin (CRB) in Namibia and Botswana and proposes to demonstrate a methodology that can be applied to disentangle the effect of environmental variability from land management decisions on changing and ecologically sensitive savanna ecosystems in transboundary contexts. We characterized the temporal dynamics (1985–2010) of vegetation productivity for the CRB using proxies of vegetation productivity and examine the relative importance of shifts in flooding and fire patterns to vegetation dynamics and effects of the association of phases of the El Niño—Southern Oscillation (ENSO) on vegetation greenness. Our results indicate that vegetation in these semi-arid environments is highly responsive to climatic fluctuations and the long-term trend is one of increased but heterogeneous vegetation cover. The increased cover and heterogeneity during the growing season is especially noted in communally-managed areas of Botswana where long-term fire suppression has been instituted, in contrast to communal areas in Namibia where heterogeneity in vegetation cover is mostly increasing primarily outside of the growing season and may correspond to mosaic early dry season burns. Observed patterns of increased vegetation productivity and heterogeneity may relate to more frequent and intense burning and higher spatial variability in surface water availability from both precipitation and regional inundation patterns, with implications for global environmental change and adaptation in subsistence-based communities.
Narcisa G. Pricope; Andrea E. Gaughan; John D. All; Michael W. Binford; Lucas P. Rutina. Spatio-Temporal Analysis of Vegetation Dynamics in Relation to Shifting Inundation and Fire Regimes: Disentangling Environmental Variability from Land Management Decisions in a Southern African Transboundary Watershed. Land 2015, 4, 627 -655.
AMA StyleNarcisa G. Pricope, Andrea E. Gaughan, John D. All, Michael W. Binford, Lucas P. Rutina. Spatio-Temporal Analysis of Vegetation Dynamics in Relation to Shifting Inundation and Fire Regimes: Disentangling Environmental Variability from Land Management Decisions in a Southern African Transboundary Watershed. Land. 2015; 4 (3):627-655.
Chicago/Turabian StyleNarcisa G. Pricope; Andrea E. Gaughan; John D. All; Michael W. Binford; Lucas P. Rutina. 2015. "Spatio-Temporal Analysis of Vegetation Dynamics in Relation to Shifting Inundation and Fire Regimes: Disentangling Environmental Variability from Land Management Decisions in a Southern African Transboundary Watershed." Land 4, no. 3: 627-655.