@article{maxwell_rovai_adame_adams_alvarez-rogel_austin_beasy_boscutti_boettcher_bouma_et al._2023, title={Global dataset of soil organic carbon in tidal marshes}, volume={10}, ISSN={["2052-4463"]}, DOI={10.1038/s41597-023-02633-x}, abstractNote={Abstract}, number={1}, journal={SCIENTIFIC DATA}, author={Maxwell, Tania L. and Rovai, Andre S. and Adame, Maria Fernanda and Adams, Janine B. and Alvarez-Rogel, Jose and Austin, William E. N. and Beasy, Kim and Boscutti, Francesco and Boettcher, Michael E. and Bouma, Tjeerd J. and et al.}, year={2023}, month={Nov} }
 @article{smart_seekamp_van berkel_vukomanovic_smith_2023, title={Socio-spatial factors influence climate change adaptation decisions of rural coastal landowners}, volume={7}, ISSN={["1572-9761"]}, DOI={10.1007/s10980-023-01734-7}, journal={LANDSCAPE ECOLOGY}, author={Smart, Lindsey S. and Seekamp, Erin and Van Berkel, Derek and Vukomanovic, Jelena and Smith, Jordan W.}, year={2023}, month={Jul} }
 @article{vukomanovic_smart_koch_dale_plassin_byrd_beier_wilson_doyon_2023, title={Translating stakeholder narratives for participatory modeling in landscape ecology}, volume={7}, ISSN={["1572-9761"]}, DOI={10.1007/s10980-023-01724-9}, journal={LANDSCAPE ECOLOGY}, author={Vukomanovic, Jelena and Smart, Lindsey S. and Koch, Jennifer and Dale, Virginia H. and Plassin, Sophie and Byrd, Kristin B. and Beier, Colin and Wilson, Madison and Doyon, Frederik}, year={2023}, month={Jul} }
 @article{randall_inglis_smart_vukomanovic_2022, title={From Meadow to Map: Integrating Field Surveys and Interactive Visualizations for Invasive Species Management in a National Park}, volume={11}, ISSN={["2220-9964"]}, DOI={10.3390/ijgi11100525}, abstractNote={Invasive species are an important and growing issue of concern for land managers, and the ability to collect and visualize species coverage data is vital to the management of invasive and native species. This is particularly true of spatial data, which provides invaluable information on location, establishment rates, and spread rates necessary for managing habitats. However, current methods of collection are rarely integrated into a full management tool, making it difficult to quickly collect and visualize multiple years of data for multiple species. We created the Geospatial Meadow Management Tool (GMMT) to provide a complete framework from geospatial data collection to web visualization. We demonstrate the utility of our approach using Valley Forge National Historical Park meadow survey data. The GMMT was created through the ArcGIS suite of software, taking advantage of the modularity of multiple processes, and incorporating an online visualization dashboard that allows for quick and efficient data analysis. Using Valley Forge National Historical Park as a case study, the GMMT provides a wide range of useful species coverage data and visualizations that provide simple yet insightful ways to understand species distribution. This tool highlights the ability of a web-based visualization tool to be modified to incorporate the needs of users, providing powerful visuals for non-GIS experts. Future avenues for this work include highlighted open-data and community engagement, such as citizen science, to address the increasing threat of invasive species both on and off public lands.}, number={10}, journal={ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION}, author={Randall, Joshua and Inglis, Nicole C. and Smart, Lindsey and Vukomanovic, Jelena}, year={2022}, month={Oct} }
 @article{smart_vukomanovic_sills_sanchez_2021, title={Cultural ecosystem services caught in a 'coastal squeeze' between sea level rise and urban expansion}, volume={66}, ISSN={["1872-9495"]}, url={https://doi.org/10.1016/j.gloenvcha.2020.102209}, DOI={10.1016/j.gloenvcha.2020.102209}, abstractNote={Sea level rise and urbanization exert complex synergistic pressures on the provision of ecosystem services (ES) in coastal regions. Anticipating when and where both biophysical and cultural ES will be affected by these two types of coastal environmental change is critical for sustainable land-use planning and management. Biophysical (provisioning and regulating) services can be mapped using secondary data. We demonstrate an approach to mapping cultural ES by engaging stakeholders in iterative participatory mapping of personally and communally valuable cultural ES. We identify hotspots where highly valued cultural ES and high values for biophysical ES co-occur and generate spatially-explicit projections of sea level rise and urban expansion through 2060 to quantify impacts of the ‘coastal squeeze’ on ES. We study Johns Island, South Carolina, USA as an example of a vulnerable community in a low-lying region experiencing both rising water levels and a rapid influx of new residents and development. Our projections of environmental change through 2060 indicate that on Johns Island, cultural ES face disproportionately greater risk of decline than biophysical ES, with almost three quarters of the island’s cultural ES affected. We find that hotspots for cultural ES, such as community heritage sites and scenic vistas of oak-lined roads and marshes, rarely co-occur (only 3% area) with biophysical ES such as high values of carbon sequestration and agricultural production. This confirms the importance of engaging with local stakeholders to map cultural ES and puts them on a more level playing field with biophysical ES in decision-making contexts. Projected declines and limited overlap between biophysical and cultural ES highlight the need for tighter coordination between conservation and community planning, and for including locally valued cultural ES in assessments of threats posed by the ‘coastal squeeze’ of sea level rise and urban expansion.}, journal={GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS}, publisher={Elsevier BV}, author={Smart, Lindsey S. and Vukomanovic, Jelena and Sills, Erin O. and Sanchez, Georgina}, year={2021}, month={Jan} }
 @article{smart_vukomanovic_taillie_singh_smith_2021, title={Quantifying Drivers of Coastal Forest Carbon Decline Highlights Opportunities for Targeted Human Interventions}, volume={10}, ISSN={["2073-445X"]}, DOI={10.3390/land10070752}, abstractNote={As coastal land use intensifies and sea levels rise, the fate of coastal forests becomes increasingly uncertain. Synergistic anthropogenic and natural pressures affect the extent and function of coastal forests, threatening valuable ecosystem services such as carbon sequestration and storage. Quantifying the drivers of coastal forest degradation is requisite to effective and targeted adaptation and management. However, disentangling the drivers and their relative contributions at a landscape scale is difficult, due to spatial dependencies and nonstationarity in the socio-spatial processes causing degradation. We used nonspatial and spatial regression approaches to quantify the relative contributions of sea level rise, natural disturbances, and land use activities on coastal forest degradation, as measured by decadal aboveground carbon declines. We measured aboveground carbon declines using time-series analysis of satellite and light detection and ranging (LiDAR) imagery between 2001 and 2014 in a low-lying coastal region experiencing synergistic natural and anthropogenic pressures. We used nonspatial (ordinary least squares regression–OLS) and spatial (geographically weighted regression–GWR) models to quantify relationships between drivers and aboveground carbon declines. Using locally specific parameter estimates from GWR, we predicted potential future carbon declines under sea level rise inundation scenarios. From both the spatial and nonspatial regression models, we found that land use activities and natural disturbances had the highest measures of relative importance (together representing 94% of the model’s explanatory power), explaining more variation in carbon declines than sea level rise metrics such as salinity and distance to the estuarine shoreline. However, through the spatial regression approach, we found spatial heterogeneity in the relative contributions to carbon declines, with sea level rise metrics contributing more to carbon declines closer to the shore. Overlaying our aboveground carbon maps with sea level rise inundation models we found associated losses in total aboveground carbon, measured in teragrams of carbon (TgC), ranged from 2.9 ± 0.1 TgC (for a 0.3 m rise in sea level) to 8.6 ± 0.3 TgC (1.8 m rise). Our predictions indicated that on the remaining non-inundated landscape, potential carbon declines increased from 29% to 32% between a 0.3 and 1.8 m rise in sea level. By accounting for spatial nonstationarity in our drivers, we provide information on site-specific relationships at a regional scale, allowing for more targeted management planning and intervention. Accordingly, our regional-scale assessment can inform policy, planning, and adaptation solutions for more effective and targeted management of valuable coastal forests.}, number={7}, journal={LAND}, author={Smart, Lindsey S. and Vukomanovic, Jelena and Taillie, Paul J. and Singh, Kunwar K. and Smith, Jordan W.}, year={2021}, month={Jul} }
 @article{ferrante_vukomanovic_smart_2021, title={Uncovering Trends and Spatial Biases of Research in a US National Park}, volume={13}, ISSN={["2071-1050"]}, DOI={10.3390/su132111961}, abstractNote={National parks are vital public resources for the preservation of species and landscapes, and for decades have provided natural laboratories for studying environmental and cultural resources. Though significant scholarship has taken place in national parks, syntheses of research trends and biases are rarely available for needs assessments and decision making. In this paper, we demonstrate procedures to close this information gap using Congaree National Park (CNP) as an example of a protected area characterized by disparate research. We conducted a systematic review of research topics and funding sources of all peer-reviewed, published research conducted since its inception as a National Monument in 1976. We next paired our evaluation of research trends with a spatial analysis of study locations to uncover patterns and biases in research. A total of 49 peer-reviewed publications describing research conducted at CNP have been published between 1976–2018, with over 75% published since 2003. Quantitative studies accounted for nearly 90% of all studies, and vegetation was the most commonly studied discipline. Most studies were funded by federal agencies, with the National Park Service providing the most funding instances. Spatial analyses revealed statistically significant (p < 0.05) hotspots of studies near the park entrance, visitor center, roads, and hiking trails. In providing a comprehensive evaluation of research patterns and trends within a single park, we developed an approach that can be applied by managers in other parks or public lands to maximize the utility of past research, identify potentially valuable but understudied park resources, and prioritize research needs.}, number={21}, journal={SUSTAINABILITY}, author={Ferrante, Daniela Agostini and Vukomanovic, Jelena and Smart, Lindsey S.}, year={2021}, month={Nov} }
 @article{smart_taillie_poulter_vukomanovic_singh_swenson_mitasova_smith_meentemeyer_2020, title={Aboveground carbon loss associated with the spread of ghost forests as sea levels rise}, volume={15}, ISSN={["1748-9326"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85092484857&partnerID=MN8TOARS}, DOI={10.1088/1748-9326/aba136}, abstractNote={Abstract}, number={10}, journal={ENVIRONMENTAL RESEARCH LETTERS}, author={Smart, Lindsey S. and Taillie, Paul J. and Poulter, Benjamin and Vukomanovic, Jelena and Singh, Kunwar K. and Swenson, Jennifer J. and Mitasova, Helena and Smith, Jordan W. and Meentemeyer, Ross K.}, year={2020}, month={Oct} }
 @article{taillie_moorman_smart_pacifici_2019, title={Bird community shifts associated with saltwater exposure in coastal forests at the leading edge of rising sea level}, volume={14}, ISSN={["1932-6203"]}, url={https://doi.org/10.1371/journal.pone.0216540}, DOI={10.1371/journal.pone.0216540}, abstractNote={Rising sea levels dramatically alter the vegetation composition and structure of coastal ecosystems. However, the implications of these changes for coastal wildlife are poorly understood. We aimed to quantify responses of avian communities to forest change (i.e., ghost forests) in a low-lying coastal region highly vulnerable to rising sea level. We conducted point counts to sample avian communities at 156 forested points in eastern North Carolina, USA in 2013–2015. We modelled avian community composition using a multi-species hierarchical occupancy model and used metrics of vegetation structure derived from Light Detection and Ranging (LiDAR) data as covariates related to variation in bird responses. We used this model to predict occupancy for each bird species in 2001 (using an analogous 2001 LiDAR dataset) and 2014 and used the change in occupancy probability to estimate habitat losses and gains at 3 spatial extents: 1) the entire study area, 2) burned forests only, and 3) unburned, low-lying coastal forests only. Of the 56 bird species we investigated, we observed parameter estimates corresponding to a higher likelihood of occurring in ghost forest for 34 species, but only 9 of those had 95% posterior intervals that did not overlap 0, thus having strong support. Despite the high vulnerability of forests in the region to sea level rise, habitat losses and gains associated with rising sea level were small relative to those resulting from wildfire. Though the extent of habitat changes associated with the development of ghost forest was limited, these changes likely are more permanent and may compound over time as sea level rises at an increasing rate. As such, the proliferation of ghost forests from rising sea level has potential to become an important driver of forest bird habitat change in coastal regions.}, number={5}, journal={PLOS ONE}, author={Taillie, Paul J. and Moorman, Christopher E. and Smart, Lindsey S. and Pacifici, Krishna}, year={2019}, month={May} }
 @article{bhattachan_jurjonas_morris_taillie_smart_emanuel_seekamp_2019, title={Linking residential saltwater intrusion risk perceptions to physical exposure of climate change impacts in rural coastal communities of North Carolina}, volume={97}, ISSN={0921-030X 1573-0840}, url={http://dx.doi.org/10.1007/s11069-019-03706-0}, DOI={10.1007/s11069-019-03706-0}, number={3}, journal={Natural Hazards}, publisher={Springer Science and Business Media LLC}, author={Bhattachan, Abinash and Jurjonas, Matthew D. and Morris, Priscilla R. and Taillie, Paul J. and Smart, Lindsey S. and Emanuel, Ryan E. and Seekamp, Erin L.}, year={2019}, month={Jul}, pages={1277–1295} }
 @article{koch_dorning_van berkel_beck_sanchez_shashidharan_smart_zhang_smith_meentemeyer_et al._2019, title={Modeling landowner interactions and development patterns at the urban fringe}, volume={182}, ISSN={["1872-6062"]}, url={http://dx.doi.org/10.1016/j.landurbplan.2018.09.023}, DOI={10.1016/j.landurbplan.2018.09.023}, abstractNote={Population growth and unrestricted development policies are driving low-density urbanization and fragmentation of peri-urban landscapes across North America. While private individuals own most undeveloped land, little is known about how their decision-making processes shape landscape-scale patterns of urbanization over time. We introduce a hybrid agent-based modeling (ABM) – cellular automata (CA) modeling approach, developed for analyzing dynamic feedbacks between landowners’ decisions to sell their land for development, and resulting patterns of landscape fragmentation. Our modeling approach builds on existing conceptual frameworks in land systems modeling by integrating an ABM into an established grid-based land-change model – FUTURES. The decision-making process within the ABM involves landowner agents whose decision to sell their land to developers is a function of heterogeneous preferences and peer-influences (i.e., spatial neighborhood relationships). Simulating landowners’ decision to sell allows an operational link between the ABM and the CA module. To test our hybrid ABM-CA approach, we used empirical data for a rapidly growing region in North Carolina for parameterization. We conducted a sensitivity analysis focusing on the two most relevant parameters—spatial actor distribution and peer-influence intensity—and evaluated the dynamic behavior of the model simulations. The simulation results indicate different peer-influence intensities lead to variable landscape fragmentation patterns, suggesting patterns of spatial interaction among landowners indirectly affect landscape-scale patterns of urbanization and the fragmentation of undeveloped forest and farmland.}, journal={LANDSCAPE AND URBAN PLANNING}, author={Koch, Jennifer and Dorning, Monica A. and Van Berkel, Derek B. and Beck, Scott M. and Sanchez, Georgina M. and Shashidharan, Ashwin and Smart, Lindsey S. and Zhang, Qiang and Smith, Jordan W. and Meentemeyer, Ross K. and et al.}, year={2019}, month={Feb}, pages={101–113} }
 @article{singh_chen_smart_gray_meentemeyer_2018, title={Intra-annual phenology for detecting understory plant invasion in urban forests}, volume={142}, ISSN={0924-2716}, url={http://dx.doi.org/10.1016/J.ISPRSJPRS.2018.05.023}, DOI={10.1016/j.isprsjprs.2018.05.023}, abstractNote={Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense.}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, publisher={Elsevier BV}, author={Singh, Kunwar K. and Chen, Yin-Hsuen and Smart, Lindsey and Gray, Josh and Meentemeyer, Ross K.}, year={2018}, month={Aug}, pages={151–161} }
 @article{leveraging big data towards functionally-based, catchment scale restoration prioritization_2018, url={http://dx.doi.org/10.1007/s00267-018-1100-z}, DOI={10.1007/s00267-018-1100-z}, abstractNote={The persistence of freshwater degradation has necessitated the growth of an expansive stream and wetland restoration industry, yet restoration prioritization at broad spatial extents is still limited and ad-hoc restoration prevails. The River Basin Restoration Prioritization tool has been developed to incorporate vetted, distributed data models into a catchment scale restoration prioritization framework. Catchment baseline condition and potential improvement with restoration activity is calculated for all National Hydrography Dataset stream reaches and catchments in North Carolina and compared to other catchments within the river subbasin to assess where restoration efforts may best be focused. Hydrologic, water quality, and aquatic habitat quality conditions are assessed with peak flood flow, nitrogen and phosphorus loading, and aquatic species distribution models. The modular nature of the tool leaves ample opportunity for future incorporation of novel and improved datasets to better represent the holistic health of a watershed, and the nature of the datasets used herein allow this framework to be applied at much broader scales than North Carolina.}, journal={Environmental Management}, year={2018}, month={Dec} }
 @article{vukomanovic_smart_sanchez_jouzi_sills_2018, title={Participatory mapping and modeling of land change in coastal South Carolina: The Johns Island Community Conservation Initiative.}, url={https://scholarsarchive.byu.edu/iemssconference/2018/Posters/13}, author={Vukomanovic, Jelena and Smart, Lindsey S and Sanchez, Georgina M and Jouzi, Zeynab S and Sills, Erin O}, year={2018} }
 @article{van berkel_tabrizian_dorning_smart_newcomb_mehaffey_neale_meentemeyer_2018, title={Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR}, volume={31}, ISSN={["2212-0416"]}, url={http://dx.doi.org/10.1016/j.ecoser.2018.03.022}, DOI={10.1016/j.ecoser.2018.03.022}, abstractNote={Landscapes are increasingly recognized for providing valuable cultural ecosystem services with numerous non-material benefits by serving as places of rest, relaxation, and inspiration that ultimately improve overall mental health and physical well-being. Maintaining and enhancing these valuable benefits through targeted management and conservation measures requires understanding the spatial and temporal determinants of perceived landscape values. Content contributed through mobile technologies and the web are emerging globally, providing a promising data source for localizing and assessing these landscape benefits. These georeferenced data offer rich in situ qualitative information through photos and comments that capture valued and special locations across large geographic areas. We present a novel method for mapping and modeling landscape values and perceptions that leverages viewshed analysis of georeferenced social media data. Using a high resolution LiDAR (Light Detection and Ranging) derived digital surface model, we are able to evaluate landscape characteristics associated with the visual-sensory qualities of outdoor recreationalists. Our results show the importance of historical monuments and attractions in addition to specific environmental features which are appreciated by the public. Evaluation of photo-image content highlights the opportunity of including temporally and spatially variable visual-sensory qualities in cultural ecosystem services (CES) evaluation like the sights, sounds and smells of wildlife and weather phenomena.}, journal={ECOSYSTEM SERVICES}, author={Van Berkel, Derek B. and Tabrizian, Payam and Dorning, Monica A. and Smart, Lindsey and Newcomb, Doug and Mehaffey, Megan and Neale, Anne and Meentemeyer, Ross K.}, year={2018}, month={Jun}, pages={326–335} }
 @article{smart_2018, title={Rising Seas and the Changing Coastal Landscape: Modeling Land Change Pattern and Social Process to Quantify the Socio-Ecological Impacts of Sea Level Rise.}, url={http://www.lib.ncsu.edu/resolver/1840.20/36305}, author={Smart, Lindsey Suzanne}, year={2018} }
 @article{bhattachan_jurjonas_moody_morris_sanchez_smart_taillie_emanuel_seekamp_2018, title={Sea level rise impacts on rural coastal social-ecological systems and the implications for decision making}, volume={90}, ISSN={1462-9011}, url={http://dx.doi.org/10.1016/j.envsci.2018.10.006}, DOI={10.1016/j.envsci.2018.10.006}, abstractNote={Many rural coastal regions are distinctly vulnerable to sea level rise because of their remoteness, isolation from central planning agencies, and poverty. To better plan for future sea level changes in these regions, an interdisciplinary approach to assess the social and environmental impacts of sea level rise and their dynamic feedbacks is important. In this paper, we use a socio-ecological system framework to investigate sea level rise impacts to the Albemarle-Pamlico Peninsula, a rural, low-lying coastal region in eastern North Carolina. Specifically, we show that 42% of the region could be inundated and property losses of up to US $14 billion could be incurred with 100 cm of sea level rise. We also synthesize the impacts of sea level rise on the region’s social-ecological system and present strategies to strengthen the adaptive capacity of the ecosystem, markets and communities. We conclude with a discussion on the differing climate change risk perceptions amongst the stakeholders as well as implications for decision-making. Sea level rise will continue to threaten the functioning of this social-ecological system of rural, low-lying coastal communities. A socio-ecological system framework provides a lens through which the impacts of sea level rise can be evaluated for rural, low-lying coastal communities. The framework presented here necessitates interdisciplinary research and highlights the importance of mutual learning amongst stakeholders in other rural coastal regions.}, journal={Environmental Science & Policy}, publisher={Elsevier BV}, author={Bhattachan, A. and Jurjonas, M.D. and Moody, A.C. and Morris, P.R. and Sanchez, G.M. and Smart, L.S. and Taillie, P.J. and Emanuel, R.E. and Seekamp, E.L.}, year={2018}, month={Dec}, pages={122–134} }
 @article{smith_smart_dorning_dupéy_méley_meentemeyer_2017, title={Bayesian methods to estimate urban growth potential}, volume={163}, ISSN={0169-2046}, url={http://dx.doi.org/10.1016/J.LANDURBPLAN.2017.03.004}, DOI={10.1016/j.landurbplan.2017.03.004}, abstractNote={Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.}, journal={Landscape and Urban Planning}, publisher={Elsevier BV}, author={Smith, Jordan W. and Smart, Lindsey S. and Dorning, Monica A. and Dupéy, Lauren Nicole and Méley, Andréanne and Meentemeyer, Ross K.}, year={2017}, month={Jul}, pages={1–16} }
 @article{three-dimensional characterization of pine forest type and red-cockaded woodpecker habitat by small-footprint, discrete-return lidar_2012, url={http://dx.doi.org/10.1016/j.foreco.2012.06.020}, DOI={10.1016/j.foreco.2012.06.020}, abstractNote={Accurate measurement of forest canopy structure is critical for understanding forest-wildlife habitat relationships. Although most theory and application have been based on in situ measurements, imaging technologies such as Light Detection and Ranging (lidar) provide measurements that are both vertically accurate and horizontally extensive. We use small-footprint, multiple-return lidar from a state-wide dataset (1-m footprint, 0.11 point/m2) to characterize the vertical and horizontal structure of successional loblolly pine (Pinus taeda) and mature, fire-maintained longleaf pine (Pinus palustris) forests on the coastal plain of North Carolina, USA. The relationship between these characteristics and the federally-endangered red-cockaded woodpecker’s (Picoides borealis, Vieillot) habitat preferences were assessed; as this species has a strong affinity for mature longleaf pine forests. Vertical structure was characterized by lidar-derived metrics (e.g., average and standard deviation of canopy height) and horizontal patterns of vertical structure were quantified by semivariograms and lacunarity analysis. Lidar metrics were compared with field measurements of stand structure and with woodpecker habitat use. We predicted woodpecker distribution using the Maxent species distribution modeling algorithm with elevation, landcover, and hydrography geospatial variables, with and without lidar-derived structural variables. Lidar successfully quantified canopy variation and differentiated between the structural characteristics of these two similar coniferous evergreen forest types (e.g. significant differences in maximum height, canopy cover, and size classes). Loblolly stands were found to have the tallest trees on average with a higher canopy cover. Both semivariograms and lacunarity analyses clearly differentiated between evergreen forest structural types (e.g. semivariogram range was 18.7 m for longleaf, 32.3 m for loblolly). By examining the immediate area around cavity nesting sites we found taller trees than those found across broader foraging sites. The species distribution model accurately predicted woodpecker distribution (tested with woodpecker presence, AUC > .85). The addition of lidar-derived variables improved the model’s predictive power by 8% compared to the model based only on elevation, landcover, and hydrography environmental variables. We show that relatively low density lidar data are valuable for wildlife studies by characterizing and separating similar canopy types, describing different use zones (foraging vs. nesting), and for use in species distribution models.}, journal={Forest Ecology and Management}, year={2012}, month={Oct} }
 @article{smart_2009, title={Characterizing Spatial Pattern and Heterogeneity of Pine Forests in North Carolina’s Coastal Plain using LiDAR}, url={http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.582.1464}, author={Smart, Lindsey}, year={2009} }