Publications

@article{Singh2017,
title = {Assessing effect of dominant land-cover types and pattern on urban forest biomass estimated using LiDAR metrics},
author = {Kunwar K. Singh and Raechel A. Bianchetti and Gang Chen and Ross K. Meentemeyer},
url = {https://doi.org/10.1007/s11252-016-0591-8},
doi = {10.1007/s11252-016-0591-8},
year = {2017},
date = {2017-04-01},
journal = {Urban Ecosystems},
volume = {20},
number = {2},
pages = {265-275},
abstract = {Accurate estimates of biomass in urban forests can help improve strategies for enhancing ecosystem services. Landscape heterogeneity, such as land-cover types and their spatial arrangements, greatly affects biomass growth, and it complicates the estimation of biomass. Application of LiDAR data is a typical approach for mapping forest biomass and carbon stocks across heterogeneous landscapes. However, little is known about how urban land uses and pattern impact biomass and estimates derived from LiDAR analysis. In this study, we examined the relationship between LiDAR-derived biomass and dominant land-cover types using field-measured estimates of aboveground forest biomass in an urbanized region of North Carolina, USA. Three objectives drove this research: 1) we examined the local effects of dominant land cover types on urban forest biomass; 2) we identified the spatial scale at which dominant land cover influences biomass estimates; 3) we investigated whether the fine-scale, spatial heterogeneity of the urban landscape contributed to forest biomass. We used multiple linear regression to relate field-measured biomass to LiDAR metrics and land cover densities derived from Landsat and LiDAR data. The biomass model developed from variables derived from LiDAR first returns produced biomass estimates similar to using all LiDAR returns. Although three land-cover types (impervious surface, managed clearings, and farmland) exhibited a negative relationship with biomass, only impervious surface was statistically significant. The biomass model that used impervious surface densities between 100 m and 175 m radial buffers produced the highest adjusted R 2 with lower RMSE values. Our study suggests that impervious surface impacted forest biomass estimates considerably in urbanizing landscapes with the greatest effect between 100 and 175 m from a forest stand. Managed clearing and farmland types negatively impacted biomass estimation albeit not as strongly as impervious surface. Overall, we found that accounting for impervious surface density and its proximity to forest in biomass models may improve urban forest biomass estimates.},
keywords = {fragmentation, land cover, Landsat, landscape metrics, LiDAR, multiple linear regression, urban forests},
pubstate = {published},
tppubtype = {article}
}

@article{Chen2017b,
title = {Uncertainties in Mapping Forest Carbon in Urban Ecosystems},
author = {Gang Chen and Emre Ozelkan and Kunwar K. Singh and Jun Zhou and Marilyn R. Brown and Ross K. Meentemeyer},
url = {https://doi.org/10.1016/j.jenvman.2016.11.062},
doi = {10.1016/j.jenvman.2016.11.062},
year = {2017},
date = {2017-02-01},
journal = {Journal of Environmental Management},
volume = {187},
pages = {229-238},
abstract = {Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density – 5.8, 4.6, 2.3, and 1.2 pt s/m2, aerial optical NAIP (National Agricultural Imagery Program) imagery – 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7–11.0% of variation) than changing NAIP image resolution (causing 6.2–8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.},
keywords = {carbon mapping, neighborhood pattern, remote sensing, uncertainty analysis, urban forests},
pubstate = {published},
tppubtype = {article}
}

@article{Davis2016,
title = {Accounting for residential propagule pressure improves prediction of urban plant invasion},
author = {Amy J. Davis and Kunwar K. Singh and Jean-Claude Thill and Ross K. Meentemeyer},
url = {http://onlinelibrary.wiley.com/doi/10.1002/ecs2.1232/full},
doi = {10.1002/ecs2.1232},
year = {2016},
date = {2016-03-08},
journal = {Ecosphere},
volume = {7},
number = {3},
pages = {e01232},
abstract = {Plant invasions substantially impact the ecosystem services provided by forests in urbanizing regions. Knowing where invasion risk is greatest helps target early detection and eradication efforts, but developing an accurate predictive model of invasive species presence and spread on the basis of habitat suitability remains a challenge due to spatial variation in propagule pressure (the number of individuals released) which is likely conflated with suitability. In addition to neighborhood propagule pressure that originates with propagules dispersing from naturalized populations within invaded habitats, we expect residential propagule pressure arising from the widespread use of exotic plants in the yards of single-family residences to be an important driver of invasions, and to notably improve the predictive accuracy of species distribution models (SDMs). To this end, we collected presence/absence data for a widespread forest invader, Ligustrum sinense (Chinese privet), from 400 stratified random plots located along an urban gradient across the Charlotte, North Carolina metropolitan area. We assessed the relative contribution of residential propagule pressure and neighborhood propagule pressure to improving the predictive performance of a probit SDM for Chinese privet that only contains environmental predictors. Our results indicate that, although the environment-only model predicted the highest geographic area to be at risk of invasion by privet, it also had the highest rate of failure to accurately predict observed privet occurrences as indicated by the omission (incorrectly predicted absence) and commission (incorrectly predicted presence) error rates. Accounting for residential propagule pressure substantially improved model performance by reducing the omission error by nearly 50%, thereby improving upon the ability of the model to predict privet invasion in suboptimal habitat. Given that this increase in detection was accompanied by a decrease in the geographic area predicted at risk, we conclude that SDMs for invasive exotic shrubs and potentially for other synanthropic generalist plants may be highly inefficient when residential propagule pressure is not accounted for. Accounting for residential propagule pressure in models of invasive plants results in a more focused and accurate prediction of the area at risk, thus enabling decision makers to feasibly prioritize regional scale monitoring and control efforts.},
keywords = {Chinese privet, invasive species, propagule pressure, species distribution model, urban forests},
pubstate = {published},
tppubtype = {article}
}

@article{Singh2016,
title = {When Big Data are Too Much: Effects of LiDAR Returns and Point Density on Estimation of Forest Biomass},
author = {Kunwar K. Singh and Gang Chen and John B. Vogler and Ross K. Meentemeyer},
url = {http://dx.doi.org/10.1109/JSTARS.2016.2522960},
doi = {10.1109/JSTARS.2016.2522960},
issn = {1939-1404},
year = {2016},
date = {2016-03-02},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = {9},
number = {7},
pages = {3210-3218},
abstract = {Analysis of light detection and ranging (LiDAR) data is becoming a mainstream approach to mapping forest biomass and carbon stocks across heterogeneous landscapes. However, large volumes of multireturn high point-density LiDAR data continue to pose challenges for large-area assessments. We are beginning to learn when and where point density can be reduced (or aggregated), but little is known regarding the degree to which multireturn data—at varying levels of point density—improve estimates of forest biomass. In this study, we examined the combined effects of LiDAR returns and data reduction on field-measured estimates of aboveground forest biomass in deciduous and mixed evergreen forests in an urbanized region of North Carolina, USA. We extracted structural metrics using first returns only, all returns, and rarely used laser pulse first returns from reduced point densities of LiDAR data. We statistically analyzed relationships between the field-measured biomass and LiDAR-derived variables for each return type and point-density combination. Overall, models using first return data performed only slightly better than models that utilized multiple returns. First-return models and multiple-return models at one percent point density resulted in 14% and 11% decrease in the amount of explained variation, respectively, compared to models with 100% point density. In addition, variance of modeled biomass across all point densities and return models was statistically similar to the field-measured biomass. Taken together, these results suggest that LiDAR first returns at reduced point density provide sufficient data for mapping urban forest biomass and may be an effective alternative to multireturn data.},
keywords = {aboveground biomass, data reduction, large-area assessment, LiDAR, multiple linear regression, point density, urban forests},
pubstate = {published},
tppubtype = {article}
}

@article{BenDor2014,
title = {A mixed-methods analysis of social-ecological feedbacks between urbanization and forest persistence},
author = {Todd BenDor and Douglas A. Shoemaker and Jean-Claude Thill and Monica A. Dorning and Ross K. Meentemeyer},
url = {http://dx.doi.org/10.5751/ES-06508-190303},
doi = {10.5751/ES-06508-190303},
year = {2014},
date = {2014-09-01},
journal = {Ecology and Society},
volume = {19},
number = {3},
pages = {3},
abstract = {We examined how social-ecological factors in the land-change decision-making process influenced neighboring decisions and trajectories of alternative landscape ecologies. We decomposed individual landowner decisions to conserve or develop forests in the rapidly growing Charlotte, North Carolina, U.S. region, exposing and quantifying the effects of forest quality, and social and cultural dynamics. We tested the hypothesis that the intrinsic value of forest resources, e.g., cultural attachment to land, influence woodland owners’ propensity to sell. Data were collected from a sample of urban, nonindustrial private forest (U-NIPF) owners using an individualized survey design that spatially matched land-owner responses to the ecological and timber values of their forest stands. Cluster analysis (n = 126) revealed four woodland owner typologies with widely ranging views on the ecosystem, cultural, and historical values of their forests. Classification tree analysis revealed woodland owners’ willingness to sell was characterized by nonlinear, interactive factors, including sense of place values regarding the retention of native vegetation, the size of forest holdings, their connectedness to nature, ‘pressure’ from surrounding development, and behavioral patterns, such as how often landowners visit their land. Several ecological values and economic factors were not found to figure in the decision to retain forests. Our study design is unique in that we address metropolitan forest persistence across urban-rural and population gradients using a unique individualized survey design that richly contextualizes survey responses. Understanding the interplay between policies and landowner behavior can also help resource managers to better manage and promote forest persistence. Given the region’s paucity of policy tools to manage the type and amount of development, the mosaic of land cover the region currently enjoys is far from stable.},
keywords = {forest persistence, land change, social-ecological feedbacks, tax policy, urban forests, urbanization},
pubstate = {published},
tppubtype = {article}
}