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{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{Singh2015b,
title = {Detecting understory plant invasion in urban forests using LiDAR},
author = {Kunwar K. Singh and Amy J. Davis and Ross K. Meentemeyer},
url = {http://www.sciencedirect.com/science/article/pii/S0303243415000203},
doi = {doi:10.1016/j.jag.2015.01.012},
year = {2015},
date = {2015-02-12},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {38},
pages = {267-279},
abstract = {Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features – optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.},
keywords = {Chinese privet, IKONOS, invasion, LiDAR, Ligustrum sinense, random forest},
pubstate = {published},
tppubtype = {article}
}

@article{Singh2012,
title = {LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy},
author = {Kunwar K. Singh and John B. Vogler and Douglas A. Shoemaker and Ross K. Meentemeyer},
url = {http://dx.doi.org/10.1016/j.isprsjprs.2012.09.009},
doi = {10.1016/j.isprsjprs.2012.09.009},
year = {2012},
date = {2012-10-23},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {74},
pages = {110-121},
abstract = {The structural characteristics of Light Detection and Ranging (LiDAR) data are increasingly used to classify urban environments at fine scales, but have been underutilized for distinguishing heterogeneous land covers over large urban regions due to high cost, limited spectral information, and the computational difficulties posed by inherently large data volumes. Here we explore tradeoffs between potential gains in mapping accuracy with computational costs by integrating structural and intensity surface models extracted from LiDAR data with Landsat Thematic Mapper (TM) imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers in the rapidly urbanizing region of Charlotte, North Carolina, USA. Using supervised maximum likelihood (ML) and classification tree (CT) methods, we classified TM data at 30 m and LiDAR data and LiDAR-TM fusions at 1 m, 5 m, 10 m, 15 m and 30 m resolutions. We assessed the relative contributions of LiDAR structural and intensity surface models to classification map accuracy and identified optimal spatial resolution of LiDAR surface models for large-area assessments of urban land cover. ML classification of 1 m LiDAR-TM fusions using both structural and intensity surface models increased total accuracy by 32% compared to LiDAR alone and by 8% over TM at 30 m. Fusion data using all LiDAR surface models improved class discrimination of spectrally similar forest, farmland, and managed clearings and produced the highest total accuracies at 1 m, 5 m, and 10 m resolutions (87.2%, 86.3% and 85.4%, respectively). At all resolutions of fusion data and using either ML or CT classifier, the relative contribution of the LiDAR structural surface models (canopy height and normalized digital surface model) to classification accuracy is greater than the intensity surface. Our evaluation of tradeoffs between data volume and thematic map accuracy for this study system suggests that a spatial resolution of 5 m for LiDAR surface models best balances classification performance and the computational challenges posed by large-area assessments of land cover.},
keywords = {fusion, land change, land cover, Landsat, large-area assessment, LiDAR, managed clearings, mapping accuracy},
pubstate = {published},
tppubtype = {article}
}