@article{inglis_vukomanovic_costanza_singh_2022, title={From viewsheds to viewscapes: Trends in landscape visibility and visual quality research}, volume={224}, ISSN={["1872-6062"]}, DOI={10.1016/j.lurbplan.2022.104424}, journal={LANDSCAPE AND URBAN PLANNING}, author={Inglis, Nicole C. and Vukomanovic, Jelena and Costanza, Jennifer and Singh, Kunwar K.}, year={2022}, month={Aug} }
 @article{inglis_vukomanovic_costanza_singh_2022, title={From viewsheds to viewscapes: Trends in landscape visibility and visual quality research}, volume={224}, ISSN={["1872-6062"]}, DOI={10.1016/j.landurbplan.2022.104424}, abstractNote={The study of visibility and visual quality (VVQ) spans scientific disciplines, methods, frameworks and eras. Recent advances in line-of-sight computation and geographic information systems (GIS) have propelled VVQ research into the realm of high performance computing via a cache of geospatial tools accessible to a broad range of research disciplines. However, in the disciplines that use VVQ analysis most (archaeology, architecture, geosciences and planning), methods and terminology can vary markedly, which may encumber interdisciplinary progress. A multidisciplinary systematic review of past VVQ research is timely to assess past efforts and effectively advance the field. In this study, we summarize the state of VVQ research in a systematic review of peer-reviewed publications spanning the past two decades. Our search yielded 528 total studies, 176 of which we reviewed in depth. VVQ analysis in peer-reviewed research increased 21-fold in the last 20 years, applied primarily in archaeology and natural resources research. We found that methods, tools and study designs varied across disciplines and scales. Research disproportionately represented the Global North and primarily employed medium resolution bare-earth elevation models, despite their known limitations. We propose a framework for standardized reporting of methods that emphasizes cross-disciplinary collaboration to propel visibility research into the future.}, journal={LANDSCAPE AND URBAN PLANNING}, author={Inglis, Nicole C. and Vukomanovic, Jelena and Costanza, Jennifer and Singh, Kunwar K.}, year={2022}, month={Aug} }
 @article{singh_bhattarai_vukomanovic_2022, title={Landscape-scale hydrologic response of plant invasion relative to native vegetation in urban forests}, volume={802}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2021.149903}, abstractNote={Large-scale invasion modifies watershed hydrology by changing surface runoff and lowering the seasonal availability of water to native plants. Due to costly field-based evapotranspiration (ET) measurements, which are highly localized and occasionally subject to instrument failure, landscape-scale water use assessments of invasive plants are infrequent. Therefore, the extent to which plant invaders alter water allocation between native and non-native vegetation in a given landscape is rarely assessed. We used a remote sensing-based ET modeling approach to measure the hydrologic response of an invasive shrub, Ligustrum sinense, across forests of the Charlotte Metropolitan Area, North Carolina. We hypothesized that this invader's widespread occurrence and dominant plant physiology significantly competes with native forests for water resources. We tested this hypothesis by comparing inter- and intra-annual variations in ET from invaded and uninvaded sites estimated using the surface-energy-balance system (SEBS) model and cloud-free Landsat images for the wettest (2003), driest (2007), and normal (2005 and 2011) water years. Our findings suggest that the water demand of L. sinense is higher than native forests (deciduous and evergreen) for most of the year except during the early spring and after high precipitation events. The daily ET flux of L. sinense was significantly different than evergreen vegetation during the driest year (2007) that, five years later (2011 - normal water year), was significantly different than both deciduous and evergreen vegetation types. This suggests that L. sinense consumes more water than native forest types, particularly during dry and normal precipitation years with increasing canopy cover over time making it a strong competitor with native vegetation for water resources in urban forests. Therefore, accounting for the hydrologic response of invasive plants and potential water savings from their removal from forests, particularly in water-scarce regions, may enable land managers and decision-makers to prioritize areas for monitoring and control efforts.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Singh, Kunwar K. and Bhattarai, Nishan and Vukomanovic, Jelena}, year={2022}, 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{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={Coastal forests sequester and store more carbon than their terrestrial counterparts but are at greater risk of conversion due to sea level rise. Saltwater intrusion from sea level rise converts freshwater-dependent coastal forests to more salt-tolerant marshes, leaving ‘ghost forests’ of standing dead trees behind. Although recent research has investigated the drivers and rates of coastal forest decline, the associated changes in carbon storage across large extents have not been quantified. We mapped ghost forest spread across coastal North Carolina, USA, using repeat Light Detection and Ranging (LiDAR) surveys, multi-temporal satellite imagery, and field measurements of aboveground biomass to quantify changes in aboveground carbon. Between 2001 and 2014, 15% (167 km2) of unmanaged public land in the region changed from coastal forest to transition-ghost forest characterized by salt-tolerant shrubs and herbaceous plants. Salinity and proximity to the estuarine shoreline were significant drivers of these changes. This conversion resulted in a net aboveground carbon decline of 0.13 ± 0.01 TgC. Because saltwater intrusion precedes inundation and influences vegetation condition in advance of mature tree mortality, we suggest that aboveground carbon declines can be used to detect the leading edge of sea level rise. Aboveground carbon declines along the shoreline were offset by inland aboveground carbon gains associated with natural succession and forestry activities like planting (2.46 ± 0.25 TgC net aboveground carbon across study area). Our study highlights the combined effects of saltwater intrusion and land use on aboveground carbon dynamics of temperate coastal forests in North America. By quantifying the effects of multiple interacting disturbances, our measurement and mapping methods should be applicable to other coastal landscapes experiencing saltwater intrusion. As sea level rise increases the landward extent of inundation and saltwater exposure, investigations at these large scales are requisite for effective resource allocation for climate adaptation. In this changing environment, human intervention, whether through land preservation, restoration, or reforestation, may be necessary to prevent aboveground carbon loss.}, 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{chen_mossa_singh_2020, title={Floodplain response to varied flows in a large coastal plain river}, volume={354}, ISSN={["1872-695X"]}, DOI={10.1016/j.geomorph.2020.107035}, abstractNote={The natural flood pulse maintains river-floodplain ecosystems through the exchange of freshwater resources between the main-stem and floodplain habitats. Few prior studies have quantified the relationship between flows and floodplain response including estimating inundation area, floodwater volumes, and slough connectivity. Floodplain modeling typically uses the flow-stage height relationship at river gauge stations. In this study, we compared a relative elevation model (REM) and the Hydrologic Engineering Center River Analysis System (HEC-RAS 1D) model using events from 2015 and 2016 covering a range of flows from the 1st to the 99th percentile for the Apalachicola River, Florida. Because digital elevation models (DEM) from LiDAR (light detection and ranging) data lack details of riverbed topography, we compared a LiDAR-alone and LiDAR-sonar combined DEM to assess their differences. Estimates from the REM and HEC-RAS models were compared to maps based on Landsat imagery-derived water and vegetation indices. In this river, we found a non-linear relationship between the inundated area and flow, increasing markedly through the 90th flow percentile after which increases are minimal. Inundated areas from both REM and HEC-RAS models were similar for all selected flow levels except at the 74th percentile (708 m3/s) flow at which the REM produced 11% higher inundated area than HEC-RAS. Near the median flows, major sloughs were fully connected with backswamps and low-lying patches being inundated. At the higher flows, only a few anthropogenic features were exposed. Floodplain inundation estimates from Landsat performed poorly, detecting 9% with the modified normalized difference water index (mNDWI) and 41% with the open water likelihood index (OWL). These estimates were much lower than the HEC-RAS model (96% flooded), largely because the satellite is unable to penetrate dense forests and examine the floodplain surface, and the Landsat pixel size is twice the width of floodplain sloughs. The LiDAR-sonar combined DEM produced a higher floodwater volume estimate with the HEC-RAS model than using LiDAR-alone. The difference of 1,368,000 m3 at the 1st percentile (142 m3/s) and 2,825,000 m3 at the 89th percentile (1133 m3/s) demonstrate the limitation of using a LiDAR-alone DEM, which cannot penetrate the water surface, and the importance of surveying floodplains using sonar. Modeling results under predict historical wetting of the floodplain because Corps dredging made the main channel approximately 13% wider from its historical width in 1941. Further, in the past few decades, droughts and low flows have become more common because of varied upstream water uses, resulting in less inundation than in the past. Frequent high flows are required to maintain river-floodplain connectivity, floodplain forests, and other hydroecological functions in the Apalachicola River floodplain. Our findings present a basis to assess the legacy of past and ongoing disturbances, inform potential policy decisions for water and floodplain management, and provide a baseline for further research.}, journal={GEOMORPHOLOGY}, author={Chen, Yin-Hsuen and Mossa, Joann and Singh, Kunwar K.}, year={2020}, month={Apr} }
 @article{singh_gray_2020, title={Mapping Understory Invasive Plants in Urban Forests with Spectral and Temporal Unmixing of Landsat Imagery}, volume={86}, ISSN={["2374-8079"]}, DOI={10.14358/PERS.86.8.509}, abstractNote={Successful eradication and management of invasive plants require frequent and accurate maps. Detection of invasive plants is difficult at moderate resolution because target species are often located in the forest understory among other vegetation types, and so produce mixed spectral
 signatures. Spectral unmixing approaches can help to decompose these spectral mixtures; however, they are typically applied to only one or a few images, and thus neglect phenological variability that may improve invasive species discrimination. We compared two approaches to multiple endmember
 spectral mixture analysis for detecting Ligustrum sinense in the southeastern United States: the use of temporal signatures of endmembers from full and select-date normalized difference vegetation index time series, and conventional spectral unmixing using a single image date. Our results
 suggest that using temporal signatures from all available imagery may be a good choice, with minimal impact to achievable accuracy, if a priori information on phenological differences between endmembers is unavailable, or imagery for periods of high phenological difference are unavailable.}, number={8}, journal={PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING}, author={Singh, Kunwar K. and Gray, Josh}, year={2020}, month={Aug}, pages={509–518} }
 @article{zhang_chen_vukomanovic_singh_liu_holden_meentemeyer_2020, title={Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping}, volume={247}, ISSN={["1879-0704"]}, DOI={10.1016/j.rse.2020.111945}, abstractNote={Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules – Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 × 500 pixels each) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies.}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Zhang, Yindan and Chen, Gang and Vukomanovic, Jelena and Singh, Kunwar K. and Liu, Yong and Holden, Samuel and Meentemeyer, Ross K.}, year={2020}, month={Sep} }
 @article{chen_singh_lopez_zhou_2020, title={Tree canopy cover and carbon density are different proxy indicators for assessing the relationship between forest structure and urban socioecological conditions}, volume={113}, ISSN={["1872-7034"]}, DOI={10.1016/j.ecolind.2020.106279}, abstractNote={Forest canopy cover and carbon density are two pivotal biophysical parameters for assessing urban forest structure and its ecosystem services. While canopy cover (horizontal structure) has been extensively studied for understanding the relationship between socio-ecological dynamics and urban forests, carbon density (vertical structure) received little attention in the urban setting. The goal of this study was twofold: (i) exploring the differences between canopy cover and carbon density, and their relationships with socio-ecological factors across an urbanizing landscape, and (ii) assessing the effect of neighborhood category (i.e., low, medium and high development intensity) on the relationships at the neighborhood level. We used Mecklenburg County located in the Charlotte Metropolitan area of North Carolina, United States as a case study area, where rapid urban sprawl has fragmented the pine-oak-hickory dominated forests into a range of low to high housing density neighborhoods. We observed two major findings. First, canopy cover and carbon density demonstrated a generally weak correlation across various types of residential neighborhoods, although such relationship became relatively stronger in areas featuring a higher level of development intensity. Second, ecological factors (e.g., landscape spatial patterns) were found to dominate the statistical models explaining the variance in both canopy cover and carbon density compared to urban socioeconomic factors (e.g., income and age). However, the models and the explanatory factors were different for the two forest parameters, and they varied across neighborhoods of diverse development intensities. Based upon these findings, we argue that canopy cover and carbon density are different proxy indicators of forest functioning in the urban setting, and should be independently treated in urban forest management. The best management practices should be developed at the inner-city, neighborhood level, rather than the typical city level, owing to the significant, variable influence of socio-ecological conditions across neighborhood types.}, journal={ECOLOGICAL INDICATORS}, author={Chen, Gang and Singh, Kunwar K. and Lopez, Jaime and Zhou, Yuyu}, year={2020}, month={Jun} }
 @article{vukomanovic_singh_petrasova_vogler_2018, title={Not seeing the forest for the trees: Modeling exurban viewscapes with LiDAR}, volume={170}, ISSN={["1872-6062"]}, DOI={10.1016/j.landurbplan.2017.10.010}, abstractNote={Viewscapes are the visible portions of a landscape that create a visual connection between a human observer and their 3-dimensional surroundings. However, most large area line-of-sight studies have modeled viewscapes using bare-earth digital elevation models, which exclude the 3-D elements of built and natural environments needed to comprehensively understand the scale, complexity and naturalness of an area. In this study, we compared viewscapes derived from LiDAR bare earth (BE) and top-of-canopy (ToC) surface models for 1000 exurban homes in a region of the Rocky Mountains, USA that is experiencing rapid low-density growth. We examined the extent to which the vertical structure of trees and neighboring houses in ToC models – not accounted for in BE models – affect the size and quality of each home’s viewscape. ToC models consistently produced significantly smaller viewscapes compared to BE models across five resolutions of LiDAR-derived models (1, 5, 10, 15, and 30-m). As resolution increased, both ToC and BE models produced increasingly larger, exaggerated viewscapes. Due to their exaggerated size, BE models overestimated the greenness and diversity of vegetation types in viewscapes and underestimated ruggedness of surrounding terrain compared to more realistic ToC models. Finally, ToC models also resulted in more private viewscapes, with exurban residents seeing almost three times fewer neighbors compared to BE models. These findings demonstrate that viewscape studies should consider both vertical and horizontal dimensions of built and natural environments in landscape and urban planning applications.}, journal={LANDSCAPE AND URBAN PLANNING}, author={Vukomanovic, Jelena and Singh, Kunwar K. and Petrasova, Anna and Vogler, John B.}, year={2018}, month={Feb}, pages={169–176} }
 @article{singh_madden_gray_meentemeyer_2018, title={The managed clearing: An overlooked land-cover type in urbanizing regions?}, volume={13}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0192822}, abstractNote={Urban ecosystem assessments increasingly rely on widely available map products, such as the U.S. Geological Service (USGS) National Land Cover Database (NLCD), and datasets that use generic classification schemes to detect and model large-scale impacts of land-cover change. However, utilizing existing map products or schemes without identifying relevant urban class types such as semi-natural, yet managed land areas that account for differences in ecological functions due to their pervious surfaces may severely constrain assessments. To address this gap, we introduce the managed clearings land-cover type–semi-natural, vegetated land surfaces with varying degrees of management practices–for urbanizing landscapes. We explore the extent to which managed clearings are common and spatially distributed in three rapidly urbanizing areas of the Charlanta megaregion, USA. We visually interpreted and mapped fine-scale land cover with special attention to managed clearings using 2012 U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) images within 150 randomly selected 1-km2 blocks in the cities of Atlanta, Charlotte, and Raleigh, and compared our maps with National Land Cover Database (NLCD) data. We estimated the abundance of managed clearings relative to other land use and land cover types, and the proportion of land-cover types in the NLCD that are similar to managed clearings. Our study reveals that managed clearings are the most common land cover type in these cities, covering 28% of the total sampled land area– 6.2% higher than the total area of impervious surfaces. Managed clearings, when combined with forest cover, constitutes 69% of pervious surfaces in the sampled region. We observed variability in area estimates of managed clearings between the NAIP-derived and NLCD data. This suggests using high-resolution remote sensing imagery (e.g., NAIP) instead of modifying NLCD data for improved representation of spatial heterogeneity and mapping of managed clearings in urbanizing landscapes. Our findings also demonstrate the need to more carefully consider managed clearings and their critical ecological functions in landscape- to regional-scale studies of urbanizing ecosystems.}, number={2}, journal={PLOS ONE}, author={Singh, Kunwar K. and Madden, Marguerite and Gray, Josh and Meentemeyer, Ross K.}, year={2018}, month={Feb} }
 @article{singh_bianchetti_chen_meentemeyer_2017, title={Assessing effect of dominant land-cover types and pattern on urban forest biomass estimated using LiDAR metrics}, volume={20}, DOI={10.1007/s11252-016-0591-8}, number={2}, journal={Urban Ecosystems}, author={Singh, K. K. and Bianchetti, R. A. and Chen, G. and Meentemeyer, Ross K.}, year={2017}, pages={265–275} }
 @article{chen_ozelkan_singh_zhou_brown_meentemeyer_2017, title={Uncertainties in mapping forest carbon in urban ecosystems}, volume={187}, ISSN={["1095-8630"]}, DOI={10.1016/j.jenvman.2016.11.062}, abstractNote={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.}, journal={JOURNAL OF ENVIRONMENTAL MANAGEMENT}, author={Chen, Gang and Ozelkan, Emre and Singh, Kunwar K. and Zhou, Jun and Brown, Marilyn R. and Meentemeyer, Ross K.}, year={2017}, month={Feb}, pages={229–238} }
 @article{singh_chen_vogler_meentemeyer_2016, title={When Big Data are Too Much: Effects of LiDAR Returns and Point Density on Estimation of Forest Biomass}, volume={9}, ISSN={["2151-1535"]}, DOI={10.1109/jstars.2016.2522960}, abstractNote={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.}, number={7}, journal={IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING}, author={Singh, Kunwar K. and Chen, Gang and Vogler, John B. and Meentemeyer, Ross K.}, year={2016}, month={Jul}, pages={3210–3218} }