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Assessing effect of dominant land-cover types and pattern on urban forest biomass estimated using LiDAR metrics

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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.

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Acknowledgments

The National Science Foundation ULTRA-Ex program (BCS-0949170) supported the fieldwork of this research. Additional financial support was provided by the Garden Club of America Zone VI fellowship in urban forestry, Casey Trees Endowment Fund, and the Association of American Geographers research grants.

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Authors and Affiliations

  1. Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, 27695, USA

    Kunwar K. Singh & Ross K. Meentemeyer

  2. Department of Geography, Michigan State University, East Lansing, MI, 48823, USA

    Raechel A. Bianchetti

  3. Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

    Gang Chen

Authors
  1. Kunwar K. Singh
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  2. Raechel A. Bianchetti
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  3. Gang Chen
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  4. Ross K. Meentemeyer
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Correspondence to Kunwar K. Singh.

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Singh, K.K., Bianchetti, R.A., Chen, G. et al. Assessing effect of dominant land-cover types and pattern on urban forest biomass estimated using LiDAR metrics. Urban Ecosyst 20, 265–275 (2017). https://doi.org/10.1007/s11252-016-0591-8

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