Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests

Asim Banskota, Randolph H. Wynne, Patrick Johnson, Bomono Emessiene

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Introduction: Accurate estimation of aboveground biomass is essential to better understand the carbon cycle in forest ecosystems. Methods: The objective of this study was to determine whether biomass in temperate hardwood forests is better estimated using very high-frequency radar data (from BioSAR) alone or in combination with small-footprint discrete-return lidar data (both profiling and scanning). The study area was in the Appomattox-Buckingham State Forest, Virginia, USA (78°41'W, 37°25'N). Aboveground biomass for 28 stands was estimated using 131 basal area factor 10 point samples. The resulting stand biomass estimates were used as the dependent variable in a multiple linear regression. Descriptors of the lidar distributions (both profiling and scanning) and averaged normalized radar cross-sections in each of these stands were used as independent variables. Results: Regression results revealed the following: (1) neither BioSAR nor scanning lidar data alone are good predictors of stand biomass (R 2=0.57, root mean squared error (RMSE)= 31.0 tonnes/ha and R 2=0.64, RMSE=28.5 tonnes/ha, respectively); (2) BioSAR data combined with small-footprint discrete lidar data (either profiling or scanning) are the best predictors of stand biomass (R 2=0.80, RMSE=21.3 tonnes/ha and R 2 =0.76, RMSE=24.2 tonnes/ha, respectively); and (3) when used with BioSAR data for stand biomass estimation, less costly profiling lidar data convey the same information as more costly scanning lidar data. Conclusion: Useful synergy can be realized by considering lidar and radar measurements jointly in estimating aboveground biomass in hardwood and mixed forests.

Original languageEnglish (US)
Pages (from-to)347-356
Number of pages10
JournalAnnals of Forest Science
Volume68
Issue number2
DOIs
StatePublished - Mar 1 2011

Keywords

  • Aboveground biomass
  • Best subsets regression
  • BioSAR
  • Carbon
  • Profiling lidar
  • Scanning lidar

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