Article

Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012–2016)

Remote Sensing of Environment, Elsevier, ISSN 1879-0704

Volume 211, 2018

DOI:10.1016/j.rse.2018.04.015, Dimensions: pub.1103234915,

Authors

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Affiliations

Organisations

  1. (1) Canadian Forest Service, grid.146611.5
  2. (2) University of Copenhagen, grid.5254.6, KU

Countries

Canada

Denmark

Continents

North America

Europe

Description

National forest inventories (NFI) provide estimates of forest resources at the national and regional level but are also increasingly used as basis for mapping forest resources based on remotely sensed data. Such maps procure local estimates of forest resources but may also improve precision of national and regional estimates. Supported by a countrywide airborne laser scanning (circa 2014) and a national land-use map (circa 2014), direct (DI), model-assisted (MA), and model calibrated (MC) estimates of wood volume (V) and aboveground biomass (AGB) densities in forest areas derived from the Danish NFI (2012–2016) are presented. Nonlinear models with three LiDAR metrics are used to predict V and AGB in forested areas. According to these models, the predicted values of V and AGB in sample plots missed in the field inventory was lower than in those visited in the field; we therefore opted for estimation with multiple (stochastic) imputations. MA estimates for the country suggested a 2% lower level of both V and AGB densities with errors 45% lower than estimated errors in DI results. National MC estimates were close to the DI estimates with an error approximately 40% lower than errors in DI estimates yet 5% greater than the MA estimates of error. Multiple imputations had the strongest impact on DI estimates, but only a weak impact on MA and MC results.

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University of Copenhagen

Danish Open Access Indicator

2018: Blocked

Research area: Science & Technology

Danish Bibliometrics Indicator

2018: Level 2

Research area: Science & Technology

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Times Cited: 16

Field Citation Ratio (FCR): 6.57