Abstract

For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of aboveground forest biomass. This technology is now employed routinely in forest management inventories of some Nordic countries, and there is eager anticipation for its application to assess changes in standing biomass in vast tropical regions of the globe in concert with the UN REDD program to limit C emissions. In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc. In many instances, too, there appears to be no recognition of different bases of statistical inference which bear importantly on uncertainty estimation. Statistically rigorous assessment of uncertainty for four large LiDAR-assisted surveys is expounded. (C) 2015 Elsevier Inc. All rights reserved.

Keywords

Sampling; Statistical inference; Variance estimation

Published in

Remote Sensing of Environment
2016, Volume: 173, pages: 98-108
Publisher: ELSEVIER SCIENCE INC

SLU Authors

• Ståhl, Göran

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Forest Science


Publication identifier

DOI: https://doi.org/10.1016/j.rse.2015.11.012

Permanent link to this page (URI)

https://res.slu.se/id/publ/82925