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Statistical rigor in LiDAR-assisted estimation of aboveground forest biomassAuthor(s): Timothy G. Gregoire; Erik Næsset; Ronald E. McRoberts; Göran Ståhl; Hans Andersen; Terje Gobakken; Liviu Ene; Ross Nelson
Source: Remote Sensing of Environment
Publication Series: Scientific Journal (JRNL)
Station: Pacific Northwest Research Station
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DescriptionFor 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.
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CitationGregoire, Timothy G.; Næsset, Erik; McRoberts, Ronald E.; Ståhl, Göran; Andersen, Hans-Erik; Gobakken, Terje; Ene, Liviu; Nelson, Ross. 2016. Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass. Remote Sensing of Environment. 173: 98-108.
KeywordsSampling, Statistical inference, Variance estimation.
- Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation
- The Pacific Northwest region vegetation and monitoring system.
- Relationship of field and LiDAR estimates of forest canopy cover with snow accumulation and melt
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