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- Title
EVALUATING DIFFERENT MODELS TO PREDICT BIOMASS INCREMENT FROM MULTI-TEMPORAL LIDAR SAMPLING AND REMEASURED FIELD INVENTORY DATA IN SOUTH-CENTRAL ALASKA.
- Authors
TEMESGEN, H.; STRUNK, J.; ANDERSEN, H.-E.; FLEWELLING, J.
- Abstract
We evaluated two sets of equations for their predictive abilities for estimating biomass increment using successively acquired airborne lidar and ground data collected on western lowlands of the Kenai Peninsula in south-central Alaska. The first set included three base equations for estimating biomass increment as a function of lidar metrics, and the remaining equations enhanced the three base equations by considering the hierarchical structure of the data. It is shown that the mixed e ect framework substantially improved the accuracy and precision of biomass increment prediction over a model without the plot e ects that assume the observations are independent for the area covered by two lidar acquisitions, 5 years apart from one another. On the average, root mean square error values were reduced by 19.8% by using a plot-level random coefficient model that account for the impacts of site (biophysical factors) on biomass increment on the western Kenai Peninsula. Mixed e ect models are e ective statistical tools, but their effective application requires some sample growth data. As such, we recommend two models for estimating biomass increment on the Kenai Peninsula. If a subsample of ground data is available to predict the plot random intercept, the enhanced model is suggested. In the absence of ground data, an alternative model a model without the plot e ects is suggested. Model coeffcients are documented to facilitate development of a multi-part estimation strategy which includes both decay and increment.
- Subjects
BIOMASS; RENEWABLE energy sources; LIDAR; LASER based sensors; RADAR -- Optical equipment; SAMPLING (Process)
- Publication
Mathematical & Computational Forestry & Natural Resource Sciences, 2015, Vol 7, Issue 2, p66
- ISSN
1946-7664
- Publication type
Article