Abstract
The demand for cost-efficient forest aboveground biomass (AGB) prediction methods is growing worldwide. The National Land Survey of Finland (NLS) began collecting airborne laser scanning (ALS) data throughout Finland in 2008 to provide a new high-detailed terrain elevation model. Similar data sets are being collected in an increasing number of countries worldwide. These data sets offer great potential in forest mapping related applications. The objectives of our study were (i) to evaluate the AGB component prediction accuracy at a resolution of 300 m2 using sparse density, leaf-off ALS data (collected by NLS) derived metrics as predictor variables; (ii) to compare prediction accuracies with existing large-scale forest mapping techniques (Multi-source National Forest Inventory, MS-NFI) based on Landsat TM satellite imagery; and (iii) to evaluate the accuracy and effect of canopy height model (CHM) derived metrics on AGB component prediction when ALS data were acquired with multiple sensors and varying scanning parameters. Results showed that ALS point metrics can be used to predict component AGBs with an accuracy of 29.7%–48.3%. AGB prediction accuracy was slightly improved using CHM-derived metrics but CHM metrics had a more clear effect on the estimated bias. Compared to the MS-NFI, the prediction accuracy was considerably higher, which was caused by differences in the remote sensing data utilized.
Highlights
Accurate forest biomass mapping methods are required globally and could be utilized e.g., for predicting forest aboveground biomass (AGB), bioenergy potential and carbon stock
Our study demonstrated that low-density, leaf-off Airborne laser scanning (ALS) data originally collected for ground elevation modeling can be used for accurate component-level forest AGB prediction
The ALS data collected using varying scanning sensors and parameters was expected to have a notable effect on forest attribute prediction accuracies
Summary
Accurate forest biomass mapping methods are required globally and could be utilized e.g., for predicting forest aboveground biomass (AGB), bioenergy potential and carbon stock. A typical approach to mapping AGB over large areas is based on generalizing field sample plot measurements using coarse- or medium-resolution remote sensing (RS) data and other numeric map data. The amount of AGB is estimated using local allometric models (e.g., [1,2]). This approach, referred to as Multi-Source National Forest Inventory (MS-NFI), was first introduced and tested in Finland in the early 1990s (e.g., [3]) and is nowadays routinely used in the Nordic countries to generate biomass maps for various purposes [3].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
