Abstract
The prediction of tropical forest attributes using airborne laser scanning (ALS) is becoming attractive as an alternative to traditional field measurements. Area-based ALS inventories require a set of representative field plots from the study area, which may be difficult to obtain in tropical forests with limited accessibility. This study investigates the effect of sample-plot selection in Nepal, based on two accessibility factors: distance to road and degree of slope. The sparse Bayesian method was employed in the model to estimate above-ground biomass (AGB) with an independent validation dataset for model validation. Study findings showed that the sample plot distance and slope had a considerable effect on the accuracy of the AGB estimation, because the forest structure varied according to the level of accessibility. Thus, the field sample plots that are used in model construction should cover the full range of sample plot distances and slopes occurring within the area.
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 callMore From: ISPRS Journal of Photogrammetry and Remote Sensing
Disclaimer: 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.
