Biomass stock and change estimation in boreal forests using remotely sensed data : results from empirical studies and simulations

Abstract

Airborne LiDAR (Light Detection and Ranging) has become an important remote sensing tool for forest inventory. In the past two decades, this technology has seen a rapid status transition from experimental to operational, mainly driven by the cost saving – precision increasing duality and paralleled by accelerating technological availability. For large-area resource estimation, airborne laser scanning (ALS) has been proposed as a sampling tool. Two-stage model assisted (MA) and two-phase hybrid (HY) estimators have been proposed for this type of survey. This thesis investigated biomass stocks and biomass change estimation using repeated ALS strip sampling survey and national forest inventory field data. Emphasis was on simulative methods to assess the properties of the estimators.