Applying terrestrial LiDAR to measure vegetation characteristics and improve remote sensing monitoring applications

Many decisions made by natural resource managers or policy makers regarding forestry are not linked with the spatial scales covered by conventional forest inventory methods. Remote sensing data often offers a solution to execute forest ecosystem studies over large areas. The basic idea of this thesis is that more objective validation and calibration methods for remote sensing products will increase the accuracy of monitoring applications. The proposed methods focus on (i) modelling  LiDAR (light detection and ranging) waveforms to solve for canopy properties and (ii) the use of ground-based LiDAR to assess forest structure and above ground biomass.

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