Large Scale Mapping of Vegetation Structure and Terrain Surface using Single Photon Lidar (SPL)

Abstract:

Accurate measurements of vegetation structure are critical for reducing uncertainties in carbon science and biodiversity studies. In recent years, Lidar has emerged as a state-of-the-art technology for characterizing vegetation structure at various spatial scales. Single photon lidar (SPL) is one of the latest developments with much greater data acquisition efficiency than conventional lidar. SPL requires only one photon to record a detection for each ranging measurement as against thousands in the case of other lidar systems. In this study, we present results from an experimental SPL instrument- the High Resolution Quantum Lidar System (HRQLS). HRQLS was flown over an entire county in western Maryland, USA in September 2013 to acquire a dataset with a mean density of 13 pts/m². We developed a multistage filtering method to remove solar noise in the raw HRQLS data, and derived a county-wide high-resolution Canopy Height (1m) and Digital Elevation Model (2 m) from the de-noised dataset. Next, we assessed the accuracy of HRQLS CHM and DEM with existing field data, National Geodetic Survey data, and an existing discrete return lidar (DRL) dataset. A comparison of canopy heights between SPL, DRL and field data showed similar results with much higher detail from SPL data. There was also a good agreement between ground elevations from SPL, DRL and geological survey data. Our results demonstrate SPL capabilities in acquiring accurate canopy structure and topographic measurements over large areas, which can greatly benefit forest carbon monitoring and habitat assessments.