Canopy Height Modeling for Improved Forest Biomass Inventory

Abstract

In Ontario, an up-to-date forest biomass inventory is critical for forest management decisions and to ensure sustainable development. Due to recent pressures of climate change, economic downturns, and rising energy prices, inventory users are demanding increasingly accurate, timely, and spatially explicit biomass data from producers. This study focuses specifically on the inventory measurement of forest height, as height is an important yet elusive variable for determining biomass quality and quantity, along with a myriad of other forest and ecological attributes. Two types of remote sensing methods are used to model height, Light Detection and Ranging (LiDAR) and Semi Global Matching (SGM), at the privately owned Haliburton Forest in the Great Lakes St. Lawrence. The modeled heights are then compared to height data found in Ontario’s Forest Resource Inventory (FRI), collected using stereo-photogrammetry and ground surveys by Ontario’s Ministry of Natural Resources. Three different stand scenarios, representing various plot characteristics, are used to determine if correlations between LiDAR, SGM and the FRI increase as plot variables are constrained. Throughout the study, height values modeled by LiDAR and SGM are strongly correlated (R2 up to .88 for maximum height, .73 for average height). As there is no additional cost to producing SGM when aerial imagery is flown, it is an attractive method for Ontario to incorporate when producing its forest inventory. Unfortunately, remote sensing values and the FRI values do not correlate as plot variables are constrained. This suggests a disconnect between traditional inventory methods and emerging remote sensing technologies. It is recommended that a field-based study in the Haliburton Forest take place to validate the results of this study, and that further research should incorporate the modeled heights from this report to examine biomass volume.