Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction

Abstract

The management of low-density savannah and woodland forests for carbon storage presents a mechanism to offset the expense of ecologically informed forest management strategies. However, existing carbon monitoring systems draw on vast amounts of either field observations or aerial light detection and ranging (LiDAR) collections, making them financially prohibitive in low productivity systems where forest management focuses on promoting resilience to disturbance and multiple uses. This study evaluates how UAS altitude and flight speed influence area-based aboveground forest biomass model predictions. The imagery was acquired across a range of UAS altitudes and flight speeds that influence the efficiency of data collection. Data were processed using common structures from motion photogrammetry algorithms and then modeled using Random Forest. These results are compared to LiDAR observations collected from fixed-wing manned aircraft and modeled using the same routine. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling accuracy. UAS predictions increasingly outperformed (2–24% increased variance explained) commercial airborne LiDAR strategies as acquisition altitude increased from 80–120 m. The reduced cost of UAS data collection and processing and improved biomass modeling accuracy over airborne LiDAR approaches could make carbon monitoring viable in low productivity forest systems.

Keywords

• structure from motion
• carbon
• monitoring
• area-based
• random forest
• uav