Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-05, Vol.14 (9), p.1989
Main Authors: Swayze, Neal C., Tinkham, Wade T., Creasy, Matthew B., Vogeler, Jody C., Hoffman, Chad M., Hudak, Andrew T.
Format: Article
Language:English
Subjects:
Accuracy
Airspeed
Algorithms
Altitude
area-based
Biomass
Carbon
Carbon sequestration
Data collection
Flight
Flight altitude
Forest biomass
Forest management
Image acquisition
Lidar
Model accuracy
Modelling
Monitoring
Photogrammetry
Predictions
Productivity
random forest
structure from motion
Trees
uav
Vegetation
Woodlands
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14091989