Post-Logging Estimation of Loblolly Pine (Pinus taeda) Stump Size, Area and Population Using Imagery from a Small Unmanned Aerial System
by
Sathishkumar Samiappan 1,*, 
Gray Turnage 1, Cary McCraine 2, Joshua Skidmore 3, Lee Hathcock 1 and Robert Moorhead 1
Sathishkumar Samiappan 1,*, Gray Turnage 1, Cary McCraine 2, Joshua Skidmore 3, Lee Hathcock 1 and Robert Moorhead 1
1
Geosystems Research Institute, Mississippi State University, Mississippi State, MS 39762, USA
2
Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
3
Mississippi Forestry Commission, French Camp, MS 39745, USA
*
Author to whom correspondence should be addressed.
Drones 2017, 1(1), 4; https://doi.org/10.3390/drones1010004
Received: 30 August 2017 / Revised: 16 October 2017 / Accepted: 17 October 2017 / Published: 20 October 2017
This study describes an unmanned aerial system (UAS) method for accurately estimating the number and diameters of harvested Loblolly Pine (Pinus taeda) stumps in a final harvest (often referred as clear-cut) situation. The study methods are potentially useful in initial detection, quantification of area and volume estimation of legal or illegal logging events to help estimate the volumes and value of removed pine timber. The study sites used included three adjacent pine stands in East-Central Mississippi. Using image pattern recognition algorithms, results show a counting accuracy of 77.3% and RMSE of 4.3 cm for stump diameter estimation. The study also shows that the area can be accurately estimated from the UAS collected data. Our experimental study shows that the proposed UAS survey method has the potential for wide use as a monitoring or investigation tool in the forestry and land management industries.
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Keywords:
Pinus taeda; timber theft; timber trespass; forest measurement; UAS; drones; sampling
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MDPI and ACS Style
Samiappan, S.; Turnage, G.; McCraine, C.; Skidmore, J.; Hathcock, L.; Moorhead, R. Post-Logging Estimation of Loblolly Pine (Pinus taeda) Stump Size, Area and Population Using Imagery from a Small Unmanned Aerial System. Drones 2017, 1, 4.
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