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Using Remote Sensing for Quantity Analysis of Chip Pile Inventory in Mill Yards

dc.contributor.advisorRunesson, Ulf
dc.contributor.authorOng, Benjamin
dc.date.accessioned2017-02-28T13:52:36Z
dc.date.available2017-02-28T13:52:36Z
dc.date.issued2016
dc.identifier.urihttp://knowledgecommons.lakeheadu.ca/handle/2453/826
dc.description.abstractAn integral part of proper wood chip inventory management is the ability to accurately monitor wood chip quantities. This thesis examines the use of a new method of capturing the volume of mill yard wood chip piles through the utilization of aerial drones. The drones are used to capture images and the images are converted into digital 3D models, which are then capable of measuring pile volume. This process allows for conversion of the volume into an accurate mass estimate by compensating for compression factors within the chip pile. These factors can change the volume by a maximum of 9.46%, but on average during simulations and real world applications, most piles exhibit a change in volume in the range of 1% to 6% difference. By performing the estimation procedure multiple times and averaging the results this method is able to generate a result that is more precise, timely and less labour intensive than the previous methods of using a ground survey to determine volume and applying a linear volume to mass conversion for the quantity of wood chips. The results suggest that this averaging technique can improve the standard deviation spread from over 5% variation in the measurement to less than 2%. This new method combines multiple techniques to improve both overall accuracy and precision. Each stage of the new method was examined to determine the accumulated degree of error. This included looking at operator error of about 2.4%, considering the precision of 3D volume capture, which adds on average of 5% to 10% error, understanding the variation in bulk density due to pile shape, and size, which adds 1% to 6% error, using different 3D software modeling for measuring pile volume, which adds about 4% error. Combined together in extreme cases, these errors can skew the results by over 20%. The results of this examination provides research-based recommendations as to how to collect the images, generate the models, and process the data for mass estimation and improve error reduction at all stages.en_US
dc.language.isoen_USen_US
dc.subject3D model captureen_US
dc.subjectBulk densityen_US
dc.subjectCompression forcesen_US
dc.subjectError reductionen_US
dc.subjectMass estimationen_US
dc.subjectMill yard inventoryen_US
dc.subjectUAV imageryen_US
dc.subjectVolume lossen_US
dc.subjectWood-chip pileen_US
dc.titleUsing Remote Sensing for Quantity Analysis of Chip Pile Inventory in Mill Yardsen_US
dc.typeThesis
etd.degree.nameMaster of Scienceen_US
etd.degree.levelMasteren_US
etd.degree.disciplineNatural Resources Managementen_US
etd.degree.grantorLakehead Universityen_US
dc.contributor.committeememberLeitch, Mathew
dc.contributor.committeememberShahi, Chander


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