using remote sensing for quantity analysis of chip pile inventory in mill yards
abstract
an 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.