Machine learning and image analysis on photos of a solitary tree in a complex background
extraction and analysis of key properties for wind-tree interaction
Casper Bekkers (TU Delft - Aerospace Engineering)
S.J. Watson – Mentor (TU Delft - Wind Energy)
A.C. Viré – Mentor (TU Delft - Wind Energy)
E. Dellwik – Mentor
A.B. Dahl – Mentor
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Abstract
A method is developed to automatically analyze over 200000 images of a single mature tree. This method is assessed and applied to a tree located at Risø in Roskilde, Denmark, next to two meteorological masts. Image aspects such as the surface area and the center of area are extracted from the images and they are related to the wind statistics. Concurrence is found between expected behaviors and observations to validate the method and unexpected behaviors are documented. Expected behaviors include the decrease in surface area with an increase in wind speed, swaying directional dependency with wind direction and swaying magnitude dependency with wind speed. New behaviors include a non-linear relationship between porosity and wind deficit and an increase in surface area with wind speed from infrequent wind directions. Furthermore, a specific period is analyzed which determined that for this tree, roughly half of the Vogel exponent is attributed to the decrease in surface area.