Lightweight Unmanned Aerial Systems

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The DraganFlyer X4, with Panasonic Lumix DMC-FX580

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Structure from Motion Process – a method of estimating three-dimensional structures from two-dimensional image sequences which may be coupled with local motion signals.

High spatial resolution measurements of vegetation structure in three-dimensions (3D) are essential for accurate estimation of vegetation biomass, carbon accounting, forestry, wildlife habitat, fire hazard evaluation and other land management and scientific applications. Light Detection and Ranging (LiDAR) is the current standard for these measurements but requires bulky instruments mounted on commercial aircraft and is cost prohibitive.

This research will determine if high spatial resolution 3D measurements of vegetation structure and spectral characteristics can be produced by applying open-source computer vision algorithms to ordinary digital photographs acquired using a consumer-grade digital camera mounted on an inexpensive hobbyist aircraft platform (i.e., remote controlled helicopter). Digital photographs will be acquired nine extensively stem-mapped plots (ranging from 0.8 to 1.9 ha in size) located near Flagstaff, Arizona. An open-source computer vision algorithm generated 3D point  cloud datasets with RGB spectral attributes will be generated from the photographs and geocorrected using ground control points located on a grid across each site. Point cloud vertical precision will be assessed and statistical comparisons will be appraised using common vegetation structural attributes (e.g., canopy height, patch and opening size) obtained from the proposed technique and tradition field methods. Suitability of using aerial photography and computer vision to develop high spatial resolution 3D measurements of vegetation structure will be discussed and challenges and potential solution will be explored.

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Picture collection of trees and soccer field on NAU’s south campus

+ Structure from Motion =

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Animation showing example 3D scene build from picture collection (left)