2. Materials and Methods
2.1. UAV Survey
2.2. Data Analysis
- First, scans with fewer than eight pulses reporting finite distances to target were discarded. Such scans contained too little information for reliable comparison to other scans, and thus for reliable UAV position estimation. To limit the computational load, we used a maximum of 16 scans, equally spaced in time among those available, for each cluster.
- The two scans closest together in time were merged by adjusting the estimated UAV horizontal-plane coordinates of the second scan () so that the length of the minimal spanning tree of the merged tree was minimized (if there was more than one candidate for this pair of scans, the pair was chosen randomly from among candidate pairs). The minimization was achieved with the optimx() function in R with the L-BFGS-B method with two fitting parameters and , and bounds of ±ϵ on the two parameters, where m was the estimated maximum error in the UAV position from the SLAM algorithm based on preliminary visual inspection of the overlaid scans. The starting estimates for and were chosen randomly from the intervals and . The algorithm repeated the call to optimx() 100 times with different starting values for the parameter estimates, to ensure the global minimum was found in each case.
- Step 2 was repeated until all scans had been merged.
- A circle was fitted to the resulting merged points using Pratt’s method , and the cluster was accepted as a physical tree if the resulting fitted circle had a diameter of less than 1 m, if the circular standard deviation of points was greater than (indicating that a sufficient arc of the putative trunk had been scanned), and if the value was greater than .
- If the cluster was accepted as a physical tree in step 4, the diameter of the fitted circle was taken as the estimated diameter of the tree ().
2.3. Manual Survey
Data Availability Statement
Conflicts of Interest
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|Object Number||DBH Manual (mm)||DBH UAV (mm)||Minimum Distance from UAV Path (m)|
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