Supplementary Information for Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure Remote Sensing

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Supplementary Information for Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure Remote Sensing 2015, 7, 13895 13920 Table S1. List of UAV flight configurations used in the study showing different levels of lighting, altitude, and photographic side overlap as well as UAV track width. The number of replicates actually flown under each set of conditions is also shown. Forward overlap is computed for a UAV traveling at 6 m s 1 with the camera taking pictures at 2 frames s 1. GSD is ground sampling distance in centimeters, FOV is field of view in meters. Lighting Forward Image FOV FOV Mean Altitude Above Track Overlap Side Overlap GSD Across Along Number of Number of Canopy Surface Width (Percent) (Percent) (cm) Track Track Images Per Replicates (Meters) (Meters) (meters) (Meters) Replicate Clear 20 87 80 6 0.84 31 23 2501 4 Clear 40 93 60 25 1.68 62 46 1220 5 Clear 40 93 80 12 1.68 62 46 2506 2 Clear 60 96 60 37 2.51 93 68 1090 5 Clear 60 96 80 25 2.51 93 68 1652 5 Clear 80 97 20 100 3.35 123 91 509 5 Clear 80 97 40 75 3.35 123 91 601 5 Clear 80 97 60 50 3.35 123 91 925 5 Clear 80 97 80 25 3.35 123 91 1219 7 Cloudy 20 87 80 6 0.84 31 23 2478 5 Cloudy 40 93 60 25 1.68 62 46 1222 5 Cloudy 40 93 80 12 1.68 62 46 2659 3 Cloudy 60 96 60 37 2.51 93 68 1093 5 Cloudy 60 96 80 25 2.51 93 68 1780 2 Cloudy 80 97 20 100 3.35 123 91 499 5 Cloudy 80 97 40 75 3.35 123 91 606 5 Cloudy 80 97 60 50 3.35 123 91 930 4 Cloudy 80 97 80 25 3.35 123 91 1213 5 Total 82

2 Table S2. Variation in Ecosynth point cloud quality traits and metrics as a function of average wind speed during each flight based on the Beaufort wind force scale. Path-XY Error Path-Z Error ICP-XY Error ICP-Z Error LLED MAD (m) Field Height LIDAR TCH Average Forest Point Density (points m -2 ) Average Forest Canopy Penetration (% CV) Mean Wind Speed during Flight Beaufort Wind Force (m s -1 ) 1 2 3 4 (0.3 1.5) (1.6 3.4) (3.5 5.4) (5.5 7.9) R R 2 F-test 1.3 1.2 1.6 0.7 0.48 0.23 NS 0.4 0.4 0.5 0.5 0.99 0.99 p = 0.006 2.5 1.8 2.2 2.0 0.52 0.27 NS 2.5 3.0 2.5 3.4 0.67 0.44 NS 2.2 3.3 2.0 3.0 0.58 0.34 NS 4.6 4.1 4.1 5.3 0.48 0.23 NS 2.1 2.5 2.8 2.5 0.63 0.40 NS 47 35 36 45 0.11 0.01 NS 17 16 18 19 0.89 0.79 NS

3 Table S3. Mean of Ecosynth point cloud quality traits and metrics for the same set of five replicates processed in Photoscan v0.91 on different computers. Computer A B a C D E 2 Intel Xeon 2 Intel Xeon 1 Intel Xeon E5-1 Intel i7-950 5670 1 Intel i7-2600 K CPU 5670 2670 3.06 GHz, 4 2.93 GHz, 6 3.08 GHz, 4 cores 2.93 GHz, 6 cores 2.6 GHz, 8 cores cores cores RAM 192 GB 15 GB 16 GB 48 GB 16 GB OS Ubuntu Linux Windows 7 Windows 7 Windows 7 Windows 7 14.02 Path-XY Error 1.2 1.2 1.2 1.2 1.2 Path-Z Error 0.4 0.4 0.4 0.4 0.4 ICP-XY Error 1.7 1.7 1.8 1.7 1.6 ICP-Z Error 1.9 1.8 1.8 1.8 1.9 Launch Location Elevation Difference (m) 1.8 1.8 1.8 1.8 1.8 Field Height 4.7 4.7 4.7 4.7 4.7 LIDAR TCH 2.0 2.0 2.0 2.0 2.0 Forest Point 36 36 36 36 36 Cloud Density (points m -2 ) Forest Canopy Penetration (% CV) 19 19 18 19 18 Computation Time (hours) 31 48 52 43 45 a Same configuration with Ubuntu Linux 14.02 was run as an Amazon EC2 g2.2xlarge instance for Ecosynther processing.

4 Table S4. Mean of Ecosynth point cloud quality traits and metrics for the same set of five replicates processed in Photoscan v0.91 at different image resolutions. Resolution (Megapixels) 10 7.5 5 2.5 1 0.3 Ground Sampling Distance (GSD) centimeters / pixel 3.4 3.9 4.7 6.7 10.6 19.3 Path-XY Error 1.3 1.2 1.3 4.5 7.2 7.1 Path-Z Error 0.4 0.4 0.4 3.4 9.9 10.1 ICP-XY Error 1.7 1.6 1.7 5.0 16.8 33.7 ICP-Z Error 1.9 1.8 1.8 11.9 94.5 112 Launch Location Elevation Difference (m) 1.8 1.8 1.6 10.1 92.1 56.2 Field Height 4.2 3.7 3.6 9.8 87.2 112 LIDAR TCH 2.2 2.1 2.2 11.5 91.1 114 Forest Point Cloud Density (points m -2 ) 30 37 35 37 22 4 Forest Canopy Penetration (% CV) 16 18 18 12 2 0 Computation Time (hours) 36 38 33 45 35 10 Figure S1. Plot showing the difference in mean sea level (MSL) corrected relative and absolute altitude as reported by the UAV telemetry compared to observations of MSLcorrected UAV altitude from a laser rangefinder for the mean of three repeat flights. UAV absolute telemetry is reported in meters above mean sea level while relative altitude is reported in meters above the launch location. MSL-correction involves simply adding the altitude in MSL of the launch location to relative altitude measurements.

5 Figure S2. (Left) Scatter plots showing the relationship between error in Ecosynth TCH estimates of field height relative to the absolute vertical positioning of the point cloud relative to the LIDAR point cloud (ICP-Z). (Right) the relationship between LLED-MAD and ICP-Z. Dashed line is regression line. Figure S3. Radiometric precision of Ecosynth point clouds per channel averaged within different landcovers across all main replicates at the Herbert Run site (n = 82). Average channel percent deviation measures variation (standard deviation) in point color within 1 m 1 m bins, interpreted as a percentage of the maximum potential channel brightness (255). Numbers at bottom axis indicate mean rugosity per land cover in meters which was correlated with average percent deviation in grayscale intensity by landcover (R 2 = 0.74).

6 Figure S4. Plot showing LIDAR estimated average canopy height per plot (TCH) relative to field measured average canopy height at Herbert Run. Figure S5. Plot of error in Ecosynth TCH compared to field average canopy height (meters RMSE) relative to the solar angle at the time of the UAV flight for all main replicates flown at Herbert Run (n = 82) symbolized by whether the flight was flown on a clear or cloudy day. Linear regression is across all flights, p < 0.00003. Solar angles calculated based on date and local time of UAV flight from http://www.esrl.noaa.gov/gmd/grad/solcalc/calcdetails.html, accessed 2015-09-05).

7 Figure S6. Computation time required for SFM processing in Photoscan v0.91 based on the number of photos. Dotted line is polynomial model. 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).