Estimating Tree Height and Volume Using Unmanned Aerial Vehicle Photography and SfM Technology, with Verification of Result Accuracy

Abstract

This study aimed to investigate the effects of differences in shooting and flight conditions for an unmanned aerial vehicle (UAV) on the processing method and estimated results of aerial images. Forest images were acquired under 80 different conditions, combining various aerial photography methods and flight conditions. We verified errors in values measured by the UAV and the measurement accuracy with respect to tree height and volume. Our results showed that aerial images could be processed under all the studied flight conditions. However, although tree height and crown were decipherable in the created 3D model in 64 conditions, they were undecipherable in 16. The standard deviation (SD) in crown area values for each target tree was 0.08 to 0.68 m2. UAV measurements of tree height tended to be lower than the actual values, and the RMSE (root mean square error) was high (5.2 to 7.1 m) through all the 64 modeled conditions. With the estimated volume being lower than the actual volume, the RMSE volume measurements for each flight condition were from 0.31 to 0.4 m3. Therefore, irrespective of flight conditions for UAV measurements, accuracy was low with respect to the actual values.

Assessment of the Influence of Survey Design and Processing Choices on the Accuracy of Tree Diameter at Breast Height (DBH) Measurements Using UAV-Based Photogrammetry

“…Our experiments showed that a camera tilt of 60 • improved the DBH estimations when compared to nadir camera orientation (90 • ), a result that is consistent with previous studies [4,45] and can be explained by the tilted camera's better viewpoint of the trunks totally or partially hidden under the canopies. Initial tests conducted in our study also indicate that forward and side overlaps below 90% are insufficient for point cloud reconstruction of the tree trunks, as previous works have demonstrated [42,46,47].…”

“…where y i is the measured value,ŷ i is the estimated value, n is the total number of samples, and y is the mean of n measured values [42,43].…”

Measures of Canopy Structure from Low-Cost UAS for Monitoring Crop Nutrient Status

“…Future experiments to observed P deficiencies in field-grown tobacco are needed to test this. Many studies have focused on validating the accuracy of measurements taken from photogrammetric surface models, like crop height and volume, through field measurements and precise ground control [53,[55][56][57]. While the importance of accuracy for many measurements taken from canopy surface models is clear, the measurements of interest in this study focus on relative shape of the canopy rather than absolute quantities.…”

“…Future implementation of this approach should incorporate oblique imagery during point cloud generation to improve representation of canopy features, especially along plant row edges. Several studies [55,58,[60][61][62][63] have evaluated the impact of these considerations on feature reconstruction, but further research is needed to understand how sensitive the canopy structural metrics used in this study are to data acquisition and processing parameters. However, the relatively strong, consistent relationships between canopy structure and nutrients observed across all dates suggest that trends in canopy shape are being represented well enough in the data to provide meaningful, useful information.…”

Development of Mathematical Models for the Estimation of Dendrometric Variables Based on Unmanned Aerial Vehicle Optical Data: A Romanian Case Study

“…Research carried out under different flight conditions (altitude 60 to 140 m, overlap between 80 and 95%) using the same UAV [34] showed that these conditions have a negligible effect on errors when measuring height. For tree heights up to 26 m, errors often reach 4 m, and tend to increase as the height of the trees increases.…”