Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud

Remote Sensing

Adam J. Mathews¹ 

关键词: structure from motion;    SfM;    bundle adjustment;    point cloud;    LAI;    vegetation;    UAV;    vineyard;   

DOI  :  10.3390/rs5052164

来源: mdpi

【 摘 要 】

This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and oblique) were collected and used to create a SfM point cloud. All points were classified as ground or non-ground points. Non-ground points, presumably representing vegetation and other above ground objects, were used to create visualizations of the study vineyard blocks. Further, the relationship between non-ground points in close proximity to 67 sample vines and collected leaf area index (LAI) measurements for those same vines was also explored. Points near sampled vines were extracted from which several metrics were calculated and input into a stepwise regression model to attempt to predict LAI. This analysis resulted in a moderate R² value of 0.567, accounting for 57 percent of the variation of LAI_SQRT using six predictor variables. These results provide further justification for SfM datasets to provide three-dimensional datasets necessary for vegetation structure visualization and biophysical modeling over areas of smaller extent. Additionally, SfM datasets can provide an increased temporal resolution compared to traditional three-dimensional datasets like those captured by light detection and ranging (lidar).

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland