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

Abstract

Abstract: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 R 2 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).

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

“…The derivatives produced during the SfM procedure consisted of a set of points, which contained X, Y, Z information, as well as the color information derived from the photographs. These SfM outputs relied on an internal arbitrary coordinate system that had to be transformed to real-world coordinates [29]. The dense 3D point cloud and the total images were used to create a 3D mesh of the scene geometry, high-resolution orthophotos, DSMs, and digital terrain models (DTM) for both study areas.…”

“…The potential use of the UAV-SfM pipeline has been evaluated in several studies [5,6,8,19,29]. However, references about the use of GEOBIA with the geographic information produced by the UAV-SfM methodology for coastal monitoring are still scarce in the recent scientific bibliography.…”

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

“…Personal remote sensing systems have enabled accurate mapping of canopy height and biomass density as well as the discrimination of individual tree structural, spectral, and phenological traits [14,[21][22][23]. Similar systems have also been used for mapping stream channel geomorphology [24,25], vineyard and orchard plant structure [26][27][28], the topography of bare substrates [29][30][31][32][33][34], and lichen and moss extent and coverage [16].…”

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

“…Single plant representation could also provide complementary data to recent studies of structure from motion vineyard canopy modelling. (Mathews & Jensen, 2013) For canopies with discrete spatial distribution, such as the goblet training system, the problem has been already profitably addressed with a point pattern analysis approach. (Robbez-Masson & Foltête, 2005) Nevertheless, for the more common training systems in which vines are planted in rows, new robust and reliable techniques need to be developed.…”