Abstract
Monitoring the development of vegetation height through time provides a key indicator of crop health and overall condition. Traditional manual approaches for monitoring crop height are generally time consuming, labor intensive and impractical for large-scale operations. Dynamic crop heights collected through the season allow for the identification of within-field problems at critical stages of the growth cycle, providing a mechanism for remedial action to be taken against end of season yield losses. With advances in unmanned aerial vehicle (UAV) technologies, routine monitoring of height is now feasible at any time throughout the growth cycle. To demonstrate this capability, five digital surface maps (DSM) were reconstructed from high-resolution RGB imagery collected over a field of maize during the course of a single growing season. The UAV retrievals were compared against LiDAR scans for the purpose of evaluating the derived point clouds capacity to capture ground surface variability and spatially variable crop height. A strong correlation was observed between structure-from-motion (SfM) derived heights and pixel-to-pixel comparison against LiDAR scan data for the intra-season bare-ground surface (R2 = 0.77 − 0.99, rRMSE = 0.44% − 0.85%), while there was reasonable agreement between canopy comparisons (R2 = 0.57 − 0.65, rRMSE = 37% − 50%). To examine the effect of resolution on retrieval accuracy and processing time, an evaluation of several ground sampling distances (GSD) was also performed. Our results indicate that a 10 cm resolution retrieval delivers a reliable product that provides a compromise between computational cost and spatial fidelity. Overall, UAV retrievals were able to accurately reproduce the observed spatial variability of crop heights within the maize field through the growing season and provide a valuable source of information with which to inform precision agricultural management in an operational context.
Highlights
Over the last decade, there has been a surge of interest in the development and use of unmanned aerial vehicles (UAVs) for agricultural and environmental applications [1,2,3]
Several characteristics of the pivot development can be depicted from the digital surface maps (DSM) and crop height retrievals
A better understanding of this behavior can be obtained by examining Figure 4, which plots the anomaly of crop height around the mean value within the field, from which the percentage of crop height localized between a certain range (−0.6:0.6 m) for each UAV campaign was estimated
Summary
There has been a surge of interest in the development and use of unmanned aerial vehicles (UAVs) for agricultural and environmental applications [1,2,3]. With advances in UAV technologies, routine monitoring of height is feasible at any time of the growth cycle, providing spatially explicit retrievals on an as-needed or taskable basis [4] This represents a valuable information resource in terms of crop production and for more general agricultural management, facilitating the detection of intra-field spatial variability that may result from ineffective irrigation practices, fertilizer variability, as well as salinity and other soil property issue. Specific objectives for this study include: (1) evaluating the capacity of a fixed-wing UAV to drive SfM based 3D canopy models at the large field scale, repeatedly and consistently over time; (2) testing and applying a repeatable processing workflow to derive crop height; (3) investigating the accuracy of SfM plant height estimates when compared directly to LiDAR ground measurements; and (4) assessing this comparison at multiple resolution scales (2.5, 5, 10 and 20 cm). To ensure reliable post-processing, the dark portion of each picture was cropped and each EXIF header file updated to include a lower image resolution compared to the other surveys (Table 1)
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