Estimating Forage Utilization with Drone-Based Photogrammetric Point Clouds

Gillan, Jeffrey K.; McClaran, Mitchel P.; Swetnam, Tyson L.; Heilman, Philip

doi:10.1016/j.rama.2019.02.009

Cited by 27 publications

(35 citation statements)

References 45 publications

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“…Each point has associated uncertainty information. Points with high uncertainty are likely to be inaccurate, so it is standard practice to improve model quality by systematic elimination of points that fail to meet defined thresholds [134]. Uncertain points were identified via two passes of Agisoft's Gradual Selection tool, with threshold set to reconstruction uncertainty = 10 [135] (as recommended by Mayer et al, 2018), and then deleted.…”

Section: Model Generation Using Agisoft Photogrammetry Softwarementioning

confidence: 99%

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Nagle-McNaughton

Cox

2019

Remote Sensing

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Repeat photogrammetry is increasingly the go-too tool for long-term geomorphic monitoring, but quantifying the differences between structure-from-motion (SfM) models is a developing field. Volumetric differencing software (such as the open-source package CloudCompare) provides an efficient mechanism for quantifying change in landscapes. In this case study, we apply this methodology to coastal boulder deposits on Inishmore, Ireland. Storm waves are known to move these rocks, but boulder transportation and evolution of the deposits are not well documented. We used two disparate SfM data sets for this analysis. The first model was built from imagery captured in 2015 using a GoPro Hero 3+ camera (fisheye lens) and the second used 2017 imagery from a DJI FC300X camera (standard digital single-lens reflex (DSLR) camera); and we used CloudCompare to measure the differences between them. This study produced two noteworthy findings: First, volumetric differencing reveals that short-term changes in boulder deposits can be larger than expected, and that frequent monitoring can reveal not only the scale but the complexities of boulder transport in this setting. This is a valuable addition to our growing understanding of coastal boulder deposits. Second, SfM models generated by different imaging hardware can be successfully compared at sub-decimeter resolution, even when one of the camera systems has substantial lens distortion. This means that older image sets, which might not otherwise be considered of appropriate quality for co-analysis with more recent data, should not be ignored as data sources in long-term monitoring studies.Despite these improvements in protocols for data collection and analysis, quantifying change via repeat photogrammetry remains a challenge, generally requiring manipulation of digital terrain models (DTMs) using Geographic Information Systems (GIS) [45,46]. But recent innovations in 3D differencing software, as implemented in the open-source package CloudCompare (www.danielgm.net/cc/) enable rapid, objective, and replicable quantitative differencing [47]. CloudCompare is a cross-platform 3D point cloud and mesh processing package, which provides a suite of features for direct comparison of dense point clouds (>10 million points) and meshes on standard consumer computer hardware [48,49]. Accessibility and ease of use make CloudCompare an attractive tool, and it is applied increasingly in a variety of fields, including mapping [50,51], archaeology [52,53], and geomorphology [54][55][56][57].Another issue relevant to long term monitoring studies is how to deal with repeat imagery captured using different kinds of hardware, with different resolutions and/or distortion levels. The ultra-wide-angle (fisheye) lenses mounted on the popular consumer camera brand GoPro capture a~120 • field of view, but have barrel distortion due to their short focal length. Despite their limitations, GoPro cameras are popular tools for acquiring UAV imagery [24,31,[58][59][60], they are relatively inexpensive, are light enou...

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Section: Model Generation Using Agisoft Photogrammetry Softwarementioning

confidence: 99%

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Nagle-McNaughton

Cox

2019

Remote Sensing

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“…Lately, Wijesingha et al (2019) investigated terrestrial laser scanning (TLS) and lowcost UAV-based image acquisition for SfM/MVS analysis for biomass prediction and report promising results for both methods. For grazing systems, Gillan et al (2019) achieved an R 2 of 0.78 and Michez et al (2019) of 0.23-0.49. Bareth and Schellberg (2018) investigated the correlation of lowcost UAV-derived DSMs with RPM data and achieved a high R 2 of 0.86 for a 3-year data analysis.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

Lussem

Schellberg²,

Bareth

2020

PFG

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Monitoring and predicting above ground biomass yield of grasslands are of key importance for grassland management. Established manual methods such as clipping or rising plate meter measurements provide accurate estimates of forage yield, but are time consuming and labor intensive, and do not provide spatially continuous data as required for precision agriculture applications. Therefore, the main objective of this study is to investigate the potential of sward height metrics derived from low-cost unmanned aerial vehicle-based image data to predict forage yield. The study was conducted over a period of 3 consecutive years (2014–2016) at the Rengen Grassland Experiment (RGE) in Germany. The RGE was established in 1941 and is since then under the same management regime of five treatments in a random block design and two harvest cuts per year. For UAV-based image acquisition, a DJI Phantom 2 with a mounted Canon Powershot S110 was used as a low-cost aerial imaging system. The data were investigated at different levels (e.g., harvest date-specific, year-specific, and plant community-specific). A pooled data model resulted in an R2 of 0.65 with a RMSE of 956.57 kg ha−1, although cut-specific or date-specific models yielded better results. In general, the UAV-based metrics outperformed the traditional rising plate meter measurements, but was affected by the timing of the harvest cut and plant community.

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“…Benefits and limitations exist with all techniques, but UAVs whether alone or in fusion with other techniques promise to be a major enabling technology. The scientific literature over the past few years shows emerging benefits from UAV-captured photogrammetric, multispectral and hyperspectral imaging of pasture condition with examples from the USA, Brazil, Europe, Australia and South Africa [13][14][15][16]. An optimum solution using data fusion possibly exists, combining the best aspects of all technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Modelling of this relationship with UAV SfM is becoming more popular and several studies have reported strong correlation between UAV derived imagery and ground-based estimates. [13,14] reported average R 2 values as high as 0.78 and 0.81 in both the native pastures of Arizona, USA and a cultivated pasture in northern Germany. This case study over a number of dominant grasslands and woodlands of the rangelands of Queensland, attempted to develop a similar technique that, as previously stated, could be both used to potentially cross calibrate satellite imagery for broader applications across the rangelands of Queensland and as a standalone measure of pasture biomass at the field, paddock and property scales for improved pasture management.…”

Section: Introductionmentioning

confidence: 99%

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

2020

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The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

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Estimating Forage Utilization with Drone-Based Photogrammetric Point Clouds

Cited by 27 publications

References 45 publications

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

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