Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring

By E. R. Hunt, W. Hively, Stephen J. Fujikawa, D. S. Linden, C. Daughtry, G. W. McCarty. Remote Sensing, 2010

503 citations recorded by the source corpus.

Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the camera-UAV system may provide important information for site-specific agriculture.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/339558632f2d718136a723009de5cbccadfc079c

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Citations in the corpus

High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision (2013, unknown): “UAS are increasingly being deployed for low-cost, on-demand aerial photography and photogrammetry applications (Harwin & Lucieer, 2012; Hunt et al., 2010; Rango et al., 2009).”
An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds (2012, background): “UAVs are capable of flying much lower and hence can collect imagery at a much higher resolution [2,3], often at a sub-decimetre resolution, even as detailed as 1 cm/pixel.”
Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure (2015, background): “It is also possible to carry out near-infrared remote sensing with UAVs using modified off-the-shelf digital cameras [19,21,23] or custom light-weight multi-spectral cameras [16,77,78] and future work should consider the potential of using such sensors for SFM mapping of canopy NDVI (Normalized Diff”
Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture (2015, methodology): “The Unmanned Aerial Vehicle (UAV) remote sensing platforms are among the technologies that have been recently applied to remote sensing of vegetated areas [3–5] and applied to PV [6–8], proving a high flexibility of use, low operational costs and very high spatial resolution [9], down to 1 cm.”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, background): “…traits of a crop can be estimated by building the digital surface model (DSM) or digital elevation model (DEM) and conducting image classification analysis, which can be used to estimate the plant height, lodging area proportion, emergence, etc. (Hunt et al., 2005, 2010; Li J. W. et al., 2015).”
Direct Georeferencing of Ultrahigh-Resolution UAV Imagery (2014, methodology): “Flying at low altitudes (50–120 m AGL), UAV imagery also provides superior spatial resolution (on the order of 1 cm/pixel) when compared with the resolution typically available from digital aerial photography platforms (25 cm/pixel) [8].”