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

Abstract

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.

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

“…In addition, radiometric corrections like histogram equalization [75] or image block homogenization [76] may be useful to reduce the influence of variable scene lighting when it is not possible to collect images under constant lighting over the duration of entire flight [14]. 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 Difference Vegetation Index) at high spatial resolution and in 3D, providing links between canopy structure, optical properties, and biophysical parameters. Future research should also consider the influence of different spectral bands on reconstructions of canopy structure from SFM, for example this and prior studies used RGB imagery for modeling canopy height [14,22], while other studies achieved comparable results using RG-NIR imagery [21,23], and it is not clear what, if any, effect the NIR information has on estimates of canopy structure compared to RGB alone.…”

“…Consumer-grade UAVs have reached a degree of technical maturity that, when equipped with digital cameras or other sensors, enable rapid and on-demand 'personal remote sensing' of landscapes at high spatial and temporal resolution [12][13][14][15][16][17][18][19]. At the same time, automated computer vision SFM algorithms enable the creation of LIDAR-like (Light Detection and Ranging) three-dimensional (3D) point clouds produced from images alone with color spectral information from images associated with each point [20].…”

Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments

“…Using texture and/or the intensity-hue-saturation components can compensate for the low radiometric and spectral resolution of consumer cameras [9,21], although the lack of a near infrared band poses certain limitations for vegetation characterization. Quality lightweight multispectral sensors suitable for use on small UAS have not been widely available in the past, and one alternative has been to alter consumer cameras to acquire images in the near infrared band [14,22,23]. A multispectral sensor that captures data over a range of relatively narrow wavelength bands is preferable for vegetation applications because of the potential for quantitative remote sensing, retrieval of biophysical parameters, better differentiation of vegetation species, and greater suitability for comparison with satellite imagery.…”

“…This increase has been observed not only in practical applications, but also in the peer-reviewed literature. Two recent special issues on UAS for environmental remote sensing applications in the journals Geocarto International [1] and GIScience and Remote Sensing [2] as well as other recent publications reflect the growing acceptance by the remote sensing community of UAS as suitable platforms for acquiring quality imagery and other data for various application such as wildfire mapping [3,4], arctic sea ice and atmospheric studies [5], detection of invasive species [6], rangeland mapping [7][8][9], hydrology and riparian applications [10][11][12], and precision agriculture [13][14][15][16]. Due to limited payload capacities on small unmanned aerial vehicles (<50 kg), consumer digital cameras are often used.…”

Preliminary Assessment of Low-cost Unmanned Aerial Systems (UAS) for Creating Aerial Photographs for Natural Resource Research

“…We investigated these characteristics for four craft, commercial fixedwing and multirotor UASes and handbuilt fixedwing and multirotor UASes, and postprocessed our imagery with both commercial and opensource software. For this study, we did not investigate the spectral characteristics of the consumergrade cameras typically used on lowcost UAS since we were largely interested in truecolor, traditional aerial photography for visual analysis (see Hunt et al 2010 andRabatel et al 2012).…”