Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras

Abstract

Consumer-grade digital cameras are recognized as a cost-effective method of monitoring plant health and phenology. The capacity to use these cameras to produce time series information contributes to a better understanding of relationships between environmental conditions, vegetation health, and productivity. In this study we evaluate the use of consumer grade digital cameras modified to capture infrared wavelengths for monitoring vegetation. The use of infrared imagery is very common in satellite remote sensing, while most current near sensing studies are limited to visible wavelengths only. The use of infrared-visible observations is theoretically superior over the use of just visible observation due to the strong contrast between infrared and visible reflection of vegetation, the high correlation of the three visible bands and the possibilities to use spectral indices like the Normalized Difference Vegetation Index. This paper presents two experiments: the first study compares infrared modified and true color cameras to detect seasonal development of understory plants species in a forest; the second is aimed at evaluation of spectrometer and camera data collected during a laboratory plant stress experiment. The main goal of the experiments is to evaluate the utility of infrared modified cameras for the monitoring of plant health and phenology.Results show that infrared converted cameras perform less than standard color cameras in a monitoring setting. Comparison of the infrared camera response to spectrometer data points at limits in dynamic range, and poor band separation as the main weaknesses of converted consumer cameras. Our results support the use of standard color cameras as simple and affordable tools for the monitoring of plant stress and phenology.3

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

“…The RGNIR camera was modified by removing the internal hot-mirror filter and adding a blue-light-blocking filter (i.e., a yellow long-pass filter). Adapted from Nijland et al (2014) We extracted from each of the image objects a set of metrics (Table 4) at eight scales of analysis. In addition to the six OBIA scales, we tested one pixel-based scale (4 pixels square objects) and the manually delineated tree crown scale.…”

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

“…As a result, cross-site prediction of leaf pigments showed 461 reasonable accuracy (Table 5). It also has important implications for the design of multi-band 462 sensors and imagers as it can select the wavelengths that are most useful for the leaf traits of 463 interest (Nijland et al 2014;Ryu et al 2010). 464…”

An Airborne Multispectral Imaging System Based on Two Consumer-Grade Cameras for Agricultural Remote Sensing

“…This process will introduce some error and smoothing effect on the image, especially in the areas where rapid color transition exists. Although the NIR-converted camera only captures NIR light, the Bayer filter remains and only the NIR values captured by the red-channel is used due to its higher sensitivity [20]. In contrast, a conventional four-camera system designates one camera for each band to capture non-interpolated digital values for each pixel.…”

“…Therefore, the transmission profiles of the Bayer color channels remain when using RGB cameras with the NIR blocking filter replaced. All three channels in the converted camera only record NIR radiation, but the red-channel has the best sensitivity [20]. As a result, the image recorded in the red-channel was used as the NIR image.…”

“…Therefore, it is possible to replace the blocking filter by a long-pass infrared filter on standard CCD or CMOS sensors for obtaining NIR images. Studies have been conducted on the use of NIR-converted digital cameras for monitoring plant conditions and results from these studies support their use as simple and affordable tools for plant stress detection and growth monitoring [20,21].…”