Determining factors that impact the calibration of consumer-grade digital cameras used for vegetation analysis

Nguy-Robertson, Anthony L.; Buckley, Emma M. Brinley; Suyker, Andrew S.; Awada, Tala

doi:10.1080/01431161.2016.1199061

Cited by 8 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Green Chromatic Coordinate (GCC) GCC = ρ green ρ red +ρ green +ρ blue GCC is originally designed for use with a digital RGB camera to measure wheat cover. [48] Green Leaf Index (GLI) GLI = 2ρ green − ρ red −ρ blue 2ρ green +ρ red +ρ blue GLI is sensitive to green leaves and can be used to measure leaf chlorophyll content. [49] Normalized Green-Red Difference Index (NGRDI) NGRDI = ρ green − ρ red ρ green +ρ red NGRDI is similar to NDVI, but uses the green band instead of the NIR band.…”

Section: Formulas Implication Referencementioning

confidence: 99%

Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8/9 Images

et al. 2022

View full text Add to dashboard Cite

Garlic is the major economic crop in China. Timely and accurate identification and mapping of garlic are significant for garlic yield prediction and garlic market management. Previous studies on garlic mapping were mainly based on all observations of the entire growing season, so the resulting maps have a hysteresis. Here, we determined the optimal identification strategy and the earliest identifiable phenophase for garlic based on all available Landsat 8/9 time series imagery in Google Earth Engine. Specifically, we evaluated the performance of different vegetation indices for each phenophase to determine the optimal classification metrics for garlic. Secondly, we identified garlic using random forest algorithm and classification metrics of different time series lengths. Finally, we determined the earliest identifiable phenophase of garlic and generated an early-season garlic distribution map. Garlic could be identified as early as March (bud differentiation period) with an F1 of 0.91. Our study demonstrates the differences in the performance of vegetation indices at different phenophases, and these differences provide a new idea for mapping crops. The generated early-season garlic distribution map provides timely data support for various stakeholders.

show abstract

Section: Formulas Implication Referencementioning

confidence: 99%

Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8/9 Images

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In order to track the seasonality ("phenology") of vegetation activity in different ecosystems, digital cameras have been deployed to record highfrequency images of the canopy at hundreds of research sites around the world (Richardson, 2018). From each image, color-channel information (e.g., RGB [red-green-blue] values of each pixel) are extracted and converted to a suite of "vegetation indices" derived from linear or nonlinear transformations of the RGB or other color spaces (Sonnentag et al, 2012;Mizunuma et al, 2014;Toomey et al, 2015;Nguy-Robertson et al, 2016). These indices have been used to identify the timing of seasonal phenomena such as leaf-out, senescence, and abscission, and to monitor how these phenomena are changing in response to ongoing climatic change (Sonnentag et al, 2012).…”

Section: Motivationmentioning

confidence: 99%

A spatial concordance correlation coefficient with an application to image analysis

et al. 2020

View full text Add to dashboard Cite

In this work we define a spatial concordance coefficient for second-order stationary processes. This problem has been widely addressed in a non-spatial context, but here we consider a coefficient that for a fixed spatial lag allows one to compare two spatial sequences along a 45 • line. The proposed coefficient was explored for the bivariate Matérn and Wendland covariance functions. The asymptotic normality of a sample version of the spatial concordance coefficient for an increasing domain sampling framework was established for the Wendland covariance function. To work with large digital images, we developed a local approach for estimating the concordance that uses local spatial models on non-overlapping windows. Monte Carlo simulations were used to gain additional insights into the asymptotic properties for finite sample sizes. As an illustrative example, we applied this methodology to two similar images of a deciduous forest canopy. The images were recorded with different cameras but similar fields-of-view and within minutes of each other. Our analysis showed that the local approach helped to explain a percentage of the non-spatial concordance and to provided additional information about its decay as a function of the spatial lag.As a consequence of the asymptotic normality of the sample CCC, an approximate hypothesis testing problem of the form H 0 : ρ c = ρ 0 versus H 1 : ρ c = ρ 0 for a fixed ρ 0 can be constructed. Alternatively, an approximate confidence interval of the form ρ c ± z α/2 σ 2 Z can be used, where z α/2 is the upper quantile of order α/2 of the standard normal distribution. Applications and extensions of Lin's coefficient can be found in Lin et al. (2012), among others.

show abstract

“…It is a simple matter of optics that low-altitude flights with the same camera will have smaller ground sample distances compared to high-altitude flights. Digital cameras used for UAS remote sensing vary substantially in price, lens quality, and features from consumer-oriented point-and-shoot cameras to professional cameras that have a high degree of control over the results (Lelong et al 2008;Nguy-Robertson et al 2016;O'Connor, Smith, and James 2017;Crusiol et al 2017). Lens quality affects the ability to resolve differences between 2 pixels; so, the total number of pixels in a camera is not a good indication that an image will have a small ground sample distance (Candiago et al 2015).…”

Section: Uas Remote Sensing In Precision Agriculturementioning

confidence: 99%

What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?

Hunt

Daughtry

2017

International Journal of Remote Sensing

201

121

View full text Add to dashboard Cite

Remote sensing from unmanned aircraft systems (UAS) was expected to be an important new technology to assist farmers with precision agriculture, especially crop nutrient management. There are three advantages using UAS platforms compared to manned aircraft platforms with the same sensor for precision agriculture: (1) smaller ground sample distances, (2) incident light sensors for image calibration, and (3) canopy height models created from structure-from-motion point clouds. These developments hold promise for future data products. In order to better match vendor capabilities with farmer requirements, we classify applications into three general niches: (1) scouting for problems, (2) monitoring to prevent yield losses, and (3) planning crop management operations. The three different niches have different requirements for sensor calibration and have different costs of operation. Planning crop management operations may have the most environmental and economic benefits. However, a USDA Economic Research Report showed that only about 20% of farmers in the USA have adopted variable rate applicators; so, most farmers in the USA may not have the technology to benefit from management plans. In the near-term, monitoring to prevent yield losses from weeds, insects, and diseases may provide the most economic and environmental benefits, but the costs for data acquisition need to be reduced.

show abstract

Determining factors that impact the calibration of consumer-grade digital cameras used for vegetation analysis

Cited by 8 publications

References 40 publications

Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8/9 Images

Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8/9 Images

A spatial concordance correlation coefficient with an application to image analysis

What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?

Contact Info

Product

Resources

About