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| Web URL(s): | https://scisoc.confex.com/crops/2012am/webprogram/Paper71850.html
Last checked: 03/03/2017 |
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| Publication Type:
| Report |
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| Content Type: | Abstract or Summary only |
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| Author(s): | Dworak, Scott;
Gaussoin, Roch;
Singh, Vishal |
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| Author Affiliation: | Dworak and Gaussoin: University of Nebraska-Lincoln; Singh: Pixobot, LLC, Lincoln, NE |
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| Title: | Drones make sense: Quantifying turf quality using unmanned aerial vehicle-based near-infrared digital image analysis |
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| Section: | Establishment, thatch, soil and water management in turfgrass graduate student competition
Other records with the "Establishment, thatch, soil and water management in turfgrass graduate student competition" Section
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| Meeting Info.: | Cincinnati, Ohio: October 21-24, 2012 |
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| Source: | ASA, CSSA and SSSA Annual Meetings [2012]. 2012, p. 71850. |
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| Publishing Information: | Madison, Wisconsin: American Society of Agronomy and Crop Science Society of America and Soil Science Society of America |
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| # of Pages: | 1 |
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| Keywords: | TIC Keywords: Aerial photography; Chlorophyll index; Image analysis; Normalized Difference Vegetation Index; Reflectance; Turfgrass quality
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| Abstract/Contents: | "Digital image analysis (DIA) is a remote sensing technique using digital cameras to collect and analyze visible light reflected from turf surfaces. DIA provides quantitative quality and cover scores, which correlate well with visual ratings and exhibit less variance. Field-based DIA has limitations, however. Changing sky conditions may alter turf reflectance. Collecting turf imagery under equal sunlight conditions averts this but is often impractical. Imaging individual plots can also be time-consuming. Autonomous unmanned aerial vehicles (UAVs) provide a practical solution. UAVs offer an imaging platform that facilitates collection of temporally dense, high-resolution imaging data. A single aerial image eliminates variables such as changing skies, resulting in improved, consistent image collection. Furthermore, UAVs can image large areas quickly and be automated with GPS, resulting in image capture of entire studies in several minutes. Traditional DIA is associated with visible wavelength reflectance, yet many turf stresses largely impact near-infrared (NIR) reflectance. An aerial DIA-based NIR evaluation method was developed to quantify NIR reflectance using NIR-modified commercially available digital cameras and software. Chlorophyll index (CI) and normalized difference vegetation index (NDVI) data were generated using digital values from red channels of true-color and NIR-modified cameras. These indices were highly correlated with handheld CI (R2 = 0.76) and NDVI (R2 = 0.67) sensors in field studies; traditional dark green color index (DGCI) correlations were weaker (R2 = 0.69 and 0.55, respectively). This suggests UAV-based NIR evaluation improves traditional DIA by providing reliable CI and NDVI data, is equivalent to using handheld sensors, and saves appreciable time. UAV-based NIR DIA is an attractive addition to existing DIA systems and is a promising technology with application in turfgrass science, golf course management and other large-scale turf areas." |
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| Language: | English |
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| References: | 0 |
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| Note: | This item is an abstract only |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Dworak, S., R. Gaussoin, and V. Singh. 2012. Drones make sense: Quantifying turf quality using unmanned aerial vehicle-based near-infrared digital image analysis. Int. Ann. Meet. p. 71850. |
| Fastlink to access this record outside TGIF: https://tic.msu.edu/tgif/flink?recno=213132 |
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| Web URL(s):
https://scisoc.confex.com/crops/2012am/webprogram/Paper71850.html
Last checked: 03/03/2017 |
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