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10 October 2018 Development of field scale model for estimating barley growth based on UAV NDVI and meteorological factors
Chan-won Park, Sang-il Na, Kyu-ho So, Ho-yong Ahn, Kyung-do Lee
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Proceedings Volume 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX; 107831P (2018) https://doi.org/10.1117/12.2326273
Event: SPIE Remote Sensing, 2018, Berlin, Germany
Abstract
Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVIUAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVIUAV and growth parameters was shown. Correlation analysis between NDVIUAV and barley growth parameters revealed that NDVIUAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVIUAV, the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVIUAV was applied to power function. From these results, NDVIUAV can be used as a new tool for monitoring barley growth.
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Chan-won Park, Sang-il Na, Kyu-ho So, Ho-yong Ahn, and Kyung-do Lee "Development of field scale model for estimating barley growth based on UAV NDVI and meteorological factors", Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 107831P (10 October 2018); https://doi.org/10.1117/12.2326273
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KEYWORDS
Unmanned aerial vehicles
Vegetation
Agriculture
Statistical analysis
Near infrared
Remote sensing
Sensors
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