DocumentCode
720482
Title
A detailed field study of direct correlations between ground truth crop water stress and normalized difference vegetation index (NDVI) from small unmanned aerial system (sUAS)
Author
Tiebiao Zhao ; Stark, Brandon ; Yangquan Chen ; Ray, Andrew L. ; Doll, David
Author_Institution
Embedded Syst. & Autom. Lab., Univ. of California, Merced, Merced, CA, USA
fYear
2015
fDate
9-12 June 2015
Firstpage
520
Lastpage
525
Abstract
Aerial images with high spatial resolution and high temporal resolution were used to detect water stress based on canopy level normalized difference vegetation index (NDVI). We attempted to determine the correlation between stem water potential (SWP) and canopy NDVI with and without shade. Results indicated that removing the shade from the canopy improved the correlation between the NDVI of canopy and SWP with coefficient of determination (R2) from 0.001 to 0.0052. We further compared SWP and the NDVI of the canopy without shade over a period of one week to four weeks. The correlation between NDVI with SWP was highest in the time range of three weeks. However, both cases show that there is no obvious relationship between NDVI of canopy and SWP. Therefore, canopy level NDVI does not indicate water stress. Further research is needed beyond pretty pictures.
Keywords
autonomous aerial vehicles; crops; image resolution; industrial robots; SWP; aerial images; canopy NDVI; canopy level normalized difference vegetation index; ground truth crop water stress; sUAS; small unmanned aerial system; spatial resolution; stem water potential; temporal resolution; water stress detection; Cameras; Correlation; Irrigation; Stress; Vegetation; Water resources; NDVI (normalized difference vegetation index); SWP (stem water potential); crop canopy; crop water stress;
fLanguage
English
Publisher
ieee
Conference_Titel
Unmanned Aircraft Systems (ICUAS), 2015 International Conference on
Conference_Location
Denver, CO
Print_ISBN
978-1-4799-6009-5
Type
conf
DOI
10.1109/ICUAS.2015.7152331
Filename
7152331
Link To Document