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ARS Home » Pacific West Area » Parlier, California » San Joaquin Valley Agricultural Sciences Center » Water Management Research » Research » Publications at this Location » Publication #339118
Research Project: Develop Water Management Strategies to Sustain Water Productivity and Protect Water Quality in Irrigated Agriculture

Location: Water Management Research

Title: A new framework for UAV-based remote sensing data processing and its application in almond water stress quantification

Author
item ZHAO, TIEBIAO - University Of California
item DOLL, DAVID - University Of California - Cooperative Extension Service
item Wang, Dong
item CHEN, YANGQUAN - University Of California

Submitted to: Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 4/12/2017
Publication Date: 6/28/2017
Citation: Zhao, T., Doll, D., Wang, D., Chen, Y. 2017. A new framework for UAV-based remote sensing data processing and its application in almond water stress quantification. In 2017 International Conference on Unmanned Aircraft Systems (ICUAS’17), June 13-16, Miami, FL. p. 1794-1799.

Interpretive Summary:

Technical Abstract: With the rapid development of small imaging sensors and unmanned aerial vehicles (UAVs), remote sensing is undergoing a revolution with greatly increased spatial and temporal resolutions. While more relevant detail becomes available, it is a challenge to analyze the large number of images to extract useful information. This research introduces a new general framework to process high-resolution multi-spectral images based on principle component analysis (PCA) for crop stress quantification. As a case study, this framework is applied in almond water stress quantification using UAV-based remote sensing images. First, crop distributions of pixel value of sample trees are obtained as histograms consisting of 255 bins, assuming the stress information lies in the overall canopy pixels and ignoring the spatial relations among pixels. Second, PCA is applied to extract principle components out of histograms of 255 dimensions. This approach is advantageous in that it makes no assumption about the underlying canopy distribution of pixel values. It is shown that the first principle component has a significant correlation with stem water potential. This method is also compared with the traditional method of using the mean values of canopy Normalized Difference Vegetation Index or NDVI as a baseline, and it shows improved performance in predicting the water stress.