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Title: Citrus greening detection using airborne hyperspectral and multispectral imaging techniques

item KUMAR, ARUN - University Of Florida
item LEE, WON - University Of Florida
item EHSANI, REZA - University Of Florida
item L., ALBRIGO - University Of Florida
item Yang, Chenghai
item Mangan, Robert

Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/30/2012
Publication Date: 8/10/2012
Citation: Kumar, A., Lee, W.S., Ehsani, R., L., A.G., Yang, C., Mangan, R.L. 2012. Citrus greening detection using airborne hyperspectral and multispectral imaging techniques. Journal of Applied Remote Sensing (JARS). 6:063542.

Interpretive Summary: Huanglongbing (HLB), also known as citrus greening, is a serious citrus disease that affects all citrus cultivars and causes rapid deterioration of citrus trees. Early detection is very critical for the management and control of the disease. This study evaluated high resolution airborne multispectral and hyperspectral imagery for identifying HLB-infected citrus trees in Florida. Several image analysis techniques were used to separate infected trees from healthy trees based on the imagery. Preliminary results showed that high resolution imagery in conjunction with image analysis techniques can identify infected trees with the yellowing symptom, but more research is needed to improve the detection accuracy.

Technical Abstract: Hyperspectral imaging can provide unique spectral signatures for diseased vegetation. Airborne multispectral and hyperspectral imaging can be used to detect potentially infected trees over a large area for rapid detection of infected zones. This paper proposes a method to detect the citrus greening disease (Huanglongbing or HLB) infected areas in citrus groves using airborne hyperspectral and multispectral imaging. Two sets of hyperspectral images were acquired for this study, first in the year 2007 and another set in late 2009, both using a different imaging system and selecting a different citrus grove in Florida. The images acquired in 2007 had a spectral range of 397-997 nm with a resolution of 5 nm and 0.7 m spatial resolution. The 2009 images had a spectral range of 457-921 nm spanning across 128 spectral bands with a 3.6 nm spectral resolution and 1 m spatial resolution. The multispectral images with four spectral bands were also acquired in 2009 for the same grove. A comprehensive ground truthing based on ground measurements and visual check of the citrus grove trees was used in validation of results using 2007 images. A more accurate polymerase chain reaction (PCR) test for selected trees from ground truthing had been carried out in the citrus grove under study in 2009. In both the groves the infected tree coordinates were recorded. An image derived spectral library was built using the above images and categories of Healthy and HLB infected pixels were created based on the ground truthing information. Ground measurements were obtained for Healthy and HLB infected citrus trees from the same grove along with their degrees of infection. HLB infected areas were identified using image-derived spectral library, the mixture tuned match filtering (MTMF), the spectral angle mapping (SAM), the spectral feature fitting (SFF) and linear spectral unmixing (LSU). It was seen that accuracy of MTMF method was greater than the other methods. The accuracy of SAM using multispectral images was comparable to the results of the MTMF and also gave higher accuracy when compared to SAM analysis on hyperspectral images.