Raman spectra signature can provide structural information based on vibrational transitions of
irradiated molecules. In this work, the quantity reflecting mechanism of soil phosphorus
concentration was studied based on Raman spectroscopy. 15 sand soil samples with different
phosphate content were made in laboratory and the Raman signatures were measured. The
relationship between sand soil Phosphorus concentration and soil Raman spectra was explored. Then
the effective Raman signal was extracted from the original Raman spectra by using bior4.4 wavelet
packet. The relationship between sand soil phosphorus and their extracted signals was analyzed and
the PLS (Partial Least Squares) model for predicting phosphorus concentration in the soil was
established and compared. The maximum accuracy model comes from the extracted effective Raman
spectra after the first level decomposing. The calibration R2 was close to 1 and the validation R2
reached to 0.937. It showed high potential in soil phosphorus detecting by using Raman
Airborne multispectral and hyperspectral imaging can be used to detect potentially diseased trees rapidly over a large area using unique spectral signatures. Ground inspection and management can be focused on these detected zones, rather than an entire grove, making it less labor-intensive and time-consuming. We propose a method to detect the areas of citrus groves infected with citrus greening disease [Huanglongbing (HLB)] using airborne hyperspectral and multispectral imaging. This would prevent further spread of infection with efficient management plans of infected areas. Two sets of hyperspectral images were acquired in 2007 and 2009, from different citrus groves in Florida. Multispectral images were acquired only in 2009. A comprehensive ground truthing based on ground measurements and visual check of the citrus trees was used for validating the results using 2007 images. In 2009, a more accurate polymerase chain reaction test for selected trees from ground truthing was carried out. With a handheld spectrometer, ground spectral measurements were obtained along with their degrees of infection. A hyperspectral imaging software (ENVI, ITT VIS) was used for the analysis. HLB infected areas were identified using image-derived spectral library, mixture tuned matched filtering (MTMF), spectral angle mapping (SAM), and linear spectral unmixing. The accuracy of the MTMF method was greater than the other methods. The accuracy of SAM using multispectral images (87%) was comparable to the results of the MTMF and also yielded higher accuracy when compared to SAM analysis on hyperspectral images. A possible inaccurate ground truthing for the grove in 2007 generated more false positives.
Eutrophication disturbs the ecological balance in the Lake Okeechobee due to high concentration of phosphorus emanated from the regions in the lake's drainage basin.
Ability of measuring phosphorus (P) concentrations of water in the Lake Okeechobee itself is very important. Furthermore, monitoring P in its drainage basins is crucial in order to find the cause of P loading and contributing regions.
Also, inexpensive real-time sensing capability for a large area in a short time would help scientist, government agents, and civilians to understand the causes, spot the high-risk areas, and develop management practices for restoring the natural equilibrium.
In order to measure P concentrations in the Lake Okeechobee drainage basin, airborne hyperspectral images were taken from five representative target sites by deploying a modified queen air twin engine aircraft. Each flight line covered a swath of approximately 365 m wide. Spatial resolution was about 1 m. Spectral range covered was between 412.65 and 991.82 nm with an approximate of 5 nm spectral resolution. Ground truthing was conducted to collect soil and vegetation samples, GPS coordinates of each location, and reflectance measurement of each sample. On the ground, spectral reflectance was measured using a handheld spectrometer in 400-2500 nm. The samples were sent to a laboratory for chemical analysis. Also diffuse reflectance of the samples was measured in a laboratory setting using a spectrophotometer with an integrating sphere. Images were geocorrected and rectified to reduce geometric effect. Calibration of images was conducted to obtain actual reflectance of the target area. Score, SAM (Spectral Angle Mapping), SFF (Spectral Feature Fitting) were computed for spectral matching with image derived spectral library.
Conference Committee Involvement (1)
Sensing Technologies for Biomaterial, Food, and Agriculture 2013