A Ground Penetrating Radar (GPR at 1.5 GHz) has been used to help determine the material type at different subsurface layers. Based on the incidence and reflected electromagnetic waves, a new method was devised which determines Material Characteristics in Fourier Domain (MCFD), which can be used for material identification. MCFD is calculated at every reflection. Each reflection is caused by sufficient change in dielectric constant of two different soil layers. Working in frequency domain the effect of the media is separated by using the wavelet of the electromagnetic signal before and after it is reflected from the media. An algorithm is developed which obtains the MCFD which defines material type by the use of a 2-layer Back-propagation Neural Network (NN). Material type can be determined irregardless of layer at which the object is buried (limited to GPR reflection intensity level), or object size, or if an extended subsurface layer is present. In this method, GPR images for different material types at different layers were obtained; up to two levels of mixed material types such as sand, clay, loam, rock, broken jar, etc. were considered.
Changes in vegetation can affect our health, the environment and the economy. Understanding this, twenty years ago scientists began to use satellite remote sensors to monitor major fluctuations in vegetation and understand how it affects the environment. The pixel accuracy of some Synthetic Aperture Radar (SAR) satellites is now at near one meter resolution. A new formulation of vegetation index using such active sensors will greatly improve the Vegetation health accuracy. Attempt has been made by M. Tokunaga to relate ERS-SAR satellite sensor data of vegetation canopies to the LANDSAT TM satellite sensor measurements, both at 30 meter resolution. A correlation was observed above Normalized Difference Vegetation Index (NDVI) of 0.4, but their experiment was not based on the data taken by the two satellite sensors at the same time period. In this research, a correlation is determined between the active and passive measurements of the vegetation index, at very high resolution. The measurements take place at the near ground level over varied vegetation health, using a Ground Penetrating Radar (GPR), and a handheld Spectrometer. The GPR and the handheld Spectrometer have the same field of view, so it is possible to compare data for the whole range of NDVI. Both measurements take place one right after the other, to allow an accurate comparison. The goal of this research is to define a new vegetation index, using active sensors. The GPR operating at 1.5 GHz produces images that contain backscatter signals obtained from vegetation. These images are processed by a filter to eliminate clutter and noise. The Fourier amplitude and phase characteristics of the vegetation health are extracted from the backscatter signal. The same vegetation is subjected to the spectrometer measurements. Our results show a linear correlation between power of GPR backscatter signal and the NDVI as calculated by the spectrometer data. As a continuity of this work, the ground validation will be compared to the active/passive satellite sensors for the measurement of vegetation health.