Simulated Landsat reflectance spectra of soil samples were compared to actual Landsat radiance values of soils in two fields (1 and 3) near Vance, Mississippi. The simulated reflectance spectra were calculated by combining Landsat spectral sensitivity with laboratory-based spectrophotometer reflectance values. The actual radiance data were obtained by extracting pixel values from Landsat images. Simple linear regression (SLR) yielded significant linear relationships for 1997 field-1 and 2001 field-3 data. Multiple linear regression (MLR) and weighted linear regression (WLR), which indirectly accounted for moisture content and spatial resolution, respectively, yielded improvement in R2 for most of the studied bands. The analyses generally satisfied the normality and constant variance assumptions, and removal of outliers improved the validity of the assumptions and R2. It was concluded that indirect measures of soil moisture content and spatial uncertainty can substantially improve the relationship between remotely sensed bare-soil spectra and laboratory spectra.

No description available.

<p> The rapid development of the use of lasers in therapeutic and diagnostic medicine in the past few years has generated interest in measuring the optical properties of tissue. In particular, the development of photodynamic therapy (PDT) has necessitated studies of the optical properties of tissue at wavelengths around 630nm, this being the wavelength at which the photosensitizer commonly used in PDT, namely dihematoporphyrin ether (DHE), is normally activated.</p> <p> The control of the volume of tissue from which information about the interaction coefficients of the tissue is obtained is an important problem in diffuse reflectance spectroscopy and other applications of light, because it is critical to understanding which tissue volumes are sampled by the injected photons that eventually are re-emitted. This report describes a simple model that predicts the parameters that control the volume of tissue interrogated by photons during reflectance spectroscopy.</p> <p> In optical fiber based diffuse reflectance spectroscopy, incident radiation is applied at one point on a tissue surface and collected at another point, a radial distance, r, away. Information about the light multiply scattered by the tissue is used to deduce optical scattering and absorption coefficients of the tissue. In this report both steady state and pulse techniques are studied. In the steady state method, the spatial dependence of the backscattered light is the measured quantity, while the pulse technique uses the temporal broadening of a picosecond (ps) pulse to determine the interaction coefficients.</p> <p> The relative contribution of a volume element of tissue to the observed signal depends on its location, the measurement geometry and the optical properties of the tissue. Knowledge of this dependence would allow some control of the volume interrogated by reflectance spectroscopy, and would provide insight into the influence of inhomogeneities.</p> <p> In the work reported here a simple diffusion model of light propagation in tissue based on the Boltzmann radiative transfer equation has been used to derive mathematical expressions for the relative time spent by photons in a given tissue volume element. Using optical interaction coefficients typical of mammalian soft tissues, results are presented for both steady state and pulse irradiation in both semi-infinite and infinite media.</p> <p> The residency time depth profile calculated by this model for index matched and zero fluence boundary conditions has the same shape as that predicted by Weiss (1989), who used a somewhat different model based on a 3-dimensional random walk theory. These profiles are characterized by a build-up region near the surface and exponential fall far away from the surface in the 'diffusion region'. The influence of the absorption coefficient μa and the fiber separation on the residency time as predicted by this model is in good agreement to that predicted by the random-walk theory (i.e the depth-profile of the residency time tends to sharpen as the absorption coefficient increases. This is attributed to the fact that long trajectories are less likely with large absorption probabilities. As well, the greater the fiber separation, the wider and flatter the depth distribution of the residency time. This is because photons that reach the surface at greater r values have, in general, migrated farther from the immediate vicinity of the source and detector and hence have sampled a larger volume of tissue). All the integrations in this report were performed numerically using the IMSL/LIB on the Microvax computer system in the Hamilton Regional Cancer Center. The adequacy of this numerical integration was tested and was found to be good.</p> <p> This model therefore suggests that during diffuse reflectance spectroscopy, the volume sampled by re-emitted photons can be controlled by changing parameters such as the fiber separation (in both steady state and time-resolved techniques) and the detection time (in the time-resolved method).</p> / Thesis / Master of Science (MSc)

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Earth and Planetary Sciences. / Microfiche copy available in Archives and Science. / Bibliography : leaves 44-51. / M.S.

Thesis. 1975. M.S.--Massachusetts Institute of Technology. Dept. of Earth and Planetary Sciences. / Includes bibliographical references. / M.S.

La rugosité est une propriété clé des sols qui contrôle de nombreux processus de surface et influence la fonction de diffusion du rayonnement incident, alias sa BRDF (Bidirectional Reflectance Distribution Function). Bien qu’elle dépende fortement de l’échelle spatiale, la rugosité est souvent considérée comme stationnaire dans les modèles photométriques de réflectance de surfaces. En particulier, celui de Hapke l’inclut sous la forme d’un angle de pente moyen, intégré sur toutes les échelles variant de la taille d’un grain du régolithe à celle de la topographie locale. Le sens physique de ce paramètre de rugosité moyenne est largement débattu car l’échelle n’est pas clairement définie. Cette thèse a pour objectifs de comprendre comment la rugosité moyenne peut décrire un phénomène multi-échelle et d’investiguer l’influence des échelles spatiales de rugosité sur la BRDF d’une surface. On teste notamment la capacité du modèle de Hapke à restituer par inversion de la BRDF une rugosité moyenne compatible avec la réalité terrain. La topographie de terrains volcaniques et sédimentaires du Piton de la Fournaise (île de La Réunion) et du rift d’Asal-Ghoubbet (République de Djibouti) a été mesurée par photogrammétrie haute résolution sur une large gamme de résolutions à partir de données multi-instrumentales : images satellite, drone et acquises manuellement. Leur BRDF a été mesurée en parallèle par Pléiades et par un spectro-goniomètre (appelé Chamelon), et simulée numériquement par tracé de rayon sur les MNT réalisés. Une analyse multi-échelle par transformée en ondelettes révèle le comportement multi-modal de la rugosité des surfaces étudiées et permet de montrer que les structures sub-centimétriques dominent à la fois le paramètre de rugosité intégré et la forme de la BRDF. La rugosité estimée par inversion sur les données simulées avec une version simplifiée du modèle de Hapke coïncide avec celle déterminée sur les modèles de surface lorsque les hypothèses du modèle sont respectées et l’albédo connu à priori. L’adéquation n’est pas systématique mais reste bonne dans le cas de terrains à rugosité modérée avec une version complète du modèle de Hapke / Surface roughness is a key property of soils that controls many surface processes and influences the scattering function, or BRDF (Bidirectional Reflectance Distribution Function), of incident radiation. While it is strongly scale-dependent, it is often considered as a stationnary parameter in photometric models. In particular, it is included in the Hapke model as a mean slope angle, integrated over all scales from the grain size to the local topography. Yet its physical meaning is still a question at issue, as the scale at which it occurs is undefined. This thesis aims at understanding how this mean parameter can describe a multiscale phenomenon and to investigate the role of spatial scale on surface BRDF. Finally, we investigate the ability of the Hapke model to retrieve a roughness parameter which is consistent with the ground truth. The topography of volcanic and sedimentary terrains in the Piton de la Fournaise (Réunion Island) and the Asal-Ghoubbet rift (Republic of Djibouti) has been measured using high resolution photogrammetry at a wide range of resolutions thanks to multi-instrumental data : satellite, drone and handheld images. Simultaneously, the BRDF has been numerically simulated, and measured by satellite and a spectrogoniometer (named Chamelon). A multiscale analysis by the means of the wavelet transform reveals the multi-modal behavior of roughness and shows that sub-centimeter surface features dominate both the integrated parameter and the shape of the BRDF. The roughness estimated by inversion of a simplified version of the Hapke model matches the roughness determined over surfaces when the assumptions of the model are verified, with a priori knowledge on surface albedo. The match is not systematic, but remains good for moderately rough terrains using the full Hapke model

Chiral Liquid Crystals (CLCs) can selectively reflect light of a specific wavelength when the period of its helically twisted structure is appropriately chosen and white light propagates along the helical axis. This phenomenon makes CLCs attractive for reflective-color display, without the needs of backlighting, polarizers, or color filters. Polymer Dispersed Chiral Liquid Crystals (PDCLC) have been developed for reflective switchable, bistable color display. However they suffer from high external driving voltage, low reflectivity, and high cost in pretreatment of the substrates. The key to solve these problems is to understand and control the anchoring behavior of CLC at a polymer surface. This research has two purposes: to develop PDCLC films with high reflectivity and to investigate the factors affecting the anchoring behavior of CLC at the polymer surface of the film. Specifically, commercially available chiral dopant and nematic liquid crystals were carefully chosen to formulate the CLCs reflecting different color. These CLCs are mixed with various acrylate and methacrylate monomers respectively, and UV cured at varied conditions to obtain PDCLC films. The anchoring behavior of these films is characterized using polarized optical microscopy, confocal microscopy, and microscopic-spectrophotometer. The factors influencing the anchoring behavior include chemical structure of the monomers, effective diameter of individual cells in PDCLC, thickness of individual cells in PDCLC, and the pitch of CLC, among which the chemical structure of the monomers is the most important. The PDCLC film made with n-hexyl methacrylate is found to selectively reflect light with the reflectivity larger than that of pure CLC with the same pitch.

Chemical vapor deposition (CVD) is an industrially important process to deposit crystalline and amorphous thin films on solid substrates. In situ sensing for CVD is necessary for process monitoring, fault detection, and process control. The challenge of in situ sensing lies in the prohibitive environment of the CVD process. Optical sensors such as the reflectometer and the ellipsometer are the most promising sensors because they can be installed outside of the deposition chamber, and are sensitive and easy to implement. However, the optical sensors do not measure film properties directly. Mathematical methods are needed to extract film properties from indirect optical measurements. Currently the most commonly used method is least squares fitting. In this project, we systematically investigated in situ reflectometry data interpretation based on state estimation theory. Optical models for light reflection on both smooth and rough surfaces were studied. The model validation results indicated that the effective medium model is better than the scalar scattering model when the surface is microscopically rough. The analysis of the observability for the sensor models indicated that the linearized observability does not always guarantee the true observability of a nonlinear system. We studied various state estimators such as batch least squares fitting (BLS), recursive least squares fitting (RLS), extended Kalman filter (EKF), and moving horizon estimation (MHE). It was shown that MHE is the general least-squares-based state estimation and BLS, RLS, and EKF are special cases of MHE. To reduce the computational requirement of MHE, a modified moving horizon estimator (mMHE) was developed which combines the advantage of the computational efficiency in RLS and the a priori estimate in MHE. State estimators were compared in simulated film growth processes, including both process model mismatch and sensor model mismatch, and reflection of both single wavelength and dual wavelength. In the case of process model mismatch and reflection on a smooth surface, there exists an optimum horizon size for both RLS and mMHE, although mMHE is less sensitive to the horizon size and performs better than RLS at all horizon sizes. The estimate with dual wavelength is more accurate than that with single wavelength indicating that estimation improves with more independent measurements. In the case of reflection on a rough surface, RLS failed to give a reasonable estimate due to the strong correlation between roughness and the extinction coefficient. However, mMHE successfully estimated the extinction coefficient and surface roughness by using the a priori estimate. MHE is much more computationally intensive than mMHE and there is no significant improvement on the estimation results. In the case of sensor model mismatch, either state estimator gave a good result, although mMHE consistently gave a better estimate, especially at a shorter horizon size. In order to test the state estimators in a real world environment, we built a cold-wall low-pressure chemical vapor deposition testbed with an in situ emissivity-correcting pyrometer. Fully automatic data-acquisition and instrument-control software was developed for the CVD testbed using LabVIEW. State estimators were compared using two experimental reflectance data sets acquired under different deposition conditions. The estimated film properties are compared with ex situ ellipsometry and AFM characterization results. In all cases, mMHE consistently yielded better estimates for processes under quite different deposition conditions. This indicated that mMHE is a useful and robust state estimator for in situ sensor data interpretation. By using the information from both the process and the sensor model, one can obtain a better estimate. A good feature of mMHE is that it provides such a versatile framework to organize all these useful information and gives a user the opportunity to interact with fitting and make wise decisions in the in situ sensor data interpretation.

Thesis (Ph. D.)--University of Delaware, 2005. / Principal faculty advisor: Chandra Kambhamettu, Dept. of Computer & Information Sciences. Includes bibliographical references.

Master of Science / Department of Statistics / Leigh Murray / Using remotely sensed reflectance data is an appealing tool for controlling invasive species of grasses by rangeland managers. Recent developments in functional data analysis include the functional boxplot (FBP) which is shown here to be a useful tool in the visualization of reflectance data. Functional boxplots are a novel method of visually inspecting functional data and determining the presence of outliers in the data. Implementation and interpretation of FBPs are both straightforward and intuitive. The goal of this study is to examine the use of FBPs for visualizing reflectance data, and to determine the efficacy of using the FBP to distinguish between native tall grasses and invasive Old World Bluestem (OWB, Bothriochloa spp.) monocultures in a Kansas prairie. Validation trials were conducted in order to determine the stability of the FBP when used to analyze spectral data. FBPs were shown to be highly stable for use with both native and OWB grasses at all times and subsets of wavelengths tested. Identification trials were conducted by introducing a single OWB observation to a test set of native tall grass observations and constructing a FBP. Results indicate that using observations recorded early in the growing season, the functional boxplot is able to successfully identify the OWB observation as an outlier in a test set of native tall grass observations with an estimated probability 100% and 95.45% when considering the visible and cellular spectrums, respectively. A 95% lower bound for the probability of successfully identifying the OWB observation using the cellular spectrum in May is found to be 89.67%.