KEYWORDS: Monte Carlo methods, 3D modeling, Photons, Remote sensing, Atmospheric modeling, Shape analysis, 3D image processing, Water, Scattering, Reflectivity
This paper describes the results of modeling the water wave surface and underwater light field as influenced by water
waves using a Monte Carlo model (MCHSIM). Model and sensor data related to water column properties and benthic
properties that influence the light upwelled from below the water - as observed from a sensor looking from below or
above the water surface is presented. Synthetic image results using Monte Carlo techniques show the influence of water
waves upon subsurface shape factors and these factors can be used in shallow water remote sensing algorithms that are
based on underlying analytical models. The upwelling angular distribution of light is calculated from the model and
results shown for 490 nm. The upwelling and downwelling shape factors are shown from model runs which compare the
results with solar zenith angle for nadir viewing geometry, and for realistic water surface wave facets. It is clearly shown
that shape factors are strongly dependent upon not only viewing geometry and zenith angle of the sun, but also upon
water waves that can focus and defocus radiance entering a wind roughened water column and influence the shape
factors due to the scattering lobe effect. This paper presents results quantifying the magnitude of water effects upon the
upwelling and downwelling shape factors in a systematic and quantifiable manner at 490 nm and demonstrates the utility
of the model to assess the influence of water waves in a full 3-D Monte Carlo hyperspectral synthetic image cube model
that accounts for adjacency effects.
Simulated and airborne imagery demonstrate the ability to see manmade and natural objects below the water wave
surface. Traditional photogrammetric imagery and airborne digital imagery both suffer from a loss in image clarity due
to a number of factors, including the forward motion of the airborne platform. Blurring due to this effect can be
calculated and an opto-mechanical system has been designed in order to help remove this effect. Forward motion blur
can be shown to occur on the order of a several centimeters and calculation results are presented. The system is
described and imagery is shown to demonstrate image blurring. Preliminary results obtained from an improved Monte
Carlo model is also used to show expected results and to begin developing image correction methods in order to remove
the blur due to water wave influences. Limitations to the hardware method suggest that the opto-mechanical system
designed may lead to additional blur due to nonuniform focal plane focusing issues. The techniques have unique
opportunities to help improve hyperspectral pushbroom sensors in addition to large frame mapping cameras that are in
use today or being developed for future use.
The purpose of this paper is to present simulation in order to compare a Hyperspectral Monte Carlo Model
(MC) which generates synthetic images with realistic water wave surface to an iterative layered radiative
transfer model used to generate hyperspectral synthetic images with realistic water wave surfaces. The MC
model developed by Bostater and Gimond (2002) and Bostater and Chiang (2002) is divided into 5 steps: (1)
Generation of the photons, (2) tracking of the photon optical path and simultaneously (3) recording of the
photon's location within the water column, (4) then a tabulation of the sampling and its conversion to
meaningful radiometric quantities and finally (5) a calculation and processing of the event probabilities
between successive photons. This model will then be compared to the ILRT which is analytical and uses an
iterative method to converge on the solution to a layered, iterative two flow radiative transfer model
developed by (Bostater et al., 2002). The purpose of this research and the
presentation will be to describe the effects of spectrally derived wave facets and the foam estimation coverage
in order to assess the differences between the above modeling approaches, and to develop a better scientific
understanding of the influence of water waves on the remote sensing signal from 400 to 750 nm, as well as
the coupled influence of water waves and shallow bottom reflectance effects due to benthic aquatic habitat
features such as submerged vegetation, corals, and other objects submerged within the water column as well
as effects due to waves at the air-sea interface. The spectral wave models used include the wave (Phillips,
Jonswap, Pierson-Moskowitz and TMA) that
will help to simulate what a sensor sees from a low flying aircraft. In order to evaluate the wave models the
Inverse Fast Fourier Transform (IFFT) is applied and results described.
It is becoming more important to understand the remote sensing systems and associated autonomous or semi-autonomous methodologies (robotic & mechatronics) that may be utilized in freshwater and marine aquatic environments. This need comes from several issues related not only to advances in our scientific understanding and technological capabilities, but also from the desire to insure that the risk associated with UXO (unexploded ordnance), related submerged mines, as well as submerged targets (such as submerged aquatic vegetation) and debris left from previous human activities are remotely sensed and identified followed by reduced risks through detection and removal. This paper will describe (a) remote sensing systems, (b) platforms (fixed and mobile, as well as to demonstrate (c) the value of thinking in terms of scalability as well as modularity in the design and application of new systems now being constructed within our laboratory and other laboratories, as well as future systems. New remote sensing systems - moving or fixed sensing systems, as well as autonomous or semi-autonomous robotic and mechatronic systems will be essential to secure domestic preparedness for humanitarian reasons. These remote sensing systems hold tremendous value, if thoughtfully designed for other applications which include environmental monitoring in ambient environments.
Modeled hyperspectral reflectance signatures with water wave influences are simulated using an analytical-based, iterative radiative transport model applicable to shallow or deep waters. Light transport within the water body is simulated using a fast, accurate radiative transfer model that calculates the light distribution in any layered media and incorporates realistic water surfaces which are synthesized using empirically-based spectral models of the water surface to generate water surface wave facets. The model simulated synthetic images are displayed as 24 bit RGB images of the water surface using selected channels from the simulated synthetic hyperspectral image cube. We show selected channels centered at 490, 530 and 676 nm. We also demonstrate the use of the model to show the capability of the sensor and image modeling approach to detect or "recover" known features or targets submerged within or on the shallow water bottom in a tidal inlet area in Indian River Lagoon, Florida. Line targets are simulated in shallow water and indicate the influence of water waves in different water quality conditions. The technique demonstrates a methodology to help to develop remote sensing protocols for shallow water remote sensing as well as to develop information useful for future hyperspectral sensor system developments.
Modeled hyperspectral reflectance signatures just above the water surface are obtained from an analytical radiative transport model applicable to shallow water types. Light transport within the water body is simulated using a fast, accurate radiative transfer model that calculates the light distribution in any layered media. A realistic water surface is synthesized using empirically-based statistical models of ocean surface waves. Images are displayed as 24 bit RGB images of the water surface using selected channels. The selected channels are centered at 480, 520 and 650 nm. Hyperspectral image cubes with two spatial and a third spectral dimension are shown to allow the detection of any optically unresolved features in the two-dimensional RGB image.
A pushbroom sensor motion control system was developed for use in conjunction with a pulsed laser fan beam, streak tube camera, and a high speed low light level camera . The LIDAR and camera control system was tested to study the influence of water waves upon active-passive remote sensing systems and associated models that require pushbroom sensor motion. A pulsed laser fan beam signal at 532 nm was recorded using a streak tube camera and a (high speed, low light level, high quantum efficiency) digital CCD camera. Tests were conducted in 3 different water tanks, including 2 tanks with water waves (the longest wave tank or channel is 60 m long). Capillary waves, ~1 cm wavelength) were generated using an acoustic wave source generator. Streak tube camera and CCD images were collected in conjunction with a 532 nm pico-second short pulse laser. Images collected demonstrate the pulse stretching around submerged water
targets as well as the ability to discriminate water depth of submerged targets in shallow water types. In turbid water, the
pulsed layer backscatter structure showed a nearly random return as a function of depth if the signal was attenuated before reaching the bottom of the water column. The data collected indicated the motion control testing system can accommodate a variety of cameras and instruments in the lab and in the outdoor water wave channel. Data from these camera systems are being used to help validate analytical and Monte Carlo models of the water surface structure, and the
underwater light field structure (pulse stretching) as well as to validate other LIDAR applications used in bathymetric and hydrographic surveys of coastal waters and marine inlets for physical and biological (submerged vegetation) surveys.
The purpose of this paper is to present results of simulations of the Florida Tech UTC-M sea-breeze model with the addition of a simplified atmospheric downwelling radiation subroutine combined and a thermal inertia subroutine into the atmospheric planetary boundary layer model, in order to calculate time dependant heat flux boundary conditions at the air-land boundary that are derived from
satellite data from AVHRR and MODIS sensors. The improved UTC-M planetary boundary layer model with this thermal sub-model subroutine is used to demonstrate the use of thermal inertia to help estimate
heat fluxes at the land-air interface which in turn influences convergence and vertical fluxes near the bottom boundary, and which may affect mesoscale meteorological wind and seabreeze over complex
land-water margins. Additionally, message passage interface (MPI) parallelizing Fortran techniques were used to improve the computational time when the model grid was decreased down to 2 or 1 km cell when simulations where performed on the FIT supercomputer based on an IBM Beowulf Linux cluster. We present some results of the UTC-M simulations and associated results due to the influence of the
parameterization of the net surface radiation and thermal inertia using the spectral or wavelength (channel) specific data from MODIS and AVHRR satellite sensors.
Submerged aquatic vegetation (SAV) is an important indicator of freshwater and marine water quality in almost all shallow water aquatic environments. Throughout the world the diversity of submerged aquatic vegetation appears to be in decline, although sufficient historical data, of sufficient quantitative quality is lacking. Hyperspectral remote sensing technology, available from low altitude aircraft sensors, may provide a basis to improve upon existing
photographic regional assessments and monitoring concerned with the aerial extent and coverage of SAV. In addition, modern low altitude remote sensing may also help in the development of environmental satellite requirements for future satellite payloads. This paper documents several important spectral reflectance signature features which may be useful in developing a protocol for remote sensing of SAV, and which is transferable to other shallow water aquatic habitats around the world. Specifically, we show that the shape or curvature of the spectral reflectance absorption feature centered near the chlorophyll absorption region of ~ 675 nm is strongly influenced not only by the relative backscatter region between 530-560 nm, but by a “submerged vegetation red edge” that appears
in the 695 to 700 nm region in extremely high density vegetative areas in very shallow waters (= 0.5m depth). This “aquatic biomass red edge” is also observable in deeper waters where there is a shallow subsurface algal boom as demonstrated in this paper. Use of this submerged aquatic red edge feature will become an important component of SAV remote sensing in shallow aquatic habitats, as well as in phytoplankton-related water quality remote sensing applications of surface phytoplankton blooms.
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