ALBERT

Description: This paper presents viewgraphs on turbulence detection and mitigation technologies in weather accident prevention. The topics include: 1) Organization; 2) Scope of Turbulence Effort; 3) Background; 4) Turbulence Detection and Mitigation Program Metrics; 5) Approach; 6) Turbulence Team Relationships; 7) WBS Structure; 8) Deliverables; 9) TDAM Changes; 10) FY-01 Results/Accomplishments; 11) Out-year Plans; and 12) Element Status.

Description: This paper presents the weather accident prevention project review during the period of June 5, through June 7, 2001. The topics include: 1) Background; 2) Guidance; 3) Plan; 4) System Elements; 5) AWIN System; 6) Market Segments; 7) Technology Development Level; 8) Aviation Safety Program Organization; 9) Partnerships; 10) NASA Facilities; 11) Timeline; 12) AWIN Research Areas; and 13) Cooperative Research with FAA. This paper is in viewgraph form.

Description: The use of hyperspectral data to determine the abundance of constituents in a certain portion of the Earth's surface relies on the capability of imaging spectrometers to provide a large amount of information at each pixel of a certain scene. Today, hyperspectral imaging sensors are capable of generating unprecedented volumes of radiometric data. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), for example, routinely produces image cubes with 224 spectral bands. This undoubtedly opens a wide range of new possibilities, but the analysis of such a massive amount of information is not an easy task. In fact, most of the existing algorithms devoted to analyzing multispectral images are not applicable in the hyperspectral domain, because of the size and high dimensionality of the images. The application of neural networks to perform unsupervised classification of hyperspectral data has been tested by several authors and also by us in some previous work. We have also focused on analyzing the intrinsic capability of neural networks to parallelize the whole hyperspectral unmixing process. The results shown in this work indicate that neural network models are able to find clusters of closely related hyperspectral signatures, and thus can be used as a powerful tool to achieve the desired classification. The present work discusses the possibility of using a Self Organizing neural network to perform unsupervised classification of hyperspectral images. In sections 3 and 4, the topology of the proposed neural network and the training algorithm are respectively described. Section 5 provides the results we have obtained after applying the proposed methodology to real hyperspectral data, described in section 2. Different parameters in the learning stage have been modified in order to obtain a detailed description of their influence on the final results. Finally, in section 6 we provide the conclusions at which we have arrived.

Description: During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or improved for remote sensing applications. Imaging spectrometry allows the detection of materials, objects and regions in a particular scene with a high degree of accuracy. Hyperspectral data typically consist of hundreds of thousands of spectra, so the analysis of this information is a key issue. Mathematical morphology theory is a widely used nonlinear technique for image analysis and pattern recognition. Although it is especially well suited to segment binary or grayscale images with irregular and complex shapes, its application in the classification/segmentation of multispectral or hyperspectral images has been quite rare. In this paper, we discuss a new completely automated methodology to find endmembers in the hyperspectral data cube using mathematical morphology. The extension of classic morphology to the hyperspectral domain allows us to integrate spectral and spatial information in the analysis process. In Section 3, some basic concepts about mathematical morphology and the technical details of our algorithm are provided. In Section 4, the accuracy of the proposed method is tested by its application to real hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imaging spectrometer. Some details about these data and reference results, obtained by well-known endmember extraction techniques, are provided in Section 2. Finally, in Section 5 we expose the main conclusions at which we have arrived.

Description: Anthropogenic aerosols are intricately linked to the climate system and to the hydrologic cycle. The net effect of aerosols is to cool the climate system by reflecting sunlight. Depending on their composition, aerosols can also absorb sunlight in the atmosphere, further cooling the surface but warming the atmosphere in the process. These effects of aerosols on the temperature profile, along with the role of aerosols as cloud condensation nuclei, impact the hydrologic cycle, through changes in cloud cover, cloud properties and precipitation. Unravelling these feedbacks is particularly difficult because aerosols take a multitude of shapes and forms, ranging from desert dust to urban pollution, and because aerosol concentrations vary strongly over time and space. To accurately study aerosol distribution and composition therefore requires continuous observations from satellites, networks of ground-based instruments and dedicated field experiments. Increases in aerosol concentration and changes in their composition, driven by industrialization and an expanding population, may adversely affect the Earth's climate and water supply.

Description: A laser spectrometer based on difference-frequency generation in periodically poled LiNbO3 (PPLN) has been used to quantify atmospheric formaldehyde with a detection limit of 0.32 parts per billion in a given volume (ppbV) using specifically developed data-processing techniques. With state-of-the-art fiber-coupled diode-laser pump sources at 1083 nm and 1561 nm, difference-frequency radiation has been generated in the 3.53-micrometers (2832-cm-1) spectral region. Formaldehyde in ambient air in the 1- to 10-ppb V range has been detected continuously for nine and five days at two separate field sites in the Greater Houston area operated by the Texas Natural Resource Conservation Commission (TNRCC) and the Houston Regional Monitoring Corporation (HRM). The acquired spectroscopic data are compared with results obtained by a well-established wet-chemical o-(2,3,4,5,6-pentafluorobenzyl) hydroxylamine (PFBHA) technique.

Description: Pulsed thermoelectrically cooled QC-DFB lasers operating at 15.6 micrometers were characterized for spectroscopic gas sensing applications. A new method for wavelength scanning based on repetition rate modulation was developed. A non-wavelength-selective pyroelectric detector was incorporated in the sensor configuration giving the advantage of room-temperature operation and low cost. Absorption lines of CO2 and H2O were observed in ambient air, providing information about the concentration of these species.

Description: Our ecological footprint analyses of coral reef fish fisheries and, in particular, the live reef fish food trade (FT), indicate many countries' current consumption exceeds estimated sustainable per capita global, regional and local coral reef production levels. Hong Kong appropriates 25% of SE Asia's annual reef fish production of 135 260-286 560 tonnes (t) through its FT demand, exceeding regional biocapacity by 8.3 times; reef fish fisheries demand out-paces sustainable production in the Indo-Pacific and SE Asia by 2.5 and 6 times. In contrast, most Pacific islands live within their own reef fisheries means with local demand at 〈 20% of total capacity in Oceania. The FT annually requisitions up to 40% of SE Asia's estimated reef fish and virtually all of its estimated grouper yields. Our results underscore the unsustainable nature of the FT and the urgent need for regional management and conservation of coral reef fisheries in the Indo-Pacific.

Description: Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

Description: We have completed a new generation of water vapor radiometers (WVR), the A- series, in order to support radio science experiments with the Cassini spacecraft. These new instruments sense three frequencies in the vicinity of the 22 GHz emission line of atmospheric water vapor within a 1 degree beamwidth from a clear aperture antenna that is co-pointed with the radio telescope down to 10 degree elevation. The radiometer electronics features almost an order of magnitude improvement in temperature stability compared with earlier WVR designs. For many radio science experiments, the error budget is likely to be dominated by path delay fluctuations due to variable atmospheric water vapor along the line-of-sight to the spacecraft. In order to demonstrate the performance of these new WVRs we are attempting to calibrate the delay fluctuations as seen by a radio interferometer operating over a 21 km baseline with a WVR near each antenna. The characteristics of these new WVRs will be described and the results of our preliminary analysis will be presented indicating an accuracy of 0.2 to 0.5 mm in tracking path delay fluctuations over time scales of 10 to 10,000 seconds.