Flood detection and monitoring with the Autonomous Sciencecraft Experiment onboard EO-1

In this paper, we present a new way of detecting and monitoring flooding through the Autonomous Sciencecraft Experiment (ASE) [Chien, S. T., Debban, C., Yen, R., Sherwood, R. Castano, B., & Cichy, A. G. et al. (2001). ASC Science Study Report, available from http://ASE.jpl.nasa.gov], which is part of the Space Technology 6 effort under NASA's New Millennium Program. Recent autonomy experiments conducted on Earth Observing 1 (EO-1) using the ASE flight software have demonstrated the ability of several science algorithms to successfully classify key features including flood-induced changes, in hyperspectral images captured by the EO-1 Hyperion instrument. Furthermore, onboard science analysis on the classified images has been performed, and then used to modify an operational plan without interaction from the ground (Sherwood, R., Chien, S., Tran, D., Cichy, B., Castano, R., Davies, A., et al. (2004). Preliminary results of the autonomous sciencecraft experiment. In: Proceedings of the IEEE Aerospace Conference, Big Sky, MT). These algorithms are used to downlink science data only when change occurs, and to detect features of scientific interests such as flooding, volcanic eruptions, and the formation and breakup of sea ice. The purpose of this paper is to demonstrate the success of ASE and its implications on detecting, mapping, and monitoring transient processes such as flooding autonomously from space. Mapping of water inundation and its change through time is part of our focus in studying transient processes from space.In 2004, hyperspectral data were acquired from the Hyperion instrument for target areas around the world that have a high potential for flooding to develop and test floodwater classifiers. In addition, classifier thresholds were determined from both normal flows and possible flood conditions. The paper introduces the development, testing, and success of the ASE software in detecting and reacting to flooding in near real-time. ASE is now operational and flight-tested, and, thus, ready to use for space-borne reconnaissance. Successful demonstration of the floodwater classifiers includes the capture of a rare flooding event of the Australian Diamantina River during ground testing in February 2004, and the detection of flood-related changes along the Brahmaputra River in Bangladesh and the Yukon River in Alaska during onboard testing on EO-1 in 2005. Both of these detections led to triggered responses onboard the spacecraft, which included acquiring additional Hyperion scenes. These results pave the way for future smart reconnaissance missions of transient processes on Earth and beyond. It is hoped that ASE will become a default in future missions to increase the science return by introducing spacecraft autonomy for detection and monitoring of science events, which otherwise would be discovered too late or altogether missed.     

Autonomous detection of cryospheric change with hyperion on-board Earth Observing-1

On-board detection of cryospheric change in sea ice, lake ice, and snow cover is being conducted as part of the Autonomous Sciencecraft Experiment (ASE), using classifiers developed for the Hyperion hyper-spectral visible/infrared spectrometer on-board the Earth Observing-1 (EO-1) spacecraft. This classifier development was done with consideration for the novel limitations of on-board processing, data calibration, spacecraft targeting error and the spectral range of the instrument. During on-board tests, these algorithms were used to measure the extent of cloud, snow, and ice cover at a global suite of targets. Coupled with baseline imaging, uploaded thresholds were used to detect cryospheric changes such as the freeze and thaw of lake ice and the formation and break-up of sea ice. These thresholds were used to autonomously trigger follow-up observations, demonstrating the capability of the technique for future planetary missions where downlink is a constrained resource and there is high interest in data covering dynamic events, including cryospheric change. Before upload classifier performance was assessed with an overall accuracy of 83.3% as measured against manual labeling of 134 scenes. Performance was further assessed against field mapping conducted at Lake Mendota, Wisconsin as well as with labeling of scenes that were classified during on-board tests.     

Remote monitoring of Indonesian volcanoes using satellite data from the Internet

The Internet now harbours vast amounts of cheap and potentially useful remote sensing data. Advanced Very High Resolution Radiometer (AVHRR) data are being increasingly used for volcano surveillance, and the provision of AVHRR Global Area Coverage (GAC) imagery at no cost over the Internet offers the possibility of cheap volcano monitoring on a global scale. Herein we use an extensive, 690-scene AVHRR GAC dataset to observe volcanic activity in the Indonesian island arc between January 1996 and November 1997. Indonesia contains over 70 active volcanoes, with styles of activity during the observation period including active lava domes, lava flows, pyroclastic flows and hot crater lakes, many in close proximity to major centres of population. The detection potential of these and other phenomena in GAC data is assessed. Thermal anomalies were identified at ～18 volcanoes during the observation period, including lava flows at Anak Krakatau, persistent open-vent activity at Semeru and a previously unreported eruption at Sangeang Api volcano. Using these results, a classification scheme for night-time Indonesian GAC data is presented. Routine use of freely available high temporal resolution data such as AVHRR GAC could help elucidate cyclic activity at active volcanoes, which would contribute significantly to hazard mitigation in affected areas. Browse images of higher resolution data (e.g. SPOT) from the daily updated archives of the Centre for Remote Imaging, Sensing and Processing (CRISP) in Singapore also show potential as an aid to volcano monitoring in the region.     

A LiDAR survey of Stromboli volcano (Italy): Digital elevation model-based geomorphology and intensity analysis

LiDAR (Light Detection and Ranging) is a novel and very useful active remote sensing system which can be used to directly identify geomorphological features as well as the properties of materials on the ground surface. In this work, LiDAR data were applied to the study of the Stromboli volcano in Italy. LiDAR data points, collected during a survey in October 2005, were used to generate a Digital Elevation Model (DEM) and a calibrated intensity map of the ground surface. The DEM, derived maps and topographic cross-sections were used to complete a geomorphological analysis of Stromboli, which led to the identification of four main geomorphological domains linked to major volcanic cycles. Moreover, we investigated and documented the potential of LiDAR intensity data for distinguishing and characterizing different volcanic products, such as fallout deposits, epiclastic sediments and lava flows.     

Characterization of a sulfur-rich Arctic spring site and field analog to Europa using hyperspectral data

A unique glacial spring system exists at Borup Fiord pass, in the Canadian High Arctic, emerging from a glacial surface and depositing elemental sulfur, gypsum and calcite across a portion of the glacier. The presence of sulfur springs associated with glacial ice is extremely rare in a terrestrial context, and the resulting deposits may provide a field analog to non-ice materials on the surface of Europa. Spectral characterization of the supraglacial deposits in the visible-near infrared (VIS-NIR) range, 0.4-2.5 µm, was carried out using reflectance spectra collected in situ using a field spectrometer during the 2006 field season. These spectra, while dominated by melting snow, ice, and sulfur, show that some absorption features of the sulfates are shifted in wavelength with respect to library spectra due to the effects of mixing or temperature. Absorption features of calcite are largely absent, potentially due to mineral partitioning effects within the deposits. Investigations into changes in mineralogy within the deposits over the course of the active season using data collected by the Hyperion hyperspectral visible/infrared spectrometer aboard the Earth Observing 1 spacecraft (EO-1) were limited by low signal-to-noise (SNR) ratios in the data, although they indicate that sulfur is remaining stable. This is confirmed by seasonal data on the extent of the deposits, obtained using a classification algorithm running onboard the satellite, which continued to detect the presence of sulfur until snow obscured the site. Ground truth for the observations is provided by mineralogical analyses obtained by X-ray diffraction (XRD) measurements and laboratory reflectance spectra from 0.2-25 µm obtained of samples returned from the site in 2006. We show that while sulfur, the main constituent of the deposits, is well represented in Hyperion data, minor constituents such as calcite and gypsum can be partially or entirely masked in the data. In spite of these effects, autonomous detection methods can be utilized to monitor the generation and extent of the deposits, whose spectral properties show similarities with those of Europa's non-ice materials.     

Commodity cluster-based parallel processing of hyperspectral imagery

The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery.     

Eruptive history of Dubbi volcano,northeast Afar (Eritrea), revealed by optical and SAR image interpretation

A study of the remote Dubbi volcano, located in the northeastern part of the Afar triangle, Eritrea, was carried out using JERS-1 Synthetic Aperture Radar (SAR) and Landsat Thematic Mapper (TM) imagery. It investigated the last known eruption of Dubbi volcano in 1861, the only volcano in Afar for which historical reports indicate a major explosive eruption. Various image processing techniques were tested and compared in order to map different volcanic units, including effusive and explosive products. Principal component analysis and optical-SAR fusion were found to be useful to determine the extent of the 1861 pumice deposits surrounding the volcano. SAR imagery revealed old lava flows buried below tephra deposits, emphasizing the ground penetrating property of the L-band (HH polarization). The interpretation obtained from satellite imagery was cross-checked with sparse historical testimonies and available ground-truth data. Two scenarios are proposed for the 1861 eruptive sequences in order to estimate the volumes of lava flows erupted and the timing of explosive and effusive activity. Identified as a bimodal basaltic-trachytic eruption, with a minimum volume of 1.2 km³ of hawaiite lava and a minimum area of 70 km² of trachytic pumice, it represents the largest known historic eruption in the Afar triangle. This paper raises the issue of the potential volcanic hazards posed by Dubbi, which concern both the local population and the maritime traffic using the strategic route of the Red Sea.     

Quality of TOPSAR topographic data for volcanology studies at Kilauea Volcano, Hawaii: An assessment using airborne lidar data

We use airborne lidar data for the summit area of Kilauea Caldera, Hawaii, to explore the utility of topographic data collected by the TOPSAR airborne interferometric radar for volcanology studies. The lidar data are processed to a spatial resolution of 1 m/pixel, compared to TOPSAR with a spatial resolution of 5 m. Over a variety of fresh volcanic surfaces (pahoehoe and aa lava flows, ash falls and fluvial fans), TOPSAR data are shown to have a typical vertical offset compared to the lidar data of no more than ∼2-3 m. Larger differences between the two data sets and TOPSAR data drop-outs are found to be concentrated around steep scarps such as the walls of pit craters and ground cracks associated with the Southwest Rift Zone. A comparison of these two data sets is used to explore the utility of TOPSAR to interpret the topography of volcanic features close to the spatial resolution of TOPSAR, such as spatter ramparts, fractures, a perched lava flow, and eroded ash deposits. Comparison of the TOPSAR elevation and the lidar first-return minus the return from the ground surface (the so-called “bald Earth” data) for vegetated areas reveals TOPSAR penetration into the tree canopy is typically at least 10% and no more than ∼50%, although a wide range of penetration values from 0% to 90% has been identified. Our results are significant because they show that TOPSAR data for volcanoes can reliably be used to measure regional slopes and the thickness of lava flows, and have value for the validation of coarser spatial resolution digital elevation data (such as SRTM) in areas where lidar data have not been collected.     

Volcano detection and monitoring using AVHRR data: the Krafla eruption, 1984

Abstract. Many volcanic eruptions go essentially unmonitored. Potentially the Advanced Very High Resolution Radiometer (AVHRR), with its global coverage, frequent return period, and sensitivity in the thermal infrared, represents a data source capable of monitoring surface volcanic activity unrecorded by ground observations or other satellite sensors. In this study an attempt is made to demonstrate this potential by extracting information for the 1984 eruption at Krafla, Iceland. Seven cloud-free AVHRR images were available for the 14 day period of eruptive activity. The surface activity was detectable as a major thermal anomaly in all three of the longer wavelength channels and was vigorous enough during one night-time pass to be detectable in the near-infrared channel (0.725-1.1μm). Channel 2 and 4 radiance data were used to calculate the size and temperature of sub-pixel heat sources within the lava flow field, and a heat source at 1050° C was estimated as occupying an area of approximately 240000 m², which was distributed across 20 pixels. Detection and measurement of volcanic heat sources at such short wavelengths using low spatial resolution data has rarely been reported before. Field reports and maps were used to guide and confirm the analysis. Digital number variations within the anomaly could be related to various known features of the eruption. To monitor the eruption a weighted average method was derived and used to sharpen up the images, and the density sliced sharpened images enabled the development of the eruption to be mapped. Results compared well with field reports, suggesting that AVHRR and similar systems could be a useful source of data for monitoring eruptions where contemporaneous field observations are unavailable or incomplete.     

A lava flow simulation model for the development of volcanic hazard maps for Mount Etna (Italy)

Volcanic hazard assessment is of paramount importance for the safeguard of the resources exposed to volcanic hazards. In the paper we present ELFM, a lava flow simulation model for the evaluation of the lava flow hazard on Mount Etna (Sicily, Italy), the most important active volcano in Europe. The major contributions of the paper are: (a) a detailed specification of the lava flow simulation model and the specification of an algorithm implementing it; (b) the definition of a methodological framework for applying the model to the specific volcano. For what concerns the former issue, we propose an extended version of an existing stochastic model that has been applied so far only to the assessment of the volcanic hazard on Lanzarote and Tenerife (Canary Islands). Concerning the methodological framework, we claim model validation is definitely needed for assessing the effectiveness of the lava flow simulation model. To that extent a strategy has been devised for the generation of simulation experiments and evaluation of their outcomes.     

Detecting marine intrusion into rivers using EO-1 ALI satellite imagery: Modaomen Waterway,Pearl River Estuary,China

Because of increasing marine intrusion into the Pearl River Estuary (PRE) in China, salinity has become one of the important and necessary hydrological and water quality monitoring parameters. In this research, we examined the relationships between the reflectance from Earth Observing-1 (EO-1) Advanced Land Imager (ALI) satellite imagery and total suspended solids (TSS) based on the synchronous in situ spectra analysis of the river water, in an attempt to detect salinity using remote sensing technique. The study site was the Modaomen Waterway in the PRE, Guangdong Province, China. We found a strong negative linear relationship between in situ reflectance at 549 nm and TSS concentrations (R ²?=?0.91, p < 0.001) when the salinity of the river was less than 1.46‰. It indicates that the TSS near Pinggang and Nanzhen in Modaomen Waterway of PRE tends to be dominated by organic mater carried by the particles and this is one major reason for the inverse relation between reflectance and TSS. Meanwhile, a strong correlation was observed between salinity and TSS (R ²?=?0.70, p < 0.001). The salinity-TSS model accounted for 70% of variation in salinity and allowed the estimation of salinity with a root mean square error (RMSE) of less than 0.036‰ when the TSS concentrations were between 7.4 and 28 mg l^?1. Therefore, we were able to develop a new method of detecting surface salinity of the river estuary from the calibrated EO-1 ALI reflectance data. The EO-1 ALI derived surface salinity and TSS concentrations were validated using in situ data that were collected on 18 December 2005, synchronous with EO-1 ALI satellite imagery acquisition. The results showed that the semi-empirical relationships are capable of deducing the TSS concentrations and then salinity from EO-1 ALI imagery in the PRE under low salinity. The methodology of detecting salinity from ALI imagery provides potential to monitor coastal saltwater intrusion and provides the water supply and conservancy authorities with useful spatial information to spatially understand and manage the marine intrusion.     

NOAA AVHRR image referencing

Abstract

A simple and fast algorithm for image referencing of the NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data has been derived to facilitate the identification of geographic co-ordinates corresponding to any pixel on an NOAA image and vice versa. The procedure assumes a spherical Earth and circular orbit and takes into account the effects due to the Earth's rotation and oblateness and the scan skew. Inputs to the procedure are the ascending nodal longitude and lime, the time of the first scan line and one ground control point (GCP). The effects of an ellipsoid Earth and an elliptical orbit are corrected by using the GCP to adjust the spacecraft altitude and inclination angle. No detailed emphemeris data arc required. The average r.m.s. errors obtained by comparing with independent sets of well-distributed GCPs for each image are about 2 pixels and 2 lines or 3 km displacement. Results from the procedure are illustrated by the rectification of NOAA images over France.     

Plumes of discolored water of volcanic origin and possible implications for algal communities. The case of the Home Reef eruption of 2006 (Tonga, Southwest Pacific Ocean)

The Home Reef volcano (Tonga, Southwest Pacific Ocean) erupted in August 2006. Initially a submarine eruption it quickly evolved into a subaerial event upon the formation of an ephemeral island.Remote sensing data from different sensors including MODIS, ASTER, EO-1 ALI and Landsat-7 ETM+ were used to analyze the event, focusing on the plumes of discolored water, ocean chlorophyll-a concentration (OCC) and sea surface temperature.An early classification system for the plumes was devised based on spectral properties and point of origin. Plumes originated at the volcano were named Type-I and those associated to the pumice rafts Type-II.Anomalies in ocean chlorophyll-a concentration, measured using MODIS data, were analyzed and a large bloom, presumably dominated by Trichodesmium sp. was identified. The bloom, which contributed to OCC values 17 times higher than the background, was spatially and temporally coincident with a Type-I plume of discolored water. The OCC increase appears to have been caused by a combined effect of both ocean fertilizations by the subsurface volcanic plume and rising sea surface temperatures.The Home Reef event offers a good candidate for a case of ocean enrichment by a submarine volcano and highlights the need for continuous monitoring of the eruptions even after the end of the explosive, more spectacular stages.     

The impact of large-scale forest fires on atmospheric aerosol characteristics

Aerosol optical thickness and aerosol index variations, caused by large-scale forest fires in central Yakutia in 2002 were studied using remote-sensing data (Earth Observing System (EOS) Terra, Earth Probe). Total emissions of CO₂, CO, CH₄, etc., were calculated using a modelling approach and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra burned area data. A comparison of the total emissions from forest fires in central Yakutia with global fire and volcanic emissions are made. Furthermore, long-range transport events of aerosol particles during maximum activity of forest fires are examined. It was shown that forest fires have a significant impact on the lower atmosphere; in particular, aerosol plumes are noticeable at a distance of around 3000 km from the study area.     

航天器地面健康管理验证系统设计与实现

航天器地面健康管理系统作为整个航天器健康管理系统的核心,主要为技术人员提供航天器试验、运行和管理过程中的数据分析、诊断、预测等服务。目前由于航天器故障机理难以获取、故障验证环境不足、缺乏统一的验证方法和性能指标,给航天器PHM的研究成果的有效验证与评价带来了困难。提出了一种基于大数据的航天器地面健康管理系统设计思路,并对其验证评估指标体系和验证方法进行了研究,在此基础上设计实现了基于仿真和试验验证的航天器地面健康管理验证系统,通过多个航天器故障仿真和在轨历史数据试验,验证了提出的航天器地面健康管理验证系统设计方法的有效性,能够有效的应用于航天器的地面健康管理过程,具有较强的航天器工程应用价值。     

Onboard object recognition for planetary exploration

Machine learning techniques have shown considerable promise for automating common visual inspection tasks such as the detection of human faces in cluttered scenes. Here, we examine whether similar techniques can be used (or adapted) for the problem of automatically locating geologic landforms in planetary images gathered by spacecraft. Beyond enabling more efficient and comprehensive ground analysis of down-linked data, we are aiming toward perceptive spacecraft that use onboard processing to autonomously analyze their collected imagery and take appropriate actions. In our current study, we have employed various supervised learning algorithms, including neural networks, ensemble methods, support vector machines (SVM), and continuously-scalable template models (CSTM) to derive detectors for craters from ground-truthed images. The resulting detectors are evaluated on a challenging set of Viking Orbiter images of Mars containing roughly one thousand craters. The SVM approach with normalized image patches provides detection and localization performance closest to that of human labelers and is shown to be substantially superior to boundary-based approaches such as the Hough transform. However, the run-time cost in applying the SVM solution in the standard way (spatial scanning in which the SVM is applied to each patch of the image on a window-by-window basis) is too high due both to the number of support vectors required and the number of test vectors generated by sliding a window across the data. We have developed an implementation using FFTs and the overlap-and-add technique, which can be used to efficiently apply SVMs to sensor data in resource-constrained environments such as on a spacecraft. The technique allows exact computation of the SVM decision function over an image using minimal RAM (typically less than 5% of the size of the image) and only ${\mathcal{O}}(n_{s} (\log_{2} d + 11))$ real multiplications per pixel, where n _s is the number of support vectors and d is the dimensionality of the vectors compared with ${\mathcal{O}}(n_{s} d)$ real multiplications per pixel for spatial scanning. Our approach is complementary to reduced set methods providing (in theory) a multiplicative gain in performance.     

Parallel techniques for information extraction from hyperspectral imagery using heterogeneous networks of workstations

Recent advances in space and computer technologies are revolutionizing the way remotely sensed data is collected, managed and interpreted. In particular, NASA is continuously gathering very high-dimensional imagery data from the surface of the Earth with hyperspectral sensors such as the Jet Propulsion Laboratory's airborne visible-infrared imaging spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) satellite platform. The development of efficient techniques for extracting scientific understanding from the massive amount of collected data is critical for space-based Earth science and planetary exploration. In particular, many hyperspectral imaging applications demand real time or near real-time performance. Examples include homeland security/defense, environmental modeling and assessment, wild-land fire tracking, biological threat detection, and monitoring of oil spills and other types of chemical contamination. Only a few parallel processing strategies for hyperspectral imagery are currently available, and most of them assume homogeneity in the underlying computing platform. In turn, heterogeneous networks of workstations (NOWs) have rapidly become a very promising computing solution which is expected to play a major role in the design of high-performance systems for many on-going and planned remote sensing missions. In order to address the need for cost-effective parallel solutions in this fast growing and emerging research area, this paper develops several highly innovative parallel algorithms for unsupervised information extraction and mining from hyperspectral image data sets, which have been specifically designed to be run in heterogeneous NOWs. The considered approaches fall into three highly representative categories: clustering, classification and spectral mixture analysis. Analytical and experimental results are presented in the context of realistic applications (based on hyperspectral data sets from the AVIRIS data repository) using several homogeneous and heterogeneous parallel computing facilities available at NASA's Goddard Space Flight Center and the University of Maryland.     

Impact of pixel size on mapping surface water in subsolar imagery

We observed surface water in a wetland, imaging in the subsolar or specular direction the exceptionally bright specular reflection of sunlight at a ground resolution of 0.3 m. We then simulated ground resolutions between 1.7 m and 1.2 km through aggregation of the 0.3 m pixels. Contrary to the expectations of some of our colleagues in the wetlands community, for these data, the accuracy of spectral mixture analysis (SMA) estimates of surface water increases as pixel ground footprint size increases. Our results suggest that regional to global scale assessments of flooded landscapes and wetlands that do not involve issues requiring 1 m resolution per se may be addressed with acceptable accuracy by applying SMA techniques to low resolution imagery. Our results indicate within-pixel estimates of surface water area derived from data measured by subsolar viewing sensors with large ground pixel footprints, such as satellite POLarization and Directionality of Earth Radiance (POLDER) data, may be highly accurate under strong surface wind conditions.     

Determination of neotectonic features of the Karasu Basin (SE Turkey) and their relationship with Quaternary volcanic activity using Landsat ETM+ imagery

The junction between the East Anatolian and the Dead Sea fault zones lies close to an unstable F: Transform fault (FFF) triple junction where the African, Eurasian and Arabian plates meet in south-eastern Turkey. The Karasu Basin is an ephemeral rifted structure located close to the junction of these plate boundaries and is expressed by a range of tectono-morphological features. This study uses remote sensing to define tectonic structures and discriminate volcanic rocks linked to rifting within the basin using Landsat Enhanced Thematic Mapper Plus (ETM+) imagery. Prior to advanced image processing stages, images were corrected to eliminate atmospheric scattering effects and determine the best band combination for multi-spectral processing techniques based on statistical methods. Following pre-processing stages, edge detection filters were applied to derive tectonic structures defining the basin. The Brovey transformation, a statistical data merging method, was used to combine reflective multi-spectral bands with the Landsat ETM+?panchromatic band. This method fuses higher spatial data with data of lower spatial value. The western margin of the Karasu Basin is defined by the left-lateral Amanos Fault Zone with a contemporary motion (c. –0.4 mm a^?1) probably accommodating most of the left-lateral strike-slip motion between Arabian and African plates on the northern continuation of the Dead Sea Fault Zone. The rift zone has been the site of extensive recent volcanism concentrated within the Brunhes Chron (<0.78 Ma) and linked to fault block rotations between the intracontinental master faults. The boundaries of this activity are defined here using a regolith mapping technique.     

MultiSpec—a tool for multispectral–hyperspectral image data analysis

MultiSpec is a multispectral image data analysis software application. It is intended to provide a fast, easy-to-use means for analysis of multispectral image data, such as that from the Landsat, SPOT, MODIS or IKONOS series of Earth observational satellites, hyperspectral data such as that from the Airborne Visible–Infrared Imaging Spectrometer (AVIRIS) and EO-1 Hyperion satellite system or the data that will be produced by the next generation of Earth observational sensors. The primary purpose for the system was to make new, otherwise complex analysis tools available to the general Earth science community. It has also found use in displaying and analyzing many other types of non-space related digital imagery, such as medical image data and in K-12 and university level educational activities.MultiSpec has been implemented for both the Apple Macintosh^® and Microsoft Windows^® operating systems (OS). The effort was first begun on the Macintosh OS in 1988. The GLOBE (http://www.globe.gov) program supported the development of a subset of MultiSpec for the Windows OS in 1995. Since then most (but not all) of the features in the Macintosh OS version have been ported to the Windows OS version.Although copyrighted, MultiSpec with its documentation is distributed without charge. The Macintosh and Windows versions and documentation on its use are available from the World Wide Web at URL:http://dynamo.ecn.purdue.edu/biehl/MultiSpec/MultiSpec is copyrighted (1991–2001) by Purdue Research Foundation, West Lafayette, Indiana 47907.