Search

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra MODIS, and Suomi-NPP VIIRS. The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding. I.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of < 1.5 days. The network produces spatially explicit lake ice maps with pixel-wise accuracies > 91% (respectively, mIoU scores > 60%) and generalises well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement.

Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatio-temporal information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and different lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model's ability to generalise with data from multiple camera views and two different winters. On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing web sites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations., Comment: Accepted for ISPRS Congress 2020, Nice, France

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters., Comment: Accepted for ISPRS Congress 2020, Nice, France

Microwave emissivity models of sea ice are poorly validated empirically. Typical validation studies involve using averaged or stereotyped profiles of ice parameters against averaged radiance measurements. Measurement sites are rarely matched and even less often point-by-point. Because of saline content, complex permittivity of sea ice is highly variable and difficult to predict. Therefore, to check the validity of a typical, plane-parallel, radiative-transfer-based ice emissivity model, we apply it to fresh water ice instead of salt-water ice. Radiance simulations for lake ice are compared with measurements over Lake Superior from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E). AMSR-E measurements are also collected over Antarctic icepack. For each pixel, a thermodynamic model is driven by four years of European Center for Medium Range Weather Forecasts (ECMWF) reanalysis data and the resulting temperature profiles used to drive the emissivity model. The results suggest that the relatively simple emissivity model is a good fit to the data. Both cases, however, show large discrepencies whose most likely explanation is scattering both within the ice sheet as well as by cloudy atmospheres. Scattering is neglected by the model. Further work is needed to refine the scattering component of ice emissivity models and to generate accurate estimates of complex permittivities within sea ice., Comment: Made some minor corrections

The ICESat-2 mission will continue NASA's spaceflight laser altimeter measurements of ice sheets, sea ice and vegetation using a new measurement approach: micropulse, single photon ranging at 532 nm. Differential penetration of green laser energy into snow, ice and water could introduce errors in sea ice freeboard determination used for estimation of ice thickness. Laser pulse scattering from these surface types, and resulting range biasing due to pulse broadening, is assessed using SIMPL airborne data acquired over icecovered Lake Erie. SIMPL acquires polarimetric lidar measurements at 1064 and 532 nm using the micropulse, single photon ranging measurement approach.

The permanent ice covers of Antarctic lakes in the McMurdo Dry Valleys develop liquid water inclusions in response to solar heating of internal aeolian-derived sediments. The ice sediment particles serve as nutrient (inorganic and organic)-enriched microzones for the establishment of a physiologically and ecologically complex microbial consortium capable of contemporaneous photosynthesis, nitrogen fixation, and decomposition. The consortium is capable of physically and chemically establishing and modifying a relatively nutrient- and organic matter-enriched microbial "oasis" embedded in the lake ice cover.

The research reported herein focused on the general hypothesis that satellite remote sensing of large-area, long-term trends in lake ice phenology (formation and breakup) is a robust, integrated measure of regional and global climate change. To validate this hypothesis, we explored the use of data from the Advanced Very High Resolution Radiometer (AVHRR) to discriminate the presence and extent of lake ice during the winter of 1990-1991 on the 45 lakes and reservoirs in Wisconsin with a surface area greater than 1,000 hectares. Our results suggest both the feasibility of using the AVHRR to determine the date of lake ice breakup as well as the strong correlation (R= -0.87) of the date so derived with local surface-based temperature measurements. These results suggest the potential of using current and archival satellite data to monitor changes in the date of lake ice breakup as a means of detecting regional 'signals' of greenhouse warming.

ERS-1 SAR imagery of lake ice growing on shallow tundra lakes in northern Alaska shows interesting radar backscatter variations. Based on the analysis of ice cores from these lakes, a multi-layer backscatter model comprised of the following elements has been developed: (1) specular air-ice; ice-water and ice-frozen soil boundaries; (2) an ice layer of variable thickness; (3) ice sub-layers with air inclusions of variable density, size and shape including spheres, prolate spheroids, and cylinders of finite length. Preliminary model results confirm that backscatter is a sensitive function of greater reflectivity than from an ice-frozen soil interface. The model has also been tested using bubble data derived from ice cores in April 1992. The modelled backscatter is compared with backscatter derived from ERS-1 SAR images obtained at the same time as the fieldwork.

Synthetic aperture radar (SAR) imagery data can provide information on types and distribution of river and lake ice needed for studying river ice processes and dynamics, monitoring ice during winter navigation, and formulating ice control strategies. Visible and IR remote sensing systems cannot provide such data and present field methods are inadequate for characterizing ice conditions over long river reaches. Our ongoing analysis of JPL's AIRSAR imagery data and concurrent ground truth of ice conditions on the Tanana River and surrounding lakes near Fairbanks, Alaska, in March 1988, has resulted in several findings: hummocked ice covers and zones of variable ice surface roughness within them can be differentiated; C- and L-band data are more sensitive than P-band to the range of surface roughnesses encountered; smooth, level ice that is clear or contains small bubbles produces little backscatter; snow-covered river ice, whether rough or smooth, is distinguishable from snow-covered river sediments on exposed river beds and unvegetated bars; and open water leads are readily distinguished.

Measurements of lake ice thickness were made during March 1975 at the Straits of Mackinac by using a short-pulse radar system aboard an all-terrain vehicle. These measurements were compared with ice thicknesses determined with an auger. Over 25 sites were explored which had ice thicknesses in the range 29 to 60 cm. The maximum difference between radar and auger measurements was less than 9.8 percent. The magnitude of the error was less than + or - 3.5 cm. The NASA operating short-pulse radar system used in monitoring lake ice thickness from an aircraft is also described.

The NASA Langley C-Band Stepped Frequency Microwave Radiometer was used to conduct airborne remote sensing measurements of the Great Lakes ice cover during the winters of 1978 and 1979. In order to evaluate the use of microwave radiometry for remote sensing of fresh water ice, an initial experiment was conducted in February 1978. After the results were analyzed an algorithm was developed, and a more comprehensive mission was completed in March 1979, which related the thermal emission from lake ice to meaningful geophysical quantities. A clear discrimination of pressure ridges and rubble from base ice was observed, and evidence was found which showed gradual changes in the thickness of the base itself. Results indicate that passive microwave sensors can provide a measure of lake ice within uncertainties presented by the attenuation coefficient, and the changes in surface reflectivity imposed by surface roughness.

The airborne short-pulse radar system described was developed to measure ice thickness in an attempt to extend the winter navigation system as a means of reducing coal and ore shipping costs. Experimental studies of the accuracy and limitations of the system are discussed, and measurements made at 25 sites are compared. The radar system was found to provide accurate lake ice thickness measurements that were not affected by snow cover or adverse weather conditions. Surface melting and rain, however, preclude measurements.

Description of the design and operation of two new short-pulse radar systems developed for use aboard aircraft for remote profiling of lake ice thickness. The principle of operation is based on the fact that the return signal is composed of a pulse return from the top of the ice and another, delayed in time, from the ice-water interface. The delay time between these two pulses directly gives the ice thickness when allowance is made for the slower RF propagation through the ice. The two systems are the S band and the C band systems, and their comparative merits are discussed.

Five snow extent maps of the 5,601 sq km American River Basin were prepared using a Zoom Transfer Scope from ERTS-1 MSS band 4 imagery. The maps were generally completed within one hour. A snowmelt curve based on ERTS-1 imagery was used as a calibration standard or comparison for maps prepared from NOAA-2 VHRR imagery in the same manner. Cost comparisons with U-2 derived imagery indicate that ERTS-1 snow mapping of the basins is six times faster. Conservative estimates of comparable aircraft snow survey flights yields a cost figure 200 times that of the ERTS-1 snow map. Snow mapping attempts in the Lake Ontario Basin demonstrated that ERTS-1 is not well suited to large basins. Optimum size of basins for ERTS studies is believed to range from about 250 sq km to 30,000 sq km. The value of the ERTS-1 MSS for Great Lake ice evaluation was proved during the past winter on Lake Erie. Not only were ice features and types of ice identified, but melting ice was detected through the combined use of band 5 and band 7. Ice movement (direction and speed) was mapped by examining imagery from two successive days.

Photographic data and microwave emission images from the Great Lake ice formation are compared for their applicability to commercial shipping interests. A synoptic view of the microwave radiation from the lake area ice shows a large variation in brightness temperature. The snow ice appears to have the highest microwave brightness temperatures, whereas the thick clear ice shows up some 30 degrees kelvin colder, and the thin clear ice is colder still with a 1.55 cm radiation.

There are no author-identified significant results in this report. Remotely sensed multispectral scanner and return beam vidicon imagery from ERTS-1 is being used for: (1) water depth measurements in the Virgin Islands and Upper Lake Michigan areas; (2) mapping of the Yellowstone National Park; (3) assessment of atmospheric effects in Colorado; (4) lake ice surveillance in Canada and Great Lakes areas; (5) recreational land use in Southeast Michigan; (6) International Field Year on the Great Lakes investigations of Lake Ontario; (7) image enhancement of multispectral scanner data using existing techniques; (8) water quality monitoring of the New York Bight, Tampa Bay, Lake Michigan, Santa Barbara Channel, and Lake Erie; (9) oil pollution detection in the Chesapeake Bay, Gulf of Mexico southwest of New Orleans, and Santa Barbara Channel; and (10) mapping iron compounds in the Wind River Mountains.

There are no author-identified significant results in this report.

There are no author-identified significant results in this report.

Simultaneous microwave radar and spectral radiometric data were collected over Lake Erie during March 1978. A theoretical development is presented which interprets the data collected at nadir in terms of changes in the ice thickness and the electromagnetic attenuation coefficient. The theory also addresses the failure of the spectral radiometer to determine ice thickness through observations of quarter wavelength excursions in the reflectivity. Radar data collected off-nadir showed a substantially different behavior compared to that collected near nadir. This difference is attributed to a change in propagation characteristics from quasi-specular return from the ice-water interface to scattering from the rough air-ice interface.

A short-pulse (one nanosecond) S-band radar system was developed to supplement the information obtained with the aid of the SLAR system of the Great Lakes ice information system. It is the objective of the ice information system to aid in extending the winter navigation season. The SLAR imagery cannot be interpreted directly to obtain information concerning the thickness of the ice. This information is to be provided by a remote ice measuring system utilizing nanosecond radar pulses. A description is given of investigations in which such a system was installed on a C-47 aircraft. In other studies reported an S-band short-pulse radar was mounted on an all-terrain vehicle.

The author has identified the following significant results. A comparison of lake freeze transition zone migration with the movement of large pressure centers reveals the following consistencies: (1) polar continental cyclones originate within and/or travel along the trend of the transition zone; (2) polar continental anticyclones fail to cross the transition zone; (3) polar outbreak anticyclones pass through the transition zone, apparently unaffected. In addition, storm centers associated with the transition zone undergo significant intensification manifest by a deepening of the pressure through and increased precipitation outside the zone.

There are no author-identified significant results in this report.

The author has identified the following significant results. Although it is readily recognized that there is a need for ground truth to provide adequate guidance for remote sensing data interpretation, it is noted that, in terms of radar remote sensing, this ground truth is often inadequate. It is necessary to make basic electrical and physical measurements of the surface and to some depth below it. A brief outline is presented of a ground truth scheme which uses measurements of the dielectric constant. Two portable instruments were designed specifically for this purpose; these were: (1) a Q-meter for measurement of dielectric constant and loss tangent; and (2) an instrument to measure electrical properties of the two operating frequencies of the imaging radar. Although extensive data are lacking, several general cases of radar-earth surface and interaction are described; also, examples of radar imagery and some data on ice and snow are presented. It is concluded that the next logical step is to begin to quantify the radar ground truth in preparation for machine interpretation and automatic data processing of the radar imagery.

There are no author-identified significant results in this report. This investigation is to identify any correlations between the freeze/ thaw cycles of lakes and regional weather variations. ERTS-1 imagery of central Canada and north central United States is examined on a seasonal basis. The ice conditions of certain major study lakes are noted and recorded on magnetic tape, from which the movement of a freeze/thaw transition zone may be deduced. Weather maps and tables are used to establish any obvious correlations. The process of selecting major study lakes is discussed, and a complete lake directory is presented. Various routines of the software support library are described, accompanied by output samples. Procedures used for ERTS imagery processing are presented along with the data analysis plan. Application of these procedures to selected ERTS imagery has demonstrated their utility. Preliminary results show that the freeze/thaw transition zone can be monitored from ERTS.

There are no author-identified significant results in this report.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework is presented for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network. This framework defines the landscape FT-cycle retrieval as a time series anomaly detection problem considering the frozen states as normal and thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is evaluated over Alaska, against in situ ground-based observations, showing reduced uncertainties compared to the traditional methods that use thresholding of the normalized polarization ratio.

Depleting lake ice is a climate change indicator, just like sea-level rise or glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because long-term freezing and thawing patterns serve as sentinels to understand regional and global climate change. We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000-2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. To train the classifier we rely on reference data annotated manually based on webcam images. From the ice maps, we derive long-term LIP trends. Since the webcam data are only available for two winters, we cross-check our results against the operational MODIS and VIIRS snow products. We find a change in complete freeze duration of -0.76 and -0.89 days per annum for lakes Sils and Silvaplana, respectively. Furthermore, we observe plausible correlations of the LIP trends with climate data measured at nearby meteorological stations. We notice that mean winter air temperature has a negative correlation with the freeze duration and break-up events and a positive correlation with the freeze-up events. Additionally, we observe a strong negative correlation of sunshine during the winter months with the freeze duration and break-up events., Comment: This version of the article has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s41064-022-00215-x

Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities. Multi-temporal satellite images and publicly available webcam streams are among the viable data sources to monitor lake ice. In this work we investigate machine learning-based image analysis as a tool to determine the spatio-temporal extent of ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from both multispectral optical satellite images (VIIRS and MODIS) and RGB webcam images. We model lake ice monitoring as a pixel-wise semantic segmentation problem, i.e., each pixel on the lake surface is classified to obtain a spatially explicit map of ice cover. We show experimentally that the proposed system produces consistently good results when tested on data from multiple winters and lakes. Our satellite-based method obtains mean Intersection-over-Union (mIoU) scores >93%, for both sensors. It also generalises well across lakes and winters with mIoU scores >78% and >80% respectively. On average, our webcam approach achieves mIoU values of 87% (approx.) and generalisation scores of 71% (approx.) and 69% (approx.) across different cameras and winters respectively. Additionally, we put forward a new benchmark dataset of webcam images (Photi-LakeIce) which includes data from two winters and three cameras., Comment: Accepted for publication in MDPI Remote Sensing Journal

Various lake observables, including lake ice, are related to climate and climate change and provide a good opportunity for long-term monitoring. Lakes (and as part of them lake ice) is therefore considered an Essential Climate Variable (ECV) of the Global Climate Observing System (GCOS). Following the need for an integrated multi-temporal monitoring of lake ice in Switzerland, MeteoSwiss in the framework of GCOS Switzerland supported this 2-year project to explore not only the use of satellite images but also the possibilities of Webcams and in-situ measurements. The aim of this project is to monitor some target lakes and detect the extent of ice and especially the ice-on/off dates, with focus on the integration of various input data and processing methods. The target lakes are: St. Moritz, Silvaplana, Sils, Sihl, Greifen and Aegeri, whereby only the first four were mainly frozen during the observation period and thus processed. The observation period was mainly the winter 2016-17. During the project, various approaches were developed, implemented, tested and compared. Firstly, low spatial resolution (250 - 1000 m) but high temporal resolution (1 day) satellite images from the optical sensors MODIS and VIIRS were used. Secondly, and as a pilot project, the use of existing public Webcams was investigated for (a) validation of results from satellite data, and (b) independent estimation of lake ice, especially for small lakes like St. Moritz, that could not be possibly monitored in the satellite images. Thirdly, in-situ measurements were made in order to characterize the development of the temperature profiles and partly pressure before freezing and under the ice-cover until melting. This report presents the results of the project work.

Image retrieval is the process of searching and retrieving images from a database based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or medical images by extracting features from the images, such as deep features, colour-based features, shape-based features and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images; the datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infection, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of 0.09 across classes of irregular patterns, outperforming alternative feature-metric combinations across all datasets. Furthermore, the low standard deviation between each class highlights DefChars' capability for a reliable image retrieval task, even in the presence of class imbalances or small-sized datasets., Comment: 35 pages, 5 figures, 19 tables (17 tables in appendix), submitted to Special Issue: Advances and Challenges in Multimodal Machine Learning 2nd Edition, Journal of Imaging, MDPI

Lake ice within three Advanced Microwave Scanning Radiometer on EOS (AMSR-E) pixels over the Great Bear and Great Slave Lakes have been simulated with the Canadian Lake Ice Model (CLIMo). The resulting thicknesses and temperatures were fed to a radiative transfer-based ice emissivity model and compared to the satellite measurements at three frequencies---6.925 GHz, 10.65 GHz and 18.7 GHz. Excluding the melt season, the model was found to have strong predictive power, returning a correlation of 0.926 and a residual of 0.78 Kelvin at 18 GHz, vertical polarization. Discrepencies at melt season are thought to be caused by the presence of dirt in the snow cover which makes the microwave signature more like soil rather than ice. Except at 18 GHz, all results showed significant bias compared to measured values. Further work needs to be done to determine the source of this bias., Comment: Corrected minor errors (e.g. sentence fragment)

Mars was characterized by cataclysmic groundwater-sourced surface flooding that formed large outflow channels and that may have altered the climate for extensive periods during the Hesperian era. In particular, it has been speculated that such events could have induced significant rainfall and caused the formation of late-stage valley networks. We present the results of 3-D Global Climate Model simulations reproducing the short and long term climatic impact of a wide range of outflow channel formation events under cold ancient Mars conditions. We find that the most intense of these events (volumes of water up to 10$^7 $km$^3$ and released at temperatures up to 320 Kelvins) cannot trigger long-term greenhouse global warming, regardless of how favorable are the external conditions (e.g. obliquity and seasons). In any case, outflow channel formation events at any atmospheric pressure are unable to produce rainfall or significant snowmelt at latitudes below 40$^{\circ}$N. On the long term, for an obliquity of $\sim $45$^{\circ}$ and atmospheric pressures $>$ 80 mbar, we find that the lake ice (formed quickly after the outflow event) is transported progressively southward through the mechanisms of sublimation and adiabatic cooling. At the same time, and as long as the initial water reservoir is not entirely sublimated, ice deposits remain in the West Echus Chasma Plateau region where hints of hydrological activity contemporaneous with outflow channel formation events have been observed. However, because the high albedo of ice drives Mars to even colder temperatures, snowmelt produced by seasonal solar forcing is difficult to attain., Comment: 67 pages, 21 figures, accepted for publication in Icarus

ICE: The Nature, the History, and the Uses of an Astonishing Substance is a recently published book by Mariana Gosnell about ice. It covers not just the ice that is readily seen, such as sea ice, lake ice, icebergs, glaciers, ice sheets, and ice cubes, but also ice in the ground, in the atmosphere, inside plants and animals, and in outer space, plus new ice forms being created in scientific laboratories. Gosnell treats the reader to a well-written, easy-going mixture of science, adventure, history, applications, science methods and controversies, and philosophy, all centered in one way or another on ice. The book is 563 pages long and is filled with fascinating anecdotes and details, such as beetles in the Canadian Rockies that can supercool to 60 C below freezing and a lake in Minnesota where each winter typically 65,000 fishing shanties are set up on the lake's ice, many with couches, beds, television sets, and bathrooms. Gosnell also includes many practical suggestions. Among them: When driving on lake ice, keep your windows open, in case your vehicle breaks through the ice and you need to make a rapid exit.

High-performance liquid chromatography (HPLC) based amino acid analysis of a Tagish Lake meteorite sample recovered 3 months after the meteorite fell to Earth have revealed that the amino acid composition of Tagish Lake is strikingly different from that of the CM and CI carbonaceous chondrites. We found that the Tagish Lake meteorite contains only trace levels of amino acids (total abundance = 880 ppb), which is much lower than the total abundance of amino acids in the CI Orgueil (4100 ppb) and the CM Murchison (16 900 ppb). Because most of the same amino acids found in the Tagish Lake meteorite are also present in the Tagish Lake ice melt water, we conclude that the amino acids detected in the meteorite are terrestrial contamination. We found that the exposure of a sample of Murchison to cold water lead to a substantial reduction over a period of several weeks in the amount of amino acids that are not strongly bound to the meteorite matrix. However, strongly bound amino acids that are extracted by direct HCl hydrolysis are not affected by the leaching process. Thus even if there had been leaching of amino acids from our Tagish Lake meteorite sample during its 3 month residence in Tagish Lake ice and melt water, a Murchison type abundance of endogenous amino acids in the meteorite would have still been readily detectable. The low amino acid content of Tagish Lake indicates that this meteorite originated fiom a different type of parent body than the CM and CI chondrites. The parent body was apparently devoid of the reagents such as aldehyldes/ketones, HCN and ammonia needed for the effective abiotic synthesis of amino acids. Based on reflectance spectral measurements, Tagish Lake has been associated with P- or D-type asteroids. If the Tagish Lake meteorite was indeed derived fiom these types of parent bodies, our understanding of these primitive asteroids needs to be reevaluated with respect to their potential inventory of biologically important organic compounds.