Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Chien, Janet Y. L.; Houser, Paul R. (Technical Monitor)

2001-01-01

Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500 m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5 km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.

Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Chien, Y. L.; Houser, Paul R. (Technical Monitor)

2001-01-01

Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500-m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5-km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.

SMAP Global Map of Surface Soil Moisture Aug. 25-27, 2015

NASA Image and Video Library

2015-09-02

A three-day composite global map of surface soil moisture as retrieved from NASA SMAP radiometer instrument between Aug. 25-27, 2015. Dry areas appear yellow/orange, such as the Sahara Desert, western Australia and the western U.S. Wet areas appear blue, representing the impacts of localized storms. White areas indicate snow, ice or frozen ground. http://photojournal.jpl.nasa.gov/catalog/PIA19877

Theoretical Accuracy of Global Snow-Cover Mapping Using Satellite Data in the Earth Observing System (EOS) Era

NASA Technical Reports Server (NTRS)

Hall, D. K.; Foster, J. L.; Salomonson, V. V.; Klein, A. G.; Chien, J. Y. L.

1998-01-01

Following the launch of the Earth Observing System first morning (EOS-AM1) satellite, daily, global snow-cover mapping will be performed automatically at a spatial resolution of 500 m, cloud-cover permitting, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. A technique to calculate theoretical accuracy of the MODIS-derived snow maps is presented. Field studies demonstrate that under cloud-free conditions when snow cover is complete, snow-mapping errors are small (less than 1%) in all land covers studied except forests where errors are greater and more variable. The theoretical accuracy of MODIS snow-cover maps is largely determined by percent forest cover north of the snowline. Using the 17-class International Geosphere-Biosphere Program (IGBP) land-cover maps of North America and Eurasia, the Northern Hemisphere is classified into seven land-cover classes and water. Snow-mapping errors estimated for each of the seven land-cover classes are extrapolated to the entire Northern Hemisphere for areas north of the average continental snowline for each month. Average monthly errors for the Northern Hemisphere are expected to range from 5 - 10%, and the theoretical accuracy of the future global snow-cover maps is 92% or higher. Error estimates will be refined after the first full year that MODIS data are available.

Mapping snow depth distribution in forested terrain using unmanned aerial vehicles and structure-from-motion

NASA Astrophysics Data System (ADS)

Webster, C.; Bühler, Y.; Schirmer, M.; Stoffel, A.; Giulia, M.; Jonas, T.

2017-12-01

Snow depth distribution in forests exhibits strong spatial heterogeneity compared to adjacent open sites. Measurement of snow depths in forests is currently limited to a) manual point measurements, which are sparse and time-intensive, b) ground-penetrating radar surveys, which have limited spatial coverage, or c) airborne LiDAR acquisition, which are expensive and may deteriorate in denser forests. We present the application of unmanned aerial vehicles in combination with structure-from-motion (SfM) methods to photogrammetrically map snow depth distribution in forested terrain. Two separate flights were carried out 10 days apart across a heterogeneous forested area of 900 x 500 m. Corresponding snow depth maps were derived using both, LiDAR-based and SfM-based DTM data, obtained during snow-off conditions. Manual measurements collected following each flight were used to validate the snow depth maps. Snow depths were resolved at 5cm resolution and forest snow depth distribution structures such as tree wells and other areas of preferential melt were represented well. Differential snow depth maps showed maximum ablation in the exposed south sides of trees and smaller differences in the centre of gaps and on the north side of trees. This new application of SfM to map snow depth distribution in forests demonstrates a straightforward method for obtaining information that was previously only available through manual spatially limited ground-based measurements. These methods could therefore be extended to more frequent observation of snow depths in forests as well as estimating snow accumulation and depletion rates.

Using Commercial Digital Cameras and Structure-for-Motion Software to Map Snow Cover Depth from Small Aircraft

NASA Astrophysics Data System (ADS)

Sturm, M.; Nolan, M.; Larsen, C. F.

2014-12-01

A long-standing goal in snow hydrology has been to map snow cover in detail, either mapping snow depth or snow water equivalent (SWE) with sub-meter resolution. Airborne LiDAR and air photogrammetry have been used successfully for this purpose, but both require significant investments in equipment and substantial processing effort. Here we detail a relatively inexpensive and simple airborne photogrammetric technique that can be used to measure snow depth. The main airborne hardware consists of a consumer-grade digital camera attached to a survey-quality, dual-frequency GPS. Photogrammetric processing is done using commercially available Structure from Motion (SfM) software that does not require ground control points. Digital elevation models (DEMs) are made from snow-free acquisitions in the summer and snow-covered acquisitions in winter, and the maps are then differenced to arrive at snow thickness. We tested the accuracy and precision of snow depths measured using this system through 1) a comparison with airborne scanning LiDAR, 2) a comparison of results from two independent and slightly different photogrameteric systems, and 3) comparison to extensive on-the-ground measured snow depths. Vertical accuracy and precision are on the order of +/-30 cm and +/- 8 cm, respectively. The accuracy can be made to approach that of the precision if suitable snow-free ground control points exists and are used to co-register summer to winter DEM maps. Final snow depth accuracy from our series of tests was on the order of ±15 cm. This photogrammetric method substantially lowers the economic and expertise barriers to entry for mapping snow.

Wet Snow Mapping in Southern Ontario with Sentinel-1A Observations

NASA Astrophysics Data System (ADS)

Chen, H.; Kelly, R. E. J.

2017-12-01

Wet snow is defined as snow with liquid water present in an ice-water mix. It is can be an indicator for the onset of the snowmelt period. Knowledge about the extent of wet snow area can be of great importance for the monitoring of seasonal snowmelt runoff with climate-induced changes in snowmelt duration having implications for operational hydrological and ecological applications. Spaceborne microwave remote sensing has been used to observe seasonal snow under all-weather conditions. Active microwave observations of snow at C-band are sensitive to wet snow due to the high dielectric contrast with non-wet snow surfaces and synthetic aperture radar (SAR) is now openly available to identify and map the wet snow areas globally at relatively fine spatial resolutions ( 100m). In this study, a semi-automated workflow is developed from the change detection method of Nagler et al. (2016) using multi-temporal Sentinel-1A (S1A) dual-polarization observations of Southern Ontario. Weather station data and visible-infrared satellite observations are used to refine the wet snow area estimates. Wet snow information from National Operational Hydrologic Remote Sensing Center (NOHRSC) is used to compare with the S1A estimates. A time series of wet snow maps shows the variations in backscatter from wet snow on a pixel basis. Different land cover types in Southern Ontario are assessed with respect to their impacts on wet snow estimates. While forests and complex land surfaces can impact the ability to map wet snow, the approach taken is robust and illustrates the strong sensitivity of the approach to wet snow backscattering characteristics. The results indicate the feasibility of the change detection method on non-mountainous large areas and address the usefulness of Sentinel-1A data for wet snow mapping.

Improving snow density estimation for mapping SWE with Lidar snow depth: assessment of uncertainty in modeled density and field sampling strategies in NASA SnowEx

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Smyth, E.; Small, E. E.

2017-12-01

The spatial distribution of snow water equivalent (SWE) is not sufficiently monitored with either remotely sensed or ground-based observations for water resources management. Recent applications of airborne Lidar have yielded basin-wide mapping of SWE when combined with a snow density model. However, in the absence of snow density observations, the uncertainty in these SWE maps is dominated by uncertainty in modeled snow density rather than in Lidar measurement of snow depth. Available observations tend to have a bias in physiographic regime (e.g., flat open areas) and are often insufficient in number to support testing of models across a range of conditions. Thus, there is a need for targeted sampling strategies and controlled model experiments to understand where and why different snow density models diverge. This will enable identification of robust model structures that represent dominant processes controlling snow densification, in support of basin-scale estimation of SWE with remotely-sensed snow depth datasets. The NASA SnowEx mission is a unique opportunity to evaluate sampling strategies of snow density and to quantify and reduce uncertainty in modeled snow density. In this presentation, we present initial field data analyses and modeling results over the Colorado SnowEx domain in the 2016-2017 winter campaign. We detail a framework for spatially mapping the uncertainty in snowpack density, as represented across multiple models. Leveraging the modular SUMMA model, we construct a series of physically-based models to assess systematically the importance of specific process representations to snow density estimates. We will show how models and snow pit observations characterize snow density variations with forest cover in the SnowEx domains. Finally, we will use the spatial maps of density uncertainty to evaluate the selected locations of snow pits, thereby assessing the adequacy of the sampling strategy for targeting uncertainty in modeled snow density.

Our sense of Snow: the myth of John Snow in medical geography.

PubMed

McLeod, K S

2000-04-01

In 1854, Dr. John Snow identified the Broad Street pump as the source of an intense cholera outbreak by plotting the location of cholera deaths on a dot-map. He had the pump handle removed and the outbreak ended...or so one version of the story goes. In medical geography, the story of Snow and the Broad Street cholera outbreak is a common example of the discipline in action. While authors in other health-related disciplines focus on Snow's "shoe-leather epidemiology", his development of a water-borne theory of cholera transmission, and/or his pioneering role in anaesthesia, it is the dot-map that makes him a hero in medical geography. The story forms part of our disciplinary identity. Geographers have helped to shape the Snow narrative: the map has become part of the myth. Many of the published accounts of Snow are accompanied by versions of the map, but which map did Snow use? What happens to the meaning of our story when the determinative use of the map is challenged? In his book On the Mode of Communication of Cholera (2nd ed., John Churchill, London, 1855), Snow did not write that he used a map to identify the source of the outbreak. The map that accompanies his text shows cholera deaths in Golden Square (the subdistrict of London's Soho district where the outbreak occurred) from August 19 to September 30, a period much longer than the intense outbreak. What happens to the meaning of the myth when the causal connection between the pump's disengagement and the end of the outbreak is examined? Snow's data and text do not support this link but show that the number of cholera deaths was abating before the handle was removed. With the drama of the pump handle being questioned and the map, our artifact, occupying a more illustrative than central role, what is our sense of Snow?

Finland Validation of the New Blended Snow Product

NASA Technical Reports Server (NTRS)

Kim, E. J.; Casey, K. A.; Hallikainen, M. T.; Foster, J. L.; Hall, D. K.; Riggs, G. A.

2008-01-01

As part of an ongoing effort to validate satellite remote sensing snow products for the recentlydeveloped U.S. Air Force Weather Agency (AFWA) - NASA blended snow product, Satellite and in-situ data for snow extent and snow water equivalent (SWE) are evaluated in Finland for the 2006-2007 snow season Finnish Meteorological Institute (FMI) daily weather station data and Finnish Environment Institute (SYKE) bi-monthly snow course data are used as ground truth. Initial comparison results display positive agreement between the AFWA NASA Snow Algorithm (ANSA) snow extent and SWE maps and in situ data, with discrepancies in accordance with known AMSR-E and MODIS snow mapping limitations. Future ANSA product improvement plans include additional validation and inclusion of fractional snow cover in the ANSA data product. Furthermore, the AMSR-E 19 GHz (horizontal channel) with the difference between ascending and descending satellite passes (Diurnal Amplitude Variations, DAV) will be used to detect the onset of melt, and QuikSCAT scatterometer data (14 GHz) will be used to map areas of actively melting snow.

Potential and limitations of webcam images for snow cover monitoring in the Swiss Alps

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2017-04-01

In Switzerland, several thousands of outdoor webcams are currently connected to the Internet. They deliver freely available images that can be used to analyze snow cover variability on a high spatio-temporal resolution. To make use of this big data source, we have implemented a webcam-based snow cover mapping procedure, which allows to almost automatically derive snow cover maps from such webcam images. As there is mostly no information about the webcams and its parameters available, our registration approach automatically resolves these parameters (camera orientation, principal point, field of view) by using an estimate of the webcams position, the mountain silhouette, and a high-resolution digital elevation model (DEM). Combined with an automatic snow classification and an image alignment using SIFT features, our procedure can be applied to arbitrary images to generate snow cover maps with a minimum of effort. Resulting snow cover maps have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or hidden from webcams' positions. Up to now, we processed images of about 290 webcams from our archive, and evaluated images of 20 webcams using manually selected ground control points (GCPs) to evaluate the mapping accuracy of our procedure. We present methodological limitations and ongoing improvements, show some applications of our snow cover maps, and demonstrate that webcams not only offer a great opportunity to complement satellite-derived snow retrieval under cloudy conditions, but also serve as a reference for improved validation of satellite-based approaches.

A Prototype MODI- SSM/I Snow Mapping Algorithm

NASA Technical Reports Server (NTRS)

Tait, Andrew B.; Barton, Jonathan S.; Hall, Dorothy K.

1999-01-01

Data in the wavelength range 0.545 - 1.652 microns from the Moderate Resolution Imaging Spectroradiometer (MODIS), to be launched aboard the Earth Observing System (EOS) Terra in the fall of 1999, will be used to map daily global snow cover at 500m resolution. However, during darkness, or when the satellite's view of the surface is obscured by cloud, snow cover cannot be mapped using MODIS data. We show that during these conditions, it is possible to supplement the MODIS product by mapping the snow cover using passive microwave data from the Special Sensor Microwave Imager (SSM/I), albeit with much poorer resolution. For a 7-day time period in March 1999, a prototype MODIS snow-cover product was compared with a prototype MODIS-SSM/I product for the same area in the mid-western United States. The combined MODIS-SSM/I product mapped 9% more snow cover than the MODIS-only product.

Merging the MODIS and NESDIS Monthly Snow-Cover Records to Study Decade-Scale Changes in Northern Hemisphere Snow Cover

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Robinson, David A.; Riggs, George A.

2004-01-01

A decade-scale record of Northern Hemisphere snow cover has been available from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite Data and Information Service (NESDIS) and has been reconstructed and validated by Rutgers University following adjustments for inconsistencies that were discovered in the early years of the data set. This record provides weekly, monthly (and, in recent years, daily) snow cover from 1966 to the present for the Northern Hemisphere. With the December 1999 launch of NASA's Earth observing System (EOS) Terra satellite, snow maps are being produced globally, using automated algorithms, on a daily, weekly and monthly basis from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The resolution of the MODIS monthly snow maps (0.05deg or about 5 km) is an improvement over that of the NESDIS-derived monthly snow maps (>approx.10 km) the maps, it is necessary to study the datasets carefully to determine if it is possible to merge the datasets into a continuous record. The months in which data are available for both the NESDIS and MODIS maps (March 2000 to the present) will be compared quantitatively to analyze differences in North American and Eurasian snow cover. Results from the NESDIS monthly maps show that for North America (including all 12 months), there is a trend toward slightly less snow cover in each succeeding decade. Interannual snow-cover extent has varied significantly since 2000 as seen in both the NESDIS and MODIS maps. As the length of the satellite record increases through the MODIS era, and into the National Polar-orbiting Environmental Satellite System (NPOESS) era, it should become easier to identify trends in areal extent of snow cover, if present, that may have climatic significance. Thus it is necessary to analyze the validity of merging the NESDIS and MODIS, and, in the future, the NPOESS datasets for determination of long-term continuity in measurement of Northern Hemisphere snow cover.

An automated approach for mapping persistent ice and snow cover over high latitude regions

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors.

Snow water equivalent mapping in Norway

NASA Astrophysics Data System (ADS)

Tveito, O. E.; Udnæs, H.-C.; Engeset, R.; Førland, E. J.; Isaksen, K.; Mengistu, Z.

2003-04-01

In high latitude area snow covers the ground large parts of the year. Information about the water volume as snow is of major importance in many respects. Flood forecasters at NVE need it in order to assess possible flood risks. Hydropower producers need it to plan the most efficient production of the water in their reservoirs, traders to estimate the potential energy available for the market. Meteorologists on their side use the information as boundary conditions in weather forecasting models. The Norwegian meteorological institute has provided snow accumulation maps for Norway for more than 50 years. These maps are now produced twice a month in the winter season. They show the accumulated precipitation in the winter season from the day the permanent snow cover is established. They do however not take melting into account, and do therefore not give a good description of the actual snow amounts during and after periods with snowmelt. Due to an increased need for a direct measure of water volumes as snow cover, met.no and NVE initialized a joint project in order to establish maps of the actual snow cover expressed in water equivalents. The project utilizes recent developments in the use of GIS in spatial modeling. Daily precipitation and temperature are distributed in space by using objective spatial interpolation methods. The interpolation considers topographical and other geographical parameters as well as weather type information. A degree-day model is used at each modeling point to calculate snow-accumulation and snowmelt. The maps represent a spatial scale of 1x1 km2. The modeled snow reservoir is validated by snow pillow values as well traditional snow depth observations. Preliminary results show that the new snow modeling approach reproduces the snow water equivalent well. The spatial approach also opens for a wide use in the terms of areal analysis.

View Angle Effects on MODIS Snow Mapping in Forests

NASA Technical Reports Server (NTRS)

Xin, Qinchuan; Woodcock, Curtis E.; Liu, Jicheng; Tan, Bin; Melloh, Rae A.; Davis, Robert E.

2012-01-01

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.

Snow Cover Mapping and Ice Avalanche Monitoring from the Satellite Data of the Sentinels

NASA Astrophysics Data System (ADS)

Wang, S.; Yang, B.; Zhou, Y.; Wang, F.; Zhang, R.; Zhao, Q.

2018-04-01

In order to monitor ice avalanches efficiently under disaster emergency conditions, a snow cover mapping method based on the satellite data of the Sentinels is proposed, in which the coherence and backscattering coefficient image of Synthetic Aperture Radar (SAR) data (Sentinel-1) is combined with the atmospheric correction result of multispectral data (Sentinel-2). The coherence image of the Sentinel-1 data could be segmented by a certain threshold to map snow cover, with the water bodies extracted from the backscattering coefficient image and removed from the coherence segment result. A snow confidence map from Sentinel-2 was used to map the snow cover, in which the confidence values of the snow cover were relatively high. The method can make full use of the acquired SAR image and multispectral image under emergency conditions, and the application potential of Sentinel data in the field of snow cover mapping is exploited. The monitoring frequency can be ensured because the areas obscured by thick clouds are remedied in the monitoring results. The Kappa coefficient of the monitoring results is 0.946, and the data processing time is less than 2 h, which meet the requirements of disaster emergency monitoring.

MODIS Collection 6 Data at the National Snow and Ice Data Center (NSIDC)

NASA Astrophysics Data System (ADS)

Fowler, D. K.; Steiker, A. E.; Johnston, T.; Haran, T. M.; Fowler, C.; Wyatt, P.

2015-12-01

For over 15 years, the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) has archived and distributed snow and sea ice products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Earth Observing System (EOS) Aqua and Terra satellites. Collection 6 represents the next revision to NSIDC's MODIS archive, mainly affecting the snow-cover products. Collection 6 specifically addresses the needs of the MODIS science community by targeting the scenarios that have historically confounded snow detection and introduced errors into the snow-cover and fractional snow-cover maps even though MODIS snow-cover maps are typically 90 percent accurate or better under good observing conditions, Collection 6 uses revised algorithms to discriminate between snow and clouds, resolve uncertainties along the edges of snow-covered regions, and detect summer snow cover in mountains. Furthermore, Collection 6 applies modified and additional snow detection screens and new Quality Assessment protocols that enhance the overall accuracy of the snow maps compared with Collection 5. Collection 6 also introduces several new MODIS snow products, including a daily Climate Modelling Grid (CMG) cloud gap-filled (CGF) snow-cover map which generates cloud-free maps by using the most recent clear observations.. The MODIS Collection 6 sea ice extent and ice surface temperature algorithms and products are much the same as Collection 5; however, Collection 6 updates to algorithm inputs—in particular, the L1B calibrated radiances, land and water mask, and cloud mask products—have improved the sea ice outputs. The MODIS sea ice products are currently available at NSIDC, and the snow cover products are soon to follow in 2016 NSIDC offers a variety of methods for obtaining these data. Users can download data directly from an online archive or use the NASA Reverb Search & Order Tool to perform spatial, temporal, and parameter subsetting, reformatting, and re-projection of the data.

Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Digirolamo, Nicolo E.; Frei, Allan

2017-01-01

Meltwater from snow that falls in the Catskill Mountains in southern New York contributes to reservoirs that supply drinking water to approximately nine million people in New York City. Using the NOAA National Ice Centers Interactive Multisensor Snow and Ice Mapping System (IMS) 4km snow maps, we have identified at least 32 lake-effect (LE) storms emanating from Lake Erie andor Lake Ontario that deposited snow in the CatskillDelaware Watershed in the Catskill Mountains of southern New York State between 2004 and 2017. This represents a large underestimate of the contribution of LE snow to the Catskills snowpack because many of the LE snowstorms are not visible in the IMS snow maps when they travel over snow-covered terrain. Most of the LE snowstorms that we identified originate from Lake Ontario but quite a few originate from both Erie and Ontario, and a few from Lake Erie alone. Using satellite, meteorological and reanalysis data we identify conditions that contributed to LE snowfall in the Catskills. Clear skies following some of the storms permitted measurement of the extent of snow cover in the watershed using multiple satellite sensors. IMS maps tend to overestimate the extent of snow compared to MODerate resolution Imaging Spectroradiometer (MODIS) and Landsat-derived snow-cover extent maps. Using this combination of satellite and meteorological data, we can begin to quantify the important contribution of LE snow to the Catskills Mountain snowpack. Changes that are predicted in LE snowfall from the Great Lakes could impact the distribution of rain vs snow in the Catskills which may affect future reservoir operations in the NYC Water Supply System.

Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

NASA Astrophysics Data System (ADS)

Çiftçi, B. B.; Kuter, S.; Akyürek, Z.; Weber, G.-W.

2017-11-01

Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

Applications systems verification and transfer project. Volume 1: Operational applications of satellite snow cover observations: Executive summary. [usefulness of satellite snow-cover data for water yield prediction

NASA Technical Reports Server (NTRS)

Rango, A.

1981-01-01

Both LANDSAT and NOAA satellite data were used in improving snowmelt runoff forecasts. When the satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. A cost benefit analysis run in conjunction with the snow mapping indicated a $36.5 million annual benefit accruing from a one percent improvement in forecast accuracy using the snow cover data for the western United States. The annual cost of employing the system would be $505,000. The snow mapping has proven that satellite snow cover data can be used to reduce snowmelt runoff forecast error in a cost effective manner once all operational satellite data are available within 72 hours after acquisition. Executive summaries of the individual snow mapping projects are presented.

Development and Evaluation of a Cloud-Gap-Filled MODIS Daily Snow-Cover Product

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Foster, James L.; Kumar, Sujay V.

2010-01-01

The utility of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover products is limited by cloud cover which causes gaps in the daily snow-cover map products. We describe a cloud-gap-filled (CGF) daily snowcover map using a simple algorithm to track cloud persistence, to account for the uncertainty created by the age of the snow observation. Developed from the 0.050 resolution climate-modeling grid daily snow-cover product, MOD10C1, each grid cell of the CGF map provides a cloud-persistence count (CPC) that tells whether the current or a prior day was used to make the snow decision. Percentage of grid cells "observable" is shown to increase dramatically when prior days are considered. The effectiveness of the CGF product is evaluated by conducting a suite of data assimilation experiments using the community Noah land surface model in the NASA Land Information System (LIS) framework. The Noah model forecasts of snow conditions, such as snow-water equivalent (SWE), are updated based on the observations of snow cover which are obtained either from the MOD1 OC1 standard product or the new CGF product. The assimilation integrations using the CGF maps provide domain averaged bias improvement of -11 %, whereas such improvement using the standard MOD1 OC1 maps is -3%. These improvements suggest that the Noah model underestimates SWE and snow depth fields, and that the assimilation integrations contribute to correcting this systematic error. We conclude that the gap-filling strategy is an effective approach for increasing cloud-free observations of snow cover.

Multi-Sensor Approach to Mapping Snow Cover Using Data From NASA's EOS Aqua and Terra Spacecraft

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.

2003-12-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. For the period 1978 to 2002, we have shown earlier that both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are, depending on season, less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Snow mapping using optical data is based on the magnitude of the surface reflectance while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Our previous work has defined the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and it is clear that a blended product is optimal. We present a multi-sensor approach to snow mapping based both on historical data as well as data from current NASA EOS sensors. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets. An example of validation at the brightness temperature level is provided through the comparison of AMSR-E with data from the well-calibrated heritage SSM/I sensor over a large homogeneous snow-covered surface (Dome C, Antarctica). Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current blended product is being developed in the 25 km EASE-Grid while the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) allowing the blended product to indicate percent snow cover over the larger grid cell. Relationships between the percent area covered by snow as indicated by the MODIS data and the threshold for the appearance of snow as indicated by the passive microwave data are presented. Both MODIS and AMSR-E data have enhanced spatial resolution compared to the earlier data sources and examples of how this increased spatial resolution results in more accurate snow cover maps are presented. A wide range of validation data sets are being employed in this study including the NASA Cold Lands Processes Field Experiment undertaken in Colorado during 2002 and 2003.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J.; Martin, K. (Principal Investigator); Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. Comparative analysis of snow-free and snow-covered imagery of the New England Test Area has resulted in a larger number of lineaments mapped from snow-covered imagery in three out of four sets of comparative imagery. Analysts unfamiliar with the New England Test Area were utilized; the quality of imagery was independently judged to be uniform. In all image sets, a greater total length of lineaments was mapped with the snow-covered imagery. The value of this technique for fracture mapping in areas with thick soil cover is suggested. A number of potentially useful environmental applications of snow enhancement related to such areas as mining, land use, and hydrology have been identified.

Design, Development and Testing of Web Services for Multi-Sensor Snow Cover Mapping

NASA Astrophysics Data System (ADS)

Kadlec, Jiri

This dissertation presents the design, development and validation of new data integration methods for mapping the extent of snow cover based on open access ground station measurements, remote sensing images, volunteer observer snow reports, and cross country ski track recordings from location-enabled mobile devices. The first step of the data integration procedure includes data discovery, data retrieval, and data quality control of snow observations at ground stations. The WaterML R package developed in this work enables hydrologists to retrieve and analyze data from multiple organizations that are listed in the Consortium of Universities for the Advancement of Hydrologic Sciences Inc (CUAHSI) Water Data Center catalog directly within the R statistical software environment. Using the WaterML R package is demonstrated by running an energy balance snowpack model in R with data inputs from CUAHSI, and by automating uploads of real time sensor observations to CUAHSI HydroServer. The second step of the procedure requires efficient access to multi-temporal remote sensing snow images. The Snow Inspector web application developed in this research enables the users to retrieve a time series of fractional snow cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) for any point on Earth. The time series retrieval method is based on automated data extraction from tile images provided by a Web Map Tile Service (WMTS). The average required time for retrieving 100 days of data using this technique is 5.4 seconds, which is significantly faster than other methods that require the download of large satellite image files. The presented data extraction technique and space-time visualization user interface can be used as a model for working with other multi-temporal hydrologic or climate data WMTS services. The third, final step of the data integration procedure is generating continuous daily snow cover maps. A custom inverse distance weighting method has been developed to combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the MODIS snow cover product. The method is demonstrated by producing a continuous daily time step snow presence probability map dataset for the Czech Republic region. The ability of the presented methodology to reconstruct MODIS snow cover under cloud is validated by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. Using crowdsourcing data (volunteer snow reports and ski tracks) improves the map accuracy by 0.7--1.2%. The output snow probability map data sets are published online using web applications and web services. Keywords: crowdsourcing, image analysis, interpolation, MODIS, R statistical software, snow cover, snowpack probability, Tethys platform, time series, WaterML, web services, winter sports.

Applications systems verification and transfer project. Volume 8: Satellite snow mapping and runoff prediction handbook

NASA Technical Reports Server (NTRS)

Bowley, C. J.; Barnes, J. C.; Rango, A.

1981-01-01

The purpose of the handbook is to update the various snowcover interpretation techniques, document the snow mapping techniques used in the various ASVT study areas, and describe the ways snowcover data have been applied to runoff prediction. Through documentation in handbook form, the methodology developed in the Snow Mapping ASVT can be applied to other areas.

Mapping snow depth in open alpine terrain from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Marti, R.; Gascoin, S.; Berthier, E.; de Pinel, M.; Houet, T.; Laffly, D.

2016-07-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.

Improving alpine-region spectral unmixing with optimal-fit snow endmembers

NASA Technical Reports Server (NTRS)

Painter, Thomas H.; Roberts, Dar A.; Green, Robert O.; Dozier, Jeff

1995-01-01

Surface albedo and snow-covered-area (SCA) are crucial inputs to the hydrologic and climatologic modeling of alpine and seasonally snow-covered areas. Because the spectral albedo and thermal regime of pure snow depend on grain size, areal distribution of snow grain size is required. Remote sensing has been shown to be an effective (and necessary) means of deriving maps of grain size distribution and snow-covered-area. Developed here is a technique whereby maps of grain size distribution improve estimates of SCA from spectral mixture analysis with AVIRIS data.

Snow Cover Mapping at the Continental to Global Scale Using Combined Visible and Passive Microwave Satellite Data

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M.; Savoie, M. H.

2007-12-01

Over the past several decades both visible and passive microwave satellite data have been utilized for snow mapping at the continental to global scale. Snow mapping using visible data has been based primarily on the magnitude of the surface reflectance, and in more recent cases on specific spectral signatures, while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. We describe the respective problems as well as the advantages and disadvantages of these two types of satellite data for snow cover mapping and demonstrate how a multi-sensor approach is optimal. For the period 1978 to present we combine data from the NOAA weekly snow charts with snow cover derived from the SMMR and SSM/I brightness temperature data. For the period since 2002 we blend NASA EOS MODIS and AMSR-E data sets. Our current product incorporates MODIS data from the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) with microwave-derived snow water equivalent (SWE) at 25 km, resulting in a blended product that includes percent snow cover in the larger grid cell whenever the microwave SWE signal is absent. Validation of AMSR-E at the brightness temperature level is provided through the comparison with data from the well-calibrated heritage SSM/I sensor over large homogeneous snow-covered surfaces (e.g. Dome C region, Antarctica). We also describe how the application of the higher frequency microwave channels (85 and 89 GHz)enhances accurate mapping of shallow and intermittent snow cover.

Towards a well-founded and reproducible snow load map for Austria

NASA Astrophysics Data System (ADS)

Winkler, Michael; Schellander, Harald

2017-04-01

"EN 1991-1-3 Eurocode 1: Part 1-3: Snow Loads" provides standard for the determination of the snow load to be used for the structural design of buildings etc. Since 2006 national specifications for Austria define a snow load map with four "load zones", allowing the calculation of the characteristic ground snow load sk for locations below 1500 m asl. A quadratic regression between altitude and sk is used, as suggested by EN 1991-1-3. The actual snow load map is based on best meteorological practice, but still it is somewhat subjective and non-reproducible. Underlying snow data series often end in the 1980s; in the best case data until about 2005 is used. Moreover, extreme value statistics only rely on the Gumbel distribution and the way in which snow depths are converted to snow loads is generally unknown. This might be enough reasons to rethink the snow load standard for Austria, all the more since today's situation is different to what it was some 15 years ago: Firstly, Austria is rich of multi-decadal, high quality snow depth measurements. These data are not well represented in the actual standard. Secondly, semi-empirical snow models allow sufficiently precise calculations of snow water equivalents and snow loads from snow depth measurements without the need of other parameters like temperature etc. which often are not available at the snow measurement sites. With the help of these tools, modelling of daily snow load series from daily snow depth measurements is possible. Finally, extreme value statistics nowadays offers convincing methods to calculate snow depths and loads with a return period of 50 years, which is the base of sk, and allows reproducible spatial extrapolation. The project introduced here will investigate these issues in order to update the Austrian snow load standard by providing a well-founded and reproducible snow load map for Austria. Not least, we seek for contact with standards bodies of neighboring countries to find intersections as well as to avoid inconsistencies and duplications of effort.

Snow survey and vegetation growth in high mountains (Swiss Alps)

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. A method for mapping snow over large areas was developed combining the possibilities of a Quantimet (QTM 72) to evaluate the exact density level of the snow cover for each individual image (or a selected section of the photo) with the higher resolution of photographic techniques. The density level established on the monitor by visual control is used as reference for the exposure time of a lithographic film, producing a clear tonal separation of all snow- and ice-covered areas from uncovered land in black and white. The data is projected onto special maps 1:500,000 or 1:100,000 showing the contour lines and the hydrographic features only. The areal extent of the snow cover may be calculated directly with the QTM 720 or on the map. Bands 4 and 5 provide the most accurate results for mapping snow. Using all four bands a separation of an old melting snow cover from a new one is possible. Regional meteorological studies combining ERTS-1 imagery and conventional sources describe synoptical evolution of meteorological systems over the Alps.

Improving the MODIS Global Snow-Mapping Algorithm

NASA Technical Reports Server (NTRS)

Klein, Andrew G.; Hall, Dorothy K.; Riggs, George A.

1997-01-01

An algorithm (Snowmap) is under development to produce global snow maps at 500 meter resolution on a daily basis using data from the NASA MODIS instrument. MODIS, the Moderate Resolution Imaging Spectroradiometer, will be launched as part of the first Earth Observing System (EOS) platform in 1998. Snowmap is a fully automated, computationally frugal algorithm that will be ready to implement at launch. Forests represent a major limitation to the global mapping of snow cover as a forest canopy both obscures and shadows the snow underneath. Landsat Thematic Mapper (TM) and MODIS Airborne Simulator (MAS) data are used to investigate the changes in reflectance that occur as a forest stand becomes snow covered and to propose changes to the Snowmap algorithm that will improve snow classification accuracy forested areas.

Improved Snow Mapping Accuracy with Revised MODIS Snow Algorithm

NASA Technical Reports Server (NTRS)

Riggs, George; Hall, Dorothy K.

2012-01-01

The MODIS snow cover products have been used in over 225 published studies. From those reports, and our ongoing analysis, we have learned about the accuracy and errors in the snow products. Revisions have been made in the algorithms to improve the accuracy of snow cover detection in Collection 6 (C6), the next processing/reprocessing of the MODIS data archive planned to start in September 2012. Our objective in the C6 revision of the MODIS snow-cover algorithms and products is to maximize the capability to detect snow cover while minimizing snow detection errors of commission and omission. While the basic snow detection algorithm will not change, new screens will be applied to alleviate snow detection commission and omission errors, and only the fractional snow cover (FSC) will be output (the binary snow cover area (SCA) map will no longer be included).

MODSNOW-Tool: an operational tool for daily snow cover monitoring using MODIS data

NASA Astrophysics Data System (ADS)

Gafurov, Abror; Lüdtke, Stefan; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Schöne, Tilo; Schmidt, Sebastian; Kalashnikova, Olga; Merz, Bruno

2017-04-01

Spatially distributed snow cover information in mountain areas is extremely important for water storage estimations, seasonal water availability forecasting, or the assessment of snow-related hazards (e.g. enhanced snow-melt following intensive rains, or avalanche events). Moreover, spatially distributed snow cover information can be used to calibrate and/or validate hydrological models. We present the MODSNOW-Tool - an operational monitoring tool offers a user-friendly application which can be used for catchment-based operational snow cover monitoring. The application automatically downloads and processes freely available daily Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data. The MODSNOW-Tool uses a step-wise approach for cloud removal and delivers cloud-free snow cover maps for the selected river basins including basin specific snow cover extent statistics. The accuracy of cloud-eliminated MODSNOW snow cover maps was validated for 84 almost cloud-free days in the Karadarya river basin in Central Asia, and an average accuracy of 94 % was achieved. The MODSNOW-Tool can be used in operational and non-operational mode. In the operational mode, the tool is set up as a scheduled task on a local computer allowing automatic execution without user interaction and delivers snow cover maps on a daily basis. In the non-operational mode, the tool can be used to process historical time series of snow cover maps. The MODSNOW-Tool is currently implemented and in use at the national hydrometeorological services of four Central Asian states - Kazakhstan, Kyrgyzstan, Uzbekistan and Turkmenistan and used for seasonal water availability forecast.

Mapping snow depth from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Gascoin, S.; Marti, R.; Berthier, E.; Houet, T.; de Pinel, M.; Laffly, D.

2016-12-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km²) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km²). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available. Based on this method we have initiated a multi-year survey of the peak snow depth in the Bassiès catchment.

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard; Caldwell, Megan K.

2014-01-01

Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

Evaluation of single-band snow-patch mapping using high-resolution microwave remote sensing: an application in the maritime Antarctic

NASA Astrophysics Data System (ADS)

Mora, Carla; Jiménez, Juan Javier; Pina, Pedro; Catalão, João; Vieira, Gonçalo

2017-01-01

The mountainous and ice-free terrains of the maritime Antarctic generate complex mosaics of snow patches, ranging from tens to hundreds of metres. These can only be accurately mapped using high-resolution remote sensing. In this paper we evaluate the application of radar scenes from TerraSAR-X in High Resolution SpotLight mode for mapping snow patches at a test area on Fildes Peninsula (King George Island, South Shetlands). Snow-patch mapping and characterization of snow stratigraphy were conducted at the time of image acquisition on 12 and 13 January 2012. Snow was wet in all studied snow patches, with coarse-grain and rounded crystals showing advanced melting and with frequent ice layers in the snow pack. Two TerraSAR-X scenes in HH and VV polarization modes were analysed, with the former showing the best results when discriminating between wet snow, lake water and bare soil. However, significant overlap in the backscattering signal was found. Average wet-snow backscattering was -18.0 dB in HH mode, with water showing -21.1 dB and bare soil showing -11.9 dB. Single-band pixel-based and object-oriented image classification methods were used to assess the classification potential of TerraSAR-X SpotLight imagery. The best results were obtained with an object-oriented approach using a watershed segmentation with a support vector machine (SVM) classifier, with an overall accuracy of 92 % and Kappa of 0.88. The main limitation was the west to north-west facing snow patches, which showed significant error, an issue related to artefacts from the geometry of satellite imagery acquisition. The results show that TerraSAR-X in SpotLight mode provides high-quality imagery for mapping wet snow and snowmelt in the maritime Antarctic. The classification procedure that we propose is a simple method and a first step to an implementation in operational mode if a good digital elevation model is available.

Analysis of MODIS snow cover time series over the alpine regions as input for hydrological modeling

NASA Astrophysics Data System (ADS)

Notarnicola, Claudia; Rastner, Philipp; Irsara, Luca; Moelg, Nico; Bertoldi, Giacomo; Dalla Chiesa, Stefano; Endrizzi, Stefano; Zebisch, Marc

2010-05-01

Snow extent and relative physical properties are key parameters in hydrology, weather forecast and hazard warning as well as in climatological models. Satellite sensors offer a unique advantage in monitoring snow cover due to their temporal and spatial synoptic view. The Moderate Resolution Imaging Spectrometer (MODIS) from NASA is especially useful for this purpose due to its high frequency. However, in order to evaluate the role of snow on the water cycle of a catchment such as runoff generation due to snowmelt, remote sensing data need to be assimilated in hydrological models. This study presents a comparison on a multi-temporal basis between snow cover data derived from (1) MODIS images, (2) LANDSAT images, and (3) predictions by the hydrological model GEOtop [1,3]. The test area is located in the catchment of the Matscher Valley (South Tyrol, Northern Italy). The snow cover maps derived from MODIS-images are obtained using a newly developed algorithm taking into account the specific requirements of mountain regions with a focus on the Alps [2]. This algorithm requires the standard MODIS-products MOD09 and MOD02 as input data and generates snow cover maps at a spatial resolution of 250 m. The final output is a combination of MODIS AQUA and MODIS TERRA snow cover maps, thus reducing the presence of cloudy pixels and no-data-values due to topography. By using these maps, daily time series starting from the winter season (November - May) 2002 till 2008/2009 have been created. Along with snow maps from MODIS images, also some snow cover maps derived from LANDSAT images have been used. Due to their high resolution (< 30 m) they have been considered as an evaluation tool. The snow cover maps are then compared with the hydrological GEOtop model outputs. The main objectives of this work are: 1. Evaluation of the MODIS snow cover algorithm using LANDSAT data 2. Investigation of snow cover, and snow cover duration for the area of interest for South Tyrol 3. Derivation and interpretation of the snow line for the seven winter seasons 4. An evaluation of the model outputs in order to determine the situations in which the remotely sensed data can be used to improve the model prediction of snow coverage and related variables References [1] Rigon R., Bertoldi G. and Over T.M. 2006. GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets, Journal of Hydrometeorology, 7: 371-388. [2] Rastner P., Irsara L., Schellenberger T., Della Chiesa S., Bertoldi G., Endrizzi S., Notarnicola C., Steurer C., Zebisch M. 2009. Monitoraggio del manto nevoso in aree alpine con dati MODIS multi-temporali e modelli idrologici, 13th ASITA National Conference, 1-4.12.2009, Bari, Italy. [3] Zanotti F., Endrizzi S., Bertoldi G. and Rigon R. 2004. The GEOtop snow module. Hydrological Processes, 18: 3667-3679. DOI:10.1002/hyp.5794.

Satellite geological and geophysical remote sensing of Iceland

NASA Technical Reports Server (NTRS)

Williams, R. S., Jr. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Under a binational, multidisciplinary experiment ERTS-1 imagery is being used to measure and map dynamic natural phenomena in Iceland. A few of the initial results from the project are: (1) a large variety of geological and volcanic features can be studied, particularly on imagery acquired at low sun angle ( 10 deg), which have not been previously recognized; (2) under optimum snow cover conditions, geothermal areas can be discerned by their snowmelt pattern or by warm spring discharge into frozen lakes; (3) a variety of map types at scale of 1:1,000,000 and 1:500,000, can be compiled, made more accurate, or updated (changes in coastline, glaciers, lakes, etc.); (4) the persistence of snow in the highland areas, during the summer months, has important ramifications to rangeland management; (5) false color composites (MSS) permitted the mapping of four types of vegetation; forested, reclaimed, cultivated areas and grasslands, and the mapping of the seasonal change of the vegetation, all of high value to rangeland management when complete, repetitive coverage of Iceland becomes a reality with an operational satellite; and (6) the volcanic eruption on Heimaey was recorded.

MODIS Snow and Ice Production

NASA Technical Reports Server (NTRS)

Hall, Dorthoy K.; Hoser, Paul (Technical Monitor)

2002-01-01

Daily, global snow cover maps, and sea ice cover and sea ice surface temperature (IST) maps are derived from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS), are available at no cost through the National Snow and Ice Data Center (NSIDC). Included on this CD-ROM are samples of the MODIS snow and ice products. In addition, an animation, done by the Scientific Visualization studio at Goddard Space Flight Center, is also included.

From drones to ASO: Using 'Structure-From-Motion' photogrammetry to quantify variations in snow depth at multiple scales

NASA Astrophysics Data System (ADS)

Skiles, M.

2017-12-01

The ability to accurately measure and manage the natural snow water reservoir in mountainous regions has its challenges, namely mapping of snowpack depth and snow water equivalent (SWE). Presented here is a scalable method that differentially maps snow depth using Structure from Motion (SfM); a photogrammetric technique that uses 2d images to create a 3D model/Digital Surface Model (DSM). There are challenges with applying SfM to snow, namely, relatively uniform snow brightness can make it difficult to produce quality images needed for processing, and vegetation can limit the ability to `see' through the canopy to map both the ground and snow beneath. New techniques implemented in the method to adapt to these challenges will be demonstrated. Results include a time series at (1) the plot scale, imaged with an unmanned areal vehicle (DJI Phantom 2 adapted with Sony A5100) over the Utah Department of Transportation Atwater Study Plot in Little Cottonwood Canyon, UT, and at (2) the mountain watershed scale, imaged from the RGB camera aboard the Airborne Snow Observatory (ASO), over the headwaters of the Uncompahgre River in the San Juan Mountains, CO. At the plot scale we present comparisons to measured snow depth, and at the watershed scale we present comparisons to the ASO lidar DSM. This method is of interest due to its low cost relative to lidar, making it an accessible tool for snow research and the management of water resources. With advancing unmanned aerial vehicle technology there are implications for scalability to map snow depth, and SWE, across large basins.

National Operational Hydrologic Remote Sensing Center - The ultimate source

Science.gov Websites

Analysis Satellite Obs Forecasts Data Archive SHEF Products Observations near City, ST Go Science Database Airborne Snow Surveys Satellite Snow Cover Mapping Snow Modeling and Data Assimilation Analyses polar-orbiting and geostationary satellite imagery. Maps are provided for the U.S. and the northern

Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Spallek, Waldemar; Witek-Kasprzak, Matylda

2018-04-01

The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red-green-blue (RGB) or near-infrared-red-green (NIRRG) or near-infrared-green-blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7-0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.

Classification of surface types using SIR-C/X-SAR, Mount Everest Area, Tibet

USGS Publications Warehouse

Albright, Thomas P.; Painter, Thomas H.; Roberts, Dar A.; Shi, Jiancheng; Dozier, Jeff; Fielding, Eric

1998-01-01

Imaging radar is a promising tool for mapping snow and ice cover in alpine regions. It combines a high-resolution, day or night, all-weather imaging capability with sensitivity to hydrologic and climatic snow and ice parameters. We use the spaceborne imaging radar-C/X-band synthetic aperture radar (SIR-C/X-SAR) to map snow and glacial ice on the rugged north slope of Mount Everest. From interferometrically derived digital elevation data, we compute the terrain calibration factor and cosine of the local illumination angle. We then process and terrain-correct radar data sets acquired on April 16, 1994. In addition to the spectral data, we include surface slope to improve discrimination among several surface types. These data sets are then used in a decision tree to generate an image classification. This method is successful in identifying and mapping scree/talus, dry snow, dry snow-covered glacier, wet snow-covered glacier, and rock-covered glacier, as corroborated by comparison with existing surface cover maps and other ancillary information. Application of the classification scheme to data acquired on October 7 of the same year yields accurate results for most surface types but underreports the extent of dry snow cover.

Assessment of Consistencies and Uncertainties between the NASA MODIS and VIIRS Snow-Cover Maps

NASA Astrophysics Data System (ADS)

Hall, D. K.; Riggs, G. A., Jr.; DiGirolamo, N. E.; Roman, M. O.

2017-12-01

Snow cover has great climatic and economic importance in part due to its high albedo and low thermal conductivity and large areal extent in the Northern Hemisphere winter, and its role as a freshwater source for about one-sixth of the world's population. The Rutgers University Global Snow Lab's 50-year climate-data record (CDR) of Northern Hemisphere snow cover is invaluable for climate studies, but, at 25-km resolution, the spatial resolution is too coarse to provide accurate snow information at the basin scale. Since 2000, global snow-cover maps have been produced from the MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites at 500-m resolution, and from the Suomi-National Polar Program (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) since 2011 at 375-m resolution. Development of a moderate-resolution (375 - 500 m) earth system data record (ESDR) that utilizes both MODIS and VIIRS snow maps is underway. There is a 6-year overlap between the data records. In late 2017 the second in a series of VIIRS sensors will be launched on the Joint Polar Satellite System-1 (JPSS-1), with the JPSS-2 satellite scheduled for launch in 2021, providing the potential to extend NASA's snow-cover ESDR for decades into the future and to create a CDR. Therefore it is important to investigate the continuity between the MODIS and VIIRS NASA snow-cover data products and evaluate whether there are any inconsistencies and biases that would affect their value as CDR. Time series of daily normalized-difference snow index (NDSI) Terra and Aqua MODIS Collection 6 (C6) and NASA VIIRS Collection 1 (C1) snow-cover tile maps (MOD10A1 and VNP10A1) are studied for North America to identify NDSI differences and possible biases between the datasets. Developing a CDR using the MODIS and VIIRS records is challenging. Though the instruments and orbits are similar, differences in bands, viewing geometry, spatial resolution, and cloud- and snow-mapping algorithms affect snow detection.

Formation, distribution and variability in snow cover on the Asian territory of the USSR

NASA Technical Reports Server (NTRS)

Pupkov, V. N.

1985-01-01

A description is given of maps compiled for annual and average multiple-year water reserves. The annual and average multiple-year maximum snow cover height for winter, extreme values of maximum snow reserves, and the average height and snow reserves at the end of each decade are shown. These maps were made for the entire Asian territory of the USSR, excluding Central Asia, Kamchatka Peninsula, and the Sakhalin Islands.

Study to develop improved spacecraft snow survey methods using Skylab/EREP data. [Sierra Nevada Mts., Wasatch Range, central Arizona, and Mississippi and Missouri River basins

NASA Technical Reports Server (NTRS)

Barnes, J. C. (Principal Investigator); Smallwood, M. D.; Cogan, J. L.

1975-01-01

The author has identified the following significant results. Of the four black and white S190A camera stations, snowcover is best defined in the two visible spectral bands, due in part to their better resolution. The overall extent of the snow can be mapped more precisely, and the snow within shadow areas is better defined in the visible bands. Of the two S190A color products, the aerial color photography is the better. Because of the contrast in color between snow and snow-free terrain and the better resolution, this product is concluded to be the best overall of the six camera stations for detecting and mapping snow. Overlapping frames permit stereo viewing, which aids in distinguishing clouds from the underlying snow. Because of the greater spatial resolution of the S190B earth terrain camera, areal snow extent can be mapped in greater detail than from the S190A photographs. The snow line elevation measured from the S190A and S190B photographs is reasonable compared to the meager ground truth data available.

Improving automated disturbance maps using snow-covered landsat time series stacks

Treesearch

Kirk M. Stueve; Ian W. Housman; Patrick L. Zimmerman; Mark D. Nelson; Jeremy Webb; Charles H. Perry; Robert A. Chastain; Dale D. Gormanson; Chengquan Huang; Sean P. Healey; Warren B. Cohen

2012-01-01

Snow-covered winter Landsat time series stacks are used to develop a nonforest mask to enhance automated disturbance maps produced by the Vegetation Change Tracker (VCT). This method exploits the enhanced spectral separability between forested and nonforested areas that occurs with sufficient snow cover. This method resulted in significant improvements in Vegetation...

Snow Depth Mapping at a Basin-Wide Scale in the Western Arctic Using UAS Technology

NASA Astrophysics Data System (ADS)

de Jong, T.; Marsh, P.; Mann, P.; Walker, B.

2015-12-01

Assessing snow depths across the Arctic has proven to be extremely difficult due to the variability of snow depths at scales from metres to 100's of metres. New Unmanned Aerial Systems (UAS) technology provides the possibility to obtain centimeter level resolution imagery (~3cm), and to create Digital Surface Models (DSM) based on the Structure from Motion method. However, there is an ongoing need to quantify the accuracy of this method over different terrain and vegetation types across the Arctic. In this study, we used a small UAS equipped with a high resolution RGB camera to create DSMs over a 1 km2 watershed in the western Canadian Arctic during snow (end of winter) and snow-free periods. To improve the image georeferencing, 15 Ground Control Points were marked across the watershed and incorporated into the DSM processing. The summer DSM was subtracted from the snowcovered DSM to deliver snow depth measurements across the entire watershed. These snow depth measurements were validated by over 2000 snow depth measurements. This technique has the potential to improve larger scale snow depth mapping across watersheds by providing snow depth measurements at a ~3 cm . The ability of mapping both shallow snow (less than 75cm) covering much of the basin and snow patches (up to 5 m in depth) that cover less than 10% of the basin, but contain a significant portion of total basin snowcover, is important for both water resource applications, as well as for testing snow models.

Archival processes of the water stable isotope signal in East Antarctic ice cores

NASA Astrophysics Data System (ADS)

Casado, Mathieu; Landais, Amaelle; Picard, Ghislain; Münch, Thomas; Laepple, Thomas; Stenni, Barbara; Dreossi, Giuliano; Ekaykin, Alexey; Arnaud, Laurent; Genthon, Christophe; Touzeau, Alexandra; Masson-Delmotte, Valerie; Jouzel, Jean

2018-05-01

The oldest ice core records are obtained from the East Antarctic Plateau. Water isotopes are key proxies to reconstructing past climatic conditions over the ice sheet and at the evaporation source. The accuracy of climate reconstructions depends on knowledge of all processes affecting water vapour, precipitation and snow isotopic compositions. Fractionation processes are well understood and can be integrated in trajectory-based Rayleigh distillation and isotope-enabled climate models. However, a quantitative understanding of processes potentially altering snow isotopic composition after deposition is still missing. In low-accumulation sites, such as those found in East Antarctica, these poorly constrained processes are likely to play a significant role and limit the interpretability of an ice core's isotopic composition. By combining observations of isotopic composition in vapour, precipitation, surface snow and buried snow from Dome C, a deep ice core site on the East Antarctic Plateau, we found indications of a seasonal impact of metamorphism on the surface snow isotopic signal when compared to the initial precipitation. Particularly in summer, exchanges of water molecules between vapour and snow are driven by the diurnal sublimation-condensation cycles. Overall, we observe in between precipitation events modification of the surface snow isotopic composition. Using high-resolution water isotopic composition profiles from snow pits at five Antarctic sites with different accumulation rates, we identified common patterns which cannot be attributed to the seasonal variability of precipitation. These differences in the precipitation, surface snow and buried snow isotopic composition provide evidence of post-deposition processes affecting ice core records in low-accumulation areas.

A new strategy for snow-cover mapping using remote sensing data and ensemble based systems techniques

NASA Astrophysics Data System (ADS)

Roberge, S.; Chokmani, K.; De Sève, D.

2012-04-01

The snow cover plays an important role in the hydrological cycle of Quebec (Eastern Canada). Consequently, evaluating its spatial extent interests the authorities responsible for the management of water resources, especially hydropower companies. The main objective of this study is the development of a snow-cover mapping strategy using remote sensing data and ensemble based systems techniques. Planned to be tested in a near real-time operational mode, this snow-cover mapping strategy has the advantage to provide the probability of a pixel to be snow covered and its uncertainty. Ensemble systems are made of two key components. First, a method is needed to build an ensemble of classifiers that is diverse as much as possible. Second, an approach is required to combine the outputs of individual classifiers that make up the ensemble in such a way that correct decisions are amplified, and incorrect ones are cancelled out. In this study, we demonstrate the potential of ensemble systems to snow-cover mapping using remote sensing data. The chosen classifier is a sequential thresholds algorithm using NOAA-AVHRR data adapted to conditions over Eastern Canada. Its special feature is the use of a combination of six sequential thresholds varying according to the day in the winter season. Two versions of the snow-cover mapping algorithm have been developed: one is specific for autumn (from October 1st to December 31st) and the other for spring (from March 16th to May 31st). In order to build the ensemble based system, different versions of the algorithm are created by varying randomly its parameters. One hundred of the versions are included in the ensemble. The probability of a pixel to be snow, no-snow or cloud covered corresponds to the amount of votes the pixel has been classified as such by all classifiers. The overall performance of ensemble based mapping is compared to the overall performance of the chosen classifier, and also with ground observations at meteorological stations.

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

USGS Publications Warehouse

Guo, J.; Tsang, L.; Josberger, E.G.; Wood, A.W.; Hwang, J.-N.; Lettenmaier, D.P.

2003-01-01

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.

Polar View Snow Service- Operational Snow Cover Mapping for Downstream Runoff Modeling and Hydropower Predictions

NASA Astrophysics Data System (ADS)

Bach, Heike; Appel, Florian; Rust, Felix; Mauser, Wolfram

2010-12-01

Information on snow cover and snow properties are important for hydrology and runoff modelling. Frequent updates of snow cover observation, especially for areas characterized by short-term snow dynamics, can help to improve water balance and discharge calculations. Within the GMES service element Polar View, VISTA offers a snow mapping service for Central Europe since several years [1, 2]. We outline the use of this near-real- time product for hydrological applications in Alpine environment. In particular we discuss the integration of the Polar View product into a physically based hydrological model (PROMET). This allows not only the provision of snow equivalent values, but also enhances river runoff modelling and its use in hydropower energy yield prediction. The GMES snow products of Polar View are thus used in a downstream service for water resources management, providing information services for renewable energy suppliers and energy traders.

Selected satellite data on snow and ice in the Great Lakes basin 1972-73 /IFYGL/. [International Field Year for Great Lakes

NASA Technical Reports Server (NTRS)

Wiesnet, D. R.; Mcginnis, D. F.; Forsyth, D. G.

1974-01-01

Three snow-extent maps of the Lake Ontario drainage basin were prepared from NOAA-2 satellite visible band images during the International Field Year for the Great Lakes. These maps are discussed and the satellite data are evaluated for snow-extent mapping. The value of ERTS-1 imagery and digital data is also discussed in relation to the Lake Ontario basin studies. ERTS-1 MSS data are excellent for ice identification and analysis but are not useful for forecasting where timely receipt of data is imperative. NOAA-2 VHRR data are timely but the lower resolution of the VHRR makes identification of certain ice features difficult. NOAA-2 VHRR is well suited for snow-extent maps and thermal maps of large areas such as the 19,000 sq-km Lake Ontario basin.

Forest Fires Darken Snow for Years following Disturbance: Magnitude, Duration, and Composition of Light Absorbing Impurities in Seasonal Snow across a Chronosequence of Burned Forests in the Colorado River Headwaters

NASA Astrophysics Data System (ADS)

Gleason, K. E.; Arienzo, M. M.; Chellman, N.; McConnell, J.

2017-12-01

Charred forests shed black carbon and burned debris, which accumulates and concentrates on winter snowpack, reducing snow surface albedo, and subsequently increasing snowmelt rates, and advancing the date of snow disappearance. Forest fires have occurred across vast areas of the seasonal snow zone in recent decades, however we do not understand the long-term implications of burned forests in montane headwaters to snow hydrology and downstream water resources. Across a chronosequence of nine burned forests in the Colorado River Headwaters, we sampled snow throughout the complete snowpack profile to conserve the composition, properties, and vertical stratigraphy of impurities in the snowpack during maximum snow accumulation. Using state-of-the-art geochemical analyses, we determined the magnitude, composition, and particle size distribution of black carbon, dust, and other impurities in the snowpack relative to years-since fire. Forest fires continue to darken snow for many years following fire, however the magnitude, composition, and particle size distribution of impurities change through time, altering the post-fire radiative forcing on snow as a burned forest ages.

The Contribution to High Asia Runoff from Ice and Snow (CHARIS): Understanding the source and trends of cryospheric contributions to the water balance

NASA Astrophysics Data System (ADS)

Rittger, K.; Armstrong, R. L.; Bair, N.; Racoviteanu, A.; Brodzik, M. J.; Hill, A. F.; Wilson, A. M.; Khan, A. L.; Ramage, J. M.; Khalsa, S. J. S.; Barrett, A. P.; Raup, B. H.; Painter, T. H.

2017-12-01

The Contribution to High Asia Runoff from Ice and Snow, or CHARIS, project is systematically assessing the role that glaciers and seasonal snow play in the freshwater resources of Central and South Asia. The study area encompasses roughly 3 million square kilometers of the Himalaya, Karakoram, Hindu Kush, Pamir and Tien Shan mountain ranges that drain to five major rivers: the Ganges, Brahmaputra, Indus, Amu Darya and Syr Darya. We estimate daily snow and glacier ice contributions to the water balance. Our automated partitioning method generates daily maps of 1) snow over ice (SOI), 2) exposed glacier ice (EGI), 3) debris covered glacier ice (DGI) and 4) snow over land (SOL) using fractional snow cover, snow grain size, and annual minimum ice and snow from the 500 m MODIS-derived MODSCAG and MODICE products. Maps of snow and ice cover are validated using high-resolution (30 m) maps of snow, ice, and debris cover from Landsat. The probability of detection is 0.91 and precision is 0.85 for MODICE. We examine trends in annual and monthly snow and ice maps and use daily maps as inputs to a calibrated temperature-index model and an uncalibrated energy balance model, ParBal. Melt model results and measurements of isotopes and specific ions used as an independent validation of melt modeling indicate a sharp geographic contrast in the role of snow and ice melt to downstream water supplies between the arid Tien Shan and Pamir ranges of Central Asia, where melt water dominates dry season flows, and the monsoon influenced central and eastern Himalaya where rain controls runoff. We also compare melt onset and duration from the melt models to the Calibrated, Enhanced Resolution Passive Microwave Brightness Temperature Earth Science Data Record. Trend analysis of annual and monthly area of permanent snow and ice (the union of SOI and EGI) for 2000 to 2016 shows statistically significant negative trends in the Ganges and Brahmaputra basins. There are no statistically significant trends in permanent snow and ice in the other basins and no statistically significant trends in SOL, the renewable and seasonal component of snow and ice cover, in any of the five basins. This work gives a better understanding of the current hydrologic regime to guide realistic estimates of the future availability and vulnerability of water resources in these regions.

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Hall, Dorothy K.; Eylander, John B.; Riggs, George A.; Nghiem, Son V.; Tedesco, Marco; Kim, Edward; Montesano, Paul M.; Kelly, Richard E. J.; Casey, Kimberly A.;

River Flow Advisory Commission: Snow Survey

Science.gov Websites

Survey River Watch Home → Snow Survey RFAC Information About Us Reports Maine Cooperative Snow Survey About the Snow Survey Snow Survey Map Compare Snow Survey Data Snow Survey Graphs River Watch MEMA Home USGS (Maine) Home Maine Cooperative Snow Survey This information is provided by a partnership with

High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

NASA Astrophysics Data System (ADS)

Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

2017-12-01

Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data may enhance understanding of physical processes controlling spatial and temporal distribution of seasonal snow, and their relative importance at varying spatial and temporal scales.

Application of satellite data for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H. A.; Andersen, T.; Ostrem, G. (Principal Investigator)

1980-01-01

The author has identified the following significant results. A close quantitative relationship was found between snow covered areas and subsequent runoff for different parts of the country despite climate differences. Digital LANDSAT data can be used for areas down to approximately 10 sq km to 20 sq km for accurate measurement of snow cover extent. On large watersheds (more than 500 sq km), digital NOAA/TIROS imagery can be used for snow mapping if the area/runoff relationship is determined by using observations from previous years.

BOREAS RSS-8 Snow Maps Derived from Landsat TM Imagery

NASA Technical Reports Server (NTRS)

Hall, Dorothy; Chang, Alfred T. C.; Foster, James L.; Chien, Janeet Y. L.; Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Remote Sensing Science (RSS)-8 team utilized Landsat Thematic Mapper (TM) images to perform mapping of snow extent over the Southern Study Area (SSA). This data set consists of two Landsat TM images that were used to determine the snow-covered pixels over the BOREAS SSA on 18 Jan 1993 and on 06 Feb 1994. The data are stored in binary image format files. The RSS-08 snow map data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

The spatial and seasonal variations in mineral particle composition on the snow surface and their possible effect on snow algae in the Tateyama Mountains, Japan

NASA Astrophysics Data System (ADS)

Umino, T.; Takeuchi, N.

2012-12-01

Snow algae are autotrophic microbes and play an important role as primary producers in food chain of glaciers and snowfield. Although their reproduction requires nutrients, snow and ice is extreamly poor in nutrients. One of the possible sources of nutrients is mineral particles blown by wind and deposited on the snow. They may contain variable elements and provide nutrients for snow algae. However, we scarcely know about the relationship between mineral particles and snow algae. In this study, we described spatial and seasonal variations in mineral particle composition and also snow algae on the snow surface in the Tateyama Mountains, Japan. We discussed the possible effect of mineral particles on snow algae. Tateyama Mountains are located in middle-north part of Japan ranging from 2000 - 3000 m above sea level and have heavy snow fall in winter due to strong monsoon wind from Siberia. The snow starts to thaw in April and remains until late summer as perennial snow patches in some valleys. Kosa eolian dust is known to be blown from Chinese deserts and deposited on the snow every spring. Also, snow algal bloom is often observed as red-colored snow in summer. Samples were collected from the snow surface during summer in 2008 - 2011 at four different sites (A - D) in this area. We examined them by X-ray diffractometer (XRD) and microscope to obtain composition of mineral particles and structure of snow algae community. XRD analysis revealed mineral particles on the snow surface were mainly composed of quartz, plagioclase, hornblende, mica, chlorite, and amorphous. In April, mineral compositions of all sites were almost similar to that of Kosa eolian dust, indicating that these mineral particles were derived from Chinese arid regions. After May, the mineral compositions changed according to sites. The proportion of hornblende at the site C significantly increased whereas that of mica increased at the site D. Since the site C was located near geological features mainly composed of hornblende, the supply of mineral particles from local sources is likely to increase after May. These results indicate mineral particles on the snow surface were blown from distant Chinese deserts in April when snow covered entire ground surface, and they may change to be supplied from the local exposed ground surface after May. Microscopy revealed over 90 % of snow algal cells attached mineral particles on their surfaces, suggesting mineral particles may be beneficial for their growth. Furthermore, algal community structure was different among study sites. The difference may be due to different composition of mineral particles. This suggests composition of mineral particles may affect algal community structure. Hornblende, which was abundant at the site C, is usually rich in Fe or Mg, and it may affect algal growth in the site.

Georectification and snow classification of webcam images: potential for complementing satellite-derrived snow maps over Switzerland

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2016-04-01

The spatial and temporal variability of snow cover has a significant impact on climate and environment and is of great socio-economic importance for the European Alps. Satellite remote sensing data is widely used to study snow cover variability and can provide spatially comprehensive information on snow cover extent. However, cloud cover strongly impedes the surface view and hence limits the number of useful snow observations. Outdoor webcam images not only offer unique potential for complementing satellite-derived snow retrieval under cloudy conditions but could also serve as a reference for improved validation of satellite-based approaches. Thousands of webcams are currently connected to the Internet and deliver freely available images with high temporal and spatial resolutions. To exploit the untapped potential of these webcams, a semi-automatic procedure was developed to generate snow cover maps based on webcam images. We used daily webcam images of the Swiss alpine region to apply, improve, and extend existing approaches dealing with the positioning of photographs within a terrain model, appropriate georectification, and the automatic snow classification of such photographs. In this presentation, we provide an overview of the implemented procedure and demonstrate how our registration approach automatically resolves the orientation of a webcam by using a high-resolution digital elevation model and the webcam's position. This allows snow-classified pixels of webcam images to be related to their real-world coordinates. We present several examples of resulting snow cover maps, which have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or not visible from webcams' positions. The procedure is expected to work under almost any weather condition and demonstrates the feasibility of using webcams for the retrieval of high-resolution snow cover information.

Multitemporal Accuracy and Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale Snow Depth Maps in Alpine Terrain

NASA Astrophysics Data System (ADS)

Adams, Marc S.; Bühler, Yves; Fromm, Reinhard

2017-12-01

Reliable and timely information on the spatio-temporal distribution of snow in alpine terrain plays an important role for a wide range of applications. Unmanned aerial system (UAS) photogrammetry is increasingly applied to cost-efficiently map the snow depth at very high resolution with flexible applicability. However, crucial questions regarding quality and repeatability of this technique are still under discussion. Here we present a multitemporal accuracy and precision assessment of UAS photogrammetry for snow depth mapping on the slope-scale. We mapped a 0.12 km2 large snow-covered study site, located in a high-alpine valley in Western Austria. 12 UAS flights were performed to acquire imagery at 0.05 m ground sampling distance in visible (VIS) and near-infrared (NIR) wavelengths with a modified commercial, off-the-shelf sensor mounted on a custom-built fixed-wing UAS. The imagery was processed with structure-from-motion photogrammetry software to generate orthophotos, digital surface models (DSMs) and snow depth maps (SDMs). Accuracy of DSMs and SDMs were assessed with terrestrial laser scanning and manual snow depth probing, respectively. The results show that under good illumination conditions (study site in full sunlight), the DSMs and SDMs were acquired with an accuracy of ≤ 0.25 and ≤ 0.29 m (both at 1σ), respectively. In case of poorly illuminated snow surfaces (study site shadowed), the NIR imagery provided higher accuracy (0.19 m; 0.23 m) than VIS imagery (0.49 m; 0.37 m). The precision of the UASSDMs was 0.04 m for a small, stable area and below 0.33 m for the whole study site (both at 1σ).

Next Generation Snow Cover Mapping: Can Future Hyperspectral Satellite Spectrometer Systems Improve Subpixel Snow-covered Area and Grain Size in the Sierra Nevada?

NASA Astrophysics Data System (ADS)

Hill, R.; Calvin, W. M.; Harpold, A.

2017-12-01

Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Building on previous retrieval of subpixel snow-covered area algorithms that take into account varying grain size we present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction, and approximate grain size. In addition, varying simulated models of the data will compare and contrast the retrieval of current snow products such as MODIS Snow-Covered Area and Grain Size (MODSCAG) and the Airborne Space Observatory (ASO). Specifically, does lower spatial resolution (MODIS), broader resolution bandwidth (MODIS), and limited spectral resolution (ASO) affect snow-cover area and grain size approximations? The implications of our findings will help refine snow mapping products for planned hyperspectral satellite spectrometer systems such as EnMAP (slated to launch in 2019), HISUI (planned for inclusion on the International Space Station in 2018), and HyspIRI (currently under consideration).

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.; Roman, Miguel O.

2017-01-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16C year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms.These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques. [geological mapping of New England test area

NASA Technical Reports Server (NTRS)

Wobber, F. J.; Martin, K. R. (Principal Investigator); Amato, R. V.; Leshendok, T.

1974-01-01

The author has identified the following significant results. The procedure for conducting a regional geological mapping program utilizing snow-enhanced ERTS-1 imagery has been summarized. While it is recognized that mapping procedures in geological programs will vary from area to area and from geologist to geologist, it is believed that the procedure tested in this project is applicable over a wide range of mapping programs. The procedure is designed to maximize the utility and value of ERTS-1 imagery and aerial photography within the initial phase of geological mapping programs. Sample products which represent interim steps in the mapping formula (e.g. the ERTS Fracture-Lineament Map) have been prepared. A full account of these procedures and products will be included within the Snow Enhancement Users Manual.

Evaluation of a New SnowPaver at McMurdo Station, Antarctica

DTIC Science & Technology

2014-09-01

Center (KRC) and the National Science Foundation (NSF) to assess the feasibility of using a new SnowPaver to build snow roads in Antarctica. KRC built...rutting. The SnowPaver was also used for reworking and compacting old and slushy snow during the height of the warm season. In November 2012, the power...6 SnowPaver configured for McMurdo snow-road use (2010) .................................................. 12 7 Map detail of SnowPaver test

An Ultra-Wideband, Microwave Radar for Measuring Snow Thickness on Sea Ice and Mapping Near-Surface Internal Layers in Polar Firn

NASA Technical Reports Server (NTRS)

Panzer, Ben; Gomez-Garcia, Daniel; Leuschen, Carl; Paden, John; Rodriguez-Morales, Fernando; Patel, Azsa; Markus, Thorsten; Holt, Benjamin; Gogineni, Prasad

2013-01-01

Sea ice is generally covered with snow, which can vary in thickness from a few centimeters to >1 m. Snow cover acts as a thermal insulator modulating the heat exchange between the ocean and the atmosphere, and it impacts sea-ice growth rates and overall thickness, a key indicator of climate change in polar regions. Snow depth is required to estimate sea-ice thickness using freeboard measurements made with satellite altimeters. The snow cover also acts as a mechanical load that depresses ice freeboard (snow and ice above sea level). Freeboard depression can result in flooding of the snow/ice interface and the formation of a thick slush layer, particularly in the Antarctic sea-ice cover. The Center for Remote Sensing of Ice Sheets (CReSIS) has developed an ultra-wideband, microwave radar capable of operation on long-endurance aircraft to characterize the thickness of snow over sea ice. The low-power, 100mW signal is swept from 2 to 8GHz allowing the air/snow and snow/ ice interfaces to be mapped with 5 c range resolution in snow; this is an improvement over the original system that worked from 2 to 6.5 GHz. From 2009 to 2012, CReSIS successfully operated the radar on the NASA P-3B and DC-8 aircraft to collect data on snow-covered sea ice in the Arctic and Antarctic for NASA Operation IceBridge. The radar was found capable of snow depth retrievals ranging from 10cm to >1 m. We also demonstrated that this radar can be used to map near-surface internal layers in polar firn with fine range resolution. Here we describe the instrument design, characteristics and performance of the radar.

Evaluation and time series analysis of mountain snow from MODIS and VIIRS fractional snow cover products

NASA Astrophysics Data System (ADS)

Bormann, K.; Rittger, K.; Painter, T. H.

2016-12-01

The continuation of large-scale snow cover records into the future is crucial for monitoring the impacts of global pressures such as climate change and weather variability on the cryosphere. With daily MODIS records since 2000 from a now ageing MODIS constellation (Terra & Aqua) and daily VIIRS records since 2012 from the Suomi-NPP platform, the consistency of information between the two optical sensors must be understood. First, we evaluated snow cover maps derived from both MODIS and VIIRS retrievals with coincident cloud-free Landsat 8 OLI maps across a range of locations. We found that both MODIS and VIIRS snow cover maps show similar errors when evaluated with Landsat OLI retrievals. Preliminary results also show a general agreement in regional snowline between the two sensors that is maintained during the spring snowline retreat where the proportion of mixed pixels is increased. The agreement between sensors supports the future use of VIIRS snow cover maps to continue the long-term record beyond the lifetime of MODIS. Second, we use snowline elevation to quantify large scale snow cover variability and to monitor potential changes in the rain/snow transition zone where climate change pressures may be enhanced. Despite the large inter-annual variability that is often observed in snow metrics, we expect that over the 16-year time series we will see a rise in seasonal elevation of the snowline and consequently an increasing rain/snow transition boundary in mountain environments. These results form the basis for global snowline elevation monitoring using optical remote sensing data and highlight regional differences in snowline elevation dynamics. The long-term variability in observed snowline elevation provides a recent climatology of mountain snowpack across several regions that will likely to be of interest to those interested in climate change impacts in mountain environments. This work will also be of interest to existing users of MODSCAG and VIIRSCAG snow cover products and those working in remote sensing of the mountain snowpack.

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

NASA Technical Reports Server (NTRS)

Crawford, Christopher J.; Manson, Steven M.; Bauer, Marvin E.; Hall, Dorothy K.

2013-01-01

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent.

Preliminary Validation of the AFWA-NASA Blended Snowcover Product Over the Lower Great Lakes region

NASA Technical Reports Server (NTRS)

Hall, D. K.; Montesano, P. M.; Foster, J. L.; Riggs, G. A.; Kelly, R. E. J.; Czajkowski, K.

2007-01-01

A new snow product created using the standard Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) snow cover and snow-water equivalent products has been evaluated for the Lower Great Lakes region during the winter of 2002- 03. National Weather Service Co-Operative Observing Network stations and student-acquired snow data were used as ground truth. An interpolation scheme was used to map snow cover on the ground from the station measurements for each day of the study period. It is concluded that this technique does not represent the actual ground conditions adequately to permit evaluation of the new snow product in an absolute sense. However, use of the new product was found to improve the mapping of snow cover as compared to using either the MODIS or AMSR-E product, alone. Plans for further analysis are discussed.

Summer snowmelt patterns in the South Shetlands using TerraSAR-X imagery

NASA Astrophysics Data System (ADS)

Mora, C.; Jimenez, J. J.; Catalao Fernades, J.; Ferreira, A.; David, A.; Ramos, M.; Vieira, G.

2014-12-01

Snow plays an important role in controlling ground thermal regime and thus influencing permafrost distribution in the lower areas of the South Shetlands archipelago, where late lying snowpatches protect the soil from summer warming. However, summer snow distribution is complex in the mountainous environments of the Maritime Antarctica and it is very difficult to obtain accurate mapping products of snow cover extent and also to monitor snowmelt. Field observations of snow cover in the region are currently based on: i) thickness data from a very scarce network of meteorological stations, ii) temperature poles allowing to estimate snow thickness, iii) and time-lapse cameras allowing for assessing snow distribution over relatively small areas. The high cloudiness of the Maritime Antarctic environment limits good mapping results from the analysis of optical remote sensing imagery such as Landsat, QuickBird or GeoEye. Therefore, microwave sensors provide the best imagery, since they are not influenced by cloudiness and are sensitive to wet-snow, typical of the melting season. We have acquired TerraSAR-X scenes for Deception and Livingston Islands for January-March 2014 in spotlight (HH, VV and HH/VV) and stripmap modes (HH) and analyse the radar backscattering for determining the differences between wet-snow, dry-snow and bare soil aiming at developing snow melt pattern maps. For ground truthing, snowpits were dug in order to characterize snow stratigraphy, grain size, grain type and snow density and to evaluate its effects on radar backscattering. Time-lapse cameras allow to identify snow patch boundaries in the field and ground surface temperatures obtained with minloggers, together with air temperatures, allow to identify the presence of snow cover in the ground. The current research is conducted in the framework of the project PERMANTAR-3 (Permafrost monitoring and modelling in Antarctic Peninsula - PTDC/AAG-GLO/3908/2012 of the FCT and PROPOLAR).

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

NASA Astrophysics Data System (ADS)

Riggs, George A.; Hall, Dorothy K.; Román, Miguel O.

2017-10-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

The Airborne Snow Observatory: fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo

USDA-ARS?s Scientific Manuscript database

Snow cover and its melt dominate regional climate and water resources in many of the world’s mountainous regions. Snowmelt timing and magnitude in mountains tend to be controlled by absorption of solar radiation and snow water equivalent, respectively, and yet both of these are very poorly known ev...

Utilizing Multiple Datasets for Snow Cover Mapping

NASA Technical Reports Server (NTRS)

Tait, Andrew B.; Hall, Dorothy K.; Foster, James L.; Armstrong, Richard L.

1999-01-01

Snow-cover maps generated from surface data are based on direct measurements, however they are prone to interpolation errors where climate stations are sparsely distributed. Snow cover is clearly discernable using satellite-attained optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Passive microwave (PM) data is unaffected by clouds, however, the snow-cover signature is significantly affected by melting snow and the microwaves may be transparent to thin snow (less than 3cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies. This paper describes a method that combines ground and satellite data to produce a Multiple-Dataset Snow-Cover Product (MDSCP). Comparisons with current snow-cover products show that the MDSCP draws together the advantages of each of its component products while minimizing their potential errors. Improved estimates of the snow-covered area are derived through the addition of two snow-cover classes ("thin or patchy" and "high elevation" snow cover) and from the analysis of the climate station data within each class. The compatibility of this method for use with Moderate Resolution Imaging Spectroradiometer (MODIS) data, which will be available in 2000, is also discussed. With the assimilation of these data, the resolution of the MDSCP would be improved both spatially and temporally and the analysis would become completely automated.

Mapping snow depth in alpine terrain with remotely piloted aerial systems and structure-from-motion photogrammetry - first results from a pilot study

NASA Astrophysics Data System (ADS)

Adams, Marc; Fromm, Reinhard; Bühler, Yves; Bösch, Ruedi; Ginzler, Christian

2016-04-01

Detailed information on the spatio-temporal distribution of seasonal snow in the alpine terrain plays a major role for the hydrological cycle, natural hazard management, flora and fauna, as well as tourism. Current methods are mostly only valid on a regional scale or require a trade-off between the data's availability, cost and resolution. During a one-year pilot study, we investigated the potential of remotely piloted aerial systems (RPAS) and structure-from-motion photogrammetry for snow depth mapping. We employed multi-copter and fixed-wing RPAS, equipped with different low-cost, off-the shelf sensors, at four test sites in Austria and Switzerland. Over 30 flights were performed during the winter 2014/15, where different camera settings, filters and lenses, as well as data collection routines were tested. Orthophotos and digital surface models (DSM) where calculated from the imagery using structure-from-motion photogrammetry software. Snow height was derived by subtracting snow-free from snow-covered DSMs. The RPAS-results were validated against data collected using a variety of well-established remote sensing (i.e. terrestrial laser scanning, large frame aerial sensors) and in-situ measurement techniques. The results show, that RPAS i) are able to map snow depth within accuracies of 0.07-0.15 m root mean square error (RMSE), when compared to traditional in-situ data; ii) can be operated at lower cost, easier repeatability, less operational constraints and higher GSD than large frame aerial sensors on-board manned aircraft, while achieving significantly higher accuracies; iii) are able to acquire meaningful data even under harsh environmental conditions above 2000 m a.s.l. (turbulence, low temperature and high irradiance, low air density). While providing a first prove-of-concept, the study also showed future challenges and limitations of RPAS-based snow depth mapping, including a high dependency on correct co-registration of snow-free and snow-covered height measurements, as well as a significant impact of the underlying vegetation and illumination of the snow surface on the fidelity of the results.

Facilitating the exploitation of ERTS-1 imagery utilizing snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Amato, R. V.

1973-01-01

The author has identified the following significant results. Snow cover in combination with low angle solar illumination has been found to provide increased tonal contrast of surface feature and is useful in the detection of bedrock fractures. Identical fracture systems were not as readily detectable in the fall due to the lack of a contrasting surface medium (snow) and a relatively high sun angle. Low angle solar illumination emphasizes topographic expressions not as apparent on imagery acquired with a higher sun angle. A strong correlation exists between the major fracture-lineament directions interpreted from multi-sensor imagery (including snow-free and snow cover ERTS) and the strike of bedrock joints recorded in the field indicating the structural origin of interpreted fracture-lineaments. A fracture-annotated ERTS-1 photo base map (1:250,000 scale) is being prepared for western Massachusetts. The map will document the utilization of ERTS-1 imagery for geological analysis in comparative snow-free and snow-covered terrain.

Cloud removing method for daily snow mapping over Central Asia and Xinjiang, China

NASA Astrophysics Data System (ADS)

Yu, Xiaoqi; Qiu, Yubao; Guo, Huadong; Chen, Lijuan

2017-02-01

Central Asia and Xinjiang, China are conjunct areas, located in the hinterland of the Eurasian continent, where the snowfall is an important water resource supplement form. The induced seasonal snow cover is vita factors to the regional energy and water balance, remote sensing plays a key role in the snow mapping filed, while the daily remote sensing products are normally contaminated by the occurrence of cloud, that obviously obstacles the utility of snow cover parameters. In this paper, based on the daily snow product from Moderate Resolution Imaging Spectroradiometer (MODIS A1), a cloud removing method was developed by considering the regional snow distribution characteristics with latitude and altitude dependence respectively. In the end, the daily cloud free products was compared with the same period of eight days MODIS standard product, revealing that the cloud free snow products are reasonable, while could provide higher temporal resolution, and more details over Center Asia and Xinjiang Province.

Facilitating the exploitation of ERTS-1 imagery using snow enhancement techniques. [geological fault maps of Massachusetts and Connecticut

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. The applications of ERTS-1 imagery for geological fracture mapping regardless of season has been repeatedly confirmed. The enhancement provided by a differential cover of snow increases the number and length of fracture-lineaments which can be detected with ERTS-1 data and accelerates the fracture mapping process for a variety of practical applications. The geological mapping benefits of the program will be realized in geographic areas where data are most needed - complex glaciated terrain and areas of deep residual soils. ERTS-1 derived fracture-lineament maps which provide detail well in excess of existing geological maps are not available in the Massachusetts-Connecticut area. The large quantity of new data provided by ERTS-1 may accelerate and improve field mapping now in progress in the area. Numerous other user groups have requested data on the techniques. This represents a major change in operating philosophy for groups who to data judged that snow obscured geological detail.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Sheffield, C.; Russell, O.; Amato, R. V.

1972-01-01

The author has identified the following significant results. Analysis of all available (Gemini, Apollo, Nimbus, NASA aircraft) small scale snow covered imagery has been conducted to develop and refine snow enhancement techniques. A detailed photographic interpretation of ERTS-simulation imagery covering the Feather River/Lake Tahoe area was completed and the 580-680nm. band was determined to be the optimum band for fracture detection. ERTS-1 MSS bands 5 and 7 are best suited for detailed fracture mapping. The two bands should provide more complete fracture detail when utilized in combination. Analysis of early ERTS-1 data along with U-2 ERTS simulation imagery indicates that snow enhancement is a viable technique for geological fracture mapping. A wealth of fracture detail on snow-free terrain was noted during preliminary analysis of ERTS-1 images 1077-15005-6 and 7, 1077-15011-5 and 7, and 1079-15124-5 and 7. A direct comparison of data yield on snow-free versus snow-covered terrain will be conducted within these areas following receipt of snow-covered ERTS-1 imagery.

A Full Snow Season in Yellowstone: A Database of Restored Aqua Band 6

NASA Technical Reports Server (NTRS)

Gladkova, Irina; Grossberg, Michael; Bonev, George; Romanov, Peter; Riggs, George; Hall, Dorothy

2013-01-01

The algorithms for estimating snow extent for the Moderate Resolution Imaging Spectroradiometer (MODIS) optimally use the 1.6- m channel which is unavailable for MODIS on Aqua due to detector damage. As a test bed to demonstrate that Aqua band 6 can be restored, we chose the area surrounding Yellowstone and Grand Teton national parks. In such rugged and difficult-to-access terrain, satellite images are particularly important for providing an estimation of snow-cover extent. For the full 2010-2011 snow season covering the Yellowstone region, we have used quantitative image restoration to create a database of restored Aqua band 6. The database includes restored radiances, normalized vegetation index, normalized snow index, thermal data, and band-6-based snow-map products. The restored Aqua-band-6 data have also been regridded and combined with Terra data to produce a snow-cover map that utilizes both Terra and Aqua snow maps. Using this database, we show that the restored Aqua-band-6-based snow-cover extent has a comparable performance with respect to ground stations to the one based on Terra. The result of a restored band 6 from Aqua is that we have an additional band-6 image of the Yellowstone region each day. This image can be used to mitigate cloud occlusion, using the same algorithms used for band 6 on Terra. We show an application of this database of restored band-6 images to illustrate the value of creating a cloud gap filling using the National Aeronautics and Space Administration s operational cloud masks and data from both Aqua and Terra.

An operational application of satellite snow cover observations, northwest United States. [using LANDSAT 1

NASA Technical Reports Server (NTRS)

Dillard, J. P.

1975-01-01

LANDSAT-1 imagery showing extent of snow cover was collected and is examined for the 1973 and 1974 snowmelt seasons for three Columbia River Basins. Snowlines were mapped and the aerial snow cover was determined using satellite data. Satellite snow mapping products were compared products from conventional information sources (computer programming and aerial photography was used). Available satellite data were successfully analyzed by radiance thresholding to determine snowlines and the attendant snow-covered area. Basin outline masks, contour elevation masks, and grid overlays were utilized as satellite data interpretation aids. Verification of the LANDSAT-1 data was generally good although there were exceptions. A major problem was lack of adequate cloud-free satellite imagery of high resolution and determining snowlines in forested areas.

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

NASA Astrophysics Data System (ADS)

Steele, Caitriana; Dialesandro, John; James, Darren; Elias, Emile; Rango, Albert; Bleiweiss, Max

2017-12-01

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM +) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS' coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCAMG), with reference SCA maps (SCAREF) generated from binary classification of TM imagery. SCAMG showed strong agreement with SCAREF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCAREF and SCAMG ranged between -2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCAREF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCAMG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91. In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km2) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.

Pollution from the 2014-15 Bárðarbunga eruption monitored by snow cores from the Vatnajökull glacier, Iceland

NASA Astrophysics Data System (ADS)

Galeczka, Iwona; Eiriksdottir, Eydis Salome; Pálsson, Finnur; Oelkers, Eric; Lutz, Stefanie; Benning, Liane G.; Stefánsson, Andri; Kjartansdóttir, Ríkey; Gunnarsson-Robin, Jóhann; Ono, Shuhei; Ólafsdóttir, Rósa; Jónasdóttir, Elín Björk; Gislason, Sigurdur R.

2017-11-01

The chemical composition of Icelandic rain and snow is dominated by marine aerosols, however human and volcanic activity can also affect these compositions. The six month long 2014-15 Bárðarbunga volcanic eruption was the largest in Iceland for more than 200 years and it released into the atmosphere an average of 60 kt/day SO2, 30 kt/day CO2, 500 t/day HCl and 280 t/day HF. To study the effect of this eruption on the winter precipitation, snow cores were collected from the Vatnajökull glacier and the highlands northeast of the glacier. In addition to 29 bulk snow cores from that precipitated from September 2014 until March 2015, two cores were sampled in 21 and 44 increments to quantify the spatial and time evolution of the chemical composition of the snow. The pH and chemical compositions of melted snow samples indicate that snow has been affected by the volcanic gases emitted during the Bárðarbunga eruption. The pH of the melted bulk snow cores ranged from 4.41 to 5.64 with an average value of 5.01. This is four times greater H+ activity than pure water saturated with the atmospheric CO2. The highest concentrations of volatiles in the snow cores were found close to the eruption site as predicted from CALPUFF SO2 gas dispersion quality model. The anion concentrations (SO4, Cl, and F) were higher and the pH was lower compared to equivalent snow samples collected during 1997-2006 from the unpolluted Icelandic Langjökull glacier. Higher SO4 and Cl concentrations in the snow compared with the unpolluted rainwater of marine origin confirm the addition of a non-seawater SO4 and Cl. The δ34S isotopic composition confirms that the sulphur addition is of volcanic aerosol origin. The chemical evolution of the snow with depth reflects changes in the lava effusion and gas emission rates. Those rates were the highest at the early stage of the eruption. Snow that fell during that time, represented by samples from the deepest part of the snow cores, had the lowest pH and highest concentrations of SO4, F, Cl and metals, compared with snow that fell later in the winter. Also the Al concentration, did exceed World Health Organisation drinking water standard of 3.7 μmol/kg in the lower part of the snow core closest to the eruption site. Collected snow represents the precipitation that fell during the eruption period. Nevertheless, only minor environmental impacts are evident in the snow due to its interaction with the volcanic aerosol gases. In addition, the microbial communities identified in the snow that fell during the eruption were similar to those found in snow from other parts of the Arctic, confirming an insignificant impact of this eruption on the snow microecology.

Remote Sensing of Snow Cover. Section; Snow Extent

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Frei, Allan; Drey, Stephen J.

2012-01-01

Snow was easily identified in the first image obtained from the Television Infrared Operational Satellite-1 (TIROS-1) weather satellite in 1960 because the high albedo of snow presents a good contrast with most other natural surfaces. Subsequently, the National Oceanic and Atmospheric Administration (NOAA) began to map snow using satellite-borne instruments in 1966. Snow plays an important role in the Earth s energy balance, causing more solar radiation to be reflected back into space as compared to most snow-free surfaces. Seasonal snow cover also provides a critical water resource through meltwater emanating from rivers that originate from high-mountain areas such as the Tibetan Plateau. Meltwater from mountain snow packs flows to some of the world s most densely-populated areas such as Southeast Asia, benefiting over 1 billion people (Immerzeel et al., 2010). In this section, we provide a brief overview of the remote sensing of snow cover using visible and near-infrared (VNIR) and passive-microwave (PM) data. Snow can be mapped using the microwave part of the electromagnetic spectrum, even in darkness and through cloud cover, but at a coarser spatial resolution than when using VNIR data. Fusing VNIR and PM algorithms to produce a blended product offers synergistic benefits. Snow-water equivalent (SWE), snow extent, and melt onset are important parameters for climate models and for the initialization of atmospheric forecasts at daily and seasonal time scales. Snowmelt data are also needed as input to hydrological models to improve flood control and irrigation management.

Application of LANDSAT imagery for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H.; Skorve, J. E. (Principal Investigator)

1977-01-01

The author has identified the following significant results. During the summer seasons of 1975 and 1976, the snow cover was successfully monitored and measured in the four basins. By using elevation distributions for these basins combined with the measured snow cover percentages, the equivalent snow line altitude was calculated. Equivalent snow line altitude was used in accordance with Mark Meier's definition. Cumulative runoff data were collected for the basins. Tables showing percentage snow cover versus cumulative runoff were worked out for 1975.

Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery

NASA Astrophysics Data System (ADS)

Lato, M. J.; Frauenfelder, R.; Bühler, Y.

2012-09-01

Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object-oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits.

Subpixel Snow Cover Mapping from MODIS Data by Nonparametric Regression Splines

NASA Astrophysics Data System (ADS)

Akyurek, Z.; Kuter, S.; Weber, G. W.

2016-12-01

Spatial extent of snow cover is often considered as one of the key parameters in climatological, hydrological and ecological modeling due to its energy storage, high reflectance in the visible and NIR regions of the electromagnetic spectrum, significant heat capacity and insulating properties. A significant challenge in snow mapping by remote sensing (RS) is the trade-off between the temporal and spatial resolution of satellite imageries. In order to tackle this issue, machine learning-based subpixel snow mapping methods, like Artificial Neural Networks (ANNs), from low or moderate resolution images have been proposed. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression tool that can build flexible models for high dimensional and complex nonlinear data. Although MARS is not often employed in RS, it has various successful implementations such as estimation of vertical total electron content in ionosphere, atmospheric correction and classification of satellite images. This study is the first attempt in RS to evaluate the applicability of MARS for subpixel snow cover mapping from MODIS data. Total 16 MODIS-Landsat ETM+ image pairs taken over European Alps between March 2000 and April 2003 were used in the study. MODIS top-of-atmospheric reflectance, NDSI, NDVI and land cover classes were used as predictor variables. Cloud-covered, cloud shadow, water and bad-quality pixels were excluded from further analysis by a spatial mask. MARS models were trained and validated by using reference fractional snow cover (FSC) maps generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also developed. The mutual comparison of obtained MARS and ANN models was accomplished on independent test areas. The MARS model performed better than the ANN model with an average RMSE of 0.1288 over the independent test areas; whereas the average RMSE of the ANN model was 0.1500. MARS estimates for low FSC values (i.e., FSC<0.3) were better than that of ANN. Both ANN and MARS tended to overestimate medium FSC values (i.e., 0.30.7).

Satellite and Surface Perspectives of Snow Extent in the Southern Appalachian Mountains

NASA Technical Reports Server (NTRS)

Sugg, Johnathan W.; Perry, Baker L.; Hall, Dorothy K.

2012-01-01

Assessing snow cover patterns in mountain regions remains a challenge for a variety of reasons. Topography (e.g., elevation, exposure, aspect, and slope) strongly influences snowfall accumulation and subsequent ablation processes, leading to pronounced spatial variability of snow cover. In-situ observations are typically limited to open areas at lower elevations (<1000 m). In this paper, we use several products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess snow cover extent in the Southern Appalachian Mountains (SAM). MODIS daily snow cover maps and true color imagery are analyzed after selected snow events (e.g., Gulf/Atlantic Lows, Alberta Clippers, and Northwest Upslope Flow) from 2006 to 2012 to assess the spatial patterns of snowfall across the SAM. For each event, we calculate snow cover area across the SAM using MODIS data and compare with the Interactive Multi-sensor Snow and ice mapping system (IMS) and available in-situ observations. Results indicate that Gulf/Atlantic Lows are typically responsible for greater snow extent across the entire SAM region due to intensified cyclogenesis associated with these events. Northwest Upslope Flow events result in snow cover extent that is limited to higher elevations (>1000 m) across the SAM, but also more pronounced along NW aspects. Despite some limitations related to the presence of ephemeral snow or cloud cover immediately after each event, we conclude that MODIS products are useful for assessing the spatial variability of snow cover in heavily forested mountain regions such as the SAM.

Geologic Mapping and Mineral Resource Assessment of the Healy and Talkeetna Mountains Quadrangles, Alaska Using Minimal Cloud- and Snow-Cover ASTER Data

USGS Publications Warehouse

Hubbard, Bernard E.; Rowan1, Lawrence C.; Dusel-Bacon, Cynthia; Eppinger, Robert G.

2007-01-01

On July 8, 2003, ASTER acquired satellite imagery of a 60 km-wide swath of parts of two 1:250,000 Alaska quadrangles, under favorable conditions of minimal cloud- and snow-cover. Rocks from eight different lithotectonic terranes are exposed within the swath of data, several of which define permissive tracts for various mineral deposit types such as: volcanic-hosted massive sulfides (VMS) and porphyry copper and molybdenum. Representative rock samples collected from 13 different lithologic units from the Bonnifield mining district within the Yukon-Tanana terrane (YTT), plus hydrothermally altered VMS material from the Red Mountain prospect, were analyzed to produce a spectral library spanning the VNIR-SWIR (0.4 - 2.5 ?m) through the TIR (8.1 - 11.7 ?m). Comparison of the five-band ASTER TIR emissivity and decorrelation stretch data to available geologic maps indicates that rocks from the YTT display the greatest range and diversity of silica composition of the mapped terranes, ranging from mafic rocks to silicic quartzites. The nine-band ASTER VNIR-SWIR reflectance data and spectral matched-filter processing were used to map several lithologic sequences characterized by distinct suites of minerals that exhibit diagnostic spectral features (e.g. chlorite, epidote, amphibole and other ferrous-iron bearing minerals); other sequences were distinguished by their weathering characteristics and associated hydroxyl- and ferric-iron minerals, such as illite, smectite, and hematite. Smectite, kaolinite, opaline silica, jarosite and/or other ferric iron minerals defined narrow (< 250 m diameter) zonal patterns around Red Mountain and other potential VMS targets. Using ASTER we identified some of the known mineral deposits in the region, as well as mineralogically similar targets that may represent potential undiscovered deposits. Some known deposits were not identified and may have been obscured by vegetation- or snow-cover, or were too small to be resolved.

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

NASA Astrophysics Data System (ADS)

Selkowitz, David J.; Forster, Richard R.

2016-07-01

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s-1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010-2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987-1988 and 2008-2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25-50 years.

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.

Satellite snowcover and runoff monitoring in central Arizona. [Salt River Project: Salt-Verde Watershed

NASA Technical Reports Server (NTRS)

Schumann, H. H.; Kirdar, E.; Warskow, W. L. (Principal Investigator)

1980-01-01

The author has identified the following significant results. Although the very high resolution experimental LANDSAT imagery permits rapid snow cover mapping at low cost, only one observation is available very 9 days. In contrast, low resolution operational imagery acquired by the ITOS and SMS/GOES satellites provide the daily synoptic observations necessary to monitor the rapid changes in snow covered areas in the entire Salt-Verde watershed. Geometric distortions in meteorological satellite imagery require specialized optical equipment or digital image processing for snow cover mapping.

Wide-area mapping of snow water equivalent by Sentinel-1&2 data

NASA Astrophysics Data System (ADS)

Conde, Vasco; Nico, Giovanni; Catalao, Joao; Kontu, Anna; Gritsevich, Maria

2017-04-01

The mapping of snow physical properties over large mountain areas of remote areas is an important topic in both climatological studies and hydrological models where the effects of snow melting are modeled and used to forecast extreme flood events. Usually, these models are run using in-situ measurements of snow which are expensive and statistically not representative of the spatial distribution of snow properties due to slope orientation of terrain, local terrain morphology and height as well as vegetation cover. In this work we investigate the use of data acquired by Sentinel-1 and 2 missions using a C-band SAR and multispectral sensor, respectively. The Sentinel-1 SAR data are processed to estimate the Snow Water Equivalent (SWE) using both the radar amplitude and the output of the SAR interferometry processing. Both approaches need in-situ data to process SAR data and calibrate SWE estimates. The use of SAR amplitude to estimate the SWE is well established and the basic idea is that the radar signal backscattered by snow is related to the SWE so, after modeling the relationship between these two quantities at the site of in-situ measurements this relationship can be used to map the SWE at all site where the SAR amplitude information is available. The physical principle used by SAR interferometry is that of phase delay due to propagation in a non-dispersive medium. This implies that the snow is supposed to be dry in order to allow the propagation of the SAR signal. Sentinel-2 images have been used to get land-use maps and identify areas covered by vegetation. Finland has been chosen as a study region with in-situ measurements acquired thanks to the availability of rich database of in-situ measurements of SWE. Sentinel data used in this work have been acquired starting from November 2015. Publication supported by FCT- project UID/GEO/50019/2013 - Instituto Dom Luiz.

Estimation of Subpixel Snow-Covered Area by Nonparametric Regression Splines

NASA Astrophysics Data System (ADS)

Kuter, S.; Akyürek, Z.; Weber, G.-W.

2016-10-01

Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th

Characterization of organic composition in snow and surface waters in the Athabasca Oil Sands Region, using ultrahigh resolution Fourier transform mass spectrometry.

PubMed

Yi, Y; Birks, S J; Cho, S; Gibson, J J

2015-06-15

This study was conducted to characterize the composition of dissolved organic compounds present in snow and surface waters in the Athabasca Oil Sands Region (AOSR) with the goal of identifying whether atmospherically-derived organic compounds present in snow are a significant contributor to the compounds detected in surface waters (i.e., rivers and lakes). We used electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS) to characterize the dissolved organic compound compositions of snow and surface water samples. The organic profiles obtained for the snow samples show compositional differences between samples from near-field sites (<5 km from oil sands activities) and those from more distant locations (i.e., far-field sites). There are also significant compositional differences between samples collected in near-field sites and surface water samples in the AOSR. The composition of dissolved organic compounds at the upstream Athabasca River site (i.e., Athabasca River at Athabasca) is found to be different from samples obtained from downstream sites in the vicinity of oil sands operations (i.e., Athabasca River at Fort McMurray and Athabasca River at Firebag confluence). The upstream Athabasca River sites tended to share some compositional similarities with far-field snow deposition, while the downstream Athabasca River sites are more similar to local lakes and tributaries. This contrast likely indicates the relative role of regional snowmelt contributions to the Athabasca River vs inputs from local catchments in the reach downstream of Fort McMurray. Copyright © 2015 Elsevier B.V. All rights reserved.

Studies of Malagasy Eugenia - IV: Seventeen new endemic species, a new combination, and three lectotypifications; with comments on distribution, ecological and evolutionary patterns.

PubMed

Snow, Neil; Callmander, Martin; Phillipson, Peter B

2015-01-01

Seventeen new endemic species of the genus Eugenia L. (Myrtaceae) are proposed from Madagascar, including: Eugeniaandapae N. Snow, Eugeniabarriei N. Snow, Eugeniabemangidiensis N. Snow, Eugeniacalciscopulorum N. Snow, Eugeniadelicatissima N. Snow, Callm. & Phillipson, Eugeniaechinulata N. Snow, Eugeniagandhii N. Snow, Eugeniahazonjia N. Snow, Eugeniaiantarensis N. Snow, Eugeniamalcomberi N. Snow, Eugeniamanomboensis N. Snow, Eugeniaobovatifolia N. Snow, Eugeniaranomafana N. Snow & D. Turk, Eugeniaravelonarivoi N. Snow & Callm., Eugeniarazakamalalae N. Snow & Callm., Eugeniatiampoka N. Snow & Callm., and Eugeniawilsoniana N. Snow, and one new combination, Eugeniarichardii (Blume) N. Snow, Callm. & Phillipson is provided. Detailed descriptions, information on distribution and ecology, distribution maps, vernacular names (where known), digital images of types, comparisons to morphologically similar species. Preliminary assessment of IUCN risk of extinction and conservation recommendations are provided, including Vulnerable (4 species), Endangered (2 species), and Critically Endangered (4 species). Lectotpyes are designated for Eugeniahovarum H. Perrier, Eugenianompa H. Perrier, and Eugeniascottii H. Perrier respectively.

Snow survey and vegetation growth in high mountains (Swiss Alps) and additional ERTS investigations in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Two different methods, an analog and a digital one, have been developed for rapid and accurate mapping of the areal extent and changes in snow cover in high mountains. The quick-look method is based on individual visual control of each image using a photo quantizer which provides exact references for density slicing with high resolution lith-film. The digital snow classification system is based on discriminant analysis with the data of the four multispectral bands as variables and contains all preprocessing, feature extraction, and mapping steps for an operational application. Two different sets of sampling groups were established which apply to different conditions of snow cover. The first one serves for the normal situation with a uniform dry and new cover. The second one serves for situations with partly thawing and/or frozen snow.

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

DOE PAGES

Wainwright, Haruko M.; Liljedahl, Anna K.; Dafflon, Baptiste; ...

2017-04-03

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE=2.9cm), with a spatial sampling of 10cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE=6.0cm) andmore » a fine spatial sampling (4cm×4cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE=6.0cm), at 0.5m resolution and over the lidar domain (750m×700m).« less

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wainwright, Haruko M.; Liljedahl, Anna K.; Dafflon, Baptiste

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE=2.9cm), with a spatial sampling of 10cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE=6.0cm) andmore » a fine spatial sampling (4cm×4cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE=6.0cm), at 0.5m resolution and over the lidar domain (750m×700m).« less

Combined Study of Snow Depth Determination and Winter Leaf Area Index Retrieval by Unmanned Aerial Vehicle Photogrammetry

NASA Astrophysics Data System (ADS)

Lendzioch, Theodora; Langhammer, Jakub; Jenicek, Michal

2017-04-01

A rapid and robust approach using Unmanned Aerial Vehicle (UAV) digital photogrammetry was performed for evaluating snow accumulation over different small localities (e.g. disturbed forest and open area) and for indirect field measurements of Leaf Area Index (LAI) of coniferous forest within the Šumava National Park, Czech Republic. The approach was used to reveal impacts related to changes in forest and snowpack and to determine winter effective LAI for monitoring the impact of forest canopy metrics on snow accumulation. Due to the advancement of the technique, snow depth and volumetric changes of snow depth over these selected study areas were estimated at high spatial resolution (1 cm) by subtracting a snow-free digital elevation model (DEM) from a snow-covered DEM. Both, downward-looking UAV images and upward-looking digital hemispherical photography (DHP), and additional widely used LAI-2200 canopy analyser measurements were applied to determine the winter LAI, controlling interception and transmitting radiation. For the performance of downward-looking UAV images the snow background instead of the sky fraction was used. The reliability of UAV-based LAI retrieval was tested by taking an independent data set during the snow cover mapping campaigns. The results showed the potential of digital photogrammetry for snow depth mapping and LAI determination by UAV techniques. The average difference obtained between ground-based and UAV-based measurements of snow depth was 7.1 cm with higher values obtained by UAV. The SD of 22 cm for the open area seemed competitive with the typical precision of point measurements. In contrast, the average difference in disturbed forest area was 25 cm with lower values obtained by UAV and a SD of 36 cm, which is in agreement with other studies. The UAV-based LAI measurements revealed the lowest effective LAI values and the plant canopy analyser LAI-2200 the highest effective LAI values. The biggest bias of effective LAI was observed between LAI-2200 and UAV-based analyses. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

Snow Depth Depicted on Mt. Lyell by NASA Airborne Snow Observatory

NASA Image and Video Library

2013-05-02

A natural color image of Mt. Lyell, the highest point in the Tuolumne River Basin top image is compared with a three-dimensional color composite image of Mt. Lyell from NASA Airborne Snow Observatory depicting snow depth bottom image.

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

NASA Technical Reports Server (NTRS)

Meier, M. F. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The area of snow cover on land was determined from ERTS-1 imagery. Snow cover in specific drainage basins was measured with the Stanford Research Institute console by electronically superimposing basin outlines on imagery, with video density slicing to measure areas. Snow covered area and snowline altitudes were also determined by enlarging ERTS-1 imagery 1:250,000 and using a transparent map overlay. Under very favorable conditions, snowline altitude was determined to an accuracy of about 60 m. Ability to map snow cover or to determine snowline altitude depends primarily on cloud cover and vegetation and secondarily on slope, terrain roughness, sun angle, radiometric fidelity, and amount of spectral information available. Glacier accumulation area ratios were determined from ERTS-1 imagery. Also, subtle flow structures, undetected on aerial photographs, were visible. Surging glaciers were identified, and the changes resulting from the surge of a large glacier were measured as were changes in tidal glacier termini.

Application of LANDSAT imagery for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H. (Principal Investigator); Ostrem, G.

1977-01-01

The author has identified the following significant results. It was shown that if the snow cover extent was determined from all four LANDSAT bands, there were significant differences in results. The MSS 4 gave the largest snow cover, but only slightly more than MSS 5, whereas MSS 6 and 7 gave the smallest snow area. A study was made to show that there was a relationship between the last date of snow fall and the area covered with snow, as determined from different bands. Imagery obtained shortly after a snow fall showed no significant difference in the snow-covered area when the four bans were compared, whereas, pronounced differences in the snow-covered area were found in images taken after a long period without precipitation.

NOAA's National Snow Analyses

NASA Astrophysics Data System (ADS)

Carroll, T. R.; Cline, D. W.; Olheiser, C. M.; Rost, A. A.; Nilsson, A. O.; Fall, G. M.; Li, L.; Bovitz, C. T.

2005-12-01

NOAA's National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous U.S. The NWP model forcings are physically downscaled from their native 13 km2 spatial resolution to a 1 km2 resolution for the CONUS. The downscaled NWP forcings drive an energy-and-mass-balance snow accumulation and ablation model at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the snow model's simulated state variables using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the snow model, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The snow model is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA's NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the available information necessary and available to produce a "best estimate" of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. The NOHRSC NSA consist of a variety of daily, operational, products that characterize real-time snowpack conditions including: snow water equivalent, snow depth, surface and internal snowpack temperatures, surface and blowing snow sublimation, and snowmelt for the CONUS. The products are generated and distributed in a variety of formats including: interactive maps, time-series, alphanumeric products (e.g., mean areal snow water equivalent on a hydrologic basin-by-basin basis), text and map discussions, map animations, and quantitative gridded products. The NOHRSC NSA products are used operationally by NOAA's National Weather Service field offices when issuing hydrologic forecasts and warnings including river and flood forecasts, water supply forecasts, and spring flood outlooks for the nation. Additionally, the NOHRSC NSA products are used by a wide variety of federal, state, local, municipal, private-sector, and general-public end-users with a requirement for real-time snowpack information. The paper discusses, in detail, the techniques and procedures used to create the NOHRSC NSA products and gives a number of examples of the real-time snow products generated and distributed over the NOHRSC web site (www.nohrsc.noaa.gov). Also discussed are major limitations of the approach, the most notable being deficiencies in observation of snow water equivalent. Snow observation networks generally lack the consistency and coverage needed to significantly improve confidence in snow model states through updating. Many regions of the world simply lack snow water equivalent observations altogether, a significant constraint on global application of the NSA approach.

Proposed hybrid-classifier ensemble algorithm to map snow cover area

NASA Astrophysics Data System (ADS)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

NASA Astrophysics Data System (ADS)

Blanchet, J.; Lehning, M.

2010-12-01

For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery

NASA Astrophysics Data System (ADS)

Cristea, Nicoleta C.; Breckheimer, Ian; Raleigh, Mark S.; HilleRisLambers, Janneke; Lundquist, Jessica D.

2017-08-01

Reliable maps of snow-covered areas at scales of meters to tens of meters, with daily temporal resolution, are essential to understanding snow heterogeneity, melt runoff, energy exchange, and ecological processes. Here we develop a parsimonious downscaling routine that can be applied to fractional snow covered area (fSCA) products from satellite platforms such as the Moderate Resolution Imaging Spectroradiometer (MODIS) that provide daily ˜500 m data, to derive higher-resolution snow presence/absence grids. The method uses a composite index combining both the topographic position index (TPI) to represent accumulation effects and the diurnal anisotropic heat (DAH, sun exposure) index to represent ablation effects. The procedure is evaluated and calibrated using airborne-derived high-resolution data sets across the Tuolumne watershed, CA using 11 scenes in 2014 to downscale to 30 m resolution. The average matching F score was 0.83. We then tested our method's transferability in time and space by comparing against the Tuolumne watershed in water years 2013 and 2015, and over an entirely different site, Mt. Rainier, WA in 2009 and 2011, to assess applicability to other topographic and climatic conditions. For application to sites without validation data, we recommend equal weights for the TPI and DAH indices and close TPI neighborhoods (60 and 27 m for downscaling to 30 and 3 m, respectively), which worked well in both our study areas. The method is less effective in forested areas, which still requires site-specific treatment. We demonstrate that the procedure can even be applied to downscale to 3 m resolution, a very fine scale relevant to alpine ecohydrology research.

Space Radar Image of Mammoth, California

NASA Technical Reports Server (NTRS)

1999-01-01

These two images were created using data from the Spaceborne Imaging Radar C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). The image on the left is a false-color composite of the Mammoth Mountain area in California's Sierra Nevada Mountains centered at 37.6 degrees north, 119.0 degrees west. It was acquired on-board the space shuttle Endeavour on its 67th orbit on April 13, 1994. In the image on the left, red is C-band HV-polarization, green is C-band HH-polarization and blue is the ratio of C-band VV-polarization to C-band HV-polarization. On the right is a classification map of the surface features which was developed by SIR-C/X-SAR science team members at the University of California, Santa Barbara. The area is about 23 by 46 kilometers (14 by 29 miles). In the classification image, the colors represent the following surfaces: White snow Red frozen lake, covered by snow Brown bare ground Blue lake (open water) Yellow short vegetation (mainly brush) Green sparse forest Dark green dense forest Maps like this one are helpful to scientists studying snow wetness and snow water equivalent in the snow pack. Across the globe, over major portions of the middle and high latitudes, and at high elevations in the tropical latitudes, snow and alpine glaciers are the largest contributors to run-off in rivers and to ground-water recharge. Snow hydrologists are using radar in an attempt to estimate both the quantity of water held by seasonal snow packs and the timing of snow melt. Snow and ice also play important roles in regional climates; understanding the processes in seasonal snow cover is also important for studies of the chemical balance of alpine drainage basins. SIR-C/X-SAR is a powerful tool because it is sensitive to most snow pack conditions and is less influenced by weather conditions than other remote sensing instruments, such as the Landsat satellite. Spaceborne Imaging Radar-C and X-Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves allowing detailed observations at any time, regardless of weather or sunlight conditions. SIR-C/X-SAR uses three microwave wavelengths: L-band (24 cm), C-band (6 cm) and X-band (3 cm). The multi-frequency data will be used by the international scientific community to better understand the global environment and how it is changing. The SIR-C/X-SAR data, complemented by aircraft and ground studies, will give scientists clearer insights into those environmental changes which are caused by nature and those changes which are induced by human activity. SIR-C was developed by NASA's Jet Propulsion Laboratory. X-SAR was developed by the Dornier and Alenia Spazio companies for the German space agency, Deutsche Agentur fuer Raumfahrtangelegenheiten (DARA), and the Italian space agency, Agenzia Spaziale Italiana (ASI), with the Deutsche Forschungsanstalt fuer Luft und Raumfahrt e.v. (DLR), the major partner in science, operations and data processing of X-SAR.

The application of Heat Capacity Mapping Mission (HCMM) thermal data to snow hydrology. [Salt Verde Watershed and the southern Sierra Nevada

NASA Technical Reports Server (NTRS)

Barnes, J. C. (Principal Investigator); Bowley, C. J.; Smallwood, M. D.; Willand, J. H.

1981-01-01

The application of HCMM thermal infrared data to snow hydrology and the prediction of snowmelt runoff was evaluated. Data for the Salt Verde watershed in central Arizona and the southern Sierra Nevada in California were analyzed and compared to LANDSAT and NOAA satellite data, U-2 thermal data, and other correlative data. It was determined that HCMM thermal imagery provides data as accurate for snow mapping as does visible imagery, and that in comparison with the reslution of other satellite imagery, it may be the most useful. Data from the HCMM thermal channel, with careful calibration, provides useful snow surface temperature data for hydrological purposes. An approach to an automated method of analysis is presented.

Role of nitrite in the photochemical formation of radicals in the snow.

PubMed

Jacobi, Hans-Werner; Kleffmann, Jörg; Villena, Guillermo; Wiesen, Peter; King, Martin; France, James; Anastasio, Cort; Staebler, Ralf

2014-01-01

Photochemical reactions in snow can have an important impact on the composition of the atmosphere over snow-covered areas as well as on the composition of the snow itself. One of the major photochemical processes is the photolysis of nitrate leading to the formation of volatile nitrogen compounds. We report nitrite concentrations determined together with nitrate and hydrogen peroxide in surface snow collected at the coastal site of Barrow, Alaska. The results demonstrate that nitrite likely plays a significant role as a precursor for reactive hydroxyl radicals as well as volatile nitrogen oxides in the snow. Pollution events leading to high concentrations of nitrous acid in the atmosphere contributed to an observed increase in nitrite in the surface snow layer during nighttime. Observed daytime nitrite concentrations are much higher than values predicted from steady-state concentrations based on photolysis of nitrate and nitrite indicating that we do not fully understand the production of nitrite and nitrous acid in snow. The discrepancy between observed and expected nitrite concentrations is probably due to a combination of factors, including an incomplete understanding of the reactive environment and chemical processes in snow, and a lack of consideration of the vertical structure of snow.

Oxygen isotope and deuterium composition of snow cover on the profile of Western Siberia from Tomsk to the Gulf of Ob

NASA Astrophysics Data System (ADS)

Vasil'chuk, Yu. K.; Shevchenko, V. P.; Lisitzin, A. P.; Budantseva, N. A.; Vorobiov, S. N.; Kirpotin, S. N.; Krizkov, I. V.; Manasypov, R. M.; Pokrovsky, O. S.; Chizhova, Ju. N.

2016-12-01

The purpose of this work is to study the variability of the isotope composition (δ18O, δD, d exc) of the snow cover on a long transect of Western Siberia from the southern taiga to the tundra. The study of the snow cover is of paleogeographic, paleogeocryological, and paleohydrological value. The snow cover of western Siberia was sampled on a broadly NS transzonal profile from the environs of Tomsk (southern taiga zone) to the eastern coast of the Gulf of Ob (tundra zone) from February 19 to March 4, 2014. Snow samples were collected at 31 sites. Most of the samples represented by fresh snow, i.e., snow that had fallen a day before the moment of sampling were collected in two areas. In the area of Yamburg, the snow specimens collected from the surface are most probably settled snow of different ages. The values of δ18O in the snow from Tomsk to Yamburg varied from-21.89 to-32.82‰, and the values of δD, from-163.3 to-261.2‰. The value of deuterium excess was in the range of 4.06-19.53‰.

Absence of snow cover reduces understory plant cover and alters plant community composition in boreal forests.

PubMed

Kreyling, Juergen; Haei, Mahsa; Laudon, Hjalmar

2012-02-01

Snow regimes affect biogeochemistry of boreal ecosystems and are altered by climate change. The effects on plant communities, however, are largely unexplored despite their influence on relevant processes. Here, the impact of snow cover on understory community composition and below-ground production in a boreal Picea abies forest was investigated using a long-term (8-year) snow cover manipulation experiment consisting of the treatments: snow removal, increased insulation (styrofoam pellets), and control. The snow removal treatment caused longer (118 vs. 57 days) and deeper soil frost (mean minimum temperature -5.5 vs. -2.2°C) at 10 cm soil depth in comparison to control. Understory species composition was strongly altered by the snow cover manipulations; vegetation cover declined by more than 50% in the snow removal treatment. In particular, the dominant dwarf shrub Vaccinium myrtillus (-82%) and the most abundant mosses Pleurozium schreberi (-74%) and Dicranum scoparium (-60%) declined strongly. The C:N ratio in V. myrtillus leaves and plant available N in the soil indicated no altered nitrogen nutrition. Fine-root biomass in summer, however, was negatively affected by the reduced snow cover (-50%). Observed effects are attributed to direct frost damage of roots and/ or shoots. Besides the obvious relevance of winter processes on plant ecology and distribution, we propose that shifts in the vegetation caused by frost damage may be an important driver of the reported alterations in biogeochemistry in response to altered snow cover. Understory plant performance clearly needs to be considered in the biogeochemistry of boreal systems in the face of climate change.

Studies of Malagasy Eugenia – IV: Seventeen new endemic species, a new combination, and three lectotypifications; with comments on distribution, ecological and evolutionary patterns

PubMed Central

Snow, Neil; Callmander, Martin; Phillipson, Peter B.

2015-01-01

Abstract Seventeen new endemic species of the genus Eugenia L. (Myrtaceae) are proposed from Madagascar, including: Eugenia andapae N. Snow, Eugenia barriei N. Snow, Eugenia bemangidiensis N. Snow, Eugenia calciscopulorum N. Snow, Eugenia delicatissima N. Snow, Callm. & Phillipson, Eugenia echinulata N. Snow, Eugenia gandhii N. Snow, Eugenia hazonjia N. Snow, Eugenia iantarensis N. Snow, Eugenia malcomberi N. Snow, Eugenia manomboensis N. Snow, Eugenia obovatifolia N. Snow, Eugenia ranomafana N. Snow & D. Turk, Eugenia ravelonarivoi N. Snow & Callm., Eugenia razakamalalae N. Snow & Callm., Eugenia tiampoka N. Snow & Callm., and Eugenia wilsoniana N. Snow, and one new combination, Eugenia richardii (Blume) N. Snow, Callm. & Phillipson is provided. Detailed descriptions, information on distribution and ecology, distribution maps, vernacular names (where known), digital images of types, comparisons to morphologically similar species. Preliminary assessment of IUCN risk of extinction and conservation recommendations are provided, including Vulnerable (4 species), Endangered (2 species), and Critically Endangered (4 species). Lectotpyes are designated for Eugenia hovarum H. Perrier, Eugenia nompa H. Perrier, and Eugenia scottii H. Perrier respectively. PMID:25987885

Crediting his critics' concerns: remaking John Snow's map of Broad Street cholera, 1854.

PubMed

Koch, Tom; Denike, Kenneth

2009-10-01

Few cases in the history of epidemiology and public health are more famous than John Snow's investigation of a neighborhood cholera outbreak in the St. James, Westminster, area of London in 1854. In this study Snow is assumed to have proven that cholera was water rather than airborne through a methodology that became, and to a great extent remains, central to the science and social science of disease studies. And yet, Snow's work did not satisfy most of his contemporaries who considered his proof of a solely waterborne cholera interesting but unconvincing. Uniquely, this paper asks whether the caution of Snow's contemporaries was reasonable, and secondly, whether Snow might have been more convincing within the science of the day. The answers significantly alter our understanding of this paradigmatic case. It does so in a manner offering insights both into the origins of nineteenth century disease analysis and more generally, the relation of mapping in the investigation of an outbreak of uncertain origin. The result has general relevance-pedagogically and practically-in epidemiology, medical geography, and public health.

Identifying environmental features for land management decisions

NASA Technical Reports Server (NTRS)

1981-01-01

The benefits of changes in management organization and facilities for the Center for Remote Sensing and Cartography in Utah are reported as well as interactions with and outreach to state and local agencies. Completed projects are described which studied (1) Unita Basin wetland/land use; (2) Davis County foothill development; (3) Farmington Bay shoreline fluctuation; (4) irrigation detection; and (5) satellite investigation of snow cover/mule deer relationships. Techniques developed for composite computer mapping, contrast enhancement, U-2 CIR/LANDSAT digital interface; factor analysis, and multivariate statistical analysis are described.

Surface Snow Density of East Antarctica Derived from In-Situ Observations

NASA Astrophysics Data System (ADS)

Tian, Y.; Zhang, S.; Du, W.; Chen, J.; Xie, H.; Tong, X.; Li, R.

2018-04-01

Models based on physical principles or semi-empirical parameterizations have used to compute the firn density, which is essential for the study of surface processes in the Antarctic ice sheet. However, parameterization of surface snow density is often challenged by the description of detailed local characterization. In this study we propose to generate a surface density map for East Antarctica from all the filed observations that are available. Considering that the observations are non-uniformly distributed around East Antarctica, obtained by different methods, and temporally inhomogeneous, the field observations are used to establish an initial density map with a grid size of 30 × 30 km2 in which the observations are averaged at a temporal scale of five years. We then construct an observation matrix with its columns as the map grids and rows as the temporal scale. If a site has an unknown density value for a period, we will set it to 0 in the matrix. In order to construct the main spatial and temple information of surface snow density matrix we adopt Empirical Orthogonal Function (EOF) method to decompose the observation matrix and only take first several lower-order modes, because these modes already contain most information of the observation matrix. However, there are a lot of zeros in the matrix and we solve it by using matrix completion algorithm, and then we derive the time series of surface snow density at each observation site. Finally, we can obtain the surface snow density by multiplying the modes interpolated by kriging with the corresponding amplitude of the modes. Comparative analysis have done between our surface snow density map and model results. The above details will be introduced in the paper.

Snow mapping from space platforms

NASA Technical Reports Server (NTRS)

Itten, K. I.

1980-01-01

The paper considers problems of optimum resolution, periodicity, and wavelength bands used for snow mapping. Analog and digital methods were used for application of satellite data; techniques were developed for producing steamflow forecasts, hydroelectric power generation regulation data, irrigation potentials, and information on the availability of drinking water supplies. Future systems will utilize improved spectral band selection, new spectral regions, higher repetition rates, and more rapid access to satellite data.

The seasonal cycle of snow cover, sea ice and surface albedo

NASA Technical Reports Server (NTRS)

Robock, A.

1980-01-01

The paper examines satellite data used to construct mean snow cover caps for the Northern Hemisphere. The zonally averaged snow cover from these maps is used to calculate the seasonal cycle of zonally averaged surface albedo. The effects of meltwater on the surface, solar zenith angle, and cloudiness are parameterized and included in the calculations of snow and ice albedo. The data allows a calculation of surface albedo for any land or ocean 10 deg latitude band as a function of surface temperature ice and snow cover; the correct determination of the ice boundary is more important than the snow boundary for accurately simulating the ice and snow albedo feedback.

Enhancement of the MODIS Daily Snow Albedo Product

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Schaaf, Crystal B.; Wang, Zhuosen; Riggs, George A.

2009-01-01

The MODIS daily snow albedo product is a data layer in the MOD10A1 snow-cover product that includes snow-covered area and fractional snow cover as well as quality information and other metadata. It was developed to augment the MODIS BRDF/Albedo algorithm (MCD43) that provides 16-day maps of albedo globally at 500-m resolution. But many modelers require daily snow albedo, especially during the snowmelt season when the snow albedo is changing rapidly. Many models have an unrealistic snow albedo feedback in both estimated albedo and change in albedo over the seasonal cycle context, Rapid changes in snow cover extent or brightness challenge the MCD43 algorithm; over a 16-day period, MCD43 determines whether the majority of clear observations was snow-covered or snow-free then only calculates albedo for the majority condition. Thus changes in snow albedo and snow cover are not portrayed accurately during times of rapid change, therefore the current MCD43 product is not ideal for snow work. The MODIS daily snow albedo from the MOD10 product provides more frequent, though less robust maps for pixels defined as "snow" by the MODIS snow-cover algorithm. Though useful, the daily snow albedo product can be improved using a daily version of the MCD43 product as described in this paper. There are important limitations to the MOD10A1 daily snow albedo product, some of which can be mitigated. Utilizing the appropriate per-pixel Bidirectional Reflectance Distribution Functions (BRDFs) can be problematic, and correction for anisotropic scattering must be included. The BRDF describes how the reflectance varies with view and illumination geometry. Also, narrow-to-broadband conversion specific for snow on different surfaces must be calculated and this can be difficult. In consideration of these limitations of MOD10A1, we are planning to improve the daily snow albedo algorithm by coupling the periodic per-pixel snow albedo from MCD43, with daily surface ref|outanoom, In this paper, we compare a daily version of MCD43B3 with the daily albedo from MOD10A1. and MCD43B3 with a 16-day average of MOD10A1, over Greenland. We also discuss some near-future planned enhancements to MOD10A1.

Enhancement of snow cover change detection with sparse representation and dictionary learning

NASA Astrophysics Data System (ADS)

Varade, D.; Dikshit, O.

2014-11-01

Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.

Optical Benson: Following the Impact of Melt Season Progression Using Landsat and Sentinel 2 - Snow Zone Formation Imaged

NASA Astrophysics Data System (ADS)

Fahnestock, M. A.; Shuman, C. A.; Alley, K. E.

2017-12-01

Snow pit observations on a glaciologically-focussed surface traverse in Greenland allowed Benson [1962, SIPRE (now CRREL) Research Report 70] to define a series of snow zones based on the extent of post-depositional diagenesis of the snowpack. At high elevations, Benson found fine-grained "dry snow" where melt (at that time) was absent year-round, followed down-elevation by a "percolation zone" where surface melt penetrated the snowpack, then a "wet snow zone" where firn became saturated during the peak of the melt season, and finally "superimposed ice" and "bare ice" zones where refrozen surface melt and glacier ice were exposed in the melt season. These snow zones can be discriminated in winter synthetic aperture radar (SAR) imagery of the ice sheet (e.g. Fahnestock et al. 2001), but summer melt reduces radar backscatter and makes it difficult to follow the progression of diagenesis beyond the initial indications of surface melting. While some of the impacts of surface melt (especially bands of blue water-saturated firn) are observed from time to time in optical satellite imagery, it has only become possible to map effects of melt over the course of a summer season with the advent of large-data analysis tools such as Google Earth Engine and the inclusion of Landsat and Sentinel-2 data streams in these tools. A map of the maximum extent of this blue saturated zone through the 2016 melt season is shown in the figure. This image is a true color (RGB) composite, but each pixel in the image shows the color of the surface when the "blueness" of the pixel was at a maximum. This means each pixel can be from a different satellite image acquisition than adjacent pixels - but it also means that the maximum extent of the saturated firn (Benson's wet snow zone) is visible. Also visible are percolation, superimposed and bare ice zones. This analysis, using Landsat 8 Operational Land Imager data, was performed using Google Earth Engine to access and analyze the entire melt season's data. Similar spatial analyses for other years in the record, combined with pixel-by-pixel analysis of each time series through the year, can be used to track the progression and overall effect of the melt season in each year. This view of the progression of a melt season provides a new set of tools to help understand changing surface conditions for ice sheets and glaciers globally.

Mapping Snow Depth with Automated Terrestrial Laser Scanning - Investigating Potential Applications

NASA Astrophysics Data System (ADS)

Adams, M. S.; Gigele, T.; Fromm, R.

2017-11-01

This contribution presents an automated terrestrial laser scanning (ATLS) setup, which was used during the winter 2016/17 to monitor the snow depth distribution on a NW-facing slope at a high-alpine study site. We collected data at high temporal [(sub-)daily] and spatial resolution (decimetre-range) over 0.8 km² with a Riegl LPM-321, set in a weather-proof glass fibre enclosure. Two potential ATLS-applications are investigated here: monitoring medium-sized snow avalanche events, and tracking snow depth change caused by snow drift. The results show the ATLS data's high explanatory power and versatility for different snow research questions.

Monitoring Food Security Indicators from Remote Sensing and Predicting Cereal Production in Afghanistan

NASA Astrophysics Data System (ADS)

Pervez, M. S.; Budde, M. E.; Rowland, J.

2015-12-01

We extract percent of basin snow covered areas above 2500m elevation from Moderate Resolution Imaging Spectroradiometer (MODIS) 500-meter 8-day snow cover composites to monitor accumulation and depletion of snow in the basin. While the accumulation and depletion of snow cover extent provides an indication of the temporal progression of the snow pack, it does not provide insight into available water for irrigation. Therefore, we use snow model results from the National Operational Hydrologic Remote Sensing Center to quantify snow water equivalent and volume of water available within the snowpack for irrigation. In an effort to understand how water availability, along with its inter-annual variability, relates to the food security of the country, we develop a simple, effective, and easy-to-implement model to identify irrigated areas across the country on both annual and mid-season basis. The model is based on applying thresholds to peak growing season vegetation indices—derived from 250-meter MODIS images—in a decision-tree classifier to separate irrigated crops from non-irrigated vegetation. The spatial distribution and areal estimates of irrigated areas from these maps compare well with irrigated areas classified from multiple snap shots of the landscape from Landsat 5 optical and thermal images over selected locations. We observed that the extents of irrigated areas varied depending on the availability of snowmelt and can be between 1.35 million hectares in a year with significant water deficit and 2.4 million hectares in a year with significant water surplus. The changes in the amount of available water generally can contribute up to a 30% change in irrigated areas. We also observed that the strong correlation between inter-annual variability of irrigated areas and the variability in the country's cereal production could be utilized to predict an annual estimate of cereal production, providing early indication of food security scenarios for the country.

Satellite geological and geophysical remote sensing of Iceland

NASA Technical Reports Server (NTRS)

Williams, R. S., Jr. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The ERTS imagery has sufficient resolution to map, from MSS color composites, areas of altered ground caused by high temperature geothermal activity at the Namafjall, Torfajokull, and Reykjanes geothermal areas. The major axes of the fallout pattern of tephra from the May - July 1970 volcanic eruption from Hekla Volcano can be mapped where sufficient depth of deposition was present to seriously affect the normal vegetation. Lava flows from the 1961 volcanic eruption at Askja; some of the lava flows from the 1947-48 eruption, and and all of the lava flows from the 1970 eruption at Hekla; and the areas covered by tephra and lava from the 1973 eruption on Heimaey could be delineated. Low sun angle imagery of less than 10 deg of snow covered terrain was particularly valuable in mapping structural and volcanic features concealed beneath glacial ice in the active volcanic zones of Iceland.

Monitoring Areal Snow Cover Using NASA Satellite Imagery

NASA Technical Reports Server (NTRS)

Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

2011-01-01

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

NASA Technical Reports Server (NTRS)

Meier, M. F. (Principal Investigator)

1972-01-01

The author has identified the following significant results. Preliminary results on the feasibility of mapping snow cover extent have been obtained from a limited number of ERTS-1 images of mountains in Alaska, British Columbia, and Washington. The snowline on land can be readily distinguished, except in heavy forest where such distinction appears to be virtually impossible. The snowline on very large glaciers can also be distinguished remarkably easily, leading to a convenient way to measure glacier accumulation area ratios or equilibrium line altitude. Monitoring of large surging glaciers appears to be possible, but only through observation of a change in area and/or medial moraine extent. Under certain conditions, ERTS-1 imagery appears to have high potential for mapping snow cover in mountainous areas. Distinction between snow and clouds appears to require use of the human eye, but in a cloud-free scene the snow cover is sufficiently distinct to allow use of automated techniques. This technique may prove very useful as an aid in the monitoring of the snowpack water resource and the prediction of summer snowmelt runoff volume.

The effects of dust on Colorado mountain snow cover albedo and compositional links to dust-source areas

NASA Astrophysics Data System (ADS)

Goldstein, H. L.; Reynolds, R. L.; Landry, C.; Derry, J. E.; Kokaly, R. F.; Breit, G. N.

2016-12-01

Dust deposited on mountain snow cover (DOS) changes snow albedo, enhances absorption of solar radiation, and effectively increases rates of snow melt, leading to earlier-than-normal runoff and overall smaller late-season water supplies for tens of millions of people and industries in the American West. Visible-spectrum reflectance of DOS samples is on the order of 0.2 (80% absorption), in stark contrast to the high reflectivity of pure snow which approaches 1.0. Samples of DOS were collected from 12 high-elevation Colorado mountain sites near the end of spring from 2013 through 2016 prior to complete snow melt, when most dust layers had merged into one layer. These samples were analyzed to measure dust properties that affect snow albedo and to link DOS to dust-source areas. Dust mass loadings to snow during water year 2014 varied from 5 to 30 g/m2. Median particle sizes centered around 20 micrometers with more than 80% of the dust <63 micrometers. Dark minerals, carbonaceous matter, and iron oxides, including nano-sized hematite and goethite, together diminished reflectance according to their variable concentrations. Documenting variations in dust-particle masses, sizes, and compositions helps determine their influences on snow-melt and may be useful for modeling snow-melt effects from future dust. Furthermore, variations in dust components and particle sizes lead to new ways to recognize sources of dust by comparison with properties of fine-grained sediments in dust-source areas. Much of the DOS in the San Juan Mountains, Colorado can be linked to southern Colorado Plateau source areas by compositional similarities and satellite imagery. Understanding dust properties that affect snow albedo and recognizing the sources of dust deposited on snow cover may guide mitigation of dust emission that affects water resources of the Colorado River basin.

Snowmelt runoff in the Green River basin derived from MODIS snow extent

NASA Astrophysics Data System (ADS)

Barton, J. S.; Hall, D. K.

2011-12-01

The Green River represents a vital water supply for southwestern Wyoming, northern Colorado, eastern Utah, and the Lower Colorado River Compact states (Arizona, Nevada, and California). Rapid development in the southwestern United States combined with the recent drought has greatly stressed the water supply of the Colorado River system, and concurrently increased the interest in long-term variations in stream flow. Modeling of snowmelt runoff represents a means to predict flows and reservoir storage, which is useful for water resource planning. An investigation is made into the accuracy of the Snowmelt Runoff Model of Martinec and Rango, driven by Moderate Resolution Imaging Spectroradiometer (MODIS) snow maps for predicting stream flow within the Green River basin. While the moderate resolution of the MODIS snow maps limits the spatial detail that can be captured, the daily coverage is an important advantage of the MODIS imagery. The daily MODIS snow extent is measured using the most recent clear observation for each 500-meter pixel. Auxiliary data used include temperature and precipitation time series from the Snow Telemetry (SNOTEL) and Remote Automated Weather Station (RAWS) networks as well as from National Weather Service records. Also from the SNOTEL network, snow-water equivalence data are obtained to calibrate the conversion between snow extent and runoff potential.

Remote sensing of snow and ice: A review of the research in the United States 1975 - 1978

NASA Technical Reports Server (NTRS)

Rango, A.

1979-01-01

Research work in the United States from 1975-1978 in the field of remote sensing of snow and ice is reviewed. Topics covered include snowcover mapping, snowmelt runoff forecasting, demonstration projects, snow water equivalent and free water content determination, glaciers, river and lake ice, and sea ice. A bibliography of 200 references is included.

Mapping known and potential mineral occurrences and host rocks in the Bonnifield Mining District using minimal cloud- and snow-cover ASTER data: Chapter E in Recent U.S. Geological Survey studies in the Tintina Gold Province, Alaska, United States, and Yukon, Canada--results of a 5-year project

USGS Publications Warehouse

Hubbard, Bernard E.; Dusel-Bacon, Cynthia; Rowan, Lawrence C.; Eppinger, Robert G.; Gough, Larry P.; Day, Warren C.

2007-01-01

On July 8, 2003, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor acquired satellite imagery of a 60-kilometer-wide swath covering a portion of the Bonnifield mining district within the southernmost part of the Tintina Gold Province, Alaska, under unusually favorable conditions of minimal cloud and snow cover. Although rocks from more than eight different lithotectonic terranes are exposed within the extended swath of data, we focus on volcanogenic massive sulfides (VMS) and porphyry deposits within the Yukon-Tanana terrane (YTT), the largest Mesozoic accretionary terrane exposed between the Denali fault system to the south of Fairbanks and the Tintina fault system to the north of Fairbanks. Comparison of thermal-infrared region (TIR) decorrelation stretch data to available geologic maps indicates that rocks from the YTT contain a wide range of rock types ranging in composition from mafic metavolcanic rocks to felsic rock types such as metarhyolites, pelitic schists, and quartzites. The nine-band ASTER visible-near-infrared region--short-wave infrared region (VNIR-SWIR) reflectance data and spectral matched-filter processing were used to map hydrothermal alteration patterns associated with VMS and porphyry deposit types. In particular, smectite, kaolinite, opaline silica, jarosite and (or) other ferric iron minerals defined narrow (less than 250-meter diameter) zonal patterns around Red Mountain and other potential VMS targets. Using ASTER we identified some of the known mineral deposits in the region, as well as mineralogically similar targets that may represent potential undiscovered deposits. Some known deposits were not identified and may have been obscured by vegetation or snow cover or were too small to be resolved.

Properties of the surface snow in Princess Elizabeth Land, East Antarctica - climate and non-climate dependent variability of the surface mass balance and stable water isotopic composition

NASA Astrophysics Data System (ADS)

Vladimirova, D.; Ekaykin, A.; Lipenkov, V.; Popov, S. V.; Petit, J. R.; Masson-Delmotte, V.

2017-12-01

Glaciological and meteorological observations conducted during the past four decades in Princess Elizabeth Land, East Antarctica, are compiled. The database is used to investigate spatial patterns of surface snow isotopic composition and surface mass balance, including detailed information near subglacial lake Vostok. We show diverse relationships between snow isotopic composition and surface temperature. In the most inland part (elevation 3200-3400 m a.s.l.), surface snow isotopic composition varies independently from surface temperature, and is closely related to the distance to the open water source (with a slope of 0.98±0.17 ‰ per 100 km). Surface mass balance values are higher along the ice sheet slope, and relatively evenly distributed inland. The minimum values of snow isotopic composition and surface mass balance are identified in an area XX km southwestward from Vostok station. The spatial distribution of deuterium excess delineates regions influenced by the Indian Ocean and Pacific Ocean air masses, with Vostok area being situated close to their boundary. Anomalously high deuterium excess values are observed near Dome A, suggesting high kinetic fractionation for its moisture source, or specifically high post-deposition artifacts. The dataset is available for further studies such as the assessment of skills of general circulation or regional atmospheric models, and the search for the oldest ice.

On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales

NASA Astrophysics Data System (ADS)

Härer, Stefan; Bernhardt, Matthias; Siebers, Matthias; Schulz, Karsten

2018-05-01

Knowledge of current snow cover extent is essential for characterizing energy and moisture fluxes at the Earth's surface. The snow-covered area (SCA) is often estimated by using optical satellite information in combination with the normalized-difference snow index (NDSI). The NDSI thereby uses a threshold for the definition if a satellite pixel is assumed to be snow covered or snow free. The spatiotemporal representativeness of the standard threshold of 0.4 is however questionable at the local scale. Here, we use local snow cover maps derived from ground-based photography to continuously calibrate the NDSI threshold values (NDSIthr) of Landsat satellite images at two European mountain sites of the period from 2010 to 2015. The Research Catchment Zugspitzplatt (RCZ, Germany) and Vernagtferner area (VF, Austria) are both located within a single Landsat scene. Nevertheless, the long-term analysis of the NDSIthr demonstrated that the NDSIthr at these sites are not correlated (r = 0.17) and different than the standard threshold of 0.4. For further comparison, a dynamic and locally optimized NDSI threshold was used as well as another locally optimized literature threshold value (0.7). It was shown that large uncertainties in the prediction of the SCA of up to 24.1 % exist in satellite snow cover maps in cases where the standard threshold of 0.4 is used, but a newly developed calibrated quadratic polynomial model which accounts for seasonal threshold dynamics can reduce this error. The model minimizes the SCA uncertainties at the calibration site VF by 50 % in the evaluation period and was also able to improve the results at RCZ in a significant way. Additionally, a scaling experiment shows that the positive effect of a locally adapted threshold diminishes using a pixel size of 500 m or larger, underlining the general applicability of the standard threshold at larger scales.

Snow survey and vegetation growth in the Swiss Alps

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Analog processing of S190A and B color and infrared color transparencies showed that it is possible to evaluate the courses of the temporary snow line and the upper limit of vegetation growth over large areas. By transferring the results from S190A onto a topo-map 1:200,000, an accuracy of + or - 50 m could be achieved. With S190B transferred onto a map 1:100,000, an accuracy of + or - 20 m was reached. Digital processing of S192 multispectral data allowed a separation of snow and clouds by combining the information from channels 9, 15, and 18.

Multi Satellites Monitoring of Land Use/Cover Change and Its Driving Forces in Kashgar Region, China

NASA Astrophysics Data System (ADS)

Maimaitiaili, Ayisulitan; Aji, xiaokaiti; Kondoh, Akihiko

2016-04-01

Multi Satellites Monitoring of Land Use/Cover Change and Its Driving Forces in Kashgar Region, China Ayisulitan Maimaitiaili1, Xiaokaiti Aji2 Akihiko Kondoh2 1Graduate School of Science, Chiba University, Japan 2Center for Environmental Remote Sensing, Chiba University The spatio-temporal changes of Land Use/Cover (LUCC) and its driving forces in Kashgar region, Xinjiang Province, China, are investigated by using satellite remote sensing and a geographical information system (GIS). Main goal of this paper is to quantify the drivers of LUCC. First, considering lack of the Land Cover (LC) map in whole study area, we produced LC map by using Landsat images. Land use information from Landsat data was collected using maximum likelihood classification method. Land use change was studied based on the change detection method of land use types. Second, because the snow provides a key water resources for stream flow, agricultural production and drinking water for sustaining large population in Kashgar region, snow cover are estimated by Spot Vegetation data. Normalized Difference Snow Index (NDSI) algorithm are applied to make snow cover map, which is used to screen the LUCC and climate change. The best agreement is found with threshold value of NDSI≥0.2 to generate multi-temporal snow cover and snowmelt maps. Third, driving forces are systematically identified by LC maps and statistical data such as climate and socio-economic data, regarding to i) the climate changes and ii) socioeconomic development that the spatial correlation among LUCC, snow cover change, climate and socioeconomic changes are quantified by using liner regression model and negative / positive trend analysis. Our results showed that water bodies, bare land and grass land have decreasing notably. By contrast, crop land and urban area have continually increasing significantly, which are dominated in study area. The area of snow/ice have fluctuated and has strong seasonal trends, total annual snow cover has two peaks in 2005 and 2009. With increasing population from 2,324,375 in 1984 to 4,228,200 in 2014 and crop land reclamation from 6031.4 km2 in 1972 to 16549km2 in 2014 at the study area. Water resources consumption increased with support to large population and irrigate whole crop land area, caused the water shortages that the surface water bodies decreased from 2531.43km2 in the 1972s to 1067.05km2 in the 2014. The grass land with an acreage larger than 6749km2 in 1972 decreased to 922.6 km2 in 2014. The transformations between water bodies, garss land and bare land are remarkbale. The results also suggested high linearity between the LUCC and socioeconomic changes that specific land cover change be cause of the fact that socioeconomic development. In the recent 42 years, average annual temperature have been increasing significantly, although, precipitation have increased but partly weaken effect of the rising temperature, in addition snow cover more sensitive to precipitation than temperature. Results the change of climate showed a nagitive relationship between the NDSI with decrased of the snow cover and climate with increasing of the tempreature. Morover, the relationship between the LUCC and snow cover recorded higher linearity, because the temperature have increased, consequence influence on snow cover that provides melt water for study area which expanding crop land.

Imaging of the CO snow line in a solar nebula analog.

PubMed

Qi, Chunhua; Öberg, Karin I; Wilner, David J; D'Alessio, Paola; Bergin, Edwin; Andrews, Sean M; Blake, Geoffrey A; Hogerheijde, Michiel R; van Dishoeck, Ewine F

2013-08-09

Planets form in the disks around young stars. Their formation efficiency and composition are intimately linked to the protoplanetary disk locations of "snow lines" of abundant volatiles. We present chemical imaging of the carbon monoxide (CO) snow line in the disk around TW Hya, an analog of the solar nebula, using high spatial and spectral resolution Atacama Large Millimeter/Submillimeter Array observations of diazenylium (N2H(+)), a reactive ion present in large abundance only where CO is frozen out. The N2H(+) emission is distributed in a large ring, with an inner radius that matches CO snow line model predictions. The extracted CO snow line radius of ~30 astronomical units helps to assess models of the formation dynamics of the solar system, when combined with measurements of the bulk composition of planets and comets.

Satellite Based Probabilistic Snow Cover Extent Mapping (SCE) at Hydro-Québec

NASA Astrophysics Data System (ADS)

Teasdale, Mylène; De Sève, Danielle; Angers, Jean-François; Perreault, Luc

2016-04-01

Over 40% of Canada's water resources are in Quebec and Hydro-Quebec has developed potential to become one of the largest producers of hydroelectricity in the world, with a total installed capacity of 36,643 MW. The Hydro-Québec fleet park includes 27 large reservoirs with a combined storage capacity of 176 TWh, and 668 dams and 98 controls. Thus, over 98% of all electricity used to supply the domestic market comes from water resources and the excess output is sold on the wholesale markets. In this perspective the efficient management of water resources is needed and it is based primarily on a good river flow estimation including appropriate hydrological data. Snow on ground is one of the significant variables representing 30% to 40% of its annual energy reserve. More specifically, information on snow cover extent (SCE) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in northern regions since the snowmelt provides the water that fills the reservoirs and is subsequently used for hydropower generation. For several years Hydro Quebec's research institute ( IREQ) developed several algorithms to map SCE and SWE. So far all the methods were deterministic. However, given the need to maximize the efficient use of all resources while ensuring reliability, the electrical systems must now be managed taking into account all risks. Since snow cover estimation is based on limited spatial information, it is important to quantify and handle its uncertainty in the hydrological forecasting system. This paper presents the first results of a probabilistic algorithm for mapping SCE by combining Bayesian mixture of probability distributions and multiple logistic regression models applied to passive microwave data. This approach allows assigning for each grid point, probabilities to the set of the mutually exclusive discrete outcomes: "snow" and "no snow". Its performance was evaluated using the Brier score since it is particularly appropriate to measure the accuracy of probabilistic discrete predictions. The scores were measured by comparing the snow probabilities produced by our models with the Hydro-Québec's snow ground data.

Utilizing ERTS-A imagery for tectonic analysis through study of Big Horn Mountains Region

NASA Technical Reports Server (NTRS)

Hoppin, R. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. NASA-provided color composite (1048-17234) which includes the southeastern portion of the Bighorn Mountains and the western Powder River basin is of excellent quality. The considerable variations in the red hues indicate that vegetational mapping will be enhanced over the black and white. Some additional delineation of rock units can be made, particularly the Chugwater formation. Preliminary look at just received winter scenes indicates that topographic features are enhanced both due to the snow cover and to the lower sun angle.

From the clouds to the ground - snow precipitation patterns vs. snow accumulation patterns

NASA Astrophysics Data System (ADS)

Gerber, Franziska; Besic, Nikola; Mott, Rebecca; Gabella, Marco; Germann, Urs; Bühler, Yves; Marty, Mauro; Berne, Alexis; Lehning, Michael

2017-04-01

Knowledge about snow distribution and snow accumulation patterns is important and valuable for different applications such as the prediction of seasonal water resources or avalanche forecasting. Furthermore, accumulated snow on the ground is an important ground truth for validating meteorological and climatological model predictions of precipitation in high mountains and polar regions. Snow accumulation patterns are determined by many different processes from ice crystal nucleation in clouds to snow redistribution by wind and avalanches. In between, snow precipitation undergoes different dynamical and microphysical processes, such as ice crystal growth, aggregation and riming, which determine the growth of individual particles and thereby influence the intensity and structure of the snowfall event. In alpine terrain the interaction of different processes and the topography (e.g. lifting condensation and low level cloud formation, which may result in a seeder-feeder effect) may lead to orographic enhancement of precipitation. Furthermore, the redistribution of snow particles in the air by wind results in preferential deposition of precipitation. Even though orographic enhancement is addressed in numerous studies, the relative importance of micro-physical and dynamically induced mechanisms on local snowfall amounts and especially snow accumulation patterns is hardly known. To better understand the relative importance of different processes on snow precipitation and accumulation we analyze snowfall and snow accumulation between January and March 2016 in Davos (Switzerland). We compare MeteoSwiss operational weather radar measurements on Weissfluhgipfel to a spatially continuous snow accumulation map derived from airborne digital sensing (ADS) snow height for the area of Dischma valley in the vicinity of the weather radar. Additionally, we include snow height measurements from automatic snow stations close to the weather radar. Large-scale radar snow accumulation patterns show a snowfall gradient consistent with the prevailing wind direction. Deriving snow accumulation based on radar data is challenging as the close-ground precipitation patters cannot be resolved by the radar due to shielding and ground clutter in highly complex terrain. Nonetheless, radar measurements show distinct patterns of snowfall and accumulation, which may be the result of orographic enhancement. Station-based snow accumulation measurements are in reasonable agreement with the estimated large-scale radar snow accumulation. The ADS-based snow accumulation maps feature much smaller scale snow accumulation patterns likely due to close-ground wind effects and snow redistribution on top of an altitudinal gradient. To evaluate microphysical processes and patterns influenced by the topography we run a hydrometeor classification on the radar data. The relative importance of topographically induced effects on snow accumulation patterns is investigated based on vertical cross sections of hydrometeor data and corresponding snow accumulation.

Building a Snow Data Management System using Open Source Software (and IDL)

NASA Astrophysics Data System (ADS)

Goodale, C. E.; Mattmann, C. A.; Ramirez, P.; Hart, A. F.; Painter, T.; Zimdars, P. A.; Bryant, A.; Brodzik, M.; Skiles, M.; Seidel, F. C.; Rittger, K. E.

2012-12-01

At NASA's Jet Propulsion Laboratory free and open source software is used everyday to support a wide range of projects, from planetary to climate to research and development. In this abstract I will discuss the key role that open source software has played in building a robust science data processing pipeline for snow hydrology research, and how the system is also able to leverage programs written in IDL, making JPL's Snow Data System a hybrid of open source and proprietary software. Main Points: - The Design of the Snow Data System (illustrate how the collection of sub-systems are combined to create a complete data processing pipeline) - Discuss the Challenges of moving from a single algorithm on a laptop, to running 100's of parallel algorithms on a cluster of servers (lesson's learned) - Code changes - Software license related challenges - Storage Requirements - System Evolution (from data archiving, to data processing, to data on a map, to near-real-time products and maps) - Road map for the next 6 months (including how easily we re-used the snowDS code base to support the Airborne Snow Observatory Mission) Software in Use and their Software Licenses: IDL - Used for pre and post processing of data. Licensed under a proprietary software license held by Excelis. Apache OODT - Used for data management and workflow processing. Licensed under the Apache License Version 2. GDAL - Geospatial Data processing library used for data re-projection currently. Licensed under the X/MIT license. GeoServer - WMS Server. Licensed under the General Public License Version 2.0 Leaflet.js - Javascript web mapping library. Licensed under the Berkeley Software Distribution License. Python - Glue code and miscellaneous data processing support. Licensed under the Python Software Foundation License. Perl - Script wrapper for running the SCAG algorithm. Licensed under the General Public License Version 3. PHP - Front-end web application programming. Licensed under the PHP License Version 3.01

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

New measurements of particulates in glacial snow and ice in the Cordillera Blanca mountains of Peru

NASA Astrophysics Data System (ADS)

All, J.; Schmitt, C.; Celestian, A. J.; Rucks, M.; Arnott, W. P.; Cole, R.

2012-12-01

During the local dry season (June/July) of 2011 and 2012, the American Climber Science Program (organized with the assistance of the American Alpine Club) conducted scientific expeditions in Huascaran National Park in Peru. The Park is located in the Cordillera Blanca mountain range and contains the world's largest collection of tropical mountain glaciers. One component of the environmental research program was sampling particulates on glacier surfaces by means of snow collection and filtration. Over 150 samples were collected during the two expeditions by volunteer climbers working with scientists in the field. Glacier snows were collected on over fifteen peaks throughout the range at altitudes from 4800 to nearly 6800 meters. Snow samples were kept frozen until the climber-scientists returned to basecamp - at which point they were rapidly melted and then immediately filtered through 0.7 micron PallFlex tissuequartz filters. The particulates captured on the filters have been analyzed for their bulk heat absorption properties as well as to determine the properties of individual particles through X-ray diffraction for bulk mineral identification, and Raman microscopy for chemical mapping of minerals. Preliminary results indicate that snow age, altitude, as well as geographic location (with respect to urban areas, mines, and predominant wind direction) all play significant roles in the amount and types of contaminants. Multiple locations were sampled during both expeditions as well as at different times during the same climbing season. Results include the relative heating capacity of the samples at various wavelengths as well as mineral composition information across the range. Local weather patterns and geographic observations will be used to identify potential sources of contaminants. Sampling will continue under the American Climber Science Program in 2013 and beyond.

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development

NASA Astrophysics Data System (ADS)

Perez Diaz, C. L.; Lakhankar, T.; Romanov, P.; Khanbilvardi, R.; Munoz Barreto, J.; Yu, Y.

2017-12-01

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development The Field Snow Research Station (also referred to as Snow Analysis and Field Experiment, SAFE) is operated by the NOAA Center for Earth System Sciences and Remote Sensing Technologies (CREST) in the City University of New York (CUNY). The field station is located within the premises of the Caribou Municipal Airport (46°52'59'' N, 68°01'07'' W) and in close proximity to the National Weather Service (NWS) Regional Forecast Office. The station was established in 2010 to support studies in snow physics and snow remote sensing. The Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) and Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (provided by the Terra and Aqua Earth Observing System satellites) were validated using in situ LST (T-skin) and near-surface air temperature (T-air) observations recorded at CREST-SAFE for the winters of 2013 and 2014. Results indicate that T-air correlates better than T-skin with VIIRS LST data and that the accuracy of nighttime LST retrievals is considerably better than that of daytime. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that, although all the data sets showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C). Additionally, we created a liquid water content (LWC)-profiling instrument using time-domain reflectometry (TDR) at CREST-SAFE and tested it during the snow melt period (February-April) immediately after installation in 2014. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Lastly, to improve on global snow cover mapping, a snow product capable of estimating snow depth and snow water equivalent (SWE) using microwave remote sensing and the CREST Snow Depth Regression Tree Model (SDRTM) was developed. Data from AMSR2 onboard the JAXA GCOM-W1 satellite is used to produce daily global snow depth and SWE maps in automated fashion at a 10-km resolution.

Twenty-four year record of Northern Hemisphere snow cover derived from passive microwave remote sensing

NASA Astrophysics Data System (ADS)

Armstrong, Richard L.; Brodzik, Mary Jo

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. It is now possible to monitor the global fluctuation of snow cover over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a smiliar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible statellite data and the visible data typically show higher monthly variability. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as on into the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Trends in annual averages are similar, decreasing at rates of approximately 2% per decade. The only region where the passive microwave data consistently indicate snow and the visible data do not is over the Tibetan Plateau and surrounding mountain areas. In the effort to determine the accuracy of the microwave algorithm over this region we are acquiring surface snow observations through a collaborative study with CAREERI/Lanzhou. In order to provide an optimal snow cover product in the future, we are developing a procedure that blends snow extent maps derived from MODIS data with snow water equivalent maps derived from both SSM/I and AMSR.

Natural resources inventory and land evaluation in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Using MSS channels 5 and 7 and a supervised classification system with a PPD classification algorithm, it was possible to map the exact areal extent of the snow cover and of the transition zone with melting snow patches and snow free parts of various sizes over a large area under different aspects such as relief, exposure, shadows etc. A correlation of the data from ground control, areal underflights and earth resources satellites provided a very accurate interpretation of the melting procedure of snow in high mountains.

Land Surface Properties near Terra Nova Bay, East Antarctica, Analyzed by Time-series Height, Coherence and Amplitude Maps Derived from COSMO-SkyMed One-day Tandem Pairs

NASA Astrophysics Data System (ADS)

Ji, Y.; Han, H.; Lee, H.

2014-12-01

Analysis of the surface properties of Antarctica is very important to study the change of environment and climate in the polar region. Synthetic aperture radar (SAR) has been widely used to study Antarctic surface properties because it is independent of sun altitude and atmospheric conditions. Interferometric SAR (InSAR) observes surface topography and deformation, by calculating the phase differences between two or more SAR images obtained over same area. InSAR technique can be used for height mapping in stable areas with a few meter accuracy. However, the InSAR-derived height map can have errors if the phase differences due to surface deformation or change of the scattering center by microwave penetration into snow are misinterpreted as the elevation. In this study, we generated the height maps around Terra Nova Bay in East Antarctica from 13 COSMO-SkyMed one-day tandem InSAR pairs obtained from December 2010 to January 2012. By analyzing the height maps averaged over the 13 interferograms and its standard deviation (STD) map, we could classify the surface types into glacier, mountains and basin areas covered with snow. The mountain areas showed very small STD because its surface property is unchanged with time, except for the small STD values caused by the errors from the unwrapping processing, satellite orbit or atmospheric phase distortion. Over the basin areas, however, the STD of the height was much larger than the mountain area due to the variation of scattering center either from the change in surface property such as snowfall and sublimation or by the surface displacement of snow mass that are too slow. A year-long constant motion of such slow-creeping snow body was positively identified by its linear relationship between the misinterpreted elevation and the baseline perpendicular component of InSAR pair. Analysis of time-series coherence maps and amplitude maps have also contributed to clarify the surface properties and its changes due to various environmental factors such as snow fall, wind, sublimation, and the freezing-thawing processes in this Antarctic land surface. Acknowledgement - This research was supported by National Research Foundation of Korea through NRF-2013R1A1A2008062 and NRF-2013M1A3A3A02041853.

Permafrost, Seasonally Frozen Ground, Snow Cover and Vegetation in the USSR

DTIC Science & Technology

1984-12-01

Snow Cover in Physical Geographic Processes (1948). He covered aspects of the dynamics of the snow cover, its properties and the connection between...Bigl, Research Physical Scientist, of the Geotechnical Research Branch, Experimental Engineering Division, un- der the general supervision of Dr...generalized from a detailed vegetation map in the volume Physical Geographic Atlas of the World (Gerasimov 1964), The tundra zone consists mostly of

The assessment of EUMETSAT HSAF Snow Products for mountainuos areas in the eastern part of Turkey

NASA Astrophysics Data System (ADS)

Akyurek, Z.; Surer, S.; Beser, O.; Bolat, K.; Erturk, A. G.

2012-04-01

Monitoring the snow parameters (e.g. snow cover area, snow water equivalent) is a challenging work. Because of its natural physical properties, snow highly affects the evolution of weather from daily basis to climate on a longer time scale. The derivation of snow products over mountainous regions has been considered very challenging. This can be done by periodic and precise mapping of the snow cover. However inaccessibility and scarcity of the ground observations limit the snow cover mapping in the mountainous areas. Today, it is carried out operationally by means of optical satellite imagery and microwave radiometry. In retrieving the snow cover area from satellite images bring the problem of topographical variations within the footprint of satellite sensors and spatial and temporal variation of snow characteristics in the mountainous areas. Most of the global and regional operational snow products use generic algorithms for flat and mountainous areas. However the non-uniformity of the snow characteristics can only be modeled with different algorithms for mountain and flat areas. In this study the early findings of Satellite Application Facilities on Hydrology (H-SAF) project, which is financially supported by EUMETSAT, will be presented. Turkey is a part of the H-SAF project, both in product generation (eg. snow recognition, fractional snow cover and snow water equivalent) for mountainous regions for whole Europe, cal/val of satellite-derived snow products with ground observations and cal/val studies with hydrological modeling in the mountainous terrain of Europe. All the snow products are operational on a daily basis. For the snow recognition product (H10) for mountainous areas, spectral thresholding methods were applied on sub pixel scale of MSG-SEVIRI images. The different spectral characteristics of cloud, snow and land determined the structure of the algorithm and these characteristics were obtained from subjective classification of known snow cover features in the MSG/SEVIRI images. The fractional snow cover area (H12) algorithm is based on a sub-pixel reflectance model applied on METOP-AVHRR data. Knowing the effects of topography on satellite-measured radiances for rough terrain, the sun zenith and azimuth angles, as well as direction of observation relative to these are taken into account in estimating the target reflectances from the satellite images. The values of SWE products (H13) were obtained using an assimilation process based on the Helsinki University of Technology model using Advanced Microwave Scanning Radiometer for EOS (AMSR-E) daily brightness-temperature values. The validation studies for three products have been performed for the water years 2010 and 2011. Average values of 70% of probability of detection for snow recognition product, 60% of overall accuracy for the fractional snow cover product and 45 mm RMSE for the snow water equivalent product have been obtained from the validation studies. Final versions of these three products will be presented and discussed. Key words: snow, satellite images, mountain, HSAF, snow cover, snow water equivalent

Improving the Accuracy of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Kumar, Sujay; Chien, Janety Y. L.; Riggs, George A.

2012-01-01

The Air Force Weather Agency (AFWA) -- NASA blended snow-cover product, called ANSA, utilizes Earth Observing System standard snow products from the Moderate- Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE with SWE values calculated from snow depths reported at 1500 National Climatic Data Center (NCDC) co-op stations in the Lower Great Lakes Basin. Compared to station data, the ANSA significantly underestimates SWE in densely-forested areas. We use two methods to remove some of the bias observed in forested areas to reduce the root-mean-square error (RMSE) between the ANSA- and station-derived SWE. First, we calculated a 5- year mean ANSA-derived SWE for the winters of 2005-06 through 2009-10, and developed a five-year mean bias-corrected SWE map for each month. For most of the months studied during the five-year period, the 5-year bias correction improved the agreement between the ANSA-derived and station-derived SWE. However, anomalous months such as when there was very little snow on the ground compared to the 5-year mean, or months in which the snow was much greater than the 5-year mean, showed poorer results (as expected). We also used a 7-day running mean (7DRM) bias correction method using days just prior to the day in question to correct the ANSA data. This method was more effective in reducing the RMSE between the ANSA- and co-op-derived SWE values, and in capturing the effects of anomalous snow conditions.

Spatial Patterns of Snow Cover in North Carolina: Surface and Satellite Perspectives

NASA Technical Reports Server (NTRS)

Fuhrmann, Christopher M.; Hall, Dorothy K.; Perry, L. Baker; Riggs, George A.

2010-01-01

Snow mapping is a common practice in regions that receive large amounts of snowfall annually, have seasonally-continuous snow cover, and where snowmelt contributes significantly to the hydrologic cycle. Although higher elevations in the southern Appalachian Mountains average upwards of 100 inches of snow annually, much of the remainder of the Southeast U.S. receives comparatively little snowfall (< 10 inches). Recent snowy winters in the region have provided an opportunity to assess the fine-grained spatial distribution of snow cover and the physical processes that act to limit or improve its detection across the Southeast. In the present work, both in situ and remote sensing data are utilized to assess the spatial distribution of snow cover for a sample of recent snowfall events in North Carolina. Specifically, this work seeks to determine how well ground measurements characterize the fine-grained patterns of snow cover in relation to Moderate- Resolution Imaging Spectroradiometer (MODIS) snow cover products (in this case, the MODIS Fractional Snow Cover product).

Snow cover retrieval over Rhone and Po river basins from MODIS optical satellite data (2000-2009).

NASA Astrophysics Data System (ADS)

Dedieu, Jean-Pierre, ,, Dr.; Boos, Alain; Kiage, Wiliam; Pellegrini, Matteo

2010-05-01

Estimation of the Snow Covered Area (SCA) is an important issue for meteorological application and hydrological modeling of runoff. With spectral bands in the visible, near and middle infrared, the MODIS optical satellite sensor can be used to detect snow cover because of large differences between reflectance from snow covered and snow free surfaces. At the same time, it allows separation between snow and clouds. Moreover, the sensor provides a daily coverage of large areas (2,500 km range). However, as the pixel size is 500m x 500m, a MODIS pixel may be partially covered by snow, particularly in Alpine areas, where snow may not be present in valleys lying at lower altitudes. Also, variation of reflectance due to differential sunlit effects as a function of slope and aspect, as well as bidirectional effects may be present in images. Nevertheless, it is possible to estimate snow cover at the Sub-Pixel level with a relatively good accuracy and with very good results if the sub-pixel estimations are integrated for a few pixels relative to an entire watershed. Integrated into the EU-FP7 ACQWA Project (www.acqwa.ch), this approach was first applied over Alpine area of Rhone river basin upper Geneva Lake: Canton du Valais, Switzerland (5 375 km²). In a second step over Alps, rolling hills and plain areas in Po catchment for Val d'Aosta and Piemonte regions, Italy (37 190 km²). Watershed boundaries were provided respectively by GRID (Ch) and ARPA (It) partners. The complete satellite images database was extracted from the U.S. MODIS/NASA website (http://modis.gsfc.nasa.gov/) for MOD09_B1 Reflectance images, and from the MODIS/NSIDC website (http://nsidc.org/index.html) for MOD10_A2 snow cover images. Only the Terra platform was used because images are acquired in the morning and are therefore better correlated with dry snow surface, avoiding cloud coverage of the afternoon (Aqua Platform). The MOD9 Image reflectance and MOD10_A2 products were respectively analyzed to retrieve (i) Fractional Snow cover at sub-pixel scale, and (ii) maximum snow cover. All products were retrieved at 8-days over a complete time period of 10 years (2000-2009), giving 500 images for each river basin. Digital Model Elevation was given by NASA/SRTM database at 90-m resolution and used (i) for illumination versus topography correction on snow cover, (ii) geometric rectification of images. Geographic projection is WGS84, UTM 32. Fractional Snow cover mapping was derived from the NDSI linear regression method (Salomonson et al., 2004). Cloud mask was given by MODIS-NASA library (radiometric threshold) and completed by inverse slope regression to avoid lowlands fog confusing with thin snow cover (Po river basin). Maximum Snow Cover mapping was retrieved from the NSIDC database classification (Hall et al., 2001). Validation step was processed using comparison between MODIS Snow maps outputs and meteorological data provided by network of 87 meteorological stations: temperature, precipitation, snow depth measurement. A 0.92 correlation was observed for snow/non snow cover and can be considered as quite satisfactory, given the radiometric problems encountered in mountainous areas, particularly in snowmelt season. The 10-years time period results indicates a main difference between (i) regular snow accumulation and depletion in Rhone and (ii) the high temporal and spatial variability of snow cover for Po. Then, a high sensitivity to low variation of air temperature, often close to 1° C was observed. This is the case in particular for the beginning and the end of the winter season. The regional snow cover depletion is both influenced by thermal positives anomalies (e.g. 2000 and 2006), and the general trend of rising atmospheric temperatures since the late 1980s, particularly for Po river basin. Results will be combined with two hydrological models: Topkapi and Fest.

Impacts of Recent Climatic Wetting on Distributed Snow and Streamflow Responses in a Terminal Lake Basin.

NASA Astrophysics Data System (ADS)

Van Hoy, D.; Mahmood, T. H.; Jeannotte, T.; Todhunter, P. E.

2017-12-01

The recent shift in hydroclimatic conditions in the Northern Great Plains (NGP) has led to an increase in precipitation, rainfall rate, and wetland connectivity over the last few decades. These changes yield an integrated response resulting in high mean annual streamflow and subsequent flooding in many NGP basins such as the terminal Devils Lake Basin (DLB). In this study, we investigate the impacts of recent climatic wetting on distributed hydrologic responses such as snow processes and streamflow using a field-tested and physically-based cold region hydrologic model (CRHM). CHRM is designed for cold prairie regions and has modules to simulate major processes such as blowing snow transport, sublimation, interception, frozen soil infiltration, snowmelt and subsequent streamflow generation. Our modeling focuses on a tributary basin of the DLB known as the Mauvais Coulee Basin (MCB). Since there were no snow observations in the MCB, we conducted a detailed snow survey at distributed locations estimating snow depth, density, and snow water equivalent (SWE) using a prairie snow tube four times during winter of 2016-17. The MCB model was evaluated against distributed snow observations and streamflow measured at the basin outlet (USGS) for the year 2016-2017. Preliminary results indicate that the simulated SWEs at distributed locations and streamflow (NSE ≈ 0.70) are in good agreement with observations. The simulated SWE maps exhibit large spatiotemporal variation during 2016-17 winter due to spatial variability in precipitation, snow redistribution from stubble field to wooded areas, and snow accumulations in small depressions across the subbasins. The main source of snow appears to be the hills and ridges of the eastern and western edges of the basin, while the main sink is the large flat central valleys. The model will be used to examine the effect of recent changes to precipitation and temperature on snow processes and subsequent streamflow for 2004-2017 season. We will also investigate the hydrologic sensitivity to precipitation and temperature changes by altering input temperature and precipitation. Finally, our findings will point toward future process-based studies and simulated hydrologic responses that can be used to prepare flood hazard maps for cities around Devils Lake.

Snow mapping and land use studies in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1977-01-01

The author has identified the following significant results. A system was developed for operational snow and land use mapping, based on a supervised classification method using various classification algorithms and representation of the results in maplike form on color film with a photomation system. Land use mapping, under European conditions, was achieved with a stepwise linear discriminant analysis by using additional ratio variables. On fall images, signatures of built-up areas were often not separable from wetlands. Two different methods were tested to correlate the size of settlements and the population with an accuracy for the densely populated Swiss Plateau between +2 or -12%.

Normalized-Difference Snow Index (NDSI)

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.

2010-01-01

The Normalized-Difference Snow Index (NDSI) has a long history. 'The use of ratioing visible (VIS) and near-infrared (NIR) or short-wave infrared (SWIR) channels to separate snow and clouds was documented in the literature beginning in the mid-1970s. A considerable amount of work on this subject was conducted at, and published by, the Air Force Geophysics Laboratory (AFGL). The objective of the AFGL work was to discriminate snow cover from cloud cover using an automated algorithm to improve global cloud analyses. Later, automated methods that relied on the VIS/NIR ratio were refined substantially using satellite data In this section we provide a brief history of the use of the NDSI for mapping snow cover.

White water: Fifty years of snow research in WRR and the outlook for the future

NASA Astrophysics Data System (ADS)

Sturm, Matthew

2015-07-01

Over the past 50 years, 239 papers related to snow have been published in Water Resources Research (WRR). Seminal papers on virtually every facet of snow physics and snow water resources have appeared in the journal. These include papers on drifting snow, the snow surface energy balance, the effect of grain size on albedo, chemical elution, water movement through snow, and canopy interception. In particular, papers in WRR have explored the distribution of snow across different landscapes, providing data, process knowledge, and the basis for virtually all of the distributed snow models in use today. In this paper, I review these key contributions and provide some personal thoughts on what is likely to be the focus and nature of papers published in the next few decades, a period that is likely to see an increasing ability to map snow cover in detail, which should serve as a basis for the further development and improvement of snow models. It will also be an uncertain future, with profound changes in snow climatology predicted. I expect WRR will continue to play a key role in documenting and understanding these important cryospheric changes.

Coupled long-term summer warming and deeper snow alters species composition and stimulates gross primary productivity in tussock tundra.

PubMed

Leffler, A Joshua; Klein, Eric S; Oberbauer, Steven F; Welker, Jeffrey M

2016-05-01

Climate change is expected to increase summer temperature and winter precipitation throughout the Arctic. The long-term implications of these changes for plant species composition, plant function, and ecosystem processes are difficult to predict. We report on the influence of enhanced snow depth and warmer summer temperature following 20 years of an ITEX experimental manipulation at Toolik Lake, Alaska. Winter snow depth was increased using snow fences and warming was accomplished during summer using passive open-top chambers. One of the most important consequences of these experimental treatments was an increase in active layer depth and rate of thaw, which has led to deeper drainage and lower soil moisture content. Vegetation concomitantly shifted from a relatively wet system with high cover of the sedge Eriophorum vaginatum to a drier system, dominated by deciduous shrubs including Betula nana and Salix pulchra. At the individual plant level, we observed higher leaf nitrogen concentration associated with warmer temperatures and increased snow in S. pulchra and B. nana, but high leaf nitrogen concentration did not lead to higher rates of net photosynthesis. At the ecosystem level, we observed higher GPP and NEE in response to summer warming. Our results suggest that deeper snow has a cascading set of biophysical consequences that include a deeper active layer that leads to altered species composition, greater leaf nitrogen concentration, and higher ecosystem-level carbon uptake.

A study to develop improved spacecraft show survey methods using Skylab/EREP data: Demonstration of the utility of the S190 and S192 data. [Sierra Nevadas in California, Cascades in Washington and Oregon, Upper Columbia Basin in Idaho and Montana, and Salt-Verde Watershed in Arizona

NASA Technical Reports Server (NTRS)

Barnes, J. C. (Principal Investigator); Bowley, C. J.; Smallwood, M. D.

1974-01-01

The author has identified the following significant results. This interim report provides a demonstration of the utility of spacecraft acquired Skylab S190A and S190B photography and S192 imagery for mapping areal extent of snow cover in western United States test site areas. The data sample is from the SL-2 mission flown in June 1973. Results of the investigation indicate that areal snow cover extent can be mapped more accurately from the S190A and S190B photography than from any other spacecraft system, including ERTS. The results of a qualitative analysis of the S192 imagery indicate considerable potential for the utility of multispectral snow cover analysis; the potential for distinguishing snow from clouds automatically is particularly significant.

Single-pass Airborne InSAR for Wide-swath, High-Resolution Cryospheric Surface Topography Mapping

NASA Astrophysics Data System (ADS)

Moller, D.; Hensley, S.; Wu, X.; Muellerschoen, R.

2014-12-01

In May 2009 a mm-wave single-pass interferometric synthetic aperture radar (InSAR) for the first time demonstrated ice surface topography swath-mapping in Greenland. This was achieved with the airborne Glacier and Ice Surface Topography Interferometer (GLISTIN-A). Ka-band (35.6GHz) was chosen for high-precision topographic mapping from a compact sensor with minimal surface penetration. In recent years, the system was comprehensively upgraded for improved performance, stability and calibration. In April 2013, after completing the upgrades, GLISTIN-A flew a brief campaign to Alaska. The primary purpose was to demonstrate the InSAR's ability to generate high-precision, high resolution maps of ice surface topography with swaths in excess of 10km. Comparison of GLISTIN-A's elevations over glacial ice with lidar verified the precision requirements and established elevation accuracies to within 2 m without tie points. Feature tracking of crevasses on Columbia Glacier using data acquired with a 3-day separation exhibit an impressive velocity mapping capability. Furthermore, GLISTIN-A flew over the Beaufort sea to determine if we could not only map sea ice, but also measure freeboard. Initial analysis has established we can measure sea-ice freeboard using height differences from the top of the sea-ice and the sea surface in open leads. In the future, a campaign with lidar is desired for a quantitative validation. Another proof-of-concept collection mapped snow-basins for hydrology. Snow depth measurements using summer and winter collections in the Sierras were compared with lidar measurements. Unsurprisingly when present, trees complicate the interpretation, but additional filtering and processing is in work. For each application, knowledge of the interferometric penetration is important for scientific interpretation. We present analytical predictions and experimental data to upper bound the elevation bias of the InSAR measurements over snow and snow-covered ice.

A novel approach for automatic snow depth estimation using UAV-taken images without ground control points

NASA Astrophysics Data System (ADS)

Mizinski, Bartlomiej; Niedzielski, Tomasz

2017-04-01

Recent developments in snow depth reconstruction based on remote sensing techniques include the use of photographs of snow-covered terrain taken by unmanned aerial vehicles (UAVs). There are several approaches that utilize visible-light photos (RGB) or near infrared images (NIR). The majority of the methods in question are based on reconstructing the digital surface model (DSM) of the snow-covered area with the use of the Structure-from-Motion (SfM) algorithm and the stereo-vision software. Having reconstructed the above-mentioned DSM it is straightforward to calculate the snow depth map which may be produced as a difference between the DSM of snow-covered terrain and the snow-free DSM, known as the reference surface. In order to use the aforementioned procedure, the high spatial accuracy of the two DSMs must be ensured. Traditionally, this is done using the ground control points (GCPs), either artificial or natural terrain features that are visible on aerial images, the coordinates of which are measured in the field using the Global Navigation Satellite System (GNSS) receiver by qualified personnel. The field measurements may be time-taking (GCPs must be well distributed in the study area, therefore the field experts should travel over long distances) and dangerous (the field experts may be exposed to avalanche risk or cold). Thus, there is a need to elaborate methods that enable the above-mentioned automatic snow depth map production without the use of GCPs. One of such attempts is shown in this paper which aims to present the novel method which is based on real-time processing of snow-covered and snow-free dense point clouds produced by SfM. The two stage georeferencing is proposed. The initial (low accuracy) one assigns true geographic, and subsequently projected, coordinates to the two dense point clouds, while the said initially-registered dense point clouds are matched using the iterative closest point (ICP) algorithm in the final (high accuracy) stage. The stable reference is offered by specially-selected trees which are located in the vicinity of the terrain of interest. The method has already been implemented and along with the presentation of its concept, a few case studies from the Izerskie Mountains (southwestern Poland) are discussed. Although the method reveals several constraints, it may serve the purpose of generating the snow depth maps with reasonable accuracy, in particular in the absence of GCPs. The snow depth estimation algorithm has been elaborated in frame of the research grant no. LIDER/012/223/L-5/13/NCBR/2014 financed by the National Centre for Research and Development of Poland.

Responses of Plant Community Composition to Long-term Changes in Snow Depth at the Great Basin Desert - Sierra Nevada ecotone.

NASA Astrophysics Data System (ADS)

Loik, M. E.

2015-12-01

Snowfall is the dominant hydrologic input for many high-elevation ecosystems of the western United States. Many climate models envision changes in California's Sierra Nevada snow pack characteristics, which would severely impact the storage and release of water for one of the world's largest economies. Given the importance of snowfall for future carbon cycling in high elevation ecosystems, how will these changes affect seedling recruitment, plant mortality, and community composition? To address this question, experiments utilize snow fences to manipulate snow depth and melt timing at a desert-montane ecotone in eastern California, USA. Long-term April 1 snow pack depth averages 1344 mm (1928-2015) but is highly variable from year to year. Snow fences increased equilibrium drift snow depth by 100%. Long-term changes in snow depth and melt timing are associated with s shift from shurbs to graminoids where snow depth was increased for >50 years. Changes in snow have impacted growth for only three plant species. Moreover, annual growth ring increments of the conifers Pinus jeffreyi and Pi. contorta were not equally sensitive to snow depth. There were over 8000 seedlings of the shrubs Artemisia tridentata and Purshia tridentata found in 6300 m2 in summer 2009, following about 1400 mm of winter snow and spring rain. The frequency of seedlings of A. tridentata and P. tridentata were much lower on increased-depth plots compared to ambient-depth, and reduced-depth plots. Survival of the first year was lowest for A. tridentata. Survival of seedlings from the 2008 cohort was much higher for P. tridentata than A. tridentata during the 2011-2015 drought. Results indicate complex interactions between snow depth and plant community characteristics, and that responses of plants at this ecotone may not respond similarly to increases vs. decreases in snow depth. These changes portend altered carbon uptake in this region under future snowfall scenarios.

Spectral Profiler Probe for In Situ Snow Grain Size and Composition Stratigraphy

NASA Technical Reports Server (NTRS)

Berisford, Daniel F.; Molotch, Noah P.; Painter, Thomas

2012-01-01

An ultimate goal of the climate change, snow science, and hydrology communities is to measure snow water equivalent (SWE) from satellite measurements. Seasonal SWE is highly sensitive to climate change and provides fresh water for much of the world population. Snowmelt from mountainous regions represents the dominant water source for 60 million people in the United States and over one billion people globally. Determination of snow grain sizes comprising mountain snowpack is critical for predicting snow meltwater runoff, understanding physical properties and radiation balance, and providing necessary input for interpreting satellite measurements. Both microwave emission and radar backscatter from the snow are dominated by the snow grain size stratigraphy. As a result, retrieval algorithms for measuring snow water equivalents from orbiting satellites is largely hindered by inadequate knowledge of grain size.

Integration of MODIS Snow, Cloud and Land Area Coverage Data with SNOTEL to Generate Inter-Annual and Within-Season Snow Depletion Curves and Maps

NASA Astrophysics Data System (ADS)

Qualls, R. J.; Woodruff, C.

2017-12-01

The behavior of inter-annual trends in mountain snow cover would represent extremely useful information for drought and climate change assessment; however, individual data sources exhibit specific limitations for characterizing this behavior. For example, SNOTEL data provide time series point values of Snow Water Equivalent (SWE), but lack spatial content apart from that contained in a sparse network of point values. Satellite observations in the visible spectrum can provide snow covered area, but not SWE at present, and are limited by cloud cover which often obscures visibility of the ground, especially during the winter and spring in mountainous areas. Cloud cover, therefore, often limits both temporal and spatial coverage of satellite remote sensing of snow. Among the platforms providing the best combination of temporal and spatial coverage to overcome the cloud obscuration problem by providing frequent overflights, the Aqua and Terra satellites carrying the MODIS instrument package provide 500 m, daily resolution observations of snow cover. These were only launched in 1999 and the early 2000's, thus limiting the historical period over which these data are available. A hybrid method incorporating SNOTEL and MODIS data has been developed which accomplishes cloud removal, and enables determination of the time series of watershed spatial snow cover when either SNOTEL or MODIS data are available. This allows one to generate spatial snow cover information for watersheds with SNOTEL stations for periods both before and after the launch of the Aqua and Terra satellites, extending the spatial information about snow cover over the period of record of the SNOTEL stations present in a watershed. This method is used to quantify the spatial time series of snow over the 9000 km2 Upper Snake River watershed and to evaluate inter-annual trends in the timing, rate, and duration of melt over the nearly 40 year period from the early 1980's to the present, and shows promise for generating snow cover depletion maps for drought and climate change scenarios.

An Inexpensive, Implantable Electronic Sensor for Autonomous Measurement of Snow Pack Parameters

NASA Astrophysics Data System (ADS)

De Roo, R. D.; Haengel, E.; Rogacki, S.

2015-12-01

Snow accumulations on the ground are an important source of water in many parts of the world. Mapping the accumulation, usually represented as the snow water equivalent (SWE), is valuable for water resource management. The longest record of regional and global maps of SWE are from orbiting microwave radiometers, which do not directly measure SWE but rather measure the scatter darkening from the snow pack. Robustly linking the scatter darkening to SWE eludes us to this day, in part because the snow pack is highly variable in both time and space. The data needed is currently collected by hand in "snow pits," and the labor-intensive process limits the size of the data sets that can be obtained. In particular, time series measurements are only a one or two samples per day at best, and come at the expense of spatial sampling. We report on the development of a low-power wireless device that can be embedded within a snow pack to report on some of the critical parameters needed to understand scatter darkening. The device autonomously logs temperature, the microwave dielectric constant and infrared backscatter local to the device. The microwave dielectric constant reveals the snow density and the presence of liquid water, while the infrared backscatter measurement, together with the density measurement, reveals a characteristic grain size of the snow pack. The devices are made to be inexpensive (less than $200 in parts each) and easily replicated, so that many can be deployed to monitor variations vertically and horizontally in the snow pack. The low-power operation is important both for longevity of observations as well as insuring minimal anomalous metamorphism of the snow pack. The hardware required for the microwave measurement is intended for wireless communications, and this feature will soon be implemented for near real-time monitoring of snow conditions. We will report on the design, construction and initial deployment of about 30 of these devices in northern lower Michigan, and, data permitting, on the measurements that these novel devices have acquired.

Improving Understanding of Glacier Melt Contribution to High Asian River Discharge through Collaboration and Capacity Building with High Asian CHARIS Partner Institutions

NASA Astrophysics Data System (ADS)

Armstrong, Richard; Brodzik, Mary Jo; Armstrong, Betsy; Barrett, Andrew; Fetterer, Florence; Hill, Alice; Jodha Khalsa, Siri; Racoviteanu, Adina; Raup, Bruce; Rittger, Karl; Williams, Mark; Wilson, Alana; Ye, Qinghua

2017-04-01

The Contribution to High Asia Runoff from Ice & Snow (CHARIS) project uses remote sensing data combined with modeling from 2000 to the present to improve proportional estimates of melt from glaciers and seasonal snow surfaces. Based at the National Snow and Ice Data Center (NSIDC), University of Colorado, Boulder, USA, the CHARIS project objectives are twofold: 1) capacity-building efforts with CHARIS partners from eight High Asian countries to better forecast future availability and vulnerability of water resources in the region, and 2) improving our ability to systematically assess the role of glaciers and seasonal snow in the freshwater resources of High Asia. Capacity-building efforts include working with CHARIS partners from Bhutan, Nepal, India, Pakistan, Afghanistan, Kazakhstan, Kyrgyzstan and Tajikistan. Our capacity-building activities include training, data sharing, supporting fieldwork, graduate student education and infrastructure development. Because of the scarcity of in situ data in this High Asian region, we are using the wealth of available remote sensing data to characterize digital elevation, daily maps of fractional snow-cover, annual maps of glacier and permanent snow cover area and downscaled reanalysis temperature data in snow melt models to estimate the relative proportions of river runoff from glacierized and seasonally snow-covered surfaces. Current collaboration with Qinghua Ye, visiting scientist at NSIDC from the Institute of Tibetan Plateau Research, CAS, focuses on remote sensing methods to detect changes in the mountain cryosphere. Collaboration with our Asian partners supports the systematic analysis of the annual cycle of seasonal snow and glacier ice melt across the High Mountain Asia region. With our Asian partners, we have derived reciprocal benefits, learning from their specialized local knowledge and obtaining access to their in situ data. We expect that the improved understanding of runoff from snow and glacier surfaces will inform the development of adaptation and mitigation measures. The CHARIS Project is funded by USAID.

UAS applications in high alpine, snow-covered terrain

NASA Astrophysics Data System (ADS)

Bühler, Y.; Stoffel, A.; Ginzler, C.

2017-12-01

Access to snow-covered, alpine terrain is often difficult and dangerous. Hence parameters such as snow depth or snow avalanche release and deposition zones are hard to map in situ with adequate spatial and temporal resolution and with spatial continuous coverage. These parameters are currently operationally measured at automated weather stations and by observer networks. However such isolated point measurements are not able to capture the information spatial continuous and to describe the high spatial variability present in complex mountain topography. Unmanned Aerial Systems (UAS) have the potential to fill this gap by frequently covering selected high alpine areas with high spatial resolution down to ground resolutions of even few millimeters. At the WSL Institute for Snow and Avalanche Research SLF we test different photogrammetric UAS with visual and near infrared bands. During the last three years we were able to gather experience in more than 100 flight missions in extreme terrain. By processing the imagery applying state-of-the-art structure from motion (SfM) software, we were able to accurately document several avalanche events and to photogrammetrically map snow depth with accuracies from 1 to 20 cm (dependent on the flight height above ground) compare to manual snow probe measurements. This was even possible on homogenous snow surfaces with very little texture. A key issue in alpine terrain is flight planning. We need to cover regions at high elevations with large altitude differences (up to 1 km) with high wind speeds (up to 20 m/s) and cold temperatures (down to - 25°C). Only a few UAS are able to cope with these environmental conditions. We will give an overview on our applications of UAS in high alpine terrain that demonstrate the big potential of such systems to acquire frequent, accurate and high spatial resolution geodata in high alpine, snow covered terrain that could be essential to answer longstanding questions in avalanche and snow hydrology research.

The mortality rates and the space-time patterns of John Snow's cholera epidemic map.

PubMed

Shiode, Narushige; Shiode, Shino; Rod-Thatcher, Elodie; Rana, Sanjay; Vinten-Johansen, Peter

2015-06-17

Snow's work on the Broad Street map is widely known as a pioneering example of spatial epidemiology. It lacks, however, two significant attributes required in contemporary analyses of disease incidence: population at risk and the progression of the epidemic over time. Despite this has been repeatedly suggested in the literature, no systematic investigation of these two aspects was previously carried out. Using a series of historical documents, this study constructs own data to revisit Snow's study to examine the mortality rate at each street location and the space-time pattern of the cholera outbreak. This study brings together records from a series of historical documents, and prepares own data on the estimated number of residents at each house location as well as the space-time data of the victims, and these are processed in GIS to facilitate the spatial-temporal analysis. Mortality rates and the space-time pattern in the victims' records are explored using Kernel Density Estimation and network-based Scan Statistic, a recently developed method that detects significant concentrations of records such as the date and place of victims with respect to their distance from others along the street network. The results are visualised in a map form using a GIS platform. Data on mortality rates and space-time distribution of the victims were collected from various sources and were successfully merged and digitised, thus allowing the production of new map outputs and new interpretation of the 1854 cholera outbreak in London, covering more cases than Snow's original report and also adding new insights into their space-time distribution. They confirmed that areas in the immediate vicinity of the Broad Street pump indeed suffered from excessively high mortality rates, which has been suspected for the past 160 years but remained unconfirmed. No distinctive pattern was found in the space-time distribution of victims' locations. The high mortality rates identified around the Broad Street pump are consistent with Snow's theory about cholera being transmitted through contaminated water. The absence of a clear space-time pattern also indicates the water-bourne, rather than the then popular belief of air bourne, nature of cholera. The GIS data constructed in this study has an academic value and would cater for further research on Snow's map.

Using mid-altitude regions as observatories of change in snow areas: the Natural Park of Cazorla, Segura y las Villas (South Spain) as study case for early snow regression.

NASA Astrophysics Data System (ADS)

Montilla, Soledad; Pimentel, Rafael; José Pérez-Palazón, María; Aguillar, Cristina; José Polo, María

2017-04-01

Snow plays a key role at the hydrological cycle in semiarid mountainous areas, modifying the energy and water balances that govern the regime of stored water in the snowpack, a key resource for the spring and summer river flow. The Natural and National Park of Sierra Nevada (SNNP), a coastal mountain range up to 3450 m a.s.l. in southern Spain, is a representative example of snow areas in Mediterranean-climate regions; its high altitudinal gradient results in a wide variety of eco-climatic environments, and it is part of the global monitoring network to study climate change. Both monitoring and modelling efforts have been performed to assess this variability and its significant scales; whereas increasing temperature trends have been found, no significant trends are observed so far regarding the precipitation regime both on a seasonal and annual basis, with a highly variable impact on the snow regime in this area, especially in the mid-altitude range. In this context, the study of the snow cover in the neighbouring Natural Park of Cazorla, Segura and Las Villas (CSLVNP), with similar climatic conditions but lower altitudes (up to 2107 m a.s.l.) is proposed as a parallel monitoring site for early warning of impacts of climate change on the snow regime. The CSLVNP is the most extensive protected area in Spain and it is located to the north of SNPN, with less influence of the Mediterranean Sea. This study carried out a first quantification of the snow importance in this area, which exhibits a large transitional zone with a dominant alpine environment, and its relationship with the observed local precipitation-temperature trends. For this, the snow cover fraction on a 30x30 m gridded resolution has been studied during a 5-yr period combining on-site meteorological observations and remote-sensing data analysis, and snow modelling by the distributed and physically based approach for Mediterranean regions proposed by Herrero et al. (2009; 2010). The analysis of the available series of satellite images Landsat 8 OLI/TIRS, Landsat 7 ETM+ and Landsat 4-5 TM were used to obtain snow cover fraction maps with 30x30 m resolution. The study period 2010-2015 was simulated with the distributed snow model and the results were compared against these snow map series. Additionally, the annual and seasonal trends of precipitation, mean daily temperature and global radiation were obtained from the available local data sets. Globally, the simulated results overestimate the snow presence in the study area, very likely due to the estimation of snowfall. However, on a local scale, the model performance improves in the region between 1750 and 2250 m altitude. On the other hand, those zones at lower altitudes, which are a transition of the clearly alpine environment above, present a high variability of results related to the spatial patterns of precipitation, temperature and radiation. Regarding the precipitation-temperature regime, an increasing 0.05 °/yr over the last 30 years (1970-2010) was found, but no significant conclusion can be achieved on precipitation trends. This is also observed in the SNNP, which confirms the potential representativeness of PNCSV as an early warning site. Further work is being carried out to improve the snow modelling at this site and generate longer snow cover fraction maps series and other characteristic variables of the snow in this area.

Improved quantification of mountain snowpack properties using observations from Unmanned Air Vehicles (UAVs)

NASA Astrophysics Data System (ADS)

Shea, J. M.; Harder, P.; Pomeroy, J. W.; Kraaijenbrink, P. D. A.

2017-12-01

Mountain snowpacks represent a critical seasonal reservoir of water for downstream needs, and snowmelt is a significant component of mountain hydrological budgets. Ground-based point measurements are unable to describe the full spatial variability of snow accumulation and melt rates, and repeat Unmanned Air Vehicle (UAV) surveys provide an unparalleled opportunity to measure snow accumulation, redistribution and melt in alpine environments. This study presents results from a UAV-based observation campaign conducted at the Fortress Mountain Snow Laboratory in the Canadian Rockies in 2017. Seven survey flights were conducted between April (maximum snow accumulation) and mid-July (bare ground) to collect imagery with both an RGB camera and thermal infrared imager with the sensefly eBee RTK platform. UAV imagery are processed with structure from motion techniques, and orthoimages, digital elevation models, and surface temperature maps are validated against concurrent ground observations of snow depth, snow water equivalent, and snow surface temperature. We examine the seasonal evolution of snow depth and snow surface temperature, and explore the spatial covariances of these variables with respect to topographic factors and snow ablation rates. Our results have direct implications for scaling snow ablation calculations and model resolution and discretization.

Trends in annual minimum exposed snow and ice cover in High Mountain Asia from MODIS

NASA Astrophysics Data System (ADS)

Rittger, Karl; Brodzik, Mary J.; Painter, Thomas H.; Racoviteanu, Adina; Armstrong, Richard; Dozier, Jeff

2016-04-01

Though a relatively short record on climatological scales, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000-2014 can be used to evaluate changes in the cryosphere and provide a robust baseline for future observations from space. We use the MODIS Snow Covered Area and Grain size (MODSCAG) algorithm, based on spectral mixture analysis, to estimate daily fractional snow and ice cover and the MODICE Persistent Ice (MODICE) algorithm to estimate the annual minimum snow and ice fraction (fSCA) for each year from 2000 to 2014 in High Mountain Asia. We have found that MODSCAG performs better than other algorithms, such as the Normalized Difference Index (NDSI), at detecting snow. We use MODICE because it minimizes false positives (compared to maximum extents), for example, when bright soils or clouds are incorrectly classified as snow, a common problem with optical satellite snow mapping. We analyze changes in area using the annual MODICE maps of minimum snow and ice cover for over 15,000 individual glaciers as defined by the Randolph Glacier Inventory (RGI) Version 5, focusing on the Amu Darya, Syr Darya, Upper Indus, Ganges, and Brahmaputra River basins. For each glacier with an area of at least 1 km2 as defined by RGI, we sum the total minimum snow and ice covered area for each year from 2000 to 2014 and estimate the trends in area loss or gain. We find the largest loss in annual minimum snow and ice extent for 2000-2014 in the Brahmaputra and Ganges with 57% and 40%, respectively, of analyzed glaciers with significant losses (p-value<0.05). In the Upper Indus River basin, we see both gains and losses in minimum snow and ice extent, but more glaciers with losses than gains. Our analysis shows that a smaller proportion of glaciers in the Amu Darya and Syr Darya are experiencing significant changes in minimum snow and ice extent (3.5% and 12.2%), possibly because more of the glaciers in this region are smaller than 1 km2 than in the Indus, Ganges, and Brahmaputra making analysis from MODIS (pixel area ~0.25 km2) difficult. Overall, we see 23% of the glaciers in the 5 river basins with significant trends (in either direction). We relate these changes in area to topography and climate to understand the driving processes related to these changes. In addition to annual minimum snow and ice cover, the MODICE algorithm also provides the date of minimum fSCA for each pixel. To determine whether the surface was snow or ice we use the date of minimum fSCA from MODICE to index daily maps of snow on ice (SOI), or exposed glacier ice (EGI) and systematically derive an equilibrium line altitude (ELA) for each year from 2000-2014. We test this new algorithm in the Upper Indus basin and produce annual estimates of ELA. For the Upper Indus basin we are deriving annual ELAs that range from 5350 m to 5450 m which is slightly higher than published values of 5200 m for this region.

Snow depth retrieval from L-band satellite measurements on Arctic and Antarctic sea ice

NASA Astrophysics Data System (ADS)

Maaß, N.; Kaleschke, L.; Wever, N.; Lehning, M.; Nicolaus, M.; Rossmann, H. L.

2017-12-01

The passive microwave mission SMOS provides daily coverage of the polar regions and measures at a low frequency of 1.4 GHz (L-band). SMOS observations have been used to operationally retrieve sea ice thickness up to 1 m and to estimate snow depth in the Arctic for thicker ice. Here, we present how SMOS-retrieved snow depths compare with airborne measurements from NASA's Operation IceBridge mission (OIB) and with AMSR-2 satellite retrievals at higher frequencies, and we show first applications to Antarctic sea ice. In previous studies, SMOS and OIB snow depths showed good agreement on spatial scales from 50 to 1000 km for some days and disagreement for other days. Here, we present a more comprehensive comparison of OIB and SMOS snow depths in the Arctic for 2011 to 2015. We find that the SMOS retrieval works best for cold conditions and depends on auxiliary information on ice surface temperature, here provided by MODIS thermal imagery satellite data. However, comparing SMOS and OIB snow depths is difficult because of the different spatial resolutions (SMOS: 40 km, OIB: 40 m). Spatial variability within the SMOS footprint can lead to different snow conditions as seen from SMOS and OIB. Ideally the comparison is made for uniform conditions: Low lead and open water fraction, low spatial and temporal variability of ice surface temperature, no mixture of multi- and first-year ice. Under these conditions and cold temperatures (surface temperatures below -25°C), correlation coefficients between SMOS and OIB snow depths increase from 0.3 to 0.6. A finding from the comparison with AMSR-2 snow depths is that the SMOS-based maps depend less on the age of the sea ice than the maps derived from higher frequencies. Additionally, we show first results of SMOS snow depths for Antarctic sea ice. SMOS observations are compared to measurements of autonomous snow buoys drifting in the Weddell Sea since 2014. For a better comparability of these point measurements with SMOS data, we use model simulations along these trajectories made with a sea ice version of SNOWPACK, a 1D multi-layer thermodynamic snow model driven by reanalysis data. These simulations are especially helpful for indicating the occurrence of snow-ice-transformation, which cannot be identified in the buoy data and contributes to the measured snow height.

Characterizing the PAHs in surface waters and snow in the Athabasca region: Implications for identifying hydrological pathways of atmospheric deposition.

PubMed

Birks, S J; Cho, S; Taylor, E; Yi, Y; Gibson, J J

2017-12-15

The composition of polycyclic aromatic hydrocarbons present in snow and surface waters in the Athabasca Oil Sands Region (AOSR) was characterized in order to identify major contributors to the organics detected in rivers and lakes in the region. PAH concentrations, measured by three monitoring programs in 2011, were used to compare the PAH compositions of snow and surface waters across the AOSR. The 2011 dataset includes total (dissolved+particulate) concentrations of thirty-four parent and alkylated PAH compounds in 105 snow, 272 river, and 3 lake samples. The concentration of PAHs in rivers varies seasonally, with the highest values observed in July. The timing of increases in PAH concentrations in rivers coincides with the high river discharge during the spring freshet, indicating that this major hydrological event may play an important role in delivering PAHs to rivers. However, the composition of PAHs present in rivers during this period differs from the composition of PAHs present in snow, suggesting that direct runoff and release of PAHs accumulated on snow may not be the major source of PAHs to the Athabasca River and its tributaries. Instead, snowmelt may contribute indirectly to increases in PAHs due to hydrological processes such as erosion of stream channels, remobilization of PAH-containing sediments, increased catchment runoff, and snowmelt-induced groundwater inputs during this dynamic hydrologic period. Better understanding of transformations of PAH profiles during transport along surface and subsurface flow paths in wetland-dominated boreal catchments would improve identification of potential sources and pathways in the region. The compositional differences highlight the challenges in identifying the origins of PAHs in a region with multiple potential natural and anthropogenic sources particularly when the potential transport pathways include air, soil and water. Copyright © 2017 Elsevier B.V. All rights reserved.

Hydrological differentiation and spatial distribution of high altitude wetlands in a semi-arid Andean region derived from satellite data

NASA Astrophysics Data System (ADS)

Otto, M.; Scherer, D.; Richters, J.

2011-05-01

High Altitude Wetlands of the Andes (HAWA) belong to a unique type of wetland within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand, HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV) indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 12 800 km2 situated in the Northwest of Lake Titicaca. The multi-temporal classification method used Normalized Differenced Vegetation Index (NDVI) and Normalized Differenced Infrared Index (NDII) data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000) and at the end of austral summer (May 2001). The mapping result indicates an unexpected high abundance of HAWA covering about 800 km2 of the study region (6 %). Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS). Analyses on the relation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43) and MODIS Eight Day Maximum Snow Extent data (MOD10A2) from 2000 to 2010. We found HAWA subtype specific dependencies on precipitation conditions. A strong relation exists between perennial HAWA and snow fall (r2: 0.82) in dry austral winter months (June to August) and between temporal HAWA and precipitation (r2: 0.75) during austral summer (March to May). Annual changes in spatial extend of perennial HAWA indicate alterations in annual water supply generated from snow melt.

Applications systems verification and transfer project. Volume 2: Operational applications of satellite snow-cover observations and data-collection systems in the Arizona test site

NASA Technical Reports Server (NTRS)

Schumann, H. H.

1981-01-01

Ground surveys and aerial observations were used to monitor rapidly changing moisture conditions in the Salt-Verde watershed. Repetitive satellite snow cover observations greatly reduce the necessity for routine aerial snow reconnaissance flights over the mountains. High resolution, multispectral imagery provided by LANDSAT satellite series enabled rapid and accurate mapping of snow-cover distributions for small- to medium-sized subwatersheds; however, the imagery provided only one observation every 9 days of about a third of the watershed. Low resolution imagery acquired by the ITOSa dn SMS/GOES meteorological satellite series provides the daily synoptic observation necessary to monitor the rapid changes in snow-covered area in the entire watershed. Short term runoff volumes can be predicted from daily sequential snow cover observations.

Simulating the Dependence of Sagebrush Steppe Vegetation on Redistributed Snow in a Semi-Arid Watershed.

NASA Astrophysics Data System (ADS)

Soderquist, B.; Kavanagh, K.; Link, T. E.; Strand, E. K.; Seyfried, M. S.

2014-12-01

In mountainous regions across the western USA, the composition of aspen (Populus tremuloides) and sagebrush steppe plant communities is often closely related to heterogeneous soil moisture subsidies resulting from redistributed snow. With decades of climate and precipitation data across elevational and precipitation gradients, the Reynolds Creek Experimental Watershed (RCEW) and critical zone observatory (CZO) in southwest Idaho provides a unique opportunity to study the relationship between vegetation types and redistributed snow. Within the RCEW, the total amount of precipitation has remained unchanged over the past 50 years, however the percentage of the precipitation falling as snow has declined by approximately 4% per decade at mid-elevation sites. As shifts in precipitation phase continue, future trends in vegetation composition and net primary productivity (NPP) of different plant functional types remains a critical question. We hypothesize that redistribution of snow may supplement drought sensitive species like aspen more so than drought tolerant species like mountain big sagebrush (Artemisia tridentata spp. vaseyana). To assess the importance of snowdrift subsidies on sagebrush steppe vegetation, NPP of aspen, shrub, and grass species was simulated at three sites using the biogeochemical process model BIOME-BGC. Each site is located directly downslope from snowdrifts providing soil moisture inputs to aspen stands and neighboring vegetation. Drifts vary in size with the largest containing up to four times the snow water equivalent (SWE) of a uniform precipitation layer. Precipitation inputs used by BIOME-BGC were modified to represent the redistribution of snow and simulations were run using daily climate data from 1985-2013. Simulated NPP of annual grasses at each site was not responsive to subsidies from drifting snow. However, at the driest site, aspen and shrub annual NPP was increased by as much as 44 and 30%, respectively, with the redistribution of snow. These results indicate that as snow water subsidies decrease, ecosystems may shift from tree and shrub dominated to grassland dominated. As climate change progresses, shifts in the precipitation regimes in semi-arid environments may lead to changes in species composition and carbon stores throughout the intermountain west.

Monitoring the spatio-temporal evolution of the snow cover in the eastern Alps from MODIS data

NASA Astrophysics Data System (ADS)

Cianfarra, P.; Salvini, F.; Valt, M.

2009-04-01

Estimating the snow cover extent in mountain ranges is important for a wide variety purposes including of scientific studies, environmental and meteo-climatic applications, as well as predicting water availability for energy resource and agriculture. Moreover, the monitoring of the spatio-temporal variation of the snow cover thickness, coupled with ground data from weather stations, allows to identify avalanche risk areas after heavy snowfall. The aim of this study is to test an automatic procedure to identify and map the snow coverage for different altitude interval in the eastern part of the Alpine range. There has been much progress since 1966 when the first operational snow mapping was done by NOAA with spaceborne sensors that provide daily, global observations to monitor the variability in space and time in the extent of snow cover. MODIS sensors offer increased improvements relative to the AVHRR that has been operational for many years on the NOAA Polar Operational Environmental Satellite System. In this context the MODIS provides observations at a nominal spatial resolution of 500 m versus the 1.1 km spatial resolution of the AVHRR and continuously available (spatially and temporally), spectral band observation that span the visible and short-wave infrared wavelengths, including those useful for recognize snow cover. The other advantage of using MODIS data is its availability and cost by the NASA's server. In this work we used MOD02 (L1B) data providing calibrated radiance values at the sensor (without atmospheric correction). Snow cover map production included the following steps: selection of the images with clear sky conditions, geometric correction and georeferencing to UTM zone 32 ,WSG 84 ellipsoid, to eliminate the distortion of and the typical bow-tie effect that produces the observed not alignment of the scan lines in the row image; spatial sub setting to produce an image covering an area of about 200 x 120 km; identification of the snow cover was done by computing the Normalised Difference Snow Index (NDSI) knowing that snow reflectance is higher in the visible (0.5-0.7 mm) wavelengths and has lower reflectance in the short wave infrared (1-4 mm) wavelengths. This allowed to separate snow from clouds and other non-snow-covered pixels. The NDSI for MODIS images is defined as the difference of reflectances observed in the visible band 4 (0.555 mm) and the short wave infrared band 6 (1.640 mm) divided by the sum of the two reflectances: NDSI=(B4 - B6)/ (B4 + B6) This approach allowed to reduce (yet not totally eliminate) the influence of the atmospheric effects and lighting conditions. A series of thresholds were tested to the ratio image to establish the best value for snow cover identification. Eventually, the snow cover extent was computed for 6 altitude intervals. Results from the different processed images were compared and statistically analysed. A complete set of ground truth of these preliminary results is still missing; yet we are confident that once the tuning of the processing will be completed, the automated processing of MODIS data will provide low cost, near real-time estimates of the snow cover distribution over the eastern Alps. This product would be a valuable tool for public administrations and authorities for environmental protection, control and risk management.

Monitoring Snow and Land Ice Using Satellite data in the GMES Project CryoLand

NASA Astrophysics Data System (ADS)

Bippus, Gabriele; Nagler, Thomas

2013-04-01

The main objectives of the project "CryoLand - GMES Service Snow and Land Ice" are to develop, implement and validate services for snow, glaciers and lake and river ice products as a Downstream Service within the Global Monitoring for Environment and Security (GMES) program of the European Commission. CryoLand exploits Earth Observation data from current optical and microwave sensors and of the upcoming GMES Sentinel satellite family. The project prepares also the basis for the cryospheric component of the GMES Land Monitoring services. The CryoLand project team consists of 10 partner organisations from Austria, Finland, Norway, Sweden, Switzerland and Romania and is funded by the 7th Framework Program of the European Commission. The CryoLand baseline products for snow include fractional snow extent from optical satellite data, the extent of melting snow from SAR data, and coarse resolution snow water equivalent maps from passive microwave data. Experimental products include maps of snow surface wetness and temperature. The products range from large scale coverage at medium resolution to regional products with high resolution, in order to address a wide user community. Medium resolution optical data (e.g. MODIS, in the near future Sentinel-3) and SAR (ENVISAT ASAR, in the near future Sentinel-1) are the main sources of EO data for generating large scale products in near real time. For generation of regional products high resolution satellite data are used. Glacier products are based on high resolution optical (e.g. SPOT-5, in the near future Sentinel-2) and SAR (TerraSAR-X, in the near future Sentinel-1) data and include glacier outlines, mapping of glacier facies, glacier lakes and ice velocity. The glacier products are generated on users demand. Current test areas are located in the Alps, Norway, Greenland and the Himalayan Mountains. The lake and river ice products include ice extent and its temporal changes and snow extent on ice. The algorithms for these products are in development. One major task of CryoLand is the performance assessment of the products, which is carried out in different environments, climate zones and land cover types, selected jointly with users. Accuracy assessment is done for test areas using in-situ data and very high resolution satellite data. This presentation gives an overview on the processing lines and demonstration products for snow, glacier and lake ice parameters including examples of the product accuracy assessment. An important point of the CryoLand project is the use of advanced information technology, which is applied to process and distribute snow and land ice products in near real time.

Snow fraction products evaluation with Landsat-8/OLI data and its spatial scale effects over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Jiang, L.

2016-12-01

Snow cover is one of important elements in the water supply of large populations, especially in those downstream from mountainous watershed. The cryosphere process in the Tibetan Plateau is paid much attention due to rapid change of snow amount and cover extent. Snow mapping from MODIS has been increased attention in the study of climate change and hydrology. But the lack of intensive validation of different snow mapping methods especially at Tibetan Plateau hinders its application. In this work, we examined three MODIS snow products, including standard MODIS fractional snow product (MOD10A1) (Kaufman et al., 2002; Salomonson & Appel, 2004, 2006), two other fractional snow product, MODSCAG (Painter et al., 2009) and MOD_MESMA (Shi, 2012). Both these two methods are based on spectral mixture analysis. The difference between MODISCAG and MOD_MESMA was the endmember selection. For MODSCAG product, snow spectral endmembers of varying grain size was obtained both from a radiative transfer model and spectra of vegetation, rock and soil collected in the field and laboratory. MOD_MESMA was obtained from automated endmember extraction method using linear spectral mixture analysis. Its endmembers are selected in each image to enhance the computational efficiency of MESMA (Multiple Endmember Spectral Analysis). Landsat-8 Operatinal Land Imager (OLI) data from 2013-2015 was used to evaluate the performance of these three snow fraction products in Tibetan Plateau. The effect of land cover types including forest, grass and bare soil was analyzed to evaluate three products. In addition, the effects of relatively flat surface in internal plateau and high mountain areas of Himalaya were also evaluated on the impact of these snow fraction products. From our comparison, MODSCAG and MOD10A1 overestimated snow cover, while MOD_MESMA underestimated snow cover. And RMSE of MOD_MESMA at each land cover type including forest, grass and mountain area decreased with the spatial resolution increasing from 500m, 1km, 2km to 5km. The RMSE of MODSCAG and MOD10A1 is very similar. In Himalaya area, these two RMSEs of MODSCAG and MOD10A1 increased with the spatial resolution increasing from 500m to 5km. For forest, grass and bare soil, RMSE decreased from 500m to 1km, then increased from 1km to 2km.

LANDSAT-D investigations in snow hydrology

NASA Technical Reports Server (NTRS)

Dozier, J.

1983-01-01

Progress on the registration of TM data to digital topographic data; on comparison of TM, MSS and NOAA meteorological satellite data for snowcover mapping; and on radiative transfer models for atmospheric correction is reported. Some methods for analyzing spatial contiguity of snow within the snow covered area were selected. The methods are based on a two-channel version of the grey level co-occurence matrix, combined with edge detection derived from an algorithm for computing slopes and exposures from digital terrain data.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Sheffield, C.; Russell, O.; Amato, R. V.

1973-01-01

The author has identified the following significant results. EarthSat has established an effective mail-based method for obtaining timely ground truth (snow depth) information over an extensive area. The method is both efficient and inexpensive compared with the cost of a similarly scaled direct field checking effort. Additional geological information has been acquired which is not shown in geological maps in the area. Excellent quality snow-free ERTS-1 transparencies of the test areas have been received and are being analyzed.

No evidence of widespread decline of snow cover on the Tibetan Plateau over 2000-2015.

PubMed

Wang, Xiaoyue; Wu, Chaoyang; Wang, Huanjiong; Gonsamo, Alemu; Liu, Zhengjia

2017-11-07

Understanding the changes in snow cover is essential for biological and hydrological processes in the Tibetan Plateau (TP) and its surrounding areas. However, the changes in snow cover phenology over the TP have not been well documented. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data, we reported daily cloud-free snow cover product over the Tibetan Plateau (TP) for 2000-2015. Snow cover start (SCS), melt (SCM) and duration (SCD) dates were calculated for each hydrological year, and their spatial and temporal variations were analyzed with elevation variations. Our results show no widespread decline in snow cover over the past fifteen years and the trends of snow cover phenology over the TP has high spatial heterogeneity. Later SCS, earlier SCM, and thus decreased SCD mainly occurred in the areas with elevation below 3500 m a.s.l., while regions in central and southwestern edges of the TP showed advanced SCS, delayed SCM and consequently longer SCD. The roles of temperature and precipitation on snow cover penology varied in different elevation zones, and the impact of both temperature and precipitation strengthened as elevation increases.

Microwave signatures of snow and fresh water ice

NASA Technical Reports Server (NTRS)

Schmugge, T.; Wilheit, T. T.; Gloersen, P.; Meier, M. F.; Frank, D.; Dirmhirn, I.

1973-01-01

During March of 1971, the NASA Convair 990 Airborne Observatory carrying microwave radiometers in the wavelength range 0.8 to 21 cm was flown over dry snow with different substrata: Lake ice at Bear Lake in Utah; wet soil in the Yampa River Valley near Steamboat Springs, Colorado; and glacier ice, firm and wet snow on the South Cascade Glacier in Washington. The data presented indicate that the transparency of the snow cover is a function of wavelength. False-color images of microwave brightness temperatures obtained from a scanning radiometer operating at a wavelength of 1.55 cm demonstrate the capability of scanning radiometers for mapping snowfields.

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

NASA Technical Reports Server (NTRS)

Meier, M. F. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The percentage of snow cover area on specific drainage basins was measured from ERTS-1 imagery by video density slicing with a repeatability of 4 percent of the snow covered area. Data from ERTS-1 images of the melt season snow cover in the Thunder Creek drainage basin in the North Cascades were combined with existing hydrologic and meteorologic observations to enable calculations of the time distribution of the water stored in this mountain snowpack. Similar data could be used for frequent updating of expected inflow to reservoirs. Equivalent snowline altitudes were determined from area measurements. Snowline altitudes were also determined by combining enlarged ERTS-1 images with maps. ERTS-1 imagery was also successfully used to measure glacier accumulation area ratios for a small test basin.

Mapping snow depth in complex alpine terrain with close range aerial imagery - estimating the spatial uncertainties of repeat autonomous aerial surveys over an active rock glacier

NASA Astrophysics Data System (ADS)

Goetz, Jason; Marcer, Marco; Bodin, Xavier; Brenning, Alexander

2017-04-01

Snow depth mapping in open areas using close range aerial imagery is just one of the many cases where developments in structure-from-motion and multi-view-stereo (SfM-MVS) 3D reconstruction techniques have been applied for geosciences - and with good reason. Our ability to increase the spatial resolution and frequency of observations may allow us to improve our understanding of how snow depth distribution varies through space and time. However, to ensure accurate snow depth observations from close range sensing we must adequately characterize the uncertainty related to our measurement techniques. In this study, we explore the spatial uncertainties of snow elevation models for estimation of snow depth in a complex alpine terrain from close range aerial imagery. We accomplish this by conducting repeat autonomous aerial surveys over a snow-covered active-rock glacier located in the French Alps. The imagery obtained from each flight of an unmanned aerial vehicle (UAV) is used to create an individual digital elevation model (DEM) of the snow surface. As result, we obtain multiple DEMs of the snow surface for the same site. These DEMs are obtained from processing the imagery with the photogrammetry software Agisoft Photoscan. The elevation models are also georeferenced within Photoscan using the geotagged imagery from an onboard GNSS in combination with ground targets placed around the rock glacier, which have been surveyed with highly accurate RTK-GNSS equipment. The random error associated with multi-temporal DEMs of the snow surface is estimated from the repeat aerial survey data. The multiple flights are designed to follow the same flight path and altitude above the ground to simulate the optimal conditions of repeat survey of the site, and thus try to estimate the maximum precision associated with our snow-elevation measurement technique. The bias of the DEMs is assessed with RTK-GNSS survey observations of the snow surface elevation of the area on and surrounding the rock glacier. Additionally, one of the challenges with processing snow cover imagery with SfM-MVS is dealing with the general homogeneity of the surface, which makes is difficult for automated-feature detection algorithms to identify key features for point matching. This challenge depends on the snow cover surface conditions, such as scale, lighting conditions (high vs. low contrast), and availability of snow-free features within a scene, among others. We attempt to explore this aspect by spatial modelling the factors influencing the precision and bias of the DEMs from image, flight, and terrain attributes.

Rapid heat-flowing surveying of geothermal areas, utilizing individual snowfalls as calorimeters

USGS Publications Warehouse

White, Donald E.

1969-01-01

Local differences in rate of heat transfer in vapor and by conduction through the ground in hot spring areas are difficult and time-consuming to measure quantitatively. Individual heavy snowfalls provide a rapid low-cost means of measuring total heat flow from such ground. After a favorable snowfall (heavy, brief duration, little wind, air temperature near 0°C), contacts between snow-covered and snow-free ground are mapped on a suitable base. Each mapped contact, as time elapses after a specific snowfall, is a heat-flow contour representing a decreasing rate of flow. Calibration of each mapped contact or snow line is made possible by the fact that snow remains on insulated surfaces (such as the boardwalks of Yellowstone's thermal areas) long after it has melted on adjacent warm ground. Heat-flow contours mapped to date range from 450 to 5500 μcal/cm2 sec, or 300 to 3700 times the world average of conductive heat flow. The very high rates of heat flow (2000 to > 10,000 μcal/cm2 sec) are probably too high, and the lower heat flows determinable by the method (2 sec) may be too low. Values indicated by the method are, however, probably within a factor of 2 of the total conductive and convective heat flow. Thermal anomalies from infrared imagery are similar in shape to heat-flow contours of a test area near Old Faithful geyser. Snowfall calorimetry provides a rapid means for evaluating the imagery and computer-derived products of the infrared data in terms of heat flow.

Time-Lapse Micro-Tomography Measurements and Determination of Effective Transport Properties of Snow Metamorphism Under Advective Conditions

NASA Astrophysics Data System (ADS)

Ebner, P. P.; Grimm, S.; Steen-Larsen, H. C.; Schneebeli, M.; Steinfeld, A.

2014-12-01

The metamorphism of snow under advective air flow, with and without temperature gradient, was never experimentally investigated. We developed a new sample holder where metamorphism under advective conditions can be observed and measured using time-lapse micro-tomography [1]. Long-term experiments were performed and direct pore-level simulation (DPLS) [2,3] was directly applied on the extracted 3D digital geometry of the snow to calculate the effective transport properties by solving the governing fluid flow equations. The results showed no effect of isothermal advection, compared to rates typical for isothermal metamorphism. Appling a temperature gradient, the results showed increased snow metamorphism compared to rates typical for temperature gradient metamorphism. However, for both cases a change in the isotopic composition in the air as well as in the snow sample could be observed. These measurements could be influential to better understand snow-air exchange processes relevant for atmospheric chemistry and isotopic composition. REFERENCES[1] Ebner P. P., Grimm S., Schneebeli M., and Steinfeld A.: An instrumented sample holder for time-lapse micro-tomography measurements of snow under advective airflow. Geoscientific Instrumentation, Methods and Data Systems 4(2014), 353-373. [2] Zermatten E., Haussener S., Schneebeli M., and Steinfeld A.: Tomography-based determination of permeability and Dupuit-Forchheimer coefficient of characteristic snow samples. Journal of Glaciology 57(2011), 811-816. [3] Zermatten E., Schneebeli M., Arakawa H., and Steinfeld A.: Tomography-based determination of porosity, specific area and permeability of snow and comparison with measurements. Cold Regions Science and Technology 97 (2014), 33-40. Fig. 1: 3-D surface rendering of a refrozen wet snow sample with fluid flow streamline.

A landsat data tiling and compositing approach optimized for change detection in the conterminous United States

USGS Publications Warehouse

Nelson, Kurtis; Steinwand, Daniel R.

2015-01-01

Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels was defined for CONUS and adjusted to consolidate smaller tiles along national borders, resulting in 98 non-overlapping tiles. Data from Landsat-5,-7, and -8 were re-projected to the tile extents, masked to remove clouds, shadows, water, and snow/ice, then composited using a cosine similarity approach. The resultant images were used in a change detection algorithm to determine areas of vegetation change. This approach enabled more efficient processing compared to using single Landsat scenes, by taking advantage of overlap between adjacent paths, and allowed an automated system to be developed for the entire process.

Pilot Tests of Satellite Snowcover/Runoff Forecasting Systems. [Arizona, Sierra Nevada Mountains (Ca), Colorado, Rocky Mountains (North America)

NASA Technical Reports Server (NTRS)

Rango, A.

1978-01-01

Major snow zones of the western U.S. were selected to test the capability of satellite systems for mapping snowcover in various snow, cloud, climatic, and vegetation regimes. Different satellite snowcover analysis methods used in each area are described along with results.

Development of remote sensing technology in New Zealand, part 1. Seismotectonic, structural, volcanologic and geomorphic study of New Zealand, part 2. Indigenous forest assessment, part 3. Mapping land use and environmental studies in New Zealand, part 4. New Zealand forest service LANDSAT projects, part 5. Vegetation map and landform map of Aupouri Peninsula, Northland, part 6. Geographical applications of LANDSAT mapping, part 7

NASA Technical Reports Server (NTRS)

Probine, M. C.; Suggate, R. P.; Mcgreevy, M. G.; Stirling, I. F. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Inspection of pixels obtained from LANDSAT of New Zealand revealed that not only can ships and their wakes be detected, but that information on the size, state of motion, and direction of movement was inferred by calculating the total number of pixels occupied by the vessel and wake, the orientation of these pixels, and the sum of their radiance values above the background level. Computer enhanced images showing the Waimihia State Forest and much of Kaingaroa State Forest on 22 December 1975 were examined. Most major forest categories were distinguished on LANDSAT imagery. However, the LANDSAT imagery seemed to be most useful for updating and checking existing forest maps, rather than making new maps with many forest categories. Snow studies were performed using two basins: Six Mile Creek and Mt. Robert. The differences in radiance levels indicated that a greater areal snow cover in Six Mile Creek Basin with the effect of lower radiance values from vegetation/snow regions. A comparison of the two visible bands (MSS 4 and 5) demonstrate this difference for the two basins.

Snow Dunes: A Controlling Factor of Melt Pond Distribution on Arctic Sea Ice

NASA Technical Reports Server (NTRS)

Petrich, Chris; Eicken, Hajo; Polashenski, Christopher M.; Sturm, Matthew; Harbeck, Jeremy P.; Perovich, Donald K.; Finnegan, David C.

2012-01-01

The location of snow dunes over the course of the ice-growth season 2007/08 was mapped on level landfast first-year sea ice near Barrow, Alaska. Landfast ice formed in mid-December and exhibited essentially homogeneous snow depths of 4-6 cm in mid-January; by early February distinct snow dunes were observed. Despite additional snowfall and wind redistribution throughout the season, the location of the dunes was fixed by March, and these locations were highly correlated with the distribution of meltwater ponds at the beginning of June. Our observations, including ground-based light detection and ranging system (lidar) measurements, show that melt ponds initially form in the interstices between snow dunes, and that the outline of the melt ponds is controlled by snow depth contours. The resulting preferential surface ablation of ponded ice creates the surface topography that later determines the melt pond evolution.

Preliminary Evaluation of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; Kelly, Richard E. J.; Chien, Janet Y. L.; Montesano, Paul M.

2009-01-01

The Air Force Weather Agency (AFWA) - NASA (ANSA) blended-snow product utilizes EOS standard snow products from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE. with SWE values calculated from snow depths reported at approx.1500 National Climatic Data Center (NCDC) coop stations in the Lower Great Lakes basin. Our preliminary results show that conversion of snow depth to SWE is very sensitive to the choice of snow density (we used either 0.2 or 03 as conversion factors). We found overall better agreement between the ANSA-derived SWE and the co-op station data when we use a snow density of 0.3 to convert the snow depths to SWE. In addition, we show that the ANSA underestimates SWE in densely-forested areas, using January and February 2008 ANSA and co-op data. Furthermore, apparent large SWE changes from one day to the next may be caused by thaw-re-freeze events, and do not always represent a real change in SWE. In the near future we will continue the analysis in the 2006-07 and 2007-08 snow seasons.

Snow cover correlation between Mt. Villarrica and Mt. Lliama in Chile

NASA Astrophysics Data System (ADS)

Kim, Jeong-Cheol; Park, Sung-Hwan; Jung, Hyung-Sup

2014-11-01

The Southern Volcanic Zone (SVZ) of Chile consists of many volcanoes, and all of the volcanoes are covered with snow at the top of mountain. Monitoring snow cover variations in these regions can give us a key parameter in order to understand the mechanisms of volcanic activity. In this study, we investigate on the volcanic activity and snow cover interaction from snow cover area mapping, snow-line extraction. The study areas cover Mt. Villarrica and Mt. Llaima, Chile. Both of them are most active volcanos in SVZ. Sixty Landsat TM and Landsat ETM+ images are used for observing snow cover variations of Mt. Villarrica and Mt. Llaima, spanning the 25 years from September 1986 to February 2011. Results show that snow cover area between volcanic activity and non-activity are largely changed from 42.84 km2 to 13.41 km2, temporarily decreased 79% at the Mt. Villarrica and from 28.98 km2 to 3.82 km2, temporarily decreased 87% at the Mt. Villarrica. The snow line elevation of snow cover retreated by approximately 260 m from 1,606m to 1,871 m at the Mt. Villarrica, approximately 266m from 1,741m to 2,007m at the Mt. Llaima. The results show that there are definitely correlations between snow cover and volcanic activity.

Measuring snow cover using satellite imagery during 1973 and 1974 melt season: North Santiam, Boise, and Upper Snake Basins, phase 1. [LANDSAT satellites, imaging techniques

NASA Technical Reports Server (NTRS)

Wiegman, E. J.; Evans, W. E.; Hadfield, R.

1975-01-01

Measurements are examined of snow coverage during the snow-melt season in 1973 and 1974 from LANDSAT imagery for the three Columbia River Subbasins. Satellite derived snow cover inventories for the three test basins were obtained as an alternative to inventories performed with the current operational practice of using small aircraft flights over selected snow fields. The accuracy and precision versus cost for several different interactive image analysis procedures was investigated using a display device, the Electronic Satellite Image Analysis Console. Single-band radiance thresholding was the principal technique employed in the snow detection, although this technique was supplemented by an editing procedure involving reference to hand-generated elevation contours. For each data and view measured, a binary thematic map or "mask" depicting the snow cover was generated by a combination of objective and subjective procedures. Photographs of data analysis equipment (displays) are shown.

Albedo Drop on the Greenland Ice Sheet: Relative Impacts of Wet and Dry Snow Processes

NASA Astrophysics Data System (ADS)

Chen, J.; Polashenski, C.

2014-12-01

The energy balance of the Greenland Ice Sheet (GIS) is strongly impacted by changes in snow albedo. MODIS (Moderate Resolution Imaging Spectroradiometer) observations indicate that the GIS albedo has dropped since the early part of this century. We analyze data from the MODIS products MOD10A1 for broadband snow albedo and MOD09A1 for surface spectral reflectance since 2001 to better explain the physical mechanisms driving these changes. The MODIS products are filtered, and the data is masked using microwave-derived surface melt maps to isolate albedo changes due to dry snow processes from those driven by melt impacts. Results show that the majority of recent changes in the GIS albedo - even at high elevations - are driven by snow wetting rather than dry snow processes such as grain metamorphosis and aerosol impurity deposition. The spectral signature of the smaller changes occurring within dry snow areas suggests that grain metamorphosis dominates the albedo decline in these regions.

Remote sensing: Snow monitoring tool for today and tomorrow

NASA Technical Reports Server (NTRS)

Rango, A.

1977-01-01

Various types of remote sensing are now available or will be in the future for snowpack monitoring. Aircraft reconnaissance is now used in a conventional manner by various water resources agencies to obtain information on snowlines, depth, and melting of the snowpack for forecasting purposes. The use of earth resources satellites for mapping snowcovered area, snowlines, and changes in snowcover during the spring has increased during the last five years. Gamma ray aircraft flights, although confined to an extremely low altitude, provide a means for obtaining valuable information on snow water equivalent. The most recently developed remote sensing technology for snow, namely, microwave monitoring, has provided initial results that may eventually allow us to infer snow water equivalent or depth, snow wetness, and the hydrologic condition of the underlying soil.

Response of Alpine Grassland Vegetation Phenology to Snow Accumulation and Melt in Namco Basin

NASA Astrophysics Data System (ADS)

Chen, S.; Cui, X.; Liang, T.

2018-04-01

Snow/ice accumulation and melt, as a vital part of hydrological processes, is close related with vegetation activities. Taking Namco basin for example, based on multisource remote sensing data and the ground observation data of temperature and precipitation, phenological information was extracted by S-G filtering and dynamic threshold method. Daily snow cover fraction was calculated with daily cloud-free snow cover maps. Evolution characteristics of grassland vegetation greening, growth length and daily snow cover fraction and their relationship were analyzed from 2001 to 2013. The results showed that most of grassland vegetation had advanced greening and prolong growth length trend in Namco basin. There were negative correlations between snow cover fraction and vegetation greening or growth length. The response of vegetation phenology to snow cover fraction is more sensitive than that to temperature in spring. Meanwhile, vegetation growth condition turned worse with advanced greening and prolong growth length. To a certain extent, our research reveals the relationship between grassland vegetation growth cycle and snow in alpine ecosystem. It has provided reference to research the response mechanism of alpine grassland ecosystem to climate changes.

Relationship between the trajectory of mid-latitude cyclones in the eastern Pacific Ocean and the isotopic composition of snowfall in the Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Vasquez, K. T.; Sickman, J. O.; Heard, A.; Lucero, D.

2013-12-01

Diatoms, preserved in lake sediments, provide a potential archive of snowfall variability in the Sierra Nevada through their sensitivity to changes in water chemistry (a proxy for runoff volume) and by recording the isotopic composition of snow-melt (potentially a proxy for sources of atmospheric moisture). In the Sierra Nevada, we hypothesize that the oxygen isotopic composition of diatom silica is principally controlled by snow and that the isotopic composition of snow varies as a function of the tracks of mid-latitude cyclonic storms in the eastern Pacific Ocean. Snow samples from discrete storms were collected from December 2012 to March 2013 at 2042 meters a.s.l. in Sequoia National Park. The δ18O and δ2H values of the snow samples were measured using a temperature-conversion elemental analyzer coupled to a Delta V isotope ratio mass spectrometer. The isotopic measurements were then coupled to 3, 5 and 7-day air mass back trajectories using the NOAA HYSPLIT model. The measured δ18O values ranged from -17.6 to -7.8 per mil and the δ2H ranged from -119.8 to -73.3 per mil. Both δ18O and δ2H were inversely related to the latitude of the storm origin (R^2 values of 0.67 and 0.57, respectively). Winter storms from the Gulf of Alaska were the most isotopically depleted while storms originating in the subtropical/tropical Pacific were the most isotopically enriched, reflecting the overall latitudinal pattern of ocean-water isotope composition in the Pacific Ocean. Our results suggest that the isotopic composition of Sierra Nevada snowfall is influenced by storm track trajectory and this relationship could be useful in interpreting the climatic significance of δ18O of diatom silica preserved in lake cores.

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Horn, J.; Painter, T. H.; Bormann, K. J.; Rittger, K.; Brodzik, M. J.; Skiles, M.; Burgess, A. B.; Mattmann, C. A.; Ramirez, P.; Joyce, M.; Goodale, C. E.; McGibbney, L. J.; Zimdars, P.; Yaghoobi, R.

2017-12-01

The Snow Data System at NASA JPL includes data processing pipelines built with open source software, Apache 'Object Oriented Data Technology' (OODT). Processing is carried out in parallel across a high-powered computing cluster. The pipelines use input data from satellites such as MODIS, VIIRS and Landsat. They apply algorithms to the input data to produce a variety of outputs in GeoTIFF format. These outputs include daily data for SCAG (Snow Cover And Grain size) and DRFS (Dust Radiative Forcing in Snow), along with 8-day composites and MODICE annual minimum snow and ice calculations. This poster will describe the Snow Data System, its outputs and their uses and applications. It will also highlight recent advancements to the system and plans for the future.

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Joyce, M.; Laidlaw, R.; Painter, T. H.; Bormann, K. J.; Rittger, K.; Brodzik, M. J.; Skiles, M.; Burgess, A. B.; Mattmann, C. A.; Ramirez, P.; Goodale, C. E.; McGibbney, L. J.; Zimdars, P.; Yaghoobi, R.

2016-12-01

The Snow Data System at NASA JPL includes data processing pipelines built with open source software, Apache 'Object Oriented Data Technology' (OODT). Processing is carried out in parallel across a high-powered computing cluster. The pipelines use input data from satellites such as MODIS, VIIRS and Landsat. They apply algorithms to the input data to produce a variety of outputs in GeoTIFF format. These outputs include daily data for SCAG (Snow Cover And Grain size) and DRFS (Dust Radiative Forcing in Snow), along with 8-day composites and MODICE annual minimum snow and ice calculations. This poster will describe the Snow Data System, its outputs and their uses and applications. It will also highlight recent advancements to the system and plans for the future.

Time Lapse Camera Images for Observation and Visualization of Snow Lines on two Benchmark Glaciers in High Asia - Zhadang and Halji

NASA Astrophysics Data System (ADS)

Schröter, B.; Buchroithner, M. F.; Pieczonka, T.

2015-12-01

Glaciers are characteristic elements of high mountain environments and represent key indicators for the ongoing climate change. The covering snowpack considerably affects the glacier-ice surface temperature and thus the meltdown of the glaciers which in recent decades has been accelerating worldwide. Therefore, the detailed investigation of the transient snow is of high importance. Zhadang Glacier is located in the Nyainqentanglha Mountain Range on the central part of the Tibetan Plateau (30°28.24' N, 90°38.69' E). The glacier is debris-free and flows from 6,090 to 5,515 m a.s.l. Recent measurements have shown that the whole glacier is below the ELA and experiences significant glacier volume loss. In May 2010 two terrestrial cameras had been installed there and were continuously operating until September 2012 generating 6,225 images of the glacier with a frequency of three resp. six images per day. In order to use this dataset for snow line mapping all images had to be georeferenced and orthorectified. The biggest challenge was the problem of shifting camera positions due to deformations of the ground and hence the offset in the image coordinates. This was resolved by combining the manual orthorectification of one image per week with a subsequent spline interpolation to determine the changed image coordinates. The actual orthorectification was finally realized by applying a fully automated batch processing of all images. The most favorable image of each day was chosen for the manual snow line mapping process. The final aim was the calculation of the mean elevation of the snow line for every day of the data collecting period materialized by intersecting the mapped snow lines with resampled SRTM 3 data. Considering the fact that there were several weeks either with full snow cover or without any snow this aim could be achieved. The findings have been used for the evaluation of a glacier mass balance model developed at RWTH Aachen, Germany, showing a high level of congruence. Another result is the proof of intense ablation due to snow drift and sublimation during the winter months. In 2014 a similar camera system was installed near Halji Glacier in northwestern Nepal on the southern edge of the Tibetan Plateau (30°15.80' N, 81°28.16' E; 5,730 - 5,270 m a.s.l.; ELA = 5,660 m a.s.l.). The images are currently being processed.

MODIS Snow and Ice Products from the NSIDC DAAC

NASA Technical Reports Server (NTRS)

Scharfen, Greg R.; Hall, Dorothy K.; Riggs, George A.

1997-01-01

The National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) provides data and information on snow and ice processes, especially pertaining to interactions among snow, ice, atmosphere and ocean, in support of research on global change detection and model validation, and provides general data and information services to cryospheric and polar processes research community. The NSIDC DAAC is an integral part of the multi-agency-funded support for snow and ice data management services at NSIDC. The Moderate Resolution Imaging Spectroradiometer (MODIS) will be flown on the first Earth Observation System (EOS) platform (AM-1) in 1998. The MODIS Instrument Science Team is developing geophysical products from data collected by the MODIS instrument, including snow and ice products which will be archived and distributed by NSIDC DAAC. The MODIS snow and ice mapping algorithms will generate global snow, lake ice, and sea ice cover products on a daily basis. These products will augment the existing record of satellite-derived snow cover and sea ice products that began about 30 years ago. The characteristics of these products, their utility, and comparisons to other data set are discussed. Current developments and issues are summarized.

Dynamics of Phase Transitions in a Snow Mass Containing Water-Soluble Salt Particles

NASA Astrophysics Data System (ADS)

Zelenko, V. L.; Heifets, L. I.; Orlov, Yu. N.; Voskresenskiy, N. M.

2018-07-01

A macrokinetic approach is used to describe the dynamics of phase transitions in a snow mass containing water-soluble salt particles. Equations are derived that describe the rate of salt granule dissolution and the change in the phase composition and temperature of a snow mass under the conditions of heat transfer with an isothermal surface. An experimental setup that models the change in the state of a snow mass placed on an isothermal surface is created to verify theoretical conclusions. Experimental observations of the change in temperature of the snow mass are compared to theoretical calculations. The mathematical model that is developed can be used to predict the state of a snow mass on roads treated with a deicing agent, or to analyze the state of snow masses containing water-soluble salt inclusions and resting on mountain slopes.

Element composition of insoluble fraction of aerosols in snow in the vicinity of oil chemistry refinery (Pavlodar City, Kazakhstan) and petrochemical plant (Tomsk City, Russia)

NASA Astrophysics Data System (ADS)

Talovskaya, Anna V.; Filimonenko, Ekaterina A.; Yazikov, Egor G.; Shakhova, Tatyana S.; Parygina, Irina A.

2015-11-01

Tomsk petrochemical plant (Russia) and Pavlodar oil chemistry refinery (Kazakhstan) are the sources of air contamination in Tomsk and Pavlodar respectively. Therefore, it is very important to study the level of air contamination with particulate matter as well as ultimate composition of these particles. Disposable solid particles fall out to the snow cover, so snow is an accumulator of the particles. The article deals with the study results of dust load and concentrations of Br, Sb, La, Ce, Sm and Nd in insoluble fraction of aerosols in snow in the vicinity of Pavlodar oil chemistry refinery and Tomsk petrochemical plant. The instrumental neutron activation analysis was used for the ultimate composition detection. Results were shown that the dust load in the vicinity of Tomsk petrochemical plant is higher than in Pavlodar. We have detected high concentrations of La, Br and Sm in insoluble fraction of aerosols in snow in the vicinity of Pavlodar refinery and high concentrations of Sb and Ce in Tomsk. Moreover, we have detected high Br concentration in insoluble fraction of aerosols in snow of the vicinity of both plants. Gas burning on the flares of these enterprises is likely a potential source of Br. La to light lanthanoids ratio have shown La is of anthropogenic origin. In addition, enrichment factor estimation reflects an anthropogenic origin of La, Sm, Br, Ce and Sb as well. These elements might be emitted from different production facilities of the plants.

A Look at Seasonal Snow Cover and Snow Mass in the Southern Hemisphere from 1979-2006 Using SMMR and SSM/I Passive Microwave Data

NASA Technical Reports Server (NTRS)

Foster, James

2009-01-01

Seasonal snow cover in extra-tropical areas of South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and from the Special Sensor Microwave Imagers (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow mass were estimated for the months of May-September. Most of the seasonal snow in South America occurs in the Patagonia region of Argentina. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 sq km. The seasonal (May-September) 2 average snow cover extent was greatest in 1984 (464,250 sq km) and least in 1990 (69,875 sq km). In terms of snow mass, 1984 was also the biggest year (1.19 x 10(exp 13) kg) and 1990 was the smallest year (0.12 X 10(exp 13) kg). A strong relationship exists between the snow cover area and snow mass, correlated at 0.95, though no significant trend was found over the 28 year record for either snow cover extent or snow mass. For this long term climatology, snow mass and snow cover extent are shown to vary considerably from month to month and season to season. This analysis presents a consistent approach to mapping and measuring snow in South America utilizing an appropriate and readily available long term snow satellite dataset. This is the optimal dataset available, thus far, for deriving seasonal snow cover and snow mass in this region. Nonetheless, shallow snow, wet snow, snow beneath forests, as well as snow along coastal areas all may confound interpretation using passive microwave approaches. More work needs to be done to reduce the uncertainties in the data and hence, increase the confidence of the interpretation

The first Earth Resources Technology Satellite - Nearly two years of operation

NASA Technical Reports Server (NTRS)

Nordberg, W.

1974-01-01

A brief status report is given of the ERTS-1 satellite system as of June, 1974, and some applications of the ERTS-1 images are discussed. The multispectral images make it possible to identify or measure the quality and composition of water, the potential water content of snow, the moisture and possible composition of soils, the types and state of vegetation cover, and factors relating to stresses on the environment. The orthographic view of the earth provided by the satellite makes it possible to rapidly produce thematic maps, on a scale of 1:250,000, of most areas of the world. The regular, repetitive coverage provided by ERTS-1 every 18 days is important in areas such as water-supply and flood-damage studies. The use of ERTS-1 imagery for land-use planning, wetlands surveying, assessing marine resources, and observing processes such as desertification in the African Sahel is discussed.

Bacterial Composition and Survival on Sahara Dust Particles Transported to the European Alps

PubMed Central

Meola, Marco; Lazzaro, Anna; Zeyer, Josef

2015-01-01

Deposition of Sahara dust (SD) particles is a frequent phenomenon in Europe, but little is known about the viability and composition of the bacterial community transported with SD. The goal of this study was to characterize SD-associated bacteria transported to the European Alps, deposited and entrapped in snow. During two distinct events in February and May 2014, SD particles were deposited and promptly covered by falling snow, thus preserving them in distinct ochre layers within the snowpack. In June 2014, we collected samples at different depths from a snow profile at the Jungfraujoch (Swiss Alps; 3621 m a.s.l.). After filtration, we performed various microbiological and physicochemical analyses of the snow and dust particles therein that originated in Algeria. Our results show that bacteria survive and are metabolically active after the transport to the European Alps. Using high throughput sequencing, we observed distinct differences in bacterial community composition and structure in SD-layers as compared to clean snow layers. Sporulating bacteria were not enriched in the SD-layers; however, phyla with low abundance such as Gemmatimonadetes and Deinococcus-Thermus appeared to be specific bio-indicators for SD. Since many members of these phyla are known to be adapted to arid oligotrophic environments and UV radiation, they are well suited to survive the harsh conditions of long-range airborne transport. PMID:26733988

Operational satellites and the global monitoring of snow and ice

NASA Technical Reports Server (NTRS)

Walsh, John E.

1991-01-01

The altitudinal dependence of the global warming projected by global climate models is at least partially attributable to the albedo-temperature feedback involving snow and ice, which must be regarded as key variables in the monitoring for global change. Statistical analyses of data from IR and microwave sensors monitoring the areal coverage and extent of sea ice have led to mixed conclusions about recent trends of hemisphere sea ice coverage. Seasonal snow cover has been mapped for over 20 years by NOAA/NESDIS on the basis of imagery from a variety of satellite sensors. Multichannel passive microwave data show some promise for the routine monitoring of snow depth over unforested land areas.

U.S. NIC

Science.gov Websites

Graphs) IMS Ice Extent Data. IMS Ice Extent for sea ice only. Total Ice Sea Ice Only View chart (2200 x Hemisphere Automated Snow and Ice Mapping NOHRSC Satellite Products NCEP MMAB Sea Ice CPC Northern Hemisphere National Snow and Ice Data Center (NSIDC) ** Multisensor Analyzed Sea Ice Extent (NSIDC) ** The NRCS NWCC

Wind and ecosystem response at the GLEES

Treesearch

Robert C. Musselman; Gene L. Wooldridge; William J. Massman; Richard A. Sommerfeld

1995-01-01

Research was conducted to determine wind patterns and snow deposition at a high elevation alpine/subalpine ecotone site using deformation response of trees to prevailing winds. The research has provided detailed maps of wind speed, wind direction, and snow depth as determined from tree deformation. The effects of prevailing wind on tree blowdown at the site have also...

Evaluating MODIS snow products for modelling snowmelt runoff: case study of the Rio Grande headwaters

USDA-ARS?s Scientific Manuscript database

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM). Landsat Thematic Mapper (TM) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and ...

Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

NASA Technical Reports Server (NTRS)

Toure, Ally M.; Rodell, Matthew; Yang, Zong-Liang; Beaudoing, Hiroko; Kim, Edward; Zhang, Yongfei; Kwon, Yonghwan

2015-01-01

This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30 deg N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of -1.54 x 10(exp 2) sq km. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow-no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044m (0.19 m) and -0.010m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.

Proceedings of the Annual Meeting of the Eastern Snow Conference (47th) Held in Bangor, Maine on 7-8 June 1990

DTIC Science & Technology

1990-06-01

western U.S. Ten additional windows contain basin boundary data sets to map snow cover for the Upper Midwest , the Great Lakes, New England, and... Midwest and in the East. The User’s Guide gives the data format. 4.3.2 Satellite Areal Extent of Snow Cover by Basin/Zone (MCI/AFOS-ASCII) Alphanumeric...acres) of lake shore land in the snowbelt and this perennial vine is susceptible to winter cold damage. County extension agents indicated that winter

Modeling of multi-phase interactions of reactive nitrogen between snow and air in Antarctica

NASA Astrophysics Data System (ADS)

McCrystall, M.; Chan, H. G. V.; Frey, M. M.; King, M. D.

2016-12-01

In polar and snow-covered regions, the snowpack is an important link between atmospheric, terrestrial and oceanic systems. Trace gases, including nitrogen oxides, produced via photochemical reactions in snow are partially released to the lower atmosphere with considerable impact on its composition. However, the post-depositional processes that change the chemical composition and physical properties of the snowpack are still poorly understood. Most current snow chemistry models oversimplify as they assume air-liquid interactions and aqueous phase chemistry taking place at the interface between the snow grain and air. Here, we develop a novel temperature dependent multi-phase (gas-liquid-ice) physical exchange model for reactive nitrogen. The model is validated with existing year-round observations of nitrate in the top 0.5-2 cm of snow and the overlying atmosphere at two very different Antarctic locations: Dome C on the East Antarctic Plateau with very low annual mean temperature (-54ºC) and accumulation rate (<30 kg m-2 yr-1); and Halley, a coastal site with at times at or above freezing temperatures during summer, high accumulation rate and high background level of sea salt aerosol. We find that below the eutectic temperature of the H2O/dominant ion mixture the surface snow nitrate is controlled by kinetic adsorption onto the surface of snow grains followed by grain diffusion. Above the eutectic temperature, in addition to the former two processes, thermodynamic equilibrium of HNO3 between interstitial air and liquid water pockets, possibly present at triple junctions or grooves at grain boundaries, greatly enhances the nitrate uptake by snow in agreement with the concentration peak observed in summer.

The Preservation and Recycling of Snow Pack Nitrate at the West Antarctic Ice Sheet (WAIS) Divide Ice Core Site from the Present Day to the Last Glacial Period.

NASA Astrophysics Data System (ADS)

Robinson, J. W.; Buffen, A.; Hastings, M. G.; Schauer, A. J.; Moore, L.; Isaacs, A.; Geng, L.; Savarino, J. P.; Alexander, B.

2017-12-01

We use observations of the nitrogen isotopic composition of nitrate (δ15N(NO3-)) from snow and ice collected at the West Antarctic ice sheet (WAIS) divide ice core site to quantify the preservation and recycling of snow nitrate. Ice-core samples cover a continuous section from 36 to 52 thousand years ago and discrete samples from the Holocene, the last glacial maximum (LGM), and the glacial-Holocene transition. Higher δ15N of nitrate is consistently associated with lower temperatures with δ15N(NO3-) varying from 26 to 45 ‰ during the last glacial period and from 1 to 45 ‰ between the Holocene and glacial periods, respectively. We attribute the higher δ15N in colder periods to lower snow accumulation rates which lead to greater loss of snow nitrate via photolysis before burial beneath the snow photic zone. Modeling of nitrate preservation in snow pack was performed for modern and LGM conditions. The model is used in conjunction with observations to estimate the fraction of snow nitrate that is photolyzed, re-oxidized, and re-deposited over WAIS divide versus the fraction of primary nitrate that is deposited via long range transport. We used these estimates of fractional loss of snow nitrate in different time periods to determine the variation in the deposition flux of primary nitrate at WAIS divide with climate. Our findings have implications for the climate sensitivity of the oxidizing capacity of the polar atmosphere and the interpretation of ice-core records of nitrate in terms of past atmospheric composition.

Distinguishing summertime atmospheric production of nitrate across the East Antarctic Ice Sheet

NASA Astrophysics Data System (ADS)

Shi, G.; Buffen, A. M.; Ma, H.; Hu, Z.; Sun, B.; Li, C.; Yu, J.; Ma, T.; An, C.; Jiang, S.; Li, Y.; Hastings, M. G.

2018-06-01

Surface snow and atmospheric samples collected along a traverse from the coast to the ice sheet summit (Dome A) are used to investigate summertime atmospheric production of nitrate (NO3-) across East Antarctica. The strong relationship observed between δ15N and δ18O of nitrate in the surface snow suggests a large (lesser) extent of nitrate photolysis in the interior (coastal) region. A linear correlation between the oxygen isotopes of nitrate (δ18O and Δ17O) indicates mixing of various oxidants that react with NOx (NOx = NO + NO2) to produce atmospheric nitrate. On the plateau, the isotopes of snow nitrate are best explained by local reoxidation chemistry of NOx, possibly occurring in both condensed and gas phases. Nitrate photolysis results in redistribution of snow nitrate, and the plateau snow is a net exporter of nitrate and its precursors. Our results suggest that while snow-sourced NOx from the plateau due to photolysis is a significant input to the nitrate budget in coastal snow (up to ∼35%), tropospheric transport from mid-low latitudes dominates (∼65%) coastal snow nitrate. The linear relationship of δ18O vs. Δ17O of the snow nitrate suggests a predominant role of hydroxyl radical (OH) and ozone (O3) in nitrate production, although a high Δ17O(O3) is required to explain the observations. Across Antarctica the oxygen isotope composition of OH appears to be dominated by exchange with water vapor, despite the very dry environment. One of the largest uncertainties in quantifying nitrate production pathways is the limited knowledge of atmospheric oxidant isotopic compositions.

Putting the Capital 'A' in CoCoRAHS: A Pilot Program to Measure Albedo using the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) Network

NASA Astrophysics Data System (ADS)

Burakowski, E. A.; Stampone, M. D.; Wake, C. P.; Dibb, J. E.

2012-12-01

The Community Collaborative Rain, Hail, and Snow (CoCoRaHS) Network, started in 1998 as a community-based network of volunteer weather observer in Colorado, is the single largest provider of daily precipitation observations in the United States. We embrace the CoCoRaHS mission to use low-cost measurement tools, provide training and education, and utilize an interactive website to collect high quality albedo data for research and education applications. We trained a select sub-set of CoCoRaHS's eighteen most enthusiastic, self-proclaimed 'weather nuts' in the state of New Hampshire to collect surface albedo, snow depth, and snow density measurements between 23-Nov-2011 and 15-Mar-2012. At less than 700 per observer, the low-cost albedo data falls within ±0.05 of albedo values collected from a First Class Kipp and Zonen Albedometer (CMA6) at local solar noon. CoCoRaHS albedo values range from 0.99 for fresh snow to 0.34 for shallow, aged snow. Snow-free albedo ranges from 0.09 to 0.39, depending on ground cover. Albedo is found to increase logarithmically with snow depth and decrease linearly with snow density. The latter relationship with snow density is inferred to be a proxy for increasing snow grain size as snowpack ages and compacts, supported by spectral albedo measurements collected with an ASD FieldSpec4 spectrometer. The newly established albedo network also serves as a development test bed for interactive online mapping and graphing applications for CoCoRaHS observers to investigate spatial and temporal patterns in albedo, snow depth, and snow density (www.cocorahs-albedo.org). The 2012-2013 field season will include low-cost infrared temperature guns (<40 each) to investigate the relationship between surface albedo and skin temperature. We have also recruited middle- and high-schools as volunteer observers and are working with the teachers to develop curriculum and lesson plans that utilize the low-cost measurement tools provided by CoCoRAHS. CoCoRAHS data will provide critical spatially distributed measurements of surface data that will be used to validate and improve land surface modeling of New Hampshire climate under different land cover scenarios. Building on the success of the first season, the newly established albedo network shows promise to put the capital 'A' in CoCoRAHS.Figure 1. (a) Map of Community Collaborative Rain, Hail, and Snow (CoCoRAHS) volunteers participating in the pilot albedo project, and (b) CoCoRAHS snow measurement kit.

Domain-averaged snow depth over complex terrain from flat field measurements

NASA Astrophysics Data System (ADS)

Helbig, Nora; van Herwijnen, Alec

2017-04-01

Snow depth is an important parameter for a variety of coarse-scale models and applications, such as hydrological forecasting. Since high-resolution snow cover models are computational expensive, simplified snow models are often used. Ground measured snow depth at single stations provide a chance for snow depth data assimilation to improve coarse-scale model forecasts. Snow depth is however commonly recorded at so-called flat fields, often in large measurement networks. While these ground measurement networks provide a wealth of information, various studies questioned the representativity of such flat field snow depth measurements for the surrounding topography. We developed two parameterizations to compute domain-averaged snow depth for coarse model grid cells over complex topography using easy to derive topographic parameters. To derive the two parameterizations we performed a scale dependent analysis for domain sizes ranging from 50m to 3km using highly-resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees. The first, simpler parameterization uses a commonly applied linear lapse rate. For the second parameterization, we first removed the obvious elevation gradient in mean snow depth, which revealed an additional correlation with the subgrid sky view factor. We evaluated domain-averaged snow depth derived with both parameterizations using flat field measurements nearby with the domain-averaged highly-resolved snow depth. This revealed an overall improved performance for the parameterization combining a power law elevation trend scaled with the subgrid parameterized sky view factor. We therefore suggest the parameterization could be used to assimilate flat field snow depth into coarse-scale snow model frameworks in order to improve coarse-scale snow depth estimates over complex topography.

Contribution of high resolution remote sensing data to the modeling of the snow cover the in Atlas Mountains

NASA Astrophysics Data System (ADS)

Baba, Wassim; Gascoin, Simon; Hanich, Lahoucine; Kinnard, Christophe

2017-04-01

Snow melt from the Atlas Mountains watersheds represent an important water resource for the semi-arid, cultivated, lowlands. Due to the high incoming solar radiation and low precipitation, the spatial-temporal variability of the snowpack is expected to be strongly influenced by the topography. We explore this hypothesis using a distributed energy balance snow model (SnowModel) in the experimental watershed of the Rheraya River in Morocco (225 km2). The digital elevation model (DEM) in SnowModel is used for the computation of the gridded meteorological forcing from the automatic weather stations data. We acquired three Pléiades stereo pairs in to produce an accurate, high resolution DEM of the Rheraya watershed at 4 m posting. Then, the DEM was resampled to different spatial resolutions (8 m, 30 m, 90 m, 250 m and 500 m) to simulate the snowpack evolution over 2008-2009 snow season. As validation data we used a time series of 15 maps of the snow cover area (SCA) from Formosat-2 imagery over the same snow season in the upper Rheraya watershed. These maps have a resolution of 8 m, which enables to capture small-scale variability in the snow cover. We found that the simulations at 90 m, 30 m and 8 m yield similar results at the catchment scale, with significant differences in areas of very steep topography only. From February to April, an overall good agreement was observed between the simulated SCA and the Formosat-2 SCA at 8 m and 90 m. Before the melting season, true positive (TP) column of confusion matrix is close to 1, but it drops to 0.6 during the melting season. Heidke Skill Score is higher than 0.7 for the most of the validation dates and averages 0.8. On the contrary, 500 m simulation underestimates the SCA throughout the snow season and the TP score is always inferior to the one obtained at 8 m and 90 m. We further analyzed the effect of topography by comparing the distribution of meteorological and snowpack variables along north-south and east-west transects. This analysis indicates that the impact of the topography on the simulated SWE and snow melt is mainly driven by changes in the solar radiations and the precipitations.

Evaluating UAV and LiDAR Retrieval of Snow Depth in a Coniferous Forest in Arizona

NASA Astrophysics Data System (ADS)

Van Leeuwen, W. J. D.; Broxton, P.; Biederman, J. A.

2017-12-01

Remote sensing of snow depth and cover in forested environments is challenging. Trees interfere with the remote sensing of snowpack below the canopy and cause large variations in the spatial distribution of the snowpack itself (e.g. between below canopy environments to shaded gaps to open clearings). The distribution of trees and topographic variation make it challenging to monitor the snowpack with in-situ observations. Airborne LiDAR has improved our ability to monitor snowpack over large areas in montane and forested environments because of its high sampling rate and ability to penetrate the canopy. However, these LiDAR flights can be too expensive and time-consuming to process, making it hard to use them for real-time snow monitoring. In this research, we evaluate Structure from Motion (SfM) as an alternative to Airborne LiDAR to generate high-resolution snow depth data in forested environments. This past winter, we conducted a snow field campaign over Arizona's Mogollon Rim where we acquired aerial LiDAR, multi-angle aerial photography from a UAV, and extensive field observations of snow depth at two sites. LiDAR and SFM derived snow depth maps were generated by comparing "snow-on" and "snow-off" LiDAR and SfM data. The SfM- and LiDAR-generated snow depth maps were similar at a site with fewer trees, though there were more discrepancies at a site with more trees. Both compared reasonably well with the field observations at the sparser forested site, with poorer agreement at the denser forested site. Finally, although the SfM produced point clouds with much higher point densities than the aerial LiDAR, the SfM was not able to produce meaningful snow depth estimates directly underneath trees and had trouble in areas with deep shadows. Based on these findings, we are optimizing our UAV data acquisition strategies for this upcoming field season. We are using these data, along with high-resolution hydrological modeling, to gain a better understanding of how different forest structural, climatic and topographic conditions affect the snowpack and consequently the water resources available to the Salt River Project, a water utility providing power and water to millions of customers in the Phoenix area

Flying Fast and High: Operational Flight Planning for Maximum Data Return for Airborne Snow Observatory Mountain Surveys

NASA Astrophysics Data System (ADS)

Berisford, D. F.; Painter, T. H.; Richardson, M.; Wallach, A.; Deems, J. S.; Bormann, K. J.

2017-12-01

The Airborne Snow Observatory (ASO - http://aso.jpl.nasa.gov) uses an airborne laser scanner to map snow depth, and imaging spectroscopy to map snow albedo in order to estimate snow water equivalent and melt rate over mountainous, hydrologic basin-scale areas. Optimization of planned flight lines requires the balancing of many competing factors, including flying altitude and speed, bank angle limitation, laser pulse rate and power level, flightline orientation relative to terrain, surface optical properties, and data output requirements. These variables generally distill down to cost vs. higher resolution data. The large terrain elevation variation encountered in mountainous terrain introduces the challenge of narrow swath widths over the ridgetops, which drive tight flightline spacing and possible dropouts over the valleys due to maximum laser range. Many of the basins flown by ASO exceed 3,000m of elevation relief, exacerbating this problem. Additionally, sun angle may drive flightline orientations for higher-quality spectrometer data, which may change depending on time of day. Here we present data from several ASO missions, both operational and experimental, showing the lidar performance and accuracy limitations for a variety of operating parameters. We also discuss flightline planning strategies to maximize data density return per dollar, and a brief analysis on the effect of short turn times/steep bank angles on GPS position accuracy.

Clear-Sky Narrowband Albedo Datasets Derived from Modis Data

NASA Astrophysics Data System (ADS)

Chen, Y.; Minnis, P.; Sun-Mack, S.; Arduini, R. F.; Hong, G.

2013-12-01

Satellite remote sensing of clouds requires an accurate estimate of the clear-sky radiances for a given scene to detect clouds and aerosols and to retrieve their microphysical properties. Knowing the spatial and angular variability of clear-sky albedo is essential for predicting the clear-sky radiance at solar wavelengths. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the near-infrared (NIR; 1.24, 1.6 or 2.13 μm) and visible (VIS; 0.63 μm) channels available on the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) to help identify clouds and retrieve their properties. Generally, clear-sky albedo for a given surface type is determined for conditions when the vegetation is either thriving or dormant and free of snow. The clear-sky albedos are derived using a radiative transfer parameterization of the impact of the atmosphere, including aerosols, on the observed reflectances. This paper presents the method of generating monthly clear-sky overhead albedo maps for both snow-free and snow-covered surfaces of these channels using one year of MODIS (Moderate Resolution Imaging Spectroradiometer) CERES products. Maps of 1.24 and 1.6 μm are being used as the background to help retrieve cloud properties (e.g., effective particle size, optical depth) in CERES cloud retrievals in both snow-free and snow-covered conditions.

TOMS UV Algorithm: Problems and Enhancements. 2

NASA Technical Reports Server (NTRS)

Krotkov, Nickolay; Herman, Jay; Bhartia, P. K.; Seftor, Colin; Arola, Antti; Kaurola, Jussi; Kroskinen, Lasse; Kalliskota, S.; Taalas, Petteri; Geogdzhaev, I.

2002-01-01

Satellite instruments provide global maps of surface ultraviolet (UV) irradiance by combining backscattered radiance measurements with radiative transfer models. The models are limited by uncertainties in input parameters of the atmosphere and the surface. We evaluate the effects of possible enhancements of the current Total Ozone Mapping Spectrometer (TOMS) surface UV irradiance algorithm focusing on effects of diurnal variation of cloudiness and improved treatment of snow/ice. The emphasis is on comparison between the results of the current (version 1) TOMS UV algorithm and each of the changes proposed. We evaluate different approaches for improved treatment of pixel average cloud attenuation, with and without snow/ice on the ground. In addition to treating clouds based only on the measurements at the local time of the TOMS observations, the results from other satellites and weather assimilation models can be used to estimate attenuation of the incident UV irradiance throughout the day. A new method is proposed to obtain a more realistic treatment of snow covered terrain. The method is based on a statistical relation between UV reflectivity and snow depth. The new method reduced the bias between the TOMS UV estimations and ground-based UV measurements for snow periods. The improved (version 2) algorithm will be applied to re-process the existing TOMS UV data record (since 1978) and to the future satellite sensors (e.g., Quik/TOMS, GOME, OMI on EOS/Aura and Triana/EPIC).

Snow cover and snow goose Anser caerulescens caerulescens distribution during spring migration

USGS Publications Warehouse

Hupp, Jerry W.; Zacheis, Amy B.; Anthony, R. Michael; Robertson, Donna G.; Erickson, Wallace P.; Palacios, Kelly C.

2001-01-01

Arctic geese often use spring migration stopover areas when feeding habitats are partially snow covered. Melting of snow during the stopover period causes spatial and temporal variability in distribution and abundance of feeding habitat. We recorded changes in snow cover and lesser snow goose Anser caerulescens caerulescens distribution on a spring migration stopover area in south-central Alaska during aerial surveys in 1993-1994. Our objectives were to determine whether geese selected among areas with different amounts of snow cover and to assess how temporal changes in snow cover affected goose distribution. We also measured temporal changes in chemical composition of forage species after snow melt. We divided an Arc/Info coverage of the approximately 210 km2 coastal stopover area into 2-km2 cells, and measured snow cover and snow goose use of cells. Cells that had 10-49.9% snow cover were selected by snow geese, whereas cells that lacked snow cover were avoided. In both years, snow cover diminished along the coast between mid-April and early May. Flock distribution changed as snow geese abandoned snow-free areas in favour of cells where snow patches were interspersed with bare ground. Snow-free areas may have been less attractive to geese because available forage had been quickly exploited as bare ground was exposed, and because soils became drier making extraction of underground forage more difficult. Fiber content of two forage species increased whereas non-structural carbohydrate concentrations of forage plants appeared to diminish after snow melt, but changes in nutrient concentrations likely occurred too slowly to account for abandonment of snow-free areas by snow geese.

Survey of the seasonal snow cover in Alaska

NASA Technical Reports Server (NTRS)

Weller, G. E. (Principal Investigator)

1973-01-01

The author has identified the following significant results. ERTS-1 data are used together with synoptic-climatological data to describe the buildup of the seasonal snow and ice covers in a north-south transect of a total length of about 1250 km across Alaska. It has been demonstrated that the ERTS-1 data may, under favorable conditions, be used for accurate mapping of snow lines in high mountain regions. The analysis shows that especially in the Brooks Range and on the Arctic Slope where snow covers generally are relatively thin, the ERTS-1 scenes can be useful for qualitative descriptions of the snow and ice covers over wide expanses. The onset and retreat of the seasonal snow cover are sensitive indicators of climatic fluctuations and the ERTS-1 data offers a possibility to record variations of the snow and ice buildup from year to year in a practical and informative way, which should be especially useful for studies of climatic trends. This is particularly true in Alaska where the density of the station network is too low to permit interpolations between the stations.

Use of MODIS Snow-Cover Maps for Detecting Snowmelt Trends in North America

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; Robinson, David A.; Hoon-Starr, Jody A.

2012-01-01

Research has shown that the snow season in the Northern Hemisphere has been getting shorter in recent decades, consistent with documented global temperature increases. Specifically, the snow is melting earlier in the spring allowing for a longer growing season and associated land-cover changes. Here we focus on North America. Using the Moderate-Resolution Imaging Radiometer (MODIS) cloud-gap-filled standard snow-cover data product we can detect a trend toward earlier spring snowmelt in the approx 12 years since the MODIS launch. However, not all areas in North America show earlier spring snowmelt over the study period. We show examples of springtime snowmelt over North America, beginning in March 2000 and extending through the winter of 2012 for all of North America, and for various specific areas such as the Wind River Range in Wyoming and in the Catskill Mountains in New York. We also compare our approx 12-year trends with trends derived from the Rutgers Global Snow Lab snow cover climate-data record.

Numerical run-out modelling used for reassessment of existing permanent avalanche paths in the Krkonose Mts., Czechia

NASA Astrophysics Data System (ADS)

Blahut, Jan; Klimes, Jan; Balek, Jan; Taborik, Petr; Juras, Roman; Pavlasek, Jiri

2015-04-01

Run-out modelling of snow avalanches is being widely applied in high mountain areas worldwide. This study presents application of snow avalanche run-out calculation applied to mid-mountain ranges - the Krkonose, Jeseniky and Kralicky Sneznik Mountains. All mentioned mountain ranges lie in the northern part of Czechia, close to the border with Poland. Its highest peak reaches only 1602 m a.s.l. However, climatic conditions and regular snowpack presence are the reason why these mountain ranges experience considerable snow avalanche activity every year, sometimes resulting in injuries or even fatalities. Within the aim of an applied project dealing with snow avalanche hazard prediction a re-assessment of permanent snow avalanche paths has been performed based on extensive statistics covering period from 1961/62 till present. On each avalanche path different avalanches with different return periods were modelled using the RAMMS code. As a result, an up-to-date snow avalanche hazard map was prepared.

Mapping of ice, snow and water using aircraft-mounted LiDAR

NASA Astrophysics Data System (ADS)

Church, Philip; Matheson, Justin; Owens, Brett

2016-05-01

Neptec Technologies Corp. has developed a family of obscurant-penetrating 3D laser scanners (OPAL 2.0) that are being adapted for airborne platforms for operations in Degraded Visual Environments (DVE). The OPAL uses a scanning mechanism based on the Risley prism pair. Data acquisition rates can go as high as 200kHz for ranges within 240m and 25kHz for ranges exceeding 240m. The scan patterns are created by rotating two prisms under independent motor control producing a conical Field-Of-View (FOV). An OPAL laser scanner with 90° FOV was installed on a Navajo aircraft, looking down through an aperture in the aircraft floor. The rotation speeds of the Risley prisms were selected to optimize a uniformity of the data samples distribution on the ground. Flight patterns simulating a landing approach over snow and ice in an unprepared Arctic environment were also performed to evaluate the capability of the OPAL LiDAR to map snow and ice elevation distribution in real-time and highlight potential obstacles. Data was also collected to evaluate the detection of wires when flying over water, snow and ice. Main results and conclusions obtained from the flight data analysis are presented.

Aerosol and snow transfer processes: An investigation on the behavior of water-soluble organic compounds and ionic species.

PubMed

Barbaro, Elena; Zangrando, Roberta; Padoan, Sara; Karroca, Ornela; Toscano, Giuseppa; Cairns, Warren R L; Barbante, Carlo; Gambaro, Andrea

2017-09-01

The concentrations of water-soluble compounds (ions, carboxylic acids, amino acids, sugars, phenolic compounds) in aerosol and snow have been determined at the coastal Italian base "Mario Zucchelli" (Antarctica) during the 2014-2015 austral summer. The main aim of this research was to investigate the air-snow transfer processes of a number of classes of chemical compounds and investigate their potential as tracers for specific sources. The composition and particle size distribution of Antarctic aerosol was measured, and water-soluble compounds accounted for 66% of the PM 10 total mass concentration. The major ions Na + , Mg 2+ , Cl - and SO 4 2- made up 99% of the total water soluble compound concentration indicating that sea spray input was the main source of aerosol. These ionic species were found mainly in the coarse fraction of the aerosol resulting in enhanced deposition, as reflected by the snow composition. Biogenic sources were identified using chemical markers such as carboxylic acids, amino acids, sugars and phenolic compounds. This study describes the first characterization of amino acids and sugar concentrations in surface snow. High concentrations of amino acids were found after a snowfall event, their presence is probably due to the degradation of biological material scavenged during the snow event. Alcohol sugars increased in concentration after the snow event, suggesting a deposition of primary biological particles, such as airborne fungal spores. Copyright © 2017 Elsevier Ltd. All rights reserved.

Snow Leopard and Himalayan Wolf: Food Habits and Prey Selection in the Central Himalayas, Nepal.

PubMed

Chetri, Madhu; Odden, Morten; Wegge, Per

2017-01-01

Top carnivores play an important role in maintaining energy flow and functioning of the ecosystem, and a clear understanding of their diets and foraging strategies is essential for developing effective conservation strategies. In this paper, we compared diets and prey selection of snow leopards and wolves based on analyses of genotyped scats (snow leopards n = 182, wolves n = 57), collected within 26 sampling grid cells (5×5 km) that were distributed across a vast landscape of ca 5000 km2 in the Central Himalayas, Nepal. Within the grid cells, we sampled prey abundances using the double observer method. We found that interspecific differences in diet composition and prey selection reflected their respective habitat preferences, i.e. snow leopards significantly preferred cliff-dwelling wild ungulates (mainly bharal, 57% of identified material in scat samples), whereas wolves preferred typically plain-dwellers (Tibetan gazelle, kiang and argali, 31%). Livestock was consumed less frequently than their proportional availability by both predators (snow leopard = 27%; wolf = 24%), but significant avoidance was only detected among snow leopards. Among livestock species, snow leopards significantly preferred horses and goats, avoided yaks, and used sheep as available. We identified factors influencing diet composition using Generalized Linear Mixed Models. Wolves showed seasonal differences in the occurrence of small mammals/birds, probably due to the winter hibernation of an important prey, marmots. For snow leopard, occurrence of both wild ungulates and livestock in scats depended on sex and latitude. Wild ungulates occurrence increased while livestock decreased from south to north, probably due to a latitudinal gradient in prey availability. Livestock occurred more frequently in scats from male snow leopards (males: 47%, females: 21%), and wild ungulates more frequently in scats from females (males: 48%, females: 70%). The sexual difference agrees with previous telemetry studies on snow leopards and other large carnivores, and may reflect a high-risk high-gain strategy among males.

Snow Leopard and Himalayan Wolf: Food Habits and Prey Selection in the Central Himalayas, Nepal

PubMed Central

Odden, Morten; Wegge, Per

2017-01-01

Top carnivores play an important role in maintaining energy flow and functioning of the ecosystem, and a clear understanding of their diets and foraging strategies is essential for developing effective conservation strategies. In this paper, we compared diets and prey selection of snow leopards and wolves based on analyses of genotyped scats (snow leopards n = 182, wolves n = 57), collected within 26 sampling grid cells (5×5 km) that were distributed across a vast landscape of ca 5000 km2 in the Central Himalayas, Nepal. Within the grid cells, we sampled prey abundances using the double observer method. We found that interspecific differences in diet composition and prey selection reflected their respective habitat preferences, i.e. snow leopards significantly preferred cliff-dwelling wild ungulates (mainly bharal, 57% of identified material in scat samples), whereas wolves preferred typically plain-dwellers (Tibetan gazelle, kiang and argali, 31%). Livestock was consumed less frequently than their proportional availability by both predators (snow leopard = 27%; wolf = 24%), but significant avoidance was only detected among snow leopards. Among livestock species, snow leopards significantly preferred horses and goats, avoided yaks, and used sheep as available. We identified factors influencing diet composition using Generalized Linear Mixed Models. Wolves showed seasonal differences in the occurrence of small mammals/birds, probably due to the winter hibernation of an important prey, marmots. For snow leopard, occurrence of both wild ungulates and livestock in scats depended on sex and latitude. Wild ungulates occurrence increased while livestock decreased from south to north, probably due to a latitudinal gradient in prey availability. Livestock occurred more frequently in scats from male snow leopards (males: 47%, females: 21%), and wild ungulates more frequently in scats from females (males: 48%, females: 70%). The sexual difference agrees with previous telemetry studies on snow leopards and other large carnivores, and may reflect a high-risk high-gain strategy among males. PMID:28178279

Application of Satellite SAR Imagery in Mapping the Active Layer of Arctic Permafrost

NASA Technical Reports Server (NTRS)

Zhang, Ting-Jun; Li, Shu-Sun

2003-01-01

The objective of this project is to map the spatial variation of the active layer over the arctic permafrost in terms of two parameters: (i) timing and duration of thaw period and (ii) differential frost heave and thaw settlement of the active layer. To achieve this goal, remote sensing, numerical modeling, and related field measurements are required. Tasks for the University of Colorado team are to: (i) determine the timing of snow disappearance in spring through changes in surface albedo (ii) simulate the freezing and thawing processes of the active layer and (iii) simulate the impact of snow cover on permafrost presence.

Observed Differences between North American Snow Extent and Snow Depth Variability

NASA Astrophysics Data System (ADS)

Ge, Y.; Gong, G.

2006-12-01

Snow extent and snow depth are two related characteristics of a snowpack, but they need not be mutually consistent. Differences between these two variables at local scales are readily apparent. However at larger scales which interact with atmospheric circulation and climate, snow extent is typically the variable used, while snow depth is often assumed to be minor and/or mutually consistent compared to snow extent, though this is rarely verified. In this study, a new regional/continental-scale gridded dataset derived from field observations is utilized to quantitatively evaluate the relationship between snow extent and snow depth over North America. Various statistical methods are applied to assess the mutual consistency of monthly snow depth vs. snow extent, including correlations, composites and principal components. Results indicate that snow depth variations are significant in their own rights, and that depth and extent anomalies are largely unrelated, especially over broad high latitude regions north of the snowline. In the vicinity of the snowline, where precipitation and ablation can affect both snow extent and snow depth, the two variables vary concurrently, especially in autumn and spring. It is also found that deeper winter snow translates into larger snow-covered area in the subsequent spring/summer season, which suggests a possible influence of winter snow depth on summer climate. The observed lack of mutual consistency at continental/regional scales suggests that snowpack depth variations may be of sufficiently large magnitude, spatial scope and temporal duration to influence regional-hemispheric climate, in a manner unrelated to the more extensively studied snow extent variations.

Tracing the origin of pollution in French Alpine snow and aerosols using lead isotopic ratios.

PubMed

Veysseyre, A M; Bollhöfer, A F; Rosman, K J; Ferrari, C P; Boutron, C F

2001-11-15

Fresh snow samples collected at 15 remote locations and aerosols collected at one location in the French Alps between November 1998 and April 1999 have been analyzed for Pb concentration and isotopic composition by thermal ionization mass spectrometry. The snow samples contained 19-1300 pg/g of Pb with isotopic ratios 206Pb/207Pb (208Pb/207Pb) of 1.1279-1.1607 (2.3983-2.4302). Airborne Pb concentrations at one sampling site ranged from 0.42 to 6.0 ng/m3 with isotopic ratios of 1.1321-1.1427 (2.4029-2.4160). Air mass trajectory analysis combined with isotopic compositions of potential source regions did not show discernible evidence of the long-range atmospheric transport of pollutants. Isotopic ratios in the Alpine snow samples and thus the free troposphere were generally higher than airborne Pb isotopic ratios in urban France, which coupled with the relatively high Pb concentrations suggested a regional anthropogenic Pb source, probably Italy but possibly Eastern Europe.

Exploitation of ERTS-1 imagery utilizing snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J.; Martin, K. R.

1973-01-01

Photogeological analysis of ERTS-simulation and ERTS-1 imagery of snowcovered terrain within the ERAP Feather River site and within the New England (ERTS) test area provided new fracture detail which does not appear on available geological maps. Comparative analysis of snowfree ERTS-1 images has demonstrated that MSS Bands 5 and 7 supply the greatest amount of geological fracture detail. Interpretation of the first snow-covered ERTS-1 images in correlation with ground snow depth data indicates that a heavy blanket of snow (more than 9 inches) accentuates major structural features while a light "dusting", (less than 1 inch) accentuates more subtle topographic expressions. An effective mail-based method for acquiring timely ground-truth (snowdepth) information was established and provides a ready correlation of fracture detail with snow depth so as to establish the working limits of the technique. The method is both efficient and inexpensive compared with the cost of similarly scaled direct field observations.

Snow cover dynamics in the Catalan Pyrenees range using remote sensing data from 2002 to 2008 period

NASA Astrophysics Data System (ADS)

Cea, C.; Cristóbal, J.; Pons, X.

2009-04-01

Snow cover dynamics in the Catalan Pyrenees range using remote sensing data from 2002 to 2008 period. C. Cea (1), J. Cristóbal (1), X. Pons (1, 2) (1) Department of Geography. Autonomous University of Barcelona. Cerdanyola del Vallès, 08193. Cristina.Cea@uab.cat, (2) Center for Ecological Research and Forestry Applications (CREAF) Cerdanyola del Vallès, 08193. Water resources and its management are essential in many alpine mountainous areas. Snow cover monitoring in the Mediterranean zone requires obtaining accurate snow cartography to estimate the volume of water derived from snow melting and species distribution modelling. Snow data is usually obtained by field campaigns, but to obtain a spatial and temporal cover of enough detail and quality it is necessary collect an important number of data. However, when a continuous surface is needed, Remote Sensing could provide better snow cover estimation due to its spatial and temporal resolution. The aim of this study is to map snow cover and analyse its spatial and temporal dynamics using medium and coarse remote sensing data at a regional scale over an heterogeneous area, the Catalan Pyrenees (NE of the Iberian Peninsula). The seasonal snow cover period is from October to June. In this period, regular snowfalls usually take place from December to April, although during the rest of the period, punctual but important episodes of snowfalls are frequent. To perform this analysis, a set of 96 Landsat images (36 Landsat-5 TM and 60 Landsat-7 ETM+) of path 197 and 198 and rows 31 and 32 from January 2002 to April 2007, and 90 Terra-MODIS images from October 2007 to July 2008, with a different percentage of cloudiness, have been chosen. The computation of the Landsat-5 TM and Landsat-7 ETM+ data used in snow cover mapping has been carried out by means of the following methodologies. Images have been geometrically corrected by means of techniques based on first order polynomials taking into account the effect of the relief of the land surface using a Digital Elevation Model. Radiometric correction (non-thermal bands) has been done following the methodology which allows us to reduce the number of undesired artefacts that are due to the effects of the atmosphere or to the differential illumination. Finally, cloud removal has been carried out by means of a semi-automatic methodology. In the case of MODIS images, these have been imported by reading all the necessary metadata to document and interpret them. These products have already been radiometrically and geometrically corrected by USGS in a sinusoidal projection. Snow cover mapping has been carried out by means of the Normalized Snow Cover Index (NDSI), one of the most widely methodologies used. This methodology proposes a normalized index using green band 2 and Medium Infrared band because of snow reflectance is higher in the visible bands than in the medium infrared bands. NDSI pixels greater than 0.4 are select as snow cover. Although this threshold also selects water bodies, we have obtained optimal results using a mask of water bodies and generating a pre-boundary snow mask around the snow cover. In shadow cast areas, we have used a hybrid classification to obtain the snow cover. Preliminary results show the snow surface evolution of the Pyrenean basis during the hydrological period, as well as the differences between the different years of study. The obtained data shows how the basins with Mediterranean influence, placed in the east, receive less nival contribution than those with Atlantic influence, situated in the west. In addition, this data allow establishing the beginning and the ending of the snowfall cycle. Eastern basins usually have a shorter period than west basins, where the snow line is lower. The snow cover area average of the studied period is about 1200 km2 and stands out that for the particular period 2006-2007, this area was only about 55% of the average, due to the lack of snow precipitation. It affected the winter sports, an important sector in this area, the vegetation and the extensive livestock, just as the volume of water that flows towards river basins and reservoir when snow melts. Technical characteristics are different between both sensors. On the one hand, Landsat spatial resolution is better to obtain accuracy cartography, however it is not suitable to determine the frequency of snow falls. On the one other hand, MODIS with its daily temporal resolution allows better temporal monitoring, but with coarse spatial resolution.

Mapping lichen color-groups in western Arctic Alaska using seasonal Landsat composites

NASA Astrophysics Data System (ADS)

Nelson, P.; Macander, M. J.; Swingley, C. S.

2016-12-01

Mapping lichens at a landscape scale has received increased recent interest due to fears that terricolous lichen mats, primary winter caribou forage, may be decreasing across the arctic and boreal zones. However, previous efforts have produced taxonomically coarse, total lichen cover maps or have covered relatively small spatial extents. Here we attempt to map lichens of differing colors as species proxies across northwestern Alaska to produce the finest taxonomic and spatial- grained lichen maps covering the largest spatial extent to date. Lichen community sampling in five western Alaskan National Parks and Preserves from 2007-2012 generated 328 FIA-style 34.7 m radius plots on which species-level macrolichen community structure and abundance was estimated. Species were coded by color and plot lichen cover was aggregated by plot as the sum of the cover of each species in a color group. Ten different lichen color groupings were used for modeling to deduce which colors were most detectable. Reflectance signatures of each plot were extracted from a series of Landsat composites (circa 2000-2010) partitioned into two-week intervals from June 1 to Sept. 15. Median reflectance values for each band in each pixel were selected based on filtering criteria to reduce likelihood of snow cover. Lichen color group cover was regressed against plot reflectance plus additional abiotic predictors in two different data mining algorithms. Brown and grey lichens had the best models explaining approximately 40% of lichen cover in those color groups. Both data mining techniques produced similarly good fitting models. Spatial patterns of lichen color-group cover show distinctly different ecological patterns of these color-group species proxies.

Snow Cover, Snowmelt Timing and Stream Power in the Wind River Range, Wyoming

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; DiGirolamo, Nicolo E.; Riggs, George A.

2011-01-01

Earlier onset of springtime weather, including earlier snowmelt, has been documented in the western United States over at least the last 50 years. Because the majority (is greater than 70%) of the water supply in the western U.S. comes from snowmelt, analysis of the declining spring snowpack (and shrinking glaciers) has important implications for the management of streamflow. The amount of water in a snowpack influences stream discharge which can also influence erosion and sediment transport by changing stream power, or the rate at which a stream can do work, such as move sediment and erode the stream bed. The focus of this work is the Wind River Range (WRR) in west-central Wyoming. Ten years of Moderate-Resolution Imaging Spectroradiometer (MODIS) snow-cover, cloud-gap-filled (CGF) map products and 30 years of discharge and meteorological station data are studied. Streamflow data from streams in WRR drainage basins show lower annual discharge and earlier snowmelt in the decade of the 2000s than in the previous three decades, though no trend of either lower streamflow or earlier snowmelt was observed within the decade of the 2000s. Results show a statistically-significant trend at the 95% confidence level (or higher) of increasing weekly maximum air temperature (for three out of the five meteorological stations studied) in the decade of the 1970s, and also for the 40-year study period as a whole. The extent of snow-cover (percent of basin covered) derived from the lowest elevation zone (2500-3000 m) of the WRR, using MODIS CGF snow-cover maps, is strongly correlated with maximum monthly discharge on 30 April, where Spearman's Rank correlation, rs,=0.89 for the decade of the 2000s. We also investigated stream power for Bull Lake Creek above Bull Lake; and found a trend (significant at the 90% confidence level) toward reduced stream power from 1970 to 2009. Observed changes in streamflow and stream power may be related to increasing weekly maximum air temperature measured during the 40-year study period, possibly contributing to a reduction in snow cover. In addition, the strong relationship between percent of basin that was snow covered, and maximum monthly streamflow indicates that MODIS snow-cover maps are useful for predicting streamflow, and can be used to improve management of water resources in the drought-prone western United States.

Unusual radar echoes from the Greenland ice sheet

NASA Technical Reports Server (NTRS)

Rignot, E. J.; Vanzyl, J. J.; Ostro, S. J.; Jezek, K. C.

1993-01-01

In June 1991, the NASA/Jet Propulsion Laboratory airborne synthetic-aperture radar (AIRSAR) instrument collected the first calibrated data set of multifrequency, polarimetric, radar observations of the Greenland ice sheet. At the time of the AIRSAR overflight, ground teams recorded the snow and firn (old snow) stratigraphy, grain size, density, and temperature at ice camps in three of the four snow zones identified by glaciologists to characterize four different degrees of summer melting of the Greenland ice sheet. The four snow zones are: (1) the dry-snow zone, at high elevation, where melting rarely occurs; (2) the percolation zone, where summer melting generates water that percolates down through the cold, porous, dry snow and then refreezes in place to form massive layers and pipes of solid ice; (3) the soaked-snow zone where melting saturates the snow with liquid water and forms standing lakes; and (4) the ablation zone, at the lowest elevations, where melting is vigorous enough to remove the seasonal snow cover and ablate the glacier ice. There is interest in mapping the spatial extent and temporal variability of these different snow zones repeatedly by using remote sensing techniques. The objectives of the 1991 experiment were to study changes in radar scattering properties across the different melting zones of the Greenland ice sheet, and relate the radar properties of the ice sheet to the snow and firn physical properties via relevant scattering mechanisms. Here, we present an analysis of the unusual radar echoes measured from the percolation zone.

Mapping forest canopy disturbance in the Upper Great Lakes, USA

Treesearch

James D. Garner; Mark D. Nelson; Brian G. Tavernia; Charles H. (Hobie) Perry; Ian W. Housman

2015-01-01

A map of forest canopy disturbance was generated for Michigan, Wisconsin, and most of Minnesota using 42 Landsat time series stacks (LTSS) and a vegetation change tracker (VCTw) algorithm. Corresponding winter imagery was used to reduce commission errors of forest disturbance by identifying areas of persistent snow cover. The resulting disturbance age map was classed...

Shifting mountain snow patterns in a changing climate from remote sensing retrieval.

PubMed

Dedieu, J P; Lessard-Fontaine, A; Ravazzani, G; Cremonese, E; Shalpykova, G; Beniston, M

2014-09-15

Observed climate change has already led to a wide range of impacts on environmental systems and society. In this context, many mountain regions seem to be particularly sensitive to a changing climate, through increases in temperature coupled with changes in precipitation regimes that are often larger than the global average (EEA, 2012). In mid-latitude mountains, these driving factors strongly influence the variability of the mountain snow-pack, through a decrease in seasonal reserves and earlier melting of the snow pack. These in turn impact on hydrological systems in different watersheds and, ultimately, have consequences for water management. Snow monitoring from remote sensing provides a unique opportunity to address the question of snow cover regime changes at the regional scale. This study outlines the results retrieved from the MODIS satellite images over a time period of 10 hydrological years (2000-2010) and applied to two case studies of the EU FP7 ACQWA project, namely the upper Rhone and Po in Europe and the headwaters of the Syr Darya in Kyrgyzstan (Central Asia). The satellite data were provided by the MODIS Terra MOD-09 reflectance images (NASA) and MOD-10 snow products (NSIDC). Daily snow maps were retrieved over that decade and the results presented here focus on the temporal and spatial changes in snow cover. This paper highlights the statistical bias observed in some specific regions, expressed by the standard deviation values (STD) of annual snow duration. This bias is linked to the response of snow cover to changes in elevation and can be used as a signal of strong instability in regions sensitive to climate change: with alternations of heavy snowfalls and rapid snow melting processes. The interest of the study is to compare the methodology between the medium scales (Europe) and the large scales (Central Asia) in order to overcome the limits of the applied methodologies and to improve their performances. Results show that the yearly snow cover duration increases by 4-5 days per 100 m elevation during the accumulation period, depending of the watershed, while during the melting season the snow depletion rate is 0.3% per day of surface loss for the upper Rhone catchment, 0.4%/day for the Syr Darya headwater basins, and 0.6%/day for the upper Po, respectively. Then, the annual STD maps of snow cover indicate higher values (more than 45 days difference compared to the mean values) for (i) the Po foothill region at medium elevation (SE orientation) and (ii) the Kyrgyzstan high plateaux (permafrost areas). These observations cover only a time-period of 10 years, but exhibit a signal under current climate that is already consistent with the expected decline in snow in these regions in the course of the 21st century. Copyright © 2014 Elsevier B.V. All rights reserved.

Landsat Time-Series Analysis Opens New Approaches for Regional Glacier Mapping

NASA Astrophysics Data System (ADS)

Winsvold, S. H.; Kääb, A.; Nuth, C.; Altena, B.

2016-12-01

The archive of Landsat satellite scenes is important for mapping of glaciers, especially as it represents the longest running and continuous satellite record of sufficient resolution to track glacier changes over time. Contributing optical sensors newly launched (Landsat 8 and Sentinel-2A) or upcoming in the near future (Sentinel-2B), will promote very high temporal resolution of optical satellite images especially in high-latitude regions. Because of the potential that lies within such near-future dense time series, methods for mapping glaciers from space should be revisited. We present application scenarios that utilize and explore dense time series of optical data for automatic mapping of glacier outlines and glacier facies. Throughout the season, glaciers display a temporal sequence of properties in optical reflection as the seasonal snow melts away, and glacier ice appears in the ablation area and firn in the accumulation area. In one application scenario presented we simulated potential future seasonal resolution using several years of Landsat 5TM/7ETM+ data, and found a sinusoidal evolution of the spectral reflectance for on-glacier pixels throughout a year. We believe this is because of the short wave infrared band and its sensitivity to snow grain size. The parameters retrieved from the fitting sinus curve can be used for glacier mapping purposes, thus we also found similar results using e.g. the mean of summer band ratio images. In individual optical mapping scenes, conditions will vary (e.g., snow, ice, and clouds) and will not be equally optimal over the entire scene. Using robust statistics on stacked pixels reveals a potential for synthesizing optimal mapping scenes from a temporal stack, as we present in a further application scenario. The dense time series available from satellite imagery will also promote multi-temporal and multi-sensor based analyses. The seasonal pattern of snow and ice on a glacier seen in the optical time series can in the summer season also be observed using radar backscatter series. Optical sensors reveal the reflective properties at the surface, while radar sensors may penetrate the surface revealing properties from a certain volume.In an outlook to this contribution we have explored how we can combine information from SAR and optical sensor systems for different purposes.

Development of an Algorithm for Satellite Remote Sensing of Sea and Lake Ice

NASA Astrophysics Data System (ADS)

Dorofy, Peter T.

Satellite remote sensing of snow and ice has a long history. The traditional method for many snow and ice detection algorithms has been the use of the Normalized Difference Snow Index (NDSI). This manuscript is composed of two parts. Chapter 1, Development of a Mid-Infrared Sea and Lake Ice Index (MISI) using the GOES Imager, discusses the desirability, development, and implementation of alternative index for an ice detection algorithm, application of the algorithm to the detection of lake ice, and qualitative validation against other ice mapping products; such as, the Ice Mapping System (IMS). Chapter 2, Application of Dynamic Threshold in a Lake Ice Detection Algorithm, continues with a discussion of the development of a method that considers the variable viewing and illumination geometry of observations throughout the day. The method is an alternative to Bidirectional Reflectance Distribution Function (BRDF) models. Evaluation of the performance of the algorithm is introduced by aggregating classified pixels within geometrical boundaries designated by IMS and obtaining sensitivity and specificity statistical measures.

Spring snow goose hunting influences body composition of waterfowl staging in Nebraska

USGS Publications Warehouse

Pearse, Aaron T.; Krapu, Gary L.; Cox, Robert R.

2012-01-01

A spring hunt was instituted in North America to reduce abundance of snow geese (Chen caerulescens) by increasing mortality of adults directly, yet disturbance from hunting activities can indirectly influence body condition and ultimately, reproductive success. We estimated effects of hunting disturbance by comparing body composition of snow geese and non-target species, greater white-fronted geese (Anser albifrons) and northern pintails (Anas acuta) collected in portions of south-central Nebraska that were open (eastern Rainwater Basin, ERB) and closed (western Rainwater Basin, WRB; and central Platte River Valley, CPRV) to snow goose hunting during springs 1998 and 1999. Lipid content of 170 snow geese was 25% (57 g) less in areas open to hunting compared to areas closed during hunting season but similar in all areas after hunting was concluded in the ERB. Protein content of snow geese was 3% (14 g) less in the region open to hunting. Greater white-fronted geese had 24% (76 g; n = 129) less lipids in the hunted portion of the study area during hunting season, and this difference persisted after conclusion of hunting season. We found little difference in lipid or protein content of northern pintails in relation to spring hunting. Indirect effects of spring hunting may be considered a collateral benefit regarding efforts to reduce overabundant snow goose populations. Disrupted nutrient storage observed in greater white-fronted geese represents an unintended consequence of spring hunting that has potential to adversely affect reproduction for this and other species of waterbirds staging in the region.

How Does Snow Persistence Relate to Annual Streamflow in Mountain Watersheds of the Western U.S. With Wet Maritime and Dry Continental Climates?

NASA Astrophysics Data System (ADS)

Hammond, John C.; Saavedra, Freddy A.; Kampf, Stephanie K.

2018-04-01

With climate warming, many regions are experiencing changes in snow accumulation and persistence. These changes are known to affect streamflow volume, but the magnitude of the effect varies between regions. This research evaluates whether variables derived from remotely sensed snow cover can be used to estimate annual streamflow at the small watershed scale across the western U.S., a region with a wide range of climate types. We compared snow cover variables derived from MODIS, snow persistence (SP), and snow season (SS), to more commonly utilized metrics, snow fraction (fraction of precipitation falling as snow, SF), and peak snow water equivalent (SWE). Each variable represents different information about snow, and this comparison assesses similarities and differences between the snow metrics. Next, we evaluated how two snow variables, SP and SWE, related to annual streamflow (Q) for 119 USGS reference watersheds and examined whether these relationships varied for wet/warm (precipitation surplus) and dry/cold (precipitation deficit) watersheds. Results showed high correlations between all snow variables, but the slopes of these relationships differed between climates, with wet/warm watersheds displaying lower SF and higher SWE for the same SP. In dry/cold watersheds, both SP and SNODAS SWE correlated with Q spatially across all watersheds and over time within individual watersheds. We conclude that SP can be used to map spatial patterns of annual streamflow generation in dry/cold parts of the region. Applying this approach to the Upper Colorado River Basin demonstrates that 50% of streamflow comes from areas >3,000 masl. If the relationship between SP and Q is similar in other dry/cold regions, this approach could be used to estimate annual streamflow in ungauged basins.

Mapping snow avalanche risk using GIS technique and 3D modeling in Ceahlau Mountain

NASA Astrophysics Data System (ADS)

Covasnianu, A.; Grigoras, I. R.; State, L. E.; Balin, D.; Hogas, S.; Balin, I.

2009-04-01

This study consisted in a precise mapping project (GPS field campaign and on-screen digitization of the topographic maps at 1:5.000 scale) of the Ceahlau mountain area in Romanian Carpathians in order to address the snow avalanche risk management, surveying and monitoring. Thus we considered the slope, aspect, altitude, landforms and roughness derived from a high resolute numerical terrain model (31 km2 at 1: 5.000 scale resulted in a spatial resolution of 3 m by the help of Topo to Raster tool). These parameters were classified according to a model applied into Tatra Mountains and used over Ceahlau Massive. The results were adapted and interpreted considering to the European Avalanche Hazard Scale. This work was made in the context of the elaboration of Risk Map and is directly concerning both the security of tourism activities but also the management of the Natural Park Ceahlau. The extension of this method to similar mountain areas is ongoing.

Snowscape Ecology: Linking Snow Properties to Wildlife Movements and Demography

NASA Astrophysics Data System (ADS)

Prugh, L.; Verbyla, D.; van de Kerk, M.; Mahoney, P.; Sivy, K. J.; Liston, G. E.; Nolin, A. W.

2017-12-01

Snow enshrouds up to one third of the global land mass annually and exerts a major influence on animals that reside in these "snowscapes," (landscapes covered in snow). Dynamic snowscapes may have especially strong effects in arctic and boreal regions where dry snow persists for much of the year. Changes in temperature and hydrology are transforming northern regions, with profound implications for wildlife that are not well understood. We report initial findings from a NASA ABoVE project examining effects of snow properties on Dall sheep (Ovis dalli dalli). We used the MODSCAG snow fraction product to map spring snowline elevations and snow-off dates from 2000-2015 throughout the global range of Dall sheep in Alaska and northwestern Canada. We found a negative effect of spring snow cover on Dall sheep recruitment that increased with latitude. Using meteorological data and a daily freeze/thaw status product derived from passive microwave remote sensing from 1983-2012, we found that sheep survival rates increased in years with higher temperatures, less winter precipitation, fewer spring freeze-thaw events, and more winter freeze-thaw events. To examine the effects of snow depth and density on sheep movements, we used location data from GPS-collared sheep and a snowpack evolution model (SnowModel). We found that sheep selected for shallow, fluffy snow at high elevations, but they selected for denser snow as depth increased. Our field measurements identified a critical snow density threshold of 329 (± 18 SE) kg/m3 to support the weight of Dall sheep. Thus, sheep may require areas of shallow, fluffy snow for foraging, while relying on hard-packed snow for winter travel. These findings highlight the importance of multiple snowscape properties on wildlife movements and demography. The integrated study of snow properties and ecological processes, which we call snowscape ecology, will greatly improve global change forecasting.

The Circumpolar Arctic Vegetation Map: AVHRR-derived base maps, environmental controls, and integrated mapping procedures

Treesearch

D. A. WALKER; W. A. GOULD; MAIERH. A.; M. K. RAYNOLDS

2002-01-01

A new false-colour-infrared image derived from biweekly 1993 and 1995 Advanced Very High Resolution Radiometer (AVHRR) data provides a snow-free and cloud-free base image for the interpretation of vegetation as part of a 1:7.5M-scale Circumpolar Arctic Vegetation Map (CAVM). A maximum-NDVI (Normalized DiVerence Vegetation Index) image prepared from the same data...

Using NASA Earth Observations to Assist the National Park Service in Assessing Snow Cover Distribution and Persistence Changes in the Sky Islands

NASA Astrophysics Data System (ADS)

Bayat, F.; Barrow, C., III; Gonsoroski, E.; Dutta, S.; Lynn, T.; Harville, K.; Spruce, J.

2017-12-01

Saguaro National Park in southeastern Arizona occupies one of several unique mountain ranges known collectively as the Sky Islands or the Madrean Archipelago. The Sky Islands are biodiversity hotspots and host different ecosystems, ranging from arid deserts to temperate forests. Snowmelt provides a source of water during the dry season for various flora and fauna inhabiting the region. Climate change and its effect on snow cover is of growing concern by resource managers in this location. Currently, the National Park Service (NPS) monitors water presence via stream gauges, but a synoptic record of snow presence does not exist due to the remote and rugged topography of the region. As a result, it is difficult to study how climate change has affected water resources in the Sky Islands and what effect this has on wildlife and vegetation. This project used NASA Earth observations (e.g., Landsat data) and GIS technology to help the NPS in understanding the role of snow cover in the Sky Islands. Historical snow cover maps were compiled using a combination of snow detection indices to provide spatio-temporal information on snow presence and phenology. With a more complete understanding of snow cover trends in the park, the NPS can further analyze snow cover impacts to improve future land management decisions.

Mapping snow cover using multi-source satellite data on big data platforms

NASA Astrophysics Data System (ADS)

Lhermitte, Stef

2017-04-01

Snowmelt is an important and dynamically changing water resource in mountainous regions around the world. In this framework, remote sensing data of snow cover data provides an essential input for hydrological models to model the water contribution from remote mountain areas and to understand how this water resource might alter as a result of climate change. Traditionally, however, many of these remote sensing products show a trade-off between spatial and temporal resolution (e.g., 16-day Landsat at 30m vs. daily MODIS at 500m resolution). With the advent of Sentinel-1 and 2 and the PROBA-V 100m products this trade-off can partially be tackled by having data that corresponds more closely to the spatial and temporal variations in snow cover typically observed over complex mountain areas. This study provides first a quantitative analysis of the trade-offs between the state-of-the-art snow cover mapping methodologies for Landsat, MODIS, PROBA-V, Sentinel-1 and 2 and applies them on big data platforms such as Google Earth Engine (GEE), RSS (ESA Research Service & Support) CloudToolbox, and the PROBA-V Mission Exploitation Platform (MEP). Second, it combines the different sensor data-cubes in one multi-sensor classification approach using newly developed spatio-temporal probability classifiers within the big data platform environments. Analysis of the spatio-temporal differences in derived snow cover areas from the different sensors reveals the importance of understanding the spatial and temporal scales at which variations occur. Moreover, it shows the importance of i) temporal resolution when monitoring highly dynamical properties such as snow cover and of ii) differences in satellite viewing angles over complex mountain areas. Finally, it highlights the potential and drawbacks of big data platforms for combining multi-source satellite data for monitoring dynamical processes such as snow cover.

Community variability of bacteria in alpine snow (Mont Blanc) containing Saharan dust deposition and their snow colonisation potential.

PubMed

Chuvochina, Maria S; Marie, Dominique; Chevaillier, Servanne; Petit, Jean-Robert; Normand, Philippe; Alekhina, Irina A; Bulat, Sergey A

2011-01-01

Microorganisms uplifted during dust storms survive long-range transport in the atmosphere and could colonize high-altitude snow. Bacterial communities in alpine snow on a Mont Blanc glacier, associated with four depositions of Saharan dust during the period 2006-2009, were studied using 16S rRNA gene sequencing and flow cytometry. Also, sand from the Tunisian Sahara, Saharan dust collected in Grenoble and Mont Blanc snow containing no Saharan dust (one sample of each) were analyzed. The bacterial community composition varied significantly in snow containing four dust depositions over a 3-year period. Out of 61 phylotypes recovered from dusty snow, only three phylotypes were detected in more than one sample. Overall, 15 phylotypes were recognized as potential snow colonizers. For snow samples, these phylotypes belonged to Actinobacteria, Proteobacteria and Cyanobacteria, while for Saharan sand/dust samples they belonged to Actinobacteria, Bacteroidetes, Deinococcus-Thermus and Proteobacteria. Thus, regardless of the time-scale, Saharan dust events can bring different microbiota with no common species set to alpine glaciers. This seems to be defined more by event peculiarities and aeolian transport conditions than by the bacterial load from the original dust source.

Long-term record of atmospheric and snow surface nitrate from Dome C (Central Antarctica)

NASA Astrophysics Data System (ADS)

Traversi, Rita; Becagli, Silvia; Brogioni, Marco; Caiazzo, Laura; Ciardini, Virginia; Giardi, Fabio; Legrand, Michel; Macelloni, Giovanni; Petkov, Boyan; Preunkert, Suzanne; Scarchilli, Claudio; Severi, Mirko; Vitale, Vito; Udisti, Roberto

2017-04-01

Nitrate is the end product of the oxidation of atmospheric nitrogen oxides and one of the most abundant ions present in polar ice and snow, mainly as nitric acid in present-climate conditions. Nitrate stratigraphies from snow and ice layers have the potential to provide records of past changes in atmospheric composition, including atmospheric NOx cycling and oxidative capacity, as well as past solar activity or major variations in Earth's magnetic field. Nevertheless, in order to exploit such a potential, chemical concentrations in the air, snow, firn and ice core need to be correlated. Hence, the knowledge of the link between atmosphere and snow composition at the time of deposition is basic to reconstruct past climate and past atmospheric chemical composition. The extent of such knowledge depends on whether the species of interest are gaseous or in the condensed phase, and if they are reversible and/or irreversibly deposited to snow. In order to provide a contribution to their air-to-snow exchange in the Antarctic plateau, as well as to the understanding of dominant sources and sinks of nitrate, we present here nitrate records in atmospheric aerosol and surface snow sampled at high resolution, all year-round, at Dome C along 9 years (November 2004 - November 2013). This represents the longest and most highly resolved record from continental Antarctica, where continuous and long-term atmosphere and snow samplings are particularly difficult due to the extreme meteorological conditions and, at the same time, need of extra-care in avoiding contamination due to the low level of ion concentrations. Results confirm, on a larger statistical data set with respect to previous observations, nitrate seasonal pattern with summer maxima both for aerosol and surface snow, in-phase with UV solar irradiance. Such a temporal pattern is likely a combination of nitrate sources and post-depositional processes that enhance during summer. Moreover, a case study of synoptic analysis for a major nitrate event showed the occurrence of a stratosphere-troposphere exchange in the sampled days. The sampling of both matrices carried out at high resolution at the same time allowed detecting a recurring lag, about one-month long, of summer maxima in snow with respect to aerosol. Such a temporal shift can be explained only by taking into account deposition and post-deposition processes taking place at the atmosphere-snow interface, including likely both a net uptake of gaseous nitric acid and a replenishment of the uppermost surface layers driven by a larger temperature gradient in summer. Such a possibility was tested in a preliminary way by a comparison with measurements of surface layers temperature carried out in 2012-13 time period. A comparison with nitrate concentration in the gas phase and total nitrate obtained from Dome C (2012-13) showed the major role of gaseous HNO3 to total nitrate budget hinting to the need of further investigation of the gas-to-particle conversion processes.

LANDSAT-4 Science Characterization Early Results. Volume 4: Applications. [agriculture, soils land use, geology, hydrology, wetlands, water quality, biomass identification, and snow mapping

NASA Technical Reports Server (NTRS)

Barker, J. L. (Editor)

1985-01-01

The excellent quality of TM data allows researchers to proceed directly with applications analyses, without spending a significant amount of time applying various corrections to the data. The early results derived of TM data are discussed for the following applications: agriculture, land cover/land use, soils, geology, hydrology, wetlands biomass, water quality, and snow.

Heavy snow loads in Finnish forests respond regionally asymmetrically to projected climate change

DOE PAGES

Lehtonen, Ilari; Kamarainen, Matti; Gregow, Hilppa; ...

2016-10-17

This study examined the impacts of projected climate change on heavy snow loads on Finnish forests, where snow-induced forest damage occurs frequently. For snow-load calculations, we used daily data from five global climate models under representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, statistically downscaled onto a high-resolution grid using a quantile-mapping method. Our results suggest that projected climate warming results in regionally asymmetric response on heavy snow loads in Finnish forests. In eastern and northern Finland, the annual maximum snow loads on tree crowns were projected to increase during the present century, as opposed to southern and western parts ofmore » the country. The change was rather similar both for heavy rime loads and wet snow loads, as well as for frozen snow loads. Only the heaviest dry snow loads were projected to decrease over almost the whole of Finland. Our results are aligned with previous snowfall projections, typically indicating increasing heavy snowfalls over the areas with mean temperature below -8 °C. In spite of some uncertainties related to our results, we conclude that the risk for snow-induced forest damage is likely to increase in the future in the eastern and northern parts of Finland, i.e. in the areas experiencing the coldest winters in the country. In conclusion, the increase is partly due to the increase in wet snow hazards but also due to more favourable conditions for rime accumulation in a future climate that is more humid but still cold enough.« less

Heavy snow loads in Finnish forests respond regionally asymmetrically to projected climate change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lehtonen, Ilari; Kamarainen, Matti; Gregow, Hilppa

This study examined the impacts of projected climate change on heavy snow loads on Finnish forests, where snow-induced forest damage occurs frequently. For snow-load calculations, we used daily data from five global climate models under representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, statistically downscaled onto a high-resolution grid using a quantile-mapping method. Our results suggest that projected climate warming results in regionally asymmetric response on heavy snow loads in Finnish forests. In eastern and northern Finland, the annual maximum snow loads on tree crowns were projected to increase during the present century, as opposed to southern and western parts ofmore » the country. The change was rather similar both for heavy rime loads and wet snow loads, as well as for frozen snow loads. Only the heaviest dry snow loads were projected to decrease over almost the whole of Finland. Our results are aligned with previous snowfall projections, typically indicating increasing heavy snowfalls over the areas with mean temperature below -8 °C. In spite of some uncertainties related to our results, we conclude that the risk for snow-induced forest damage is likely to increase in the future in the eastern and northern parts of Finland, i.e. in the areas experiencing the coldest winters in the country. In conclusion, the increase is partly due to the increase in wet snow hazards but also due to more favourable conditions for rime accumulation in a future climate that is more humid but still cold enough.« less

Comparison of the Snow Simulations in Community Land Model Using Two Snow Cover Fraction Parameterizations

NASA Astrophysics Data System (ADS)

Xie, Zhipeng; Hu, Zeyong

2016-04-01

Snow cover is an important component of local- and regional-scale energy and water budgets, especially in mountainous areas. This paper evaluates the snow simulations by using two snow cover fraction schemes in CLM4.5 (NY07 is the original snow-covered area parameterization used in CLM4, and SL12 is the default scheme in CLM4.5). Off-line simulations are carried out forced by the China Meteorological forcing dataset from January 1, 2001 to December 31, 2010 over the Tibetan Plateau. Simulated snow cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product, the daily snow depth dataset of China, and China Meteorological Administration (CMA) in-situ snow depth and SWE observations. The comparison results indicate significant differences existing between those two SCF parameterizations simulations. Overall, the SL12 formulation shows a certain improvement compared to the NY07 scheme used in CLM4, with the percentage of correctly modeled snow/no snow being 75.8% and 81.8% when compared with the IMS snow product, respectively. Yet, this improvement varies both temporally and spatially. Both these two snow cover schemes overestimated the snow depth, in comparison with the daily snow depth dataset of China, the average biases of simulated snow depth are 7.38cm (8.77cm), 6.97cm (8.2cm) and 5.49cm (5.76cm) NY07 (and SL12) in the snow accumulation period (September through next February), snowmelt period (March through May) and snow-free period (June through August), respectively. When compared with the CMA in-situ snow depth observations, averaged biases are 3.18cm (4.38cm), 2.85cm (4.34cm) and 0.34cm (0.34cm) for NY07 (SL12), respectively. Though SL12 does worse snow depth simulation than NY07, the simulated SWE by SL12 is better than that by NY07, with average biases being 2.64mm, 6.22mm, 1.33mm for NY07, and 1.47mm, 2.63mm, 0.31mm for SL12, respectively. This study demonstrates that future improvements on snow simulation over the Tibetan Plateau are in urgent need for better representing the variability of snow in CLM. Furthermore, these findings lay a foundation for follow-up studies on the modification of snow cover parameterization in the land surface model. Keywords: snow cover, CLM, Tibetan Plateau, simulation.

[Monitoring on spatial and temporal changes of snow cover in the Heilongjiang Basin based on remote sensing].

PubMed

Yu, Ling-Xue; Zhang, Shu-Wen; Guan, Cong; Yan, Feng-Qin; Yang, Chao-Bin; Bu, Kun; Yang, Jiu-Chun; Chang, Li-Ping

2014-09-01

This paper extracted and verified the snow cover extent in Heilongjiang Basin from 2003 to 2012 based on MODIS Aqua and Terra data, and the seasonal and interannual variations of snow cover extent were analyzed. The result showed that the double-star composite data reduced the effects of clouds and the overall accuracy was more than 91%, which could meet the research requirements. There existed significant seasonal variation of snow cover extent. The snow cover area was almost zero in July and August while in January it expanded to the maximum, which accounted for more than 80% of the basin. According to the analysis on the interannual variability of snow cover, the maximum winter snow cover areas in 2003-2004 and 2009-2010 (>180 x 10(4) km2) were higher than that of 2011 (150 x 10(4) km2). Meanwhile, there were certain correlations between the interannual fluctuations of snow cover and the changes of average annual temperature and precipitation. The year with the low snow cover was corresponding to less annual rainfall and higher average temperature, and vice versa. The spring snow cover showed a decreasing trend from 2003 to 2012, which was closely linked with decreasing precipitation and increasing temperature.

Digital outlines and topography of the glaciers of the American West

USGS Publications Warehouse

Fountain, Andrew G.; Hoffman, Matthew; Jackson, Keith; Basagic, Hassan; Nylen, Thomas; Percy, David

2007-01-01

Alpine glaciers have generally receded during the past century (post-“Little Ice Age”) because of climate warming (Oerlemans and others, 1998; Mann and others, 1999; Dyurgerov and Meier, 2000; Grove, 2001). This general retreat has accelerated since the mid 1970s, when a shift in atmospheric circulation occurred (McCabe and Fountain, 1995; Dyurgerov and Meier, 2000). The loss in glacier cover has had several profound effects. First, the shrinkage of glaciers results in a net increase in stream flow, typically in late summer when water supplies are at the lowest levels (Fountain and Tangborn, 1985). This additional water is important to ecosystems (Hall and Fagre, 2003) and to human water needs (Tangborn, 1980). However, if shrinkage continues, the net contribution to stream flow will diminish, and the effect upon these benefactors will be adverse. Glacier shrinkage is also a significant factor in current sea level rise (Meier, 1984; Dyurgerov and Meier, 2000). Second, many of the glaciers in the West Coast States are located on stratovolcanoes, and continued recession will leave oversteepened river valleys. These valleys, once buttressed by ice are now subject to failure, creating conditions for lahars (Walder and Driedger, 1994; O’Connor and others, 2001). Finally, reduction or loss of glaciers reduce or eliminate glacial activity as an important geomorphic process on landscape evolution and alters erosion rates in high alpine areas (Hallet and others, 1996). Because of the importance of glaciers to studies of climate change, hazards, and landscape modification, glacier inventories have been published for Alaska (Manley, in press), China (http://wdcdgg.westgis.ac.cn/DATABASE/Glacier/Glacier.asp), Nepal (Mool and others, 2001), Switzerland (Paul and others, 2002), and the Tyrolian Alps of Austria (Paul, 2002), among other locales. To provide the necessary data for assessing the magnitude and rate of glacier change in the American West, exclusive of Alaska (fig. 1), we are constructing a geographic information system (GIS) database. The data on glacier location and change will be derived from maps, ground-based photographs, and aerial and satellite images. Our first step, reported here, is the compilation of a glacier inventory of the American West. The inventory is compiled from the 1:100,000 (100K) and 1:24,000 (24K)-scale topographic maps published by the U.S. Geological Survey (USGS) and U.S. Forest Service (USFS). The 24K-scale maps provide the most detailed mapping of perennial snow and ice features. This report informs users of the data about the challenges we faced in compiling the data and discusses its errors and uncertainties. We rely on the expertise of the original cartographers in distinguishing “permanent snow and ice” from seasonal snow, although we know, through personal experience, of cartographic misjudgments. Whether “permanent” means indefinite or resident for several years is impossible to determine within the scope of this study. We do not discriminate between “glacier,” defined as permanent snow or ice that moves (Paterson, 1994), and stagnant snow and ice features. Therefore, we leave to future users the final determination of seasonal versus permanent snow features and the discrimination between true glaciers and stagnant snow and ice bodies. We believe that future studies of more regional focus and knowledge can most accurately refine our initial inventory. For simplicity we refer to all snow and ice bodies in this report as glaciers, although we recognize that most probably do not strictly meet the requirements; many may be snow patches.

Mapping Snow Grain Size over Greenland from MODIS

NASA Technical Reports Server (NTRS)

Lyapustin, Alexei; Tedesco, Marco; Wang, Yujie; Kokhanovsky, Alexander

2008-01-01

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Space Radar Image of Long Valley, California - 3-D view

NASA Image and Video Library

1999-05-01

This is a three-dimensional perspective view of Long Valley, California by the Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar on board the space shuttle Endeavour. This view was constructed by overlaying a color composite SIR-C image on a digital elevation map. The digital elevation map was produced using radar interferometry, a process by which radar data are acquired on different passes of the space shuttle and, which then, are compared to obtain elevation information. The data were acquired on April 13, 1994 and on October 3, 1994, during the first and second flights of the SIR-C/X-SAR radar instrument. The color composite radar image was produced by assigning red to the C-band (horizontally transmitted and vertically received) polarization; green to the C-band (vertically transmitted and received) polarization; and blue to the ratio of the two data sets. Blue areas in the image are smooth and yellow areas are rock outcrops with varying amounts of snow and vegetation. The view is looking north along the northeastern edge of the Long Valley caldera, a volcanic collapse feature created 750,000 years ago and the site of continued subsurface activity. Crowley Lake is off the image to the left. http://photojournal.jpl.nasa.gov/catalog/PIA01757

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

NASA Astrophysics Data System (ADS)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

Operational applications of satellite snowcover observations in Rio Grande drainage of Colorado

NASA Technical Reports Server (NTRS)

Washicheck, J. N.; Mikesell, T.

1975-01-01

Various mapping techniques were tried and evaluated. There were many problems encountered such as distinquishing clouds from snow and snow under trees. A partial solution to some of the problems involves ground reconnaissance and low air flights. Snow areas, cloud cover, and total areas were planimetered after transferring imagery by use of zoom transfer scope. These determinations were then compared to areas determined by use of a density slicer. Considerable adjustment is required for these two values to compare. NOAA pictures were also utilized in the evaluation. Forest cover is one of the parameters used in the modeling process. The determination of this percentage is being explored.

Evaluation of ERTS-1 data for certain hydrological uses

NASA Technical Reports Server (NTRS)

Wiesnet, D. R. (Principal Investigator); Mcginnis, D. F.; Mcmillan, M. C.

1974-01-01

The author has identified the following significant results. ERTS-1 MSS data have been used in a variety of hydrologic research including snow-extent mapping; studies of snowmelt, snowmelt runoff, spectral reflectance of snow for assessing snowpack conditions, and snow albedo; lake ice formation, breakup, and migration; lake current measurements; multispectral studies of lake ice; and flood studies. MSS sensing of soil moisture over a well-vegetated test site was unsuccessfully attempted. Although a powerful research tool, ERTS-1 has very limited use as an operational system for hydrologic communities because of its 18-day revisit cycle and its lack of a quick look capability.

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

USGS Publications Warehouse

Hansen, M.C.; Reed, B.

2000-01-01

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

Snow cover variability in a forest ecotone of the Oregon Cascades via MODIS Terra products

Treesearch

Tihomir Sabinov Kostadinov; Todd R. Lookingbill

2015-01-01

Snowcover pattern and persistence have important implications for planetary energy balance, climate sensitivity to forcings, and vegetation structure, function, and composition. Variability in snow cover within mountainous regions of the Pacific Northwest, USA is attributable to a combination of anthropogenic climate change and climate oscillations. However,...

Remote sensing applied to land-use studies in Wyoming

NASA Technical Reports Server (NTRS)

Breckenridge, R. M.; Marrs, R. W.; Murphy, D. J.

1973-01-01

Impending development of Wyoming's vast fuel resources requires a quick and efficient method of land use inventory and evaluation. Preliminary evaluations of ERTS-1 imagery have shown that physiographic and land use inventory maps can be compiled by using a combination of visual and automated interpretation techniques. Test studies in the Powder River Basin showed that ERTS image interpretations can provide much of the needed physiographic and land use information. Water impoundments as small as one acre were detected and water bodies larger than five acres could be mapped and their acreage estimated. Flood plains and irrigated lands were successfully mapped, and some individual crops were identified and mapped. Coniferous and deciduous trees were mapped separately using color additive analysis on the ERTS multispectral imagery. Gross soil distinctions were made with the ERTS imagery, and were found to be closely related to the bedrock geology. Several broad unstable areas were identified. These were related to specific geologic and slope conditions and generally extended through large regions. Some new oil fields and all large open-cut coal mines were mapped. The most difficult task accomplished was that of mapping urban areas. Work in the urban areas provides a striking example of snow enhancement and the detail available from a snow enhanced image.

European In-Situ Snow Measurements: Practices and Purposes.

PubMed

Pirazzini, Roberta; Leppänen, Leena; Picard, Ghislain; Lopez-Moreno, Juan Ignacio; Marty, Christoph; Macelloni, Giovanni; Kontu, Anna; von Lerber, Annakaisa; Tanis, Cemal Melih; Schneebeli, Martin; de Rosnay, Patricia; Arslan, Ali Nadir

2018-06-22

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called "A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction". Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.

Snow observations in Mount Lebanon (2011-2016)

NASA Astrophysics Data System (ADS)

Fayad, Abbas; Gascoin, Simon; Faour, Ghaleb; Fanise, Pascal; Drapeau, Laurent; Somma, Janine; Fadel, Ali; Bitar, Ahmad Al; Escadafal, Richard

2017-08-01

We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011-2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m-3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200-2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean. The DOI for the data is https://doi.org/10.5281/zenodo.583733.

Snow contribution to first-year and second-year Arctic sea ice mass balance north of Svalbard

NASA Astrophysics Data System (ADS)

Granskog, Mats A.; Rösel, Anja; Dodd, Paul A.; Divine, Dmitry; Gerland, Sebastian; Martma, Tõnu; Leng, Melanie J.

2017-03-01

The salinity and water oxygen isotope composition (δ18O) of 29 first-year (FYI) and second-year (SYI) Arctic sea ice cores (total length 32.0 m) from the drifting ice pack north of Svalbard were examined to quantify the contribution of snow to sea ice mass. Five cores (total length 6.4 m) were analyzed for their structural composition, showing variable contribution of 10-30% by granular ice. In these cores, snow had been entrained in 6-28% of the total ice thickness. We found evidence of snow contribution in about three quarters of the sea ice cores, when surface granular layers had very low δ18O values. Snow contributed 7.5-9.7% to sea ice mass balance on average (including also cores with no snow) based on δ18O mass balance calculations. In SYI cores, snow fraction by mass (12.7-16.3%) was much higher than in FYI cores (3.3-4.4%), while the bulk salinity of FYI (4.9) was distinctively higher than for SYI (2.7). We conclude that oxygen isotopes and salinity profiles can give information on the age of the ice and enables distinction between FYI and SYI (or older) ice in the area north of Svalbard.