7

komatsu manuals pdf

LINK 1 ENTER SITE >>> Download PDF
LINK 2 ENTER SITE >>> Download PDF

File Name:komatsu manuals pdf.pdf
Size: 2383 KB
Type: PDF, ePub, eBook

Category: Book
Uploaded: 22 May 2019, 12:44 PM
Rating: 4.6/5 from 774 votes.

Status: AVAILABLE

Last checked: 1 Minutes ago!

In order to read or download komatsu manuals pdf ebook, you need to create a FREE account.

Download Now!

eBook includes PDF, ePub and Kindle version

✔ Register a free 1 month Trial Account.

✔ Download as many books as you like (Personal use)

✔ Cancel the membership at any time if not satisfied.

✔ Join Over 80000 Happy Readers

komatsu manuals pdfA set of image-processing techniques applied to the satellite images in digital form for the enhancement and extraction of the required information. Mapping of the snow-covered areas in the satellite image by application of the specialized digital image information extraction techniques. Over the years, gradual technological progress in mapping of snow-covered areas using remote sensing data has been influenced by several interrelated factors. These include advancements in the satellite sensor and image-processing technologies, and increasing demand for accurate and frequent monitoring of snow-covered areas due to mounting pressures from rapid industrialization, urbanization, and changing climate. Thus, regular and precise mapping of snow-covered regions is important at various scales due to several reasons such as:Bibliography Allen, N. W., and Mosher, F. R., 1985. Interactive snow cover mapping with geostationary satellite data over the western United States. In Summaries, 19th International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, p. 153. Google Scholar Andersen, T., 1982. Operational snow mapping by satellites. In Hydrological aspects of Alpine and high mountain areas. Proceedings of the Exeter Symposium, July 1982. Google Scholar Bamber, J., 2006. Remote sensing in glaciology. In Knight, P. G. (ed.), Glacier Science and Environmental Change. Google Scholar Berthier, E., Arnaud, Y., Kumar, R., Ahmad, S., Wagnon, P., and Chevallier, P., 2007. Remote sensing estimates of glacier mass balances in the Himachal Pradesh (Western Himalaya, India). Google Scholar Bezdek, J. C., Ehrlich, R., and Full, W., 1984. FCM: The fuzzy c-means clustering algorithm. Google Scholar Binaghi, E., Madella, P., Montesano, M. P., and Rampini, A., 1997. Fuzzy contextual classification of multisource remote sensing images. IEEE Transactions on Geoscience and Remote Sensing. Google Scholar Bronge, L. B., and Bronge, C., 1999.http://www.senecaconsulting.com/7strategy/multichem/assets/fck_upload_files/image/hoyt-recurve-riser-manual.xml

Tags:
• komatsu forklift manual pdf, komatsu excavator manual pdf, komatsu wb93r manual pdf, komatsu service manual pdf, komatsu wa180 manual pdf, komatsu manual pdf, komatsu wa200 manual pdf, komatsu pc200 manual pdf, komatsu shop manual pdf, montacargas komatsu manual pdf, komatsu manuals pdf, komatsu manual pdf, komatsu manuals free.

Ice and snow type classification in Vestfold Hills, East Antarctica, using LANDSAT TM data and ground radiometer measurements. Google Scholar Brown, M., Gunn, S. R., and Lewis, H. G., 1999. Support vector machines for optimal classification and spectral unmixing. Google Scholar Della Ventura, A., Rampini, A., Rabagliati, R., and Serandrei-Barbero, R., 1983. Glacier monitoring by satellite. Google Scholar Della Ventura, A., Rampini, A., and Serandrei-Barbero, R., 1987. Development of a satellite remote sensing technique for the study of alpine glaciers. Google Scholar Dozier, J., 1989. Spectral signature of alpine snow-cover from the Landsat Thematic Mapper. Google Scholar Ehrler, C., and Seidel, K., 1995. Mutual effects of the climate change and the alpine snow cover and their influence on the runoff regime evaluated with the aid of satellite remote sensing. In Stein, T. I. (ed.), IGARSS’95; Quantitative Remote Sensing for Science and Applications. Google Scholar Foster, J. L., Hall, D. K., and Chang, 7., 1987. Remote sensing of snow. Google Scholar Fukushima, Y., Watanabe, O., and Higuchi, K., 1991. Estimation of streamflow change by global warming in a glacier-covered high mountain area of the Nepal Himalaya. International association of hydrological sciences. Google Scholar Gupta, R. P., 2003. Remote Sensing Geology, 2nd edn. Google Scholar Haeberli, W., Hoelzle, M., and Suter, S., 1998. Into the second century of worldwide glacier monitoring: prospects and strategies. A contribution to the International Hydrological Programme (IHP) and the Global Environment Monitoring System ( GEMS ), UNESCO Studies and Reports in Hydrology, 56, 228 p. Google Scholar Haeberli, W., Kaab, A., Vonder Muhll, D., and Teysseire, P., 2001. Prevention of outburst floods from periglacial lakes at Grubengletscher, Valais, Swiss Alps. Google Scholar Hall, D. K., Ormsby, J. P., Bindschadler, R. A., and Siddalingaiah, H., 1987.http://kor-ra.ru/UserFiles/hoyt-recurve-manual-2014.xml Characterization of snow and ice zones on glaciers using Landsat Thematic Mapper data. Google Scholar Hall, D. K., Chang, A. T. C., and Siddalingaiah, H., 1988. Reflectances of Glaciers as calculated from the Landsat 5 Thematic Mapper data. Google Scholar Hall, D. K., Riggs, G. A., and Salomonson, V. V., 1995a. Development of methods for mapping global snow-cover using moderate resolution spectroradiometer data. Google Scholar Hall, D. K., Benson, C. S., and Field, W. O., 1995b. Changes of glaciers in Glacier Bay, Alaska, using ground and satellite measurements. Google Scholar Hall, D. K., Foster, J. K., Verbyla, D. L., Klein, A. G., and Benson, C. S., 1998. Assessment of snow-cover mapping accuracy in a variety of vegetation-cover densities in Central Alaska. Google Scholar Hall, D. K., Kelly, R. EJ., Foster, J. L., and Chang, A. T. C., 2005. Estimation of snow extent and snow properties. In Anderson, M. G., (eds.), Encyclopedia of Hydrological Sciences. Climate Change 2007: The physical science basis. Cambridge: Cambridge University Press, 996 pp. Google Scholar Jacobs, J. D., Simms, E. L., and Simms, A., 1997. Recession of the southern part of Barnes Ice Cap, Baffin Island, Canada, between 1961 and 1993, determined from digital mapping of Landsat TM. Google Scholar Justice, C. O., Wharton, S. W., and Holben, B. N., 1981. Application of digital terrain data to quantify and reduce the topographic effect on Landsat data. Google Scholar Khromova, T. E., Osipova, G. B., Tsvetkov, D. G., Dyurgerov, M. B., and Barry, R. G., 2006. Changes in glacier extent in the eastern Pamir, Central Asia, determined from historical data and ASTER imagery. Google Scholar Klein, A. G., Hall, D. K., and Riggs, G. A., 1998. Improving snow-cover mapping in forests through the use of caopy reflectance model. Google Scholar Konig, M., Winther, J. G., and Isaksson, E., 2001. Measuring snow and glacier ice properties from satellite. Google Scholar Kulkarni, A. V., Singh, S. K., Mathur, P.https://skazkina.com/ru/elaboracion-manual-de-funciones-y-procedimientos, and Mishra, V. D., 2006. Algorithm to monitor snow cover using AWiFS data of RESOURCESAT-1 for the Himalayan region. Google Scholar Kulkarni, A. V., Bahuguna, I. M., Rathore, B. P., Singh, S. K., Randhawa, S. S., Sood, R. K., and Dhar, S., 2007. Glacier retreat in Himalayas using Indian Remote Sensing satellite data. Google Scholar Liang, T., Zhang, T., Xie, H., Wu, C., Feng, Q., Huang, Q., and Chen, Q., 2008. Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements. Google Scholar Lillesand, T. M., Meisner, D. E., LaMois-Downs, A., and Denell, R. L., 1982. Use of GOES and TirosINOAA satellite data for snow cover mapping. Google Scholar Miller, D. H., 1953. Snow cover depletion and runoff. Snow Hydrology. Portland: U. S Army Corps of Engineers. Google Scholar Min, X., Watanachaturaporn, P., Varshney, P. K., and Arora, M. K., 2005. Decision tree regression for soft classification of remote sensing data. Google Scholar Nolin, A., and Liang, S., 2000. Progress in bidirectional reflectance modelling and application for surface particulate media: snow and soils. Google Scholar Nolin, A. W., Dozier, J., and Mertes, L. A. K., 1993. Mapping alpine snow using spectral mixture modeling technique. Google Scholar Oerlemans, J., Dyurgerov, M., and Van de Wal, R. S. W., 2007. Reconstructing the glacier contribution to sea-level rise back to 1850. Google Scholar Paul, F., 2000. Evaluation of different methods for glacier mapping using Landsat TM. Google Scholar Paul, F., 2003. The new Swiss glacier inventory 2000: application of remote sensing and GIS. PhD thesis. Zurich, Department of Geography, University of Zurich, 198 pp. Google Scholar Paul, F., Kaab, A., Maisch, M., Kellenberger, T., and Haeberli, W., 2002. The newremote-sensing-derived Swiss glacier inventory: I Methods. Google Scholar Paul, F., Kaab, A., Maisch, M., Kellenberger, T., and Haeberli, W., 2004. Rapid disintegration of Alpine glaciers observed with satellite data. Google Scholar Potts, H. L., 1944. A photographic survey method of forecasting runoff. Google Scholar Quincey, D. C., Lucas, R. M., Richardson, S. D., Glasser, N. F., Hambrey, M. J., and Reynolds, J. M., 2005. Optical remote sensing techniques in high-mountain environments: application to glacial hazards. Google Scholar Racoviteanu, A. E., Williams, M. W., and Barry, R. G., 2008. Optical remote sensing of glacier characteristics: a review with focus on the Himalaya. Google Scholar Rampini, A., Brivio, P. A., Rota Nodari, F., and Binaghi, E., 2002. Mapping Alpine glaciers changes from space. Google Scholar Rango, A., and Martinec, J., 1981. Accuracy of snowmelt runoff simulation. Google Scholar Riggs, G. A., Hall, D. K., and Salomonson, V. V., 1994. A snow index for the Landsat thematic mapper and moderate resolution imaging spectroradiometer. Google Scholar Rott, H., and Markl, G., 1989. Improved snow and glacier monitoring by the Landsat Thematic Mapper. Google Scholar Salomonson, V. V., and Appel, I., 2006. Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results. Google Scholar Settle, J. J., and Drake, N. A., 1993. Linear mixing and the estimation of ground cover proportions. Google Scholar Shiyin, L., Wenxin, S., Yongping, S., and Gang, L., 2003. Glacier changes since the Little Ice Age maximum in the western Qilian Shan, northwest China, and consequences of glacier runoff for water supply. Google Scholar Sidjak, R. W., and Wheate, R. D., 1999. Glacier mapping of the Illecillewaet icefield, British Columbia, Canada, using Landsat TM and digital elevation data. Google Scholar Singh, P., and Singh, V. P., 2001. Snow and Glacier Hydrology. The Netherlands: Kluwer Academic Publishers, pp. 742. Google Scholar Sirguey, P., Mathieu, R., Arnaud, Y., Khan, M. M., and Chanussot, J., 2008. Improving MODIS spatial resolution for snow mapping using wavelet fusion and ARSIS concept. Google Scholar Sirguey, P., Mathieu, R., and Arnaud, Y., 2009. Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand: methodology and accuracy assessment. In Proceedings of EARSeL LIS-SIG Workshop, Berne, February, 2005. Google Scholar Tait, A. B., Hall, D. K., Foster, J. L., and Armstrong, R. L., 1999. Utilizing multiple datasets for snow-cover mapping. Google Scholar Tarble, R. D., 1963. Areal distribution of snow as determined from satellite photographs. Google Scholar Vikhamar, D., and Solberg, R., 2002. Subpixel mapping of snow cover in forests by optical remote sensing. Google Scholar Vikhamar, D., and Solberg, R., 2003. Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data. Google Scholar Wang, J., and Li, W., 2003. Comparison of methods of snow cover mapping by analyzing the solar spectrum of satellite remote sensing data in China. Google Scholar Warren, S. G., 1982. Optical properties of snow. Google Scholar Warren, S. G., and Wiscombe, W. J., 1980. A model for the spectral albedo of snow, II, Snow containing atmospheric aerosols. Google Scholar Williams, R. S., Jr., and Hall, D. K., 1993. Glaciers. In Gurey, R. J., Foster, J. L., and Parkinson, C. L. (eds.), Atlas of Satellite Observations Related to Global Change. Google Scholar Williams, R. S., Hall, D. K., and Benson, C. S., 1991. Analysis of glacier facies using satellite techniques. Google Scholar Winther, J. G., and Hall, D. K., 1999. Satellite derived- snow coverage related to hydropower production in Norway: present and future. Google Scholar Winther, J. G., Gerland, S., Orback, J. B., Ivanov, B., Blanco, A., and Boike, J., 1999. Spectral reflectance of melting snow in a high Arctic watershed on Svalbard: some implications for optical satellite remote sensing studies. Google Scholar Wiscombe, W. J., and Warren, S. G., 1980. A model for the spectral albedo of snow, I, Pure snow. Google Scholar Xiao, X., Shen, Z., and Qin, X., 2001. Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: a normalized difference snow and ice index. Google Scholar Zeng, Q., Cao, M., Feng, X., Liang, F., Chen, X., and Sheng, W., 1984. A study of spectral reflection characteristics for snow, ice and water in the north of China. In Hydrological Applications of Remote Sensing and Remote Data Transmission: Proceedings of the Hamburg Symposium.In: Singh V.P., Singh P., Haritashya U.K. (eds) Encyclopedia of Snow, Ice and Glaciers. Encyclopedia of Earth Sciences Series. Springer, Dordrecht. For an actual basin the required input to the Snowmelt Runoff Model (SRM) for different elevation zones is given by the snow covered area (S), temperature (T) and precipitation (P) values. The daily runoff can be simulated or forecasted. The curves are labelled with the average water equivalent of snow on the starting date 1 May.The daily runoff can be simulated or fore- casted. In addition, maps of regional water equivalent based on the seasonal snow cover accumulation during winter time are derived. INTRODUCTION Periodical monitoring of snow covered areas during the snowmelt season is an essential requirement for short-term forecasts of daily river flows as well as for seasonal forecasts of runoff volume. The progress in remote sensing by satellites makes it increasingly possible to obtain such data for numerous mountain basins in the world. Thanks to these developments, the Snowmelt Runoff Model (SRM), which directly incorporated the snow covered area even before the advent of remote sensing, has been already applied by various institutes and agencies in over 50 basins on 5 continents, including the Himalayan region. This paper brings illustrative examples of the evaluation of satellite data, day-to- day runoff computations and assessment of snow accumulation from areal snow cover monitoring. SATELLITE SNOW COVER MAPPING The gradual decrease of the areal extent of seasonal snow cover is a typical phenomenon of the snowmelt season in mountain basins. Fig. 1 shows a sequence of satellite images of the snow cover in the Rhine basin at Felsberg (3250 km 2, 560-3614 m a.s.l.). The daily values of snow coverage constitute an important input variable for the SRM, as is evident from the model flow chart shown in Fig. 2. For snowmelt runoff computations by the SRM model, the basin was divided into 5 elevation zones (A,B,C,D,E) and depletion curves of snow covered areas were interpolated between measured points for each zone separately (Fig. 3). The capabilities of various satellites for snow cover monitoring and mapping are summa- rised in TABLE 1. Various time lags can be introduced.Runoff data serve to evaluate the model accuracy and pos- sibly for a better derivation of recession coefficients. In the forecast mode, the values of the variables must be known or estimated in advance for the duration of each forecast period. From the forecasted temperatures, the future course of the depletion curves of snow coverage is derived by using the so-called modified depletion curves (Martinec, 1985; Hall and Martinec, 1985; Martinec and Rango, 1987) which are drawn in advance for the given basin. In real time conditions, snow covered areas have to be evaluated as quickly as possible after each satellite overflight. The Electric Company of North-East Switzerland (NOK) requires runoff forecasts for an improved operation of two hydroelectric stations in the Felsberg basin. As an example of model test runs, Fig. 4 shows a runoff simulation for the catchment area of the station Tavanasa (215 km 2, 1277-3210 m a.s.l.) (Seidel et al., 1989). The daily snow covered areas were provided by the depletion curves which were derived from satellite data similar as those in Fig. 2. Temperatures and precipitation amounts were provided by the station Disentis (1170 m a.s.l.). For real time forecasts which are envisaged in 1993, it will be necessary to use the fore- casted values of temperature and precipitation always several days ahead. It is to be ex- pected that possible deviations of meteorological forecasts from actual values will deterio- rate the accuracy of runoff forecasts. However, the SRM computer program (Martinec et al., 1992) includes an automatic updating by the measured runoff each 1 to 7 days so that major errors can be avoided. Forecasted temperature and precipitation values can also be subsequently corrected by actual measurements. Operational runoff forecasts on a weekly basis are in progress for the Beas-Thalot basin (5144 km 2, 1100-6400 m a.s.l.) and Parbati basin (1154 km 2, 1500-6400 m a.s.l.) in the Himalayas (Kumar, 1992, private communication). To this effect, the so called modified depletion curves of the snow coverage are derived, which relate the gradual decrease of the snow covered area to cumulative daily snowmelt depths computed by the degree-day method or, if possible, in a more refined way. Fig. 5 shows modified depletion curves for the respective elevation zones of the Felsberg basin with a starting date of 1 April 1985. Consequently, the average water equivalent on 1 April 1985 for each zone can be evaluated from the area under each curve. Furthermore, Fig. 5 shows the so-called exclusive modified depletion curves, in which the energy (or degree-days) necessary to melt snow fallen after 1 April 1985 is excluded from computations of the cumulative snowmelt depth. The curves thus indicate the snow accumulation on 1 April 1985 in terms of the areal average water equivalent. As expected, the snow accumulation increases with the altitude. In the highest zone E, the curve levels off at 21 of the zone area, which corresponds to the glacierized area. This method has been verified by comparing the areal values with point measurements carried out in a special research project (Martinec and Rango, 1987). Normal snow gauging networks do not provide a sufficient density of measurements for a reliable as- sessment of snow reserves, especially in high parts of basins. The average water equiva- lent of snow can be evaluated not only in hydrological basins, but also on any desired area sufficiently large with regard to the spartial resolution of satellites. For example, as shown in Fig. 7, there was twice as much snow on 1 April 1982 in the partial area 2 (zone C, 1600-2100 m a.s.l.) than in the same zone of the area 9 (Martinec et al., 1991). Incidentially, there are no much usable snow gauging stations in the areas 1, 5, 6, 7 so that the described method provides the only means of assessing the snow distribution there. The stored water volume thus evaluated can serve for seasonal runoff forecasts. It can be objected that the in- formation is available only at the end of the snowmelt season, when the derivation of modified depletion curves is completed. However, a set of modified depletion curves can be prepared from years with various accumulations of snow, as shown in Fig. 8 for the Dischma basin (43.3 km 2, 1668-3146 m a.s.l.), belonging to the area 9 in Fig. 6. In a The curves are labelled with the average water equivalent of snow on the starting date 1 May. The positions of points for the respective years indicate different snow reserves on 1 May. The points in Fig. 9 refer to the cumulative snowmelt depth (new snow excluded) in the elevation zone 2100-2600 m a.s.l. one month after the starting date of the snowmelt season (1 May at this elevation) and to the corresponding snow covered areas. The years with a low accumulation fall near to the modified depletion curve labelled with 35 cm, while snow-rich years 1970 and 1975 fall near the 100 cm - curve. If, in a future year, the cumulative snowmelt depth reaches for example 30 cm and the snow coverage at the same time decreases to 50, the modified depletion curve which indicates the initial water equivalent of 35 cm can be identified for that year and for the elevation zone 2100-2600 m a.s.l. In 1970, the total water equivalent of melted new snow slightly exceeded the total melt depth of the snow cover of 1 May so that a negative x-coordinate results. In such years it is difficult to identify the proper modified depletion curve within a few weeks. However, a correlation can be derived between areal values from snow cover mapping and a reliable snow gauging station. This can then be used as index to accelerate the decision. For example, station Weissfluhjoch (2540 m a.s.l.) measured 1192 mm of water equivalent on 1 May 1970 and only 621 mm on 1 May 1971 which would have helped at once to select the proper modified depletion curve. At all events, the snow cover monitoring and the totalling of daily snowmelt depths should be continued during the snowmelt season in order either to confirm the original selection of the modified depletion curve (and the seasonal runoff forecast) or, if necessary, to revise it. CONCLUSIONS AND OUTLOOK In mountainous areas and particularly in the Himalayan region, snow is a major runoff factor. Consequently, snowmelt runoff modelling is important for water resources man- agement and for water power generation. The Himalayan Snowmelt Management System Thus the described combination of satellite snow cover mapping with the SRM model can be used for the following purposes: 1. Runoff simulations in order to verify the predetermined model parameters and quality of hydrometerological data. 2. Runoff simulation in basins in which no runoff measurements exist, for planning water management. 3. Real time short-term forecasts for improved operation of reservoirs and electricity production. 4. Seasonal forecasts of the runoff volume based on the evaluation of the water storage in the snow cover by the modified depletion curves of snow coverage. In view of the expected global warming and climate change, the SRM computer program is now providing following option: 5. Runoff simulation and simulation of snow covered areas for various climate scenarios, that is to say for changes of temperature and precipitation assumed for the future.For snowmelt runoff computations, it is advisable to evaluate SCA for different elevation zones, in w hich the basin is subdivided.. Remote Sensing of Snow Cover Chapter Full-text available Jan 2005 Dieter Scherer Dorothy K. Hall Volker Hochschild Anne E. Walker This chapter is dedicated to remote sensing of snow as one of the mostAfter a short introduction, general approaches in optical, thermalFinally, conclusionsView Show abstract. Modelling attempts focusing on glaciers and ice sheets began in connection with the growing concern about potentially enhanced greenhouse warming and the effects on global sea level (e.g., Braithwaite and Olesen, 1990a;Oerlemans and Fortuin, 1992), as well as an increased interest in tapping glacial water for hydroelectric purposes (e.g., Braithwaite and Thomsen, 1989;van de Wal and Russel, 1994). The most recent development concerns the incorporation of remote sensing data into melt models, providing a particularly useful tool in basins inaccessible for detailed ground surveys (e.g., Seidel and Martinec, 1993; Reeh et al., 2002). In addition, much effort is focused on enhancing the spatial and temporal resolution of melt models by moving from point-scale to distributed modelling and from, for example, daily time steps to hourly time steps (Burlando et al., 2002).. Glacier Melt: A Review of Processes and Their Modelling Article Jan 2002 PROG PHYS GEOG Regine Hock Long duration stratospheric balloons offer an exciting and complementaryVarious current projects, suchA simplified OSSE (Observation. Sys- tem Simulation Experiment) is used to assimilate horizontal windResults from these experiments will be presented, with the emphasis onView Show abstract. The results should be regarded in the light of these difficulties. In Alpine regions high accuracy runoff modelling is possible due to more detailed data available, as reported earlier (Seidel and Martinec., 1992).. Modelling Runoff And Impact Of Climate Change In Large Himalayan Basins Article Full-text available Nov 2000 Klaus Seidel Joann Lacey Martinec Michael F. Baumgartner The runoff regime in the basins of the rivers Ganges (917'444 km ) and Brahmaputra (547'346 km ) is modelled from precipitation, remotely sensed snow covered areas and temperatures. The runoff cycle roughly corresponds to a calendar year. In view of the small proportion of snowmelt, it is mainly governed by the distribution of rainfalls resulting in flow peaks in the summer and recession flow in the winter. The accuracy of runoff simulations is acceptable in view of the available data and because the SRM model was for the first time used in basins of this order of magnitude. View Show abstract. Snowmelt runoff modelling in high mountain areas based on periodical snow cover mapping derived from earth observation satellites has been regularily reported in the last decades (Martinec 1973, Baumgartner et al., 1985, Kumar et al., 1991, Martinec et al, 1991, Seidel and Martinec, 1992, Rango and Martinec, 1999. Advanced methods of satellite data processing make it possible to account for specific features of glacier melt if high resolution iamges are available (Ehrler et al., 1997, Schaper et al., 1999, Schaper et al., 2000a, Schaper et al., 2000b.. Mapping Of Snow Cover And Glaciers With High Resolution Remote Sensing Data For Improved Runoff Modelling Article Full-text available Nov 2000 Klaus Seidel Jesko Schaper Joann Lacey Martinec The study presents investigations of the runoff from snow and ice, carried out in the high alpine basin of the Rhne river at Sion (3371 km, 491 -- 4634 m a.s.l.). Using satellite remote sensing data, features like the snow coverage in the whole basin, the gradually decreasing snow coverage on glaciers and the area of exposed ice have been mapped. The periodical monitoring of the basin is based on Landsat-TM data enabling snow and ice areas to be distinguished. The satellite imagery has been geocoded with high accuracy and interpreted using supervised classification techniques. Due to the high part of glaciers in the basin (17 glaciered) special attention has been paid to the melt conditions of ice in comparison with the melt conditions of snow. Conditions for a norm year in terms of normalized daily values derived for the time period 1961 to 1990 for snow cover depletion, temperature and precipitation have been established. Based on norm year conditions the effect of climate change was evaluated for the scenarios 2030 and 2100, which are characterized by increasing temperatures during winter and summer and increased precipitation during winter. The results show the influence of increased summer icemelt in the basin Rhne-Sion. View Show abstract Snow Runoff Models Using Remotely Sensed Data Chapter Jul 2011 E. Parlow The paper shows how satellite data can be used to analyse snow coverage in mountainous areas and to use satellite data toSnow melt models are mostly based on a degree-day-factor approach which is taken to parameteriseAnother approach are physically based models with a complete description of theAn examples of snow coverage classification using a degree-days-approachThe analysis of the spatially distributed net radiation as the driving energetic factor of snowView Show abstract Statistical Evaluation of MODIS Snow Cover Products With Constraints From Streamflow and SNOTEL Measurement Article Jan 2005 REMOTE SENS ENVIRON Xiaobing Zhou Hongjie Xie Jan M. H. Hendrickx Using streamflow and Snowpack Telemetry (SNOTEL) measurements as constraints, the evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily and 8-day snow-cover products is carried out using the Upper Rio Grande River Basin as a test site. A time series of the snow areal extent (SAE) of the Upper Rio Grande Basin is retrieved from the MODIS tile h09v05 covering the time period from February 2000 to June 2004 using an automatic Geographic Information System (GIS)-based algorithm developed for this study. Statistical analysis between the streamflow at Otowi (NM) station and the SAE retrieved from the two MODIS snow-cover products shows that there is a statistically significant correlation between the streamflow and SAE for both products. This relationship can be disturbed by heavy rainstorms in the later springtime, especially in May. Clouds are the major cause for reduction of the overall accuracy of the MODIS daily product. Improvement in suppressing clouds in the 8-day product is obvious from this comparison study. The sacrifice is the temporal resolution that is reduced from 1 to 8 days.