Snow and Glacier Studies using GIS and Remote Sensing

In present study, an attempt has been made to understand the spatial distribution pattern of suitability of groundwater quality for domestic use in Hisar district of Haryana state, India by using Geographical Information System (GIS) techniques. Groundwater quality data of year 2008 of Hisar district of Haryana state, India was analyzed for its suitability for drinking purposes. Groundwater samples from 87 borehole locations were used for analysis of different water quality parameters such as Hydrogen ion concentration (pH), Total Dissolved Solids (TDS), Electric Conductivity (EC), Sodium Adsorption Ratio (SAR) and Residual Sodium Carbonate (RSC) and then classified under various categories as per various national and international standards to determine the suitability of water for domestic use. Spatial distribution maps of pH, EC, TDS, RSC, and SAR were prepared in GIS environment. These maps were integrated using GIS for demarcating different groundwater quality zones for domestic usage. Out of the total area of 4174.52 km2, only 4.48 km2 is under medium salinity whereas maximum area (3778.8 km2) falls under very high and 391.2 km2 under very-very high category. Overall the groundwater is not fit for domestic use almost in the entire district.

Fire is a phenomenon occurs in most parts of the world and causes severe financial losses and sometimes, irreparable damages. Many parameters are involved in the occurrence of a fire; some of which are constant over time (at least in a fire cycle), but the others are dynamic and vary over time. Unlike the earthquake, the disturbance of fire depends on a set of physical, chemical, and biological relations. Monitoring the changes to predict the occurrence of fire is efficient in forest management. Method: In this research, the Persian and English databases were structurally searched using the keywords of fire risk modeling, fire risk, fire risk prediction, and remote sensing and the reviewed papers that reviewed predicted the fire risk in the field of Remote Sensing and Geographic Information System were retrieved. Then, the modeling and zoning data of fire risk prediction were extracted and analyzed in a descriptive manner. Accordingly, the study was conducted in 1995-2017. Findings: FuzzyAnalytic Hierarchy Process(AHP) zoning method was more practical among the applied methods and the plant moisture stress measurement was the most efficient among the remote sensing indices. Discussion and Conclusion: The findings of the study indicate that RS and GIS are an effective tool in the study of fire risk prediction.

RESUMEN La utilización de imágenes satelitales permite recolectar información en sitios remotos, abarcando una gran superficie de análisis y monitoreando visualmente áreas de difícil acceso, lo cual implicaría un gran costo tanto en tiempo como de recursos económicos. Esto es importante en una zona como la del Parque Nacional Los Glaciares en la provincia de Santa Cruz, donde se ubica el glaciar Viedma. A pesar de ser el mayor en tamaño del Parque Nacional, este glaciar ha sido muy poco estudiado, por lo que resulta esencial realizar algunas aproximaciones en materia de reconocimiento de su morfología y comportamiento, ya que ocupa una región de importancia natural, hídrica y paisajística. En el presente trabajo se buscó probar metodologías de estudio y clasificación de estructuras de hielo en el Glaciar Viedma combinando imágenes ópticas y de radar. Se estudió la capacidad de la imagen radar de la misión Sentinel 1A de la ESA (European Space Agency: Agencia Espacial Europea) en combinación con una imagen óptica del satélite SPOT 5 (Satellite Pour l'Observation de la Terre: Satélite Para la Observación de la Tierra), a partir de técnicas de procesamiento digitales que integran la información de ambas imágenes. Se destaca la importancia del uso de la tecnología radar como una ayuda invalorable para el estudio de zonas con características climáticas típicas de la Cordillera Patagónica, donde la mayor parte del tiempo se encuentra cubierto de nubes. Se evaluó de esta forma el aporte que ofrecen ambos sensores para definir y caracterizar los tipos de hielo y sus combinaciones con detritos rocosos, agua y nieve, que puede encontrarse en un glaciar de estas magnitudes, generando una cartografía temática de las unidades identificadas. ABSTRACT Remote sensing approaches allow for regular monitoring in remote areas, with large coverage. The increased availability of imagery from these platforms allows extending the measurements of hardly accessible terrains, with low financial costs and longer time spans. This is of main interest in Parque Nacional Los Glaciares in Santa Cruz, where Viedma glacier is placed. Despite it is the greatest glacier in the National Park, it has been slightly investigated, consequently it is of paramount importance to start some approaches regarding its morphology and behavior, since it is located in a natural and enchanting scenery and it is hydric resource. In the following, we present the methodology for classifying ice structures in Viedma glacier working with optical and radar imagery. The capability of the combination of ESA Sentinel 1A radar image and SPOT 5 (Satellite Pour l'Observation de la Terre) optical image was studied. For this, digital processing techniques where applied, incorporating information from both images. We emphasize the importance of radar technology as a valuable support for the monitoring of landscapes with unstable weather, as typical in the Patagonian Cordillera, where most of the time the meteorological condition is cloudy. The contribution of both sensors was evaluated in order to define and characterize different sorts of ice, also combined with soil debris, water and snow, found in such glacier. As a result, we obtained a thematic map with the identified classes.

In this study, we have developed a new thermal snow index viz. S3 thermal snow index (S3TSI) and determined its relationship with two existing thermal snow indices (i.e., normalized difference snow thermal index (NDSTI) and normalized difference thermal snow index (NDTSI)) and with snow cover indices (i.e., normalized difference snow index (NDSI) and S3) and snow cover characteristic indices (i.e., snow grain size index (SGI) and snow contamination index (SCI)) for two different sites such as bare snow cover and snow cover mixed with vegetation, respectively, based on Pearson's correlation coefficients. Whereas NDSTI uses blue band, NDTSI incorporates NDSI. With this analogy, we have developed "S3TSI" by substituting S3 for NDSI since S3 better demarcates snow cover mixed with vegetation. The study was conducted in snow cover test sites of the Chenab basin, Western Himalayas using LANDSAT-8 Operational Land Imager and Thermal Infrared Sensor data of November 2013. The investigation revealed that S3TSI exhibits considerably higher correlation with NDTSI and also with NDSI and S3 in either site except in the case of snow cover mixed with vegetation where NDTSI correlates better with NDSI. All the three thermal snow indices exhibit very high correlation with the snow cover indices in snow cover mixed with vegetation as compared to bare snow cover. However, the thermal snow indices show weak correlation with the snow cover characteristic indices as compared to the snow cover indices in either site. The findings demonstrate the significance of the new thermal snow index as compared to NDSTI and NDTSI.

Snow cover characteristics play a vital role in hydrological and climatological analyses. Snow characteristics have been retrieved using different techniques but no study has been conducted hitherto to determine its relationship with snow cover indices. In the present study the relationship of snow cover characteristics i.e., snow grain size index (SGI) and snow contamination index (SCI) with the snow cover indices viz. normalized difference snow index (NDSI) and S3 index is investigated using LANDSAT 8 OLI data in parts of Chenab Basin, western Himalayas. This task has been accomplished through comparative assessment of the relationship of snow cover characteristics with NDSI and S3: first, over two distinct illumination conditions i.e., sunlit snow cover and snow cover under shadow and second, for two different time periods i.e., November 2013 and February 2014 respectively. The results reveal the following observations. First, in the sunlit snow cover, there occurs positive correlation of both NDSI and S3 with SGI whereas they are negatively correlated with SCI, but in the snow cover under shadow, both NDSI and S3 exhibit negative correlation with SGI and SCI each. Second, S3 shows higher correlation with SGI and SCI than NDSI in the respective illumination conditions and time periods. Third, SGI and SCI portray highly positive correlation between them in the shadow side and a smaller negative correlation in the sunlit side. The results provide improved understanding regarding the relationship of the snow cover characteristics with the snow cover indices.

The Glacial Archaeology in the Austrian Alps (GAAA) project represents pioneer work being first of its kind in Austria. Due to the urgency of losing culturally significant archaeological sites in the region, the GAAA project seeks to determine areas of high interest using GIS. Spatial analyses and predictive methods can be used to diagnose areas of high archaeological potential by incorporating geographic, cultural and historic parameters into GIS.
This poster presents the methodological framework developed by the project thus far, as well as its preliminary results. Implications for further work within the parameters of the project are also introduced. Currently glacierized or recently deglacierized high altitude mountain passes located in our study area are selected to perform locational analyses based on the physical characteristics of the terrain. Glacier extents from the Austrian Glacier Inventory 1969, 1998 and 2004-2012 as well as of the Little Ice Age are used to conduct Emerging Hot Spot Analysis. Data on glacier outlines from 1985, 2003 and 2013 for the Hohe Tauern National Park (East Tyrol) has been kindly provided by B. Robson & D. Hölbling (Robson et al., 2016). Further, the Global Land Ice Measurements from Space (GLIMS) dataset is cross-checked and of which glacier outlines on the border of Austria and Italy were used.  Zones of archaeological significance are predicted using a so-called connectivity surface. The surface represents a calculation of the associated increases in the cost of travel in relation to the best routes through the Alps via calories burnt. Adjustments for carrying up to 25kg in extra weight are also included. Areas that are easier to cross are assumed to have been used more frequently and would thus be more likely to hold cultural remains.

Frozen Pasts - 4th International Glacial Archaeology Symposium, Innsbruck 2016

The hydrograph separation method, previously proposed to quantify base flow, seasonal snowmelt, and glacial melt components in river flows within Upper Indus basin underestimates glacial melt component. This is particularly limiting for highly glacierized watersheds. The limitation has been corrected by a further refinement of the method. The results with the refined procedure are highly consistent with the physical characteristics such as hypsometry and glacier extents of the watershed even though the method itself is completely independent of the physical characteristics of the watershed where it is applied. Glacial melt far outweigh snowmelt in the rivers draining the Karakoram and Zanskar ranges. In the Karakoram, on an annualized basis, glacial melt proportion varies from 43 – 50% whereas snowmelt varies from 27 – 31%. On the other hand, snowmelt dominates over glacial melt in the rivers draining the western Greater Himalayas and the Hindu Kush. Here snowmelt percentage in river discharge varies from 31 – 53% whereas that of glacial melt ranges from 16 to 30%. In the main stem of Upper Indus River, snowmelt fraction in most cases is slightly greater than the glacial melt fraction. In the main stem, snowmelt percentage ranges from 35 – 44% whereas glacial melt percentage ranges from 25 – 36%. Upper Indus River just upstream of Tarbela Reservoir carries annual flows constituted of 70% melt water of which 26% is contributed by glacial melts and 44% by snowmelts. We also show that during the later part of twentieth century and continuing into the early part of twenty first century glacial melt contributions to river discharge has decreased compared to the previous decades. This phenomenon can be ascribed to either basin wide loss of glacial mass in the recent decades in the elevation range from where most of the glacial melt originates or glacier growth and stability due to either reduction in energy inputs or increase in precipitation or both at the high altitude bands wherefrom glacial melt water originates.

The present investigation was conducted to analyze the temporal patterns of snow cover area% (SCA%), air temperature, snowfall and river discharge in parts of Chenab basin, western Himalayas. The relationship of mean SCA% with mean air temperature and river discharge was also tested using Pearson's product-moment correlation at 95% confidence limit and further sensitivity analysis of river discharge to SCA and SCA to air temperature was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day surface reflectance product MOD09A1 was used to delineate SCA during the period 2000-2013. Moreover, variation in the lowest elevation from where snow cover area starts (LESCA) was also analyzed and its relationship with mean air temperature was also studied. Non-parametric method, Mann-Kendall test was employed to determine the trend in the SCA%, air temperature, snowfall and river discharge. The investigation carried out for three meteorological stations i.e. Batote, Reasi and Tandi revealed significant findings. At Batote and Reasi, statistically significant decreasing trends were observed over the period 2000 to 2012, for maximum, minimum and mean air temperature. Mean minimum SCA% exhibited a significant upward trend during 2000-2013 which is corroborated by the significantly increasing trend of mean annual snowfall (Tandi station) from 2000 to 2010. Further, significant decreasing trend of river discharge for the winter season at Batote station from 2000 to 2011 and decreasing trends in the maximum, minimum and mean air temperature at Batote and Reasi stations are also consistent with the increasing trend of SCA%. At both Batote and Reasi stations, mean SCA% exhibited significant negative relationship with the mean air temperature. On the other hand, LESCA exhibited positive correlation with the mean air temperature except in a few months, where negative relationship was seen. Sensitivity analysis of river discharge to SCA exhibited very low values of sensitivity coefficient in most of the months, indicating less sensitivity of river discharge to SCA. On the other hand, sensitivity coefficient of SCA to air temperature exhibited comparatively higher values which indicate SCA is more sensitive to air temperature.

Terrain variables are the main factors affecting the spatial distribution of snow cover. This paper aims to find a relationship between snow-cover area (SCA) and topographic variables (elevation, slope and aspect), using MODIS Terra data (MOD09A1) in parts of the Chenab basin, western Himalayas. The inter-annual variability of SCA% for each month has been analysed for the years 2000 to 2011. The analysis reveals that mean annual SCA value was maximum (37.89%) in 2005 and minimum (32.07%) in 2001. The slope classes with maximum and minimum SCA% are 5°-10°a nd 30°-35°, respectively. Among the 16 aspect classes, the ESE-facing slope evinces maximum SCA%. During the snow accumulation period, the expanse at 3600-4300 m elevation, and in the depletion period, 4300-5000 m elevation are found to have maximum rate of change in SCA% per 100 m rise in elevation, i.e. 3.37% and 3.67%, respectively.