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The study's attempt is to investigate snow depth estimation in a GIS environment for observation points in the NW Himalaya. Using ASTER-GDEM with a 30 m resolution, snow density was mapped. The snow depth was collected on different days in the winter season, especially from January to March 2014. For this study, we used nine spatially distributed observation points. Out of nine spatially distributed observation points, eight locations were observed, and one out of each location was used for the cross-validation method. IDW and Kriging, with semivariogram models Gaussian and Linear, were attempting to derive the snow depth from routine manual observation points in the entire area. The study makes an effort to measure the 3D, 2D, and elevation distance of target points from other observation points, which improves the quality of the study. Three error estimation parameters were used: mean absolute error, mean percentage error, and root mean square error by cross-validation test. The result revealed that IDW values (32.51, −37.87, 33.94), Kriging Gaussian (52.54, −1.42, 51.45), and Kriging Linear (38.72, −38.66, 40.11). The minimum and maximum errors are then compared for five days at one observatory point in order to make a decision. According to the study, continually verifying the amount of snow in neighboring avalanche-prone areas offers useful information for assessing the integrity of the snowpack.

Monitoring of snow cover variability is crucial because it is closely linked with mountain water resources, ecosystems, and climate change. For this, moderate-resolution imaging spectroradiometer (MODIS) daily snow cover products (SCPs, version 6) were used over the Chenab river basin (CRB) during 2001–2017. In these data, cloud cover is a significant problem that produces a discontinuity in spatial and temporal scale for long-term snow cover monitoring. Therefore, a sequential non-spectral composite methodology (with five successive steps) was applied to reduce cloud obscuration. Further, the cloud gap-filled SCPs were validated with the indirect method as well as high-resolution satellite data (Landsat-8). Results indicate that the cloud-removed SCPs show an overall efficiency of 92.8 $$ \pm $$ 1.6% with an indirect approach, while an overestimation (9.3%) was observed between Landsat and MODIS snow cover area (SCA) along with higher correlation (R = 0.99, p

The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA) detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs) and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping.

Snow and urban land cover are important due to their role in hydrological management and utility, climate response, social aspects and economic viability, along with influencing the Earth's environment at local, regional and global scale. Hyperspectral data enable identification, characterization and retrieval of these land-cover features based on physical and chemical properties of compositional materials. AVIRISNG hyperspectral airborne data, with synchronous ground observations using field spectroradiometer and collateral instruments, were collected over two widely varied land-cover types, viz. a relatively homogenous area covered by snow in the extreme cold environment of the Himalaya (Bhaga sub-basin, Himachal Pradesh), and a completely heterogeneous urban area of a metropolitan city (Ahmedabad, Gujarat). AVIRIS-NG airborne data were analysed to understand the effect of terrain parameters such as slope and aspect on snow reflectance. Snow grain index using visible and near-infrared (VNIR) bands and absorption peak in the near-infrared (NIR) were used to retrieve grain size in parts of the Himalayan region. A radiative transfer model was used to understand the grain size variability and its effect on absorption peak in NIR. Continuum removal was performed for snow spectral observations obtained from airborne, modelled and field platforms to estimate band depth at 1030 nm. Grain size was observed to vary with altitude from 100 to 500 μm using AVIRIS-NG image. In the urban area, the data also separated pervious and impervious surface cover using spectral unmixing technique, identified several urban features over multispectral data such as buildings with red tiled roofs, metallic surfaces and tarpaulin sheets using the material spectral profiles. Two single-frame superresolution methods namely sparse regression and natural prior (SRP), and gradient profile prior (GPP) were applied on AVIRIS-NG data for the mixed environment around Kankaria Lake in the city of Ahmedabad, which revealed that SRP method was better than GPP, and affirmed by eight indices. Preliminary analysis of AVIRIS-NG imaging over snow-covered areas and densely populated cities indicated utility of future spaceborne hyperspectral missions, particularly for hydrological and climatological applications in such diverse environments.

In the past decade, the availability of voluminous and high-dimensional datasets has immensely emerged with continuous technological innovations to foster new ways to gather and analyze data. Thus, feature selection has become a challenging task in areas of application like text classification, data analysis, prediction, information retrieval etc. Knowledge extraction using machine learning models usually depends on the quality and quantity of data that they operate on. Feature selection is one of the core concepts to identify and remove irrelevant as well as redundant information that may impact accuracy or have no impact on the results. Feature selection methods are discussed in this review paper along with practical limitations. Subsequently, the workflow to solve a feature selection problem is also elaborated with feature selection methods. In feature selection, many surveys and empirical assessments were performed in many areas like classification, prediction, regression, and clustering, respectively. [ABSTRACT FROM AUTHOR]

The present research study assesses the accuracy of the SRTM X band DEM with respect to high accuracy photogrammetric Digital Elevation Model (DEM) for parts of the Himalaya. The high resolution DEM was generated for Manali and nearby areas using digital aerial photogrammetric survey data of 40 cm Ground Sampling Distance (GSD) captured through airborne ADS80 pushbroom camera for the first time in Indian Himalayan context. This high resolution DEM was evaluated with Differential Global Positioning System (DGPS) points for accuracy assessment. The ADS80-DEM gave root mean square error (RMSE) of ~

Release of snow avalanche from a mountain slope depends on various parameters such as snow cover, terrain and meteorological conditions of the region. The precise information of avalanche occurrence in terms of its location and extent is essentially important for hazard mapping and for avalanche occurrence feedback. In the present study, various techniques have been explored for automatic detection and mapping of snow avalanche debris for a part of Western Himalayan region using Sentinel-2 satellite data. Spectral signatures of avalanche and non-avalanche snow collected from the field spectroradiometer survey are used for identifying suitable spectral bands of Sentinel-2 for avalanche debris detection. Techniques such as Ratio Method, Gray Level Co-occurrence Matrix, a new proposed index, i.e. Avalanche Debris Index and Object-Based Image Analysis (OBIA) are applied on satellite images to retrieve the avalanche debris. Retrieved avalanche debris are further compared with the manually digitized avalanche occurred boundaries. The OBIA method has been found the most suitable for avalanche debris detection and mapping using the medium resolution satellite data. [ABSTRACT FROM AUTHOR]