scholarly journals SPATIO-TEMPORAL CHANGE IN SNOW COVER AREA USING RS & GIS IN THE GORI GANGA WATERSHED, KUMAUN HIGHER HIMALAYA

2021 ◽  
Vol 9 (03) ◽  
pp. 30-34
Author(s):  
D.S. Parihar ◽  
◽  
J.S. Rawat ◽  
Keyword(s):  
Global Warming ◽  
Snow Cover ◽  
Temporal Change ◽  
Average Rate ◽  
Landsat Tm ◽  
Snow Cover Area ◽  
Time Periods ◽  
Spatio Temporal ◽  
Normalized Difference Snow Index ◽  
Landsat Satellite

Present research paper is an attempt to examine the dynamics of snow cover by using Normalized Difference Snow Index (NDSI) in Gori Ganga watershed, Kumaun Himalaya, Uttarakhand (India). For the study of snow cover of Landsat satellite imageries of three different time periods like Landsat TM of 1990, Landsat TM of 1999 and Landsat TM 2016 were used. Geographical distribution of snow cover reveals that in 1990 about 30.97% (678.87 km2), in 1999 about 25.77% (564.92 km2) area of the Gori Ganga watershed was under snow cover while in 2016 the snow cover was found only 15.08% (330.44 km2). These data suggest that due to global warming about 348.43 km2 snow cover of Gori Ganga watershed has been converted into non-snow cover area at an average rate 13.40 km2/year during the last 26 years.

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

2017 ◽  
Author(s):  
Stefan Härer ◽  
Matthias Bernhardt ◽  
Matthias Siebers ◽  
Karsten Schulz
Keyword(s):  
Snow Cover ◽  
Threshold Value ◽  
Threshold Dynamics ◽  
General Applicability ◽  
Abstract Knowledge ◽  
Snow Cover Extent ◽  
Moisture Fluxes ◽  
Spatio Temporal ◽  
Normalized Difference Snow Index ◽  
Landsat Satellite

Abstract. Knowledge about the current snow cover extent is essential for characterising energy and moisture fluxes at the earth 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 spatio-temporal 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. Both sites, the Research Catchment Zugspitzplatt (RCZ, Germany) and the Vernagtferner area (VF, Austria), are located within a single Landsat scene. Nevertheless, the long-term analysis of the NDSIthr demonstrated that the NDSIthr at these sites are not correlated and different to the standard threshold of 0.4. For further comparison, a dynamic and locally optimized NDSI threshold was used as well as another literature threshold value. It was shown that large uncertainties in the prediction of the SCA of up to 24.1 % exist in satellite snow cover maps in case the standard threshold of 0.4 is used, but a newly developed calibrated quadratic polynomial model which is accounting 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 has shown that the positive effect of a locally adapted threshold diminishes from a pixel size of 500 m and more which underlines the general applicability of the standard threshold at larger scales.

scholarly journals Cross-Comparison between MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

Climate ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 57 ◽  
Author(s):  
Shubhechchha Thapa ◽  
Parveen K. Chhetri ◽  
Andrew G. Klein
Keyword(s):  
Snow Cover ◽  
Infrared Imaging ◽  
Environmental Data ◽  
Data Series ◽  
Climate Data ◽  
Snow Cover Area ◽  
Transition Zones ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Difference Snow Index

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP (National Polar-Orbiting Partnership) satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS (Moderate Resolution Imaging Spectroradiometer). Although it is speculated that MODIS and VIIRS snow cover products may differ because of their differing spatial resolutions and spectral coverage, quantitative comparisons between their snow products are currently limited. Therefore, this study intercompares MODIS and VIIRS snow products for the 2016 Hydrological Year over the Midwestern United States and southern Canada. Two hundred and forty-four swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (Environmental Data Records) (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI (Normalized Difference Snow Index) Snow Cover product at a snow cover fraction of 30% generated binary snow maps are most comparable to the NOAA VIIRS binary snow product. Overall agreement between MODIS and VIIRS was found to be approximately 98%. This exceeds the VIIRS accuracy requirements of 90% probability of correct typing. The agreement was highest during the winter but lower during late fall and spring. MODIS and VIIRS often mapped snow/no-snow transition zones as a cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicate that VIIRS is mapping more snow cover and less cloud cover compared to MODIS. This is evidenced by the average area of snow in MYD10L2 and VSCMO being 5.72% and 11.43%, no-snow 26.65% and 28.67% and cloud 65.02% and 59.91%, respectively. While VIIRS and MODIS have a similar capacity to map snow cover, VIIRS has the potential to map snow cover area more accurately, for the successful development of climate data records.

Revealing snow cover dynamics in the Hindu Kush Himalaya over the past decades

2020 ◽  
Author(s):  
Kathrin Naegeli ◽  
Carlo Marin ◽  
Valentina Premier ◽  
Gabriele Schwaizer ◽  
Martin Stengel ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Satellite Data ◽  
Weather Forecast ◽  
Short Wave ◽  
Climate Modelling ◽  
Snow Cover Area ◽  
Hindu Kush ◽  
Spatio Temporal ◽  
Area Percentage

<p>Knowledge about the snow cover distribution is of high importance for climate studies, weather forecast, hydrological investigations, irrigation or tourism, respectively. The Hindu Kush Himalayan (HKH) region covers almost 3.5 million km<sup>2</sup> and extends over eight different countries. The region is known as ‘water tower’ as it contains the largest volume of ice and snow outside of the polar ice sheets and it is the source of Asia’s largest rivers. These rivers provide ecosystem services, the basis for livelihoods and most importantly living water for drinking, irrigation, energy production and industry for two billion people, a fourth of the world’s population, living in the mountains and downstream.</p><p>The spatio-temporal variability of snow cover in the HKH is high and studies reported average snow-covered area percentage of 10–18%, with greater variability in winter (21–42%) than in summer (2–4%). However, no study systematically investigated snow cover metrics, such as snow cover area percentage (SCA), snow cover duration (SCD) or snow cover onset (SCOD) and melt-out day (SCMD), for the entire region so far. Here, we thus present unique in-sights of regional and sub-regional snow cover dynamics for the HKH based on almost four decades, an exceptionally long and in view of the climate modelling community valuable timeseries, of satellite data obtained within the ESA CCI+ Snow project.</p><p>Our results are based on Advanced Very High Resolution Radiometer (AVHRR) data, collected onboard the polar orbiting satellites NOAA-7 to -19, providing daily, global imagery at a spatial resolution of 5 km. Calibrated and geocoded reflectance data and a consistent cloud mask pre-processed and provided by the ESA Cloud_cci project as global 0.05° composites are used. The retrieval of snow extent considers the high reflectance of snow in the visible spectra and the low reflectance values in the short-wave infrared expressed in the Normalized Difference Snow Index (NDSI). Additional thresholds related to topography and land cover are included to derive the fractional snow cover of every pixel. A temporal gap-filling was applied to mitigate the influence of clouds. Reference snow maps from high-resolution optical satellite data as well as in-situ station data were used to validate the time series.</p>

scholarly journals ESTIMATION OF SNOW DEPLETION CURVE FOR GANGOTRI BASIN USING MULTI-SOURCE REMOTE SENSING DATA

2021 ◽  
Vol V-3-2021 ◽  
pp. 197-202
Author(s):  
P. Verma ◽  
S. K. Ghosh ◽  
R. Ramsankaran
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Snowmelt Runoff ◽  
Multiple Sources ◽  
Snow Cover Area ◽  
Depletion Curve ◽  
Normalized Difference Snow Index ◽  
Runoff Model ◽  
Key Parameter

Abstract. Snow Depletion Curve derived from satellite images is a key parameter in Snowmelt Runoff Model. The fixed temporal resolution of a satellite and presence of cloud cover in Himalayas restricts accuracy of generated SDC. This study presents an effective approach of reducing temporal interval between two consecutive dates by integrating normalized Snow Cover Area estimated from multiple sources of satellite data. SCA is extracted by using Normalized Difference Snow Index for six snowmelt seasons from 2013 to 2018 for Gangotri basin situated in Indian Himalayas. This work also explores potential of recently launched Sentinel-3A for estimating SCA. Normalized SCA is utilized to eliminate the effect of difference in spatial resolution of various satellites. The result develops an important linear relation between SDC and time with a decrease in snow cover of 0.005/day that may be further refined by increasing the number of snowmelt seasons. This relationship may help scientific community in understanding hydrological response of glaciers to climate change.

Dynamics of Maximum Snow Cover Area and Snow Line Altitude Across Nepal (2003-2018) Using Improved MODIS Data

2020 ◽  
Vol 25 (2) ◽  
pp. 17-24
Author(s):  
Nitesh Khadka ◽  
Nitesh Khadka ◽  
Shravan Kumar Ghimire ◽  
Xiaoqing Chen ◽  
Sudeep Thakuri ◽  
...  
Keyword(s):  
Snow Cover ◽  
Monsoon Season ◽  
Temporal Variations ◽  
Vital Role ◽  
Eastern Region ◽  
Snow Cover Area ◽  
Post Monsoon ◽  
Snow Line ◽  
Elevation Zone ◽  
Spatio Temporal

Snow is one of the main components of the cryosphere and plays a vital role in the hydrology and regulating climate. This study presents the dynamics of maximum snow cover area (SCA) and snow line altitude (SLA) across the Western, Central, and Eastern Nepal using improved Moderate Resolution Imaging Spectroradiometer (MODIS; 500 m) data from 2003 to 2018. The results showed a heterogeneous behavior of the spatial and temporal variations of SCA in different months, seasons, and elevation zones across three regions of Nepal. Further, the maximum and minimum SCA was observed in winter (December-February) and post-monsoon (October-November) seasons, respectively. The inter-annual variation of winter SCA showed an overall negative trend of SCA between 2003 to 2018 at the national and regional scales. The SLA was assessed in the post-monsoon season. At the national scale, the SLA lies in an elevation zone of 4500-5000 m, and the approximate SLA of Nepal was 4750 m in 2018. Regionally, the SLA lies in an elevation zone of 4500-5000 m in the Western and Central regions (approx. SLA at 4750 m) and 5000-5500 m in the Eastern region (approx. SLA at 5250 m) in 2018. The SLA fluctuated with the changes in SCA, and the spatio-temporal variations of SLAs were observed in three regions of Nepal. We observed an upward shift of SLA by 33.3 m yr-1 in the Western and Central Nepal and by 66.7 m yr-1 in Eastern Nepal. This study will help to understand the impacts of climate change on snow cover, and the information will be useful for the hydrologist and water resource managers.

Snow cover mapping using IRS-P6 AWiFS data and the relationships between some climatic factors with snowpack in the northwest of Iran

2015 ◽  
Vol 7 (2) ◽  
pp. 415-429
Author(s):  
M. Seyedielmabad ◽  
H. R. Moradi
Keyword(s):  
Snow Cover ◽  
Climatic Factors ◽  
Unsupervised Classification ◽  
Wide Field ◽  
Snow Cover Area ◽  
Multi Temporal ◽  
Moderate Resolution ◽  
Field Sensor ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Difference Snow Index

In this study, we explored the potential of the multispectral and multi-temporal IRS Advanced Wide Field Sensor (AWiFS) data for mapping of the snow cover in the northwest regions of Iran. The AWiFS snow cover maps, based on the unsupervised classification method, were compared with the estimates of snow cover area derived from the moderate resolution imaging spectroradiometer (MODIS) images based on the normalized difference snow index. Good concurrence was observed with respect to the snow area between the AWiFS features and the MODIS features; however, the snow spatial distribution of the AWiFS features differed from those of the MODIS based on the nonentity of the temporal accordance between two types of features. Also, we explored the relationships between some climatic and topographic factors with the snowpack in the northwest part of Iran. Relationships between some climatic factors with snowpack specifications were obtained, which showed significant correlation only between the components of daily temperature and snow density. The other results showed that the amounts of snowpack depth have significant correlations with the height of the stations and the height classes in 1% surface and snowpack depths showed significant differences together within the different height classes.

scholarly journals Spatio-Temporal Change of Snow Cover and Its Response to Climate over the Tibetan Plateau Based on an Improved Daily Cloud-Free Snow Cover Product

2014 ◽  
Vol 7 (1) ◽  
pp. 169-194 ◽  
Author(s):  
Wei Wang ◽  
Xiaodong Huang ◽  
Jie Deng ◽  
Hongjie Xie ◽  
Tiangang Liang
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Temporal Change ◽  
The Tibetan Plateau ◽  
Spatio Temporal

scholarly journals Changes in Snow Cover Dynamics over the Indus Basin: Evidences from 2008 to 2018 MODIS NDSI Trends Analysis

2020 ◽  
Vol 12 (17) ◽  
pp. 2782
Author(s):  
Sikandar Ali ◽  
Muhammad Jehanzeb Masud Cheema ◽  
Muhammad Mohsin Waqas ◽  
Muhammad Waseem ◽  
Usman Khalid Awan ◽  
...  
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
River Basins ◽  
Google Earth ◽  
Indus Basin ◽  
Least Squares Regression ◽  
Snow Cover Area ◽  
Standard Product ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Difference Snow Index

The frozen water reserves on the Earth are not only very dynamic in their nature, but also have significant effects on hydrological response of complex and dynamic river basins. The Indus basin is one of the most complex river basins in the world and receives most of its share from the Asian Water Tower (Himalayas). In such a huge river basin with high-altitude mountains, the regular quantification of snow cover is a great challenge to researchers for the management of downstream ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily (MOD09GA) and 8-day (MOD09A1) products were used for the spatiotemporal quantification of snow cover over the Indus basin and the western rivers’ catchments from 2008 to 2018. The high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) was used as a standard product with a minimum Normalized Difference Snow Index (NDSI) threshold (0.4) to delineate the snow cover for 120 scenes over the Indus basin on different days. All types of errors of commission/omission were masked out using water, sand, cloud, and forest masks at different spatiotemporal resolutions. The snow cover comparison of MODIS products with Landsat ETM+, in situ snow data and Google Earth imagery indicated that the minimum NDSI threshold of 0.34 fits well compared to the globally accepted threshold of 0.4 due to the coarser resolution of MODIS products. The intercomparison of the time series snow cover area of MODIS products indicated R2 values of 0.96, 0.95, 0.97, 0.96 and 0.98, for the Chenab, Jhelum, Indus and eastern rivers’ catchments and Indus basin, respectively. A linear least squares regression analysis of the snow cover area of the Indus basin indicated a declining trend of about 3358 and 2459 km2 per year for MOD09A1 and MOD09GA products, respectively. The results also revealed a decrease in snow cover area over all the parts of the Indus basin and its sub-catchments. Our results suggest that MODIS time series NDSI analysis is a useful technique to estimate snow cover over the mountainous areas of complex river basins.

Snow Cover Dynamics and its Spatio-Temporal Distribution in the Central Higher Himalaya of India

2021 ◽  
Vol 14 (9) ◽  
pp. 15-22
Author(s):  
Masoom Reza ◽  
Ramesh Chandra Joshi
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Geographical Area ◽  
Temporal Distribution ◽  
Melting Rate ◽  
Optical Data ◽  
Snow Cover Area ◽  
Central Himalayas ◽  
Spatio Temporal ◽  
The Impact

Retreating glaciers, changing timber line and decreasing accumulation of snow in the Himalaya are considered the indicators of climate change. In this study, an attempt is made to observe the snow cover change in the higher reaches of the Central Himalayas. Investigation of climate change through snow cover is very important to understand the impact and adaptation in an area. Landsat thematic and multi spectral optical data with a spatial resolution of 60m and 30m are considered for the estimation and extraction of snow cover. Total 3,369 Km2 snow cover area is lost since 1972 out of total geographical area i.e. 17,227 Km2. The accumulation of snow during winter is lower than the melting rate during summer. The current study identified the decrease of 19.6 % snow cover in 47 years since 1972 to 2019. Composite satellite imageries of September to December show that the major part of the study area covered with snow lies above 3600m. Overall observation indicates that in 47 years, permanent snow cover is decreasing in Central Himalayas.

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