scholarly journals Mapping Fluctuations of Hispar Glacier, Karakoram, using Normalized Difference Snow Index (NDSI) and Normalized Difference Principal Component Snow Index (NDSPCSI)

2021 ◽  
Vol 11 (4) ◽  
pp. 48-55
Author(s):  
Nausheen Mazhar ◽  
Dania Amjad ◽  
Kanwal Javid ◽  
Rumana Siddiqui ◽  
Muhammad Ameer Nawaz ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Principal Component ◽  
Maximum Temperature ◽  
Mountain Range ◽  
Snow Covered Area ◽  
Covered Area ◽  
Hazard Zone ◽  
Glacier Ice ◽  
Normalized Difference Snow Index ◽  
Hunza River

Investigation of the fluctuations in the snow-covered area of the major glaciers of the Karakoram range is essential for proper water resource management in Pakistan, since its glaciers are responding differently to the rising temperatures. The objective of this paper is to map snow covered area of Hispar glacier in Hunza river basin for the years 1990, 2010 and 2018. Two techniques, (NDPCSI) Normalized Difference Principal Component Snow Index and (NDSI) Normalized Difference Snow Index were used. Hispar glacier of the Hunza basin has lost 114 km2 of its ice cover area, during the last 28 years, with an alarming annual retreat rate of 1.67 km2 of glacier ice from 1990 to 2018. Hunza basin experienced a +1°C rise in both mean minimum and mean maximum temperature during 2007 to 2018.as a result, Karakorum ice reserves have been affected by rising temperature of the region. Due to temperature rise, retreat of snowcovered area of Hispar, Karakoram mountain range shows a shift in the cryospheric hazard zone.

scholarly journals Modern glacier dynamics in Mt. Tsambagarav in the Mongolian Altai Mountain Range

2020 ◽  
Vol 51 ◽  
pp. 12-20
Author(s):  
Tuyagerel Davaagatan ◽  
Alexander Orkhonselenge
Keyword(s):  
Remote Sensing Data ◽  
Principal Component ◽  
Mountain Range ◽  
Glacier Area ◽  
Altai Mountain ◽  
Mongolian Altai ◽  
Surface Water Resources ◽  
Highly Sensitive ◽  
Glacier Dynamics ◽  
Normalized Difference Snow Index

This study presents the modern glacier dynamics in Mt. Tsambagarav in the Mongolian Altai Mountain Range over the last four decades. This is the first review of this type of glacier dynamics for this massif. Changes in glacier area in Mt. Tsambagarav are estimated using normalized indexes (Normalized Difference Snow Index and Normalized Difference Principal Component Snow Index). Spatial distribution of the modern glaciers delineated with Landsat Multispectral Scanner (MSS: resolution of 80 m), Landsat Thematic Mapper (TM: resolution of 30 m) and Landsat Operational Land Imager (OLI: resolution of 30 m) imageries. Result shows that Mt. Tsambagarav has lost 51.7% of the glacier area from 132.24 km2 in 1977 to 63.92 km2 in 2017. The loss in glacier area for Mt. Tsambagarav during the last 40 years reflect the rapid response of the modern glacier to climate change, i.e., it is highly sensitive to solar insolation and/or rapidly rising local and regional mean annual temperatures. The remote sensing data and field survey suggest that the modern glaciers would be disappeared on a scale of decades. Rapid melting of the glacier in this massif contributes to surface water resources in western Mongolia. This study demonstrates the importance of spatial analysis in the remote area for understanding the context of changes in the modern glaciers.

scholarly journals Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

2017 ◽  
Author(s):  
Hanneke Luijting ◽  
Dagrun Vikhamar-Schuler ◽  
Trygve Aspelien ◽  
Mariken Homleid
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Water Resource Management ◽  
Snow Accumulation ◽  
Surface Model ◽  
Snow Melt ◽  
Grid Spacing ◽  
Snow Covered Area ◽  
Covered Area ◽  
Southern Norway

Abstract. In Norway, thirty percent of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of approximately 1 km over an area in southern Norway for two years (01 September 2014–31 August 2016), using two different forcing data sets: 1) hourly meteorological forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing), and 2) gridded hourly observations of temperature and precipitation (1 km grid spacing) in combination with the meteorological forecasts from AROME MetCoOp. We present an evaluation of the modeled snow depth and snow cover, as compared to point observations of snow depth and to MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snow melt. The results are promising. Both experiments are capable of simulating the snow pack over the two winter seasons, but there is an overestimation of snow depth when using only meteorological forecasts from AROME MetCoOp, although the snow-covered area throughout the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season, showing that assimilation of snow depth observations into SURFEX/Crocus might be necessary when using only meteorological forecasts as forcing. When using gridded observations, the simulation of snow depth is significantly improved, which shows that using a combination of gridded observations and meteorological forecasts to force a snowpack model is very useful and can give better results than only using meteorological forecasts. There is however an underestimation of snow ablation in both experiments. This is mainly due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snow melt and biases in the forcing data.

scholarly journals A Spectral Analysis of Snow in Mt. Rainier

2018 ◽  
Vol 10 (3) ◽  
pp. 20
Author(s):  
Shrinidhi Ambinakudige ◽  
Pushkar Inamdar ◽  
Aynaz Lotfata
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Thermal Emission ◽  
Major Influence ◽  
Landsat 8 ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Local Water ◽  
Normalized Difference Snow Index

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

2016 ◽  
Vol 9 (1) ◽  
pp. 307-321 ◽  
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Normalized Difference Snow Index ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed, e.g. in case of the frequently used normalized-difference snow index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user, but not by reproducible independent information. PRACTISE V.2.1 addresses this important aspect and shows additional improvements. The Matlab-based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilized as in situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs, was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results show that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible, and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

scholarly journals Towards Automatic UAS-Based Snow-Field Monitoring for Microclimate Research

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1945 ◽  
Author(s):  
Petr Gabrlik ◽  
Premysl Janata ◽  
Ludek Zalud ◽  
Josef Harcarik
Keyword(s):  
Rapid Development ◽  
Field Work ◽  
Satellite System ◽  
Mountain Range ◽  
Ground Control Points ◽  
Snow Covered Area ◽  
Covered Area ◽  
Gnss Receivers ◽  
Global Navigation Satellite ◽  
Optimum Solution

This article presents unmanned aerial system (UAS)-based photogrammetry as an efficient method for the estimation of snow-field parameters, including snow depth, volume, and snow-covered area. Unlike similar studies employing UASs, this method benefits from the rapid development of compact, high-accuracy global navigation satellite system (GNSS) receivers. Our custom-built, multi-sensor system for UAS photogrammetry facilitates attaining centimeter- to decimeter-level object accuracy without deploying ground control points; this technique is generally known as direct georeferencing. The method was demonstrated at Mapa Republiky, a snow field located in the Krkonose, a mountain range in the Czech Republic. The location has attracted the interest of scientists due to its specific characteristics; multiple approaches to snow-field parameter estimation have thus been employed in that area to date. According to the results achieved within this study, the proposed method can be considered the optimum solution since it not only attains superior density and spatial object accuracy (approximately one decimeter) but also significantly reduces the data collection time and, above all, eliminates field work to markedly reduce the health risks associated with avalanches.

Factors Impacting Performance of the NDSI-Based Operational Snow Cover Monitoring Algorithm in Forested Landscapes

2021 ◽  
Author(s):  
Arnab Muhuri ◽  
Simon Gascoin ◽  
Lucas Menzel ◽  
Tihomir S. Kostadinov ◽  
Adrian A. Harpold ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Solar Irradiance ◽  
Forest Cover ◽  
Strong Dependence ◽  
Mountain Range ◽  
Snow Covered Area ◽  
Covered Area ◽  
Forested Landscapes ◽  
Monitoring Algorithm

<p>In cold regions of the world with significant forest cover, a notable volume of precipitated snow resides under the forest cover. In such regions, snow is an abundant and valuable natural resource and assessing the winter extent of snow precipitation is particularly important for forecasting hydroelectric power potential, managing forests for maximizing the spring snowmelt yield, and monitoring animal habitats.</p><p>Forest presents challenging scenarios by obscuring much of the underlying snow over the forest floor from the view of the imaging spaceborne sensors. Moreover, due to the prevalence of mixed pixels, particularly in the forested landscapes, merely binarizing pixels into snow/snow-free can introduce errors while integrating the snow-covered area (SCA) information for hydro-climatological modeling. Therefore, the fractional snow-covered area (fSCA), which is a finer representation of the binary SCA and defines the snow-covered fraction of the pixel area, is a more reliable indicator. </p><p>The recently launched High Resolution Snow & Ice (HRSI) monitoring service by Copernicus allows exploitation of the high-resolution Sentinel-2 data by facilitating free distribution of NDSI-based operational snow cover maps. It also offers the feasibility to estimate the fractional snow cover (FSC) without the requirement of any end-member spectra. In this investigation, we assessed the performance of the NDSI-based operational snow cover area (SCA) monitoring algorithm and the associated FSC with respect to factors influencing the algorithm's performance. The investigation focused over test sites located in the northern Sierra Nevada mountain range in California, US and the central Spanish Pyrenees. The analyses indicated that terrestrial characteristics like tree cover density (TCD) and meteorological factors like incoming solar irradiance impacts the performance of the optical satellite-based snow cover monitoring algorithms. A strong dependence of the algorithm's performance on TCD (negatively correlated) and solar irradiance (positively correlated) was observed.</p>

A Spectral Analysis of Snow in Mt. Rainier

2018 ◽  
Vol 10 (3) ◽  
pp. 18
Author(s):  
Shrinidhi Ambinakudige ◽  
Pushkar Inamdar ◽  
Aynaz Lotfata
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Thermal Emission ◽  
Major Influence ◽  
Landsat 8 ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Local Water ◽  
Normalized Difference Snow Index

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

scholarly journals Glacier albedo reduction and drought effects in the extratropical Andes, 1986–2020

2020 ◽  
pp. 1-12
Author(s):  
Thomas E. Shaw ◽  
Genesis Ulloa ◽  
David Farías-Barahona ◽  
Rodrigo Fernandez ◽  
Jose M. Lattus ◽  
...  
Keyword(s):  
Water Security ◽  
Late Summer ◽  
Severe Drought ◽  
Drought Period ◽  
Ice Melt ◽  
Mountain Glaciers ◽  
Snow Covered Area ◽  
Covered Area ◽  
Glacier Ice ◽  
Long Term Trends

Abstract Surface albedo typically dominates the mass balance of mountain glaciers, though long-term trends and patterns of glacier albedo are seldom explored. We calculated broadband shortwave albedo for glaciers in the central Chilean Andes (33–34°S) using end-of-summer Landsat scenes between 1986 and 2020. We found a high inter-annual variability of glacier-wide albedo that is largely a function of the glacier fractional snow-covered area and the total precipitation of the preceding hydrological year (up to 69% of the inter-annual variance explained). Under the 2010–2020 ‘Mega Drought’ period, the mean albedo, regionally averaged ranging from ~0.25–0.5, decreased by −0.05 on average relative to 1986–2009, with the greatest reduction occurring 3500–5000 m a.s.l. In 2020, differences relative to 1986–2009 were −0.14 on average as a result of near-complete absence of late summer snow cover and the driest hydrological year since the Landsat observation period began (~90% reduction of annual precipitation relative to the 1986–2009 period). We found statistically significant, negative trends in glacier ice albedo of up to −0.03 per decade, a trend that would have serious implications for the future water security of the region, because glacier ice melt acts to buffer streamflow shortages under severe drought conditions.

scholarly journals Estimating relationships between snow water equivalent, snow covered area, and topography to extend the Airborne Snow Observatory dataset

2017 ◽  
Author(s):  
Dominik Schneider ◽  
Noah P. Molotch ◽  
Jeffrey S. Deems
Keyword(s):  
Resource Management ◽  
Water Resource Management ◽  
Satellite Images ◽  
Snow Water Equivalent ◽  
Spatial And Temporal Patterns ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Spatio Temporal ◽  
To Come

Abstract. A new spatio-temporal dataset from the ongoing Airborne Snow Observatory (ASO) provides an unprecedented look at the spatial and temporal patterns of snow water equivalent (SWE) over multiple years in the Tuolumne Basin, California, USA. We found that fractional snow covered area (fSCA) significantly improves our ability to model the distribution of SWE based on relationships between SWE, fSCA, and topography. Further, the broad availability of satellite images of fSCA facilitates the transfer of these relationship to different years with minimal degradation in performance (r2 = 0.85, % MAE = 33 %, % Bias = 1 %) compared with models fit on the same day, by considering variations in SWE depth as expressed by differences in fSCA between years. The crux of this proposition is in selecting the model to transfer. We offer a method with which to select a model from another year based on the similarity in SWE distribution at existing snow pillows in the area. Comparison of the best transferred predictions and the selected predictions results in a mild decrease in r2 (0.02) and moderate increases in % MAE (15 %) and % Bias (10 %). The results motivate further refinement in the technique used to select the best model because if these dates can be identified then SWE can be modeled at accuracy levels equivalent to models generated from ASO data collected on the day of interest. Lastly, we found that models from ASO observations of anomalously low snowpacks in 2015 still transferred to other years, although the same cannot be said for the reverse. This research provides a first attempt at extending the value of ASO beyond the observations and we expect ASO will continue to provide insights for improving water resource management for years to come.

Improving Runoff Simulations using Satellite-observed Time-series of Snow Covered Area

2003 ◽  
Vol 34 (4) ◽  
pp. 281-294 ◽  
Author(s):  
R.V. Engeset ◽  
H-C. Udnæs ◽  
T. Guneriussen ◽  
H. Koren ◽  
E. Malnes ◽  
...  
Keyword(s):  
Time Series ◽  
Radar Sensors ◽  
Noaa Avhrr ◽  
Runoff Forecasting ◽  
Snow Covered Area ◽  
Covered Area ◽  
Satellite Sensors ◽  
Major Floods ◽  
Using Data ◽  
Southern Norway

Snowmelt can be a significant contributor to major floods, and hence updated snow information is very important to flood forecasting services. This study assesses whether operational runoff simulations could be improved by applying satellite-derived snow covered area (SCA) from both optical and radar sensors. Currently the HBV model is used for runoff forecasting in Norway, and satellite-observed SCA is used qualitatively but not directly in the model. Three catchments in southern Norway are studied using data from 1995 to 2002. The results show that satellite-observed SCA can be used to detect when the models do not simulate the snow reservoir correctly. Detecting errors early in the snowmelt season will help the forecasting services to update and correct the models before possible damaging floods. The method requires model calibration against SCA as well as runoff. Time-series from the satellite sensors NOAA AVHRR and ERS SAR are used. Of these, AVHRR shows good correlation with the simulated SCA, and SAR less so. Comparison of simultaneous data from AVHRR, SAR and Landsat ETM+ for May 2000 shows good inter-correlation. Of a total satellite-observed area of 1,088 km2, AVHRR observed a SCA of 823 km2 and SAR 720 km2, as compared to 889 km2 using ETM+.

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