Assessment of Snow-Cover Mapping Accuracy in a Variety of Vegetation-Cover Densities in Central Alaska

Hall, Dorothy K.; Foster, J. L.; Verbyla, David L.; Klein, Andrew G.; Benson, Carl S.

doi:10.1016/s0034-4257(98)00051-0

Cited by 142 publications

(94 citation statements)

References 15 publications

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“…An NDSI value is calculated using the at-satellite reflectance in MODIS band 4 (0.545-0.565 μm) and band 6 (1.628-1.652 μm). The classification of a cell as snow or non-snow is based on the NDSI value, the reflectance in MODIS band 2 (0.841-0.876 μm) as well as the Normalized Difference Vegetation Index (NDVI) value for forested area (Hall et al 1998(Hall et al , 2002. The NDVI values are calculated in the similar manner as the NDSI using the reflectance in visible red and near-infrared bands (Running et al 2004).…”

Section: Methodsmentioning

confidence: 99%

An analysis of snow cover changes in the Himalayan region using MODIS snow products and in-situ temperature data

Maskey

Uhlenbrook

Ojha³

2011

Climatic Change

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Amidst growing concerns over the melting of the Himalayas' snow and glaciers, we strive to answer some of the questions related to snow cover changes in the Himalayan region covering Nepal and its vicinity using Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products from 2000 to 2008 as well as in-situ temperature data from two high altitude stations and net radiation and wind speed data from one station. The analysis consists of trend analysis based on the Spearman's rank correlation on monthly, seasonal and annual snow cover changes over five different elevation zones above 3,000 m. There are decreasing trends in January and in winter for three of the five elevation zones (all below 6,000 m), increasing trends in March for two elevation zones above 5,000 m and increasing trends in autumn for four of the five elevation zones (all above 4,000 m). Some of these observed trends, if continue, may result in changes in the spring and autumn season river flows in the region. Dominantly negative correlations are observed between the monthly snow cover and the in-situ temperature, net radiation and wind speed from the Pyramid station at 5,035 m (near Mount Everest). Similar correlations are also observed between the snow cover and the in-situ temperature from the Langtang station at 3,920 m elevation. These correlations explain some of the observed trends and substantiate the reliability of the MODIS snow cover products.

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Section: Methodsmentioning

confidence: 99%

An analysis of snow cover changes in the Himalayan region using MODIS snow products and in-situ temperature data

Maskey

Uhlenbrook

Ojha³

2011

Climatic Change

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“…Snow can be discriminated from other features such as clouds due to higher Normalized Difference Snow Index (NDSI) values (Hall et al, 1998). The MODIS snow maps use a conservative cloud mask to determine clouds with the MODIS cloud mask data product (MOD35 L2) (Riggs et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products

Tong

Déry

Jackson

2009

Hydrol. Earth Syst. Sci.

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Abstract. A spatial filter (SF) is used to reduce cloud coverage in Moderate ResolutionImaging Spectroradiometer (MODIS) 8-day maximum snow cover extent products (MOD10A2) from 2000-2007, which are obtained from MODIS daily snow cover extent products (MOD10A1), to assess the topographic control on snow cover fraction (SCF) and snow cover duration (SCD) in the Quesnel River Basin (QRB) of British Columbia, Canada. Results show that the SF reduces cloud coverage and improves by 2% the accuracy of snow mapping in the QRB. The new product developed using the SF method shows larger SCF and longer SCD than MOD10A2, with higher altitudes experiencing longer snow cover and perennial snow above 2500 m. The gradient of SCF with elevation (d(SCF)/dz) during the snowmelt season is 8% (100 m) −1 . The average ablation rates of SCF are similar for different 100 m elevation bands at about 5.5% (8 days) −1 for altitudes <1500 m with decreasing values with elevation to near 0% (8 days) −1 for altitudes >2500 m. Different combinations of slopes and aspects also affect the SCF with a maximum difference of 20.9% at a given time. Correlation coefficients between SCD and elevation attain 0.96 (p<0.001). Mean gradients of SCD with elevation are 3.8, 4.3, and 11.6 days (100 m) −1 for the snow onset season, snowmelt season, and entire year, respectively. The SF decreases the standard deviations of SCDs compared to MOD10A2 with a maximum difference near 0.6 day, 0.9 day, and 1.0 day for the snow onset season, snowmelt season, and entire year, respectively.

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“…Additionally, water and snow/ice classes were added to the masks. To do so, the Normalized Difference Snow Index (NDSI) [23] and tassel-cap brightness [24] were used to locate potential water and snow/ice regions in the imagery. These proposed masks were then hand-edited to include regions missed by the thresholding and to remove regions incorrectly included.…”

Section: Data Setsmentioning

confidence: 99%

Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing

2014

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The use of Landsat data to answer ecological questions is greatly increased by the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, SPARCS: Spatial Procedures for Automated Removal of Cloud and Shadow. The method uses a neural network approach to determine cloud, cloud shadow, water, snow/ice and clear sky classification memberships of each pixel in a Landsat scene. It then applies a series of spatial procedures to resolve pixels with ambiguous membership by using information, such as the membership values of neighboring pixels and an estimate of cloud shadow locations from cloud and solar geometry. In a comparison with FMask, a high-quality cloud and cloud shadow classification algorithm currently available, SPARCS performs favorably, with substantially lower omission errors for cloud shadow (8.0% and 3.2%), only slightly higher omission errors for clouds (0.9% and 1.3%, respectively) and fewer errors of commission (2.6% and 0.3%). Additionally, SPARCS provides a measure of uncertainty in its classification that can be exploited by other algorithms that require clear sky pixels. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of a method for vegetation change detection.

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Assessment of Snow-Cover Mapping Accuracy in a Variety of Vegetation-Cover Densities in Central Alaska

Cited by 142 publications

References 15 publications

An analysis of snow cover changes in the Himalayan region using MODIS snow products and in-situ temperature data

An analysis of snow cover changes in the Himalayan region using MODIS snow products and in-situ temperature data

Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products

Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing

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