scholarly journals Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

2021 ◽  
Vol 13 (11) ◽  
pp. 2045
Author(s):  
Anaí Caparó Bellido ◽  
Bradley C. Rundquist
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Important Variable ◽  
Field Station ◽  
Near Surface ◽  
Fractional Snow Cover ◽  
Rgb Images ◽  
Phenological Metrics

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

scholarly journals Mapping the Distribution of Buried Glacier Ice – An Example From Lago Delle Locce, Monte Rosa, Italian Alps

1986 ◽  
Vol 8 ◽  
pp. 78-81 ◽  
Author(s):  
W. Haeberli ◽  
F. Epifani
Keyword(s):  
Snow Cover ◽  
Seismic Refraction ◽  
Italian Alps ◽  
Frozen Ground ◽  
Near Surface ◽  
Winter Snow ◽  
Glacier Ice ◽  
Geoelectrical Resistivity ◽  
Monte Rosa

Techniques for mapping the distribution of buried glacier ice are discussed and the results, from a study carried out within the framework of flood protection work in the Italian Alps, are presented. Bottom temperatures of the winter snow cover (BTS) primarily indicate the heat flow conditions in the underlying ground and mainly depend on the presence or absence of an ice layer beneath the surface. Determination of BTS values is therefore an inexpensive method for quickly mapping the near-surface underground ice in areas where there is 1 m or more of winter snow cover. At greater depths, and/or when more detail is required, geoelectrical resistivity soundings and seismic refraction soundings are most commonly used to investigate underground ice. A combination of the two sounding techniques allows the vertical extent and the main characteristics (frozen ground, dead glacier ice) to be determined in at least a semi-quantitative way. Complications mainly arise from irregularity in the horizontal extension of the studied underground ice bodies, and they may have to be overcome by expensive core drillings and borehole measurements. Widespread occurrence of buried glacier ice was observed in morainic deposits, surrounding an ice-dammed lake near Macugnaga, Italy.

scholarly journals Mapping the Distribution of Buried Glacier Ice – An Example From Lago Delle Locce, Monte Rosa, Italian Alps

1986 ◽  
Vol 8 ◽  
pp. 78-81 ◽  
Author(s):  
W. Haeberli ◽  
F. Epifani
Keyword(s):  
Snow Cover ◽  
Seismic Refraction ◽  
Italian Alps ◽  
Frozen Ground ◽  
Near Surface ◽  
Winter Snow ◽  
Glacier Ice ◽  
Geoelectrical Resistivity ◽  
Monte Rosa

Techniques for mapping the distribution of buried glacier ice are discussed and the results, from a study carried out within the framework of flood protection work in the Italian Alps, are presented. Bottom temperatures of the winter snow cover (BTS) primarily indicate the heat flow conditions in the underlying ground and mainly depend on the presence or absence of an ice layer beneath the surface. Determination of BTS values is therefore an inexpensive method for quickly mapping the near-surface underground ice in areas where there is 1 m or more of winter snow cover. At greater depths, and/or when more detail is required, geoelectrical resistivity soundings and seismic refraction soundings are most commonly used to investigate underground ice. A combination of the two sounding techniques allows the vertical extent and the main characteristics (frozen ground, dead glacier ice) to be determined in at least a semi-quantitative way. Complications mainly arise from irregularity in the horizontal extension of the studied underground ice bodies, and they may have to be overcome by expensive core drillings and borehole measurements. Widespread occurrence of buried glacier ice was observed in morainic deposits, surrounding an ice-dammed lake near Macugnaga, Italy.

Accuracy Assessment of MODIS Fractional Snow Cover Product for Eastern Himalayan Catchment

2016 ◽  
Vol 44 (6) ◽  
pp. 977-985 ◽  
Author(s):  
P. Mishra ◽  
V. V. Zaphu ◽  
N. Monica ◽  
A. Bhadra ◽  
A. Bandyopadhyay
Keyword(s):  
Snow Cover ◽  
Accuracy Assessment ◽  
Fractional Snow Cover

scholarly journals Development and Evaluation of a New “Snow Water Index (SWI)” for Accurate Snow Cover Delineation

2019 ◽  
Vol 11 (23) ◽  
pp. 2774 ◽  
Author(s):  
Abhilasha Dixit ◽  
Ajanta Goswami ◽  
Sanjay Jain
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Spectral Characteristics ◽  
Index Theory ◽  
Kappa Statistics ◽  
Water Index ◽  
Snow Water ◽  
Spatio Temporal ◽  
The Individual

The current study started by examining the three most established snow indices, namely the NDSI (normalized difference snow index), S3, and NDSII-1 (normalized difference snow and ice index), based on their capabilities to differentiate snow pixels from cloud, debris, vegetation, and water pixels. Furthermore, considering the limitations of these indices, a new spectral index called the snow water index (SWI) is proposed. SWI uses spectral characteristics of the visible, SWIR (shortwave infrared), and NIR (near infrared) bands to achieve significant contrast between snow/ice pixels and other pixels including water bodies. A three-step accuracy assessment technique established the dominance of SWI over NDSI, S3, and NDSII-1. In the first step, image thresholding using standard value (>0), individual index theory (fixed threshold), histogram, and GCPs (ground control points) derived threshold were used to assess the performance of the selected indices. In the second step, comparisons of the spectral separation of features in the individual band were made from the field spectral observations collected using a spectroradiometer. In the third step, GCPs collected using field surveys were used to derive the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient for each index. The SWI threshold varied between 0.21 to 0.25 in all of the selected observations from both ablation and accumulation time. Spectral separability plots justify the SWI’s capability of extraction and removal of the most critical water pixels in integration with other impure classes from snow/ice pixels. GCP enabled accuracy assessment resulted in a maximum overall accuracy (0.93) and kappa statistics (0.947) value for the SWI. Thus, the results of the accuracy assessment justified the supremacy of the SWI over other indices. The study revealed that SWI demonstrates a considerably higher correlation with actual snow/ice cover and is prominent for spatio-temporal snow cover studies globally.

Development and Evaluation of a New “Snow Water Index (SWI)” for Accurate Snow Cover Delineation

2020 ◽  
Author(s):  
Abhilasha Dixit ◽  
Ajanta Goswami
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Spectral Characteristics ◽  
Index Theory ◽  
Kappa Statistics ◽  
Water Index ◽  
Snow Water ◽  
Spatio Temporal ◽  
The Individual

<p>The current study started by examining the three most established snow indices, namely the NDSI (normalized difference snow index), S3, and NDSII-1 (normalized difference snow and ice index), based on their capabilities to differentiate snow pixels from cloud, debris, vegetation, and water pixels. Furthermore, considering the limitations of these indices, a new spectral index called the snow water index (SWI) is proposed. SWI uses spectral characteristics of the visible, SWIR (shortwave infrared), and NIR (near infrared) bands to achieve significant contrast between snow/ice pixels and other pixels including water bodies. A three-step accuracy assessment technique established the dominance of SWI over NDSI, S3, and NDSII-1. In the first step, image thresholding using standard value (>0), individual index theory (fixed threshold), histogram, and GCPs (ground control points) derived threshold were used to assess the performance of the selected indices. In the second step, comparisons of the spectral separation of features in the individual band were made from the field spectral observations collected using a spectroradiometer. In the third step, GCPs collected using field surveys were used to derive the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient for each index. The SWI threshold varied between 0.21 to 0.25 in all of the selected observations from both ablation and accumulation time. Spectral separability plots justify the SWI’s capability of extraction and removal of the most critical water pixels in integration with other impure classes from snow/ice pixels. GCP enabled accuracy assessment resulted in a maximum overall accuracy (0.93) and kappa statistics (0.947) value for the SWI. Thus, the results of the accuracy assessment justified the supremacy of the SWI over other indices. The study revealed that SWI demonstrates a considerably higher correlation with actual snow/ice cover and is prominent for spatio-temporal snow cover studies globally.</p>

Determination of uncertainty characteristics for the satellite data-based estimation of fractional snow cover

2018 ◽  
Vol 212 ◽  
pp. 103-113 ◽  
Author(s):  
Miia Salminen ◽  
Jouni Pulliainen ◽  
Sari Metsämäki ◽  
Jaakko Ikonen ◽  
Kirsikka Heinilä ◽  
...  
Keyword(s):  
Snow Cover ◽  
Satellite Data ◽  
Fractional Snow Cover
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