Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
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 Ph...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/11/2045 |
| _version_ | 1797533099812192256 |
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| author | Anaí Caparó Bellido Bradley C. Rundquist |
| author_facet | Anaí Caparó Bellido Bradley C. Rundquist |
| author_sort | Anaí Caparó Bellido |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:09:46Z |
| format | Article |
| id | doaj.art-d81443c7d3dd426cb30ce5e72e889634 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:09:46Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d81443c7d3dd426cb30ce5e72e8896342023-11-21T20:55:48ZengMDPI AGRemote Sensing2072-42922021-05-011311204510.3390/rs13112045Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in GrasslandAnaí Caparó Bellido0Bradley C. Rundquist1Department of Geography & GISc, University of North Dakota, P.O. Box 9020, Grand Forks, ND 58202, USADepartment of Geography & GISc, University of North Dakota, P.O. Box 9020, Grand Forks, ND 58202, USASnow 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.https://www.mdpi.com/2072-4292/13/11/2045Fractional Snow Coverimage processingPhenoCamgrasslandsNorth Dakota |
| spellingShingle | Anaí Caparó Bellido Bradley C. Rundquist Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland Remote Sensing Fractional Snow Cover image processing PhenoCam grasslands North Dakota |
| title | Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland |
| title_full | Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland |
| title_fullStr | Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland |
| title_full_unstemmed | Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland |
| title_short | Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland |
| title_sort | semi automatic fractional snow cover monitoring from near surface remote sensing in grassland |
| topic | Fractional Snow Cover image processing PhenoCam grasslands North Dakota |
| url | https://www.mdpi.com/2072-4292/13/11/2045 |
| work_keys_str_mv | AT anaicaparobellido semiautomaticfractionalsnowcovermonitoringfromnearsurfaceremotesensingingrassland AT bradleycrundquist semiautomaticfractionalsnowcovermonitoringfromnearsurfaceremotesensingingrassland |