Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions
Fractional snow cover (FSC) is an important parameter to estimate snow water equivalent (SWE) and surface albedo important to climatic and hydrological applications. The presence of forest creates challenges to retrieve FSC accurately from satellite data, as forest canopy can block the sensor’s view...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Geosciences |
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| Online Access: | https://www.mdpi.com/2076-3263/7/3/55 |
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| author | Ali Nadir Arslan Cemal Melih Tanis Sari Metsämäki Mika Aurela Kristin Böttcher Maiju Linkosalmi Mikko Peltoniemi |
| author_facet | Ali Nadir Arslan Cemal Melih Tanis Sari Metsämäki Mika Aurela Kristin Böttcher Maiju Linkosalmi Mikko Peltoniemi |
| author_sort | Ali Nadir Arslan |
| collection | DOAJ |
| description | Fractional snow cover (FSC) is an important parameter to estimate snow water equivalent (SWE) and surface albedo important to climatic and hydrological applications. The presence of forest creates challenges to retrieve FSC accurately from satellite data, as forest canopy can block the sensor’s view of snow cover. In addition to the challenge related to presence of forest, in situ data of FSC—necessary for algorithm development and validation—are very limited. This paper investigates the estimation of FSC using digital imagery to overcome the obstacle caused by forest canopy, and the possibility to use this imagery in the validation of FSC derived from satellite data. FSC is calculated here using an algorithm based on defining a threshold value according to the histogram of an image, to classify a pixel as snow-covered or snow-free. Images from the MONIMET camera network, producing a continuous image series in Finland, are used in the analysis of FSC. The results obtained from automated image analysis of snow cover are compared with reference data estimated by visual inspection of same images. The results show the applicability and usefulness of digital imagery in the estimation of fractional snow cover in forested areas, with a Root Mean Squared Error (RMSE) in the range of 0.1–0.3 (with the full range of 0–1). |
| first_indexed | 2024-12-12T06:00:28Z |
| format | Article |
| id | doaj.art-18fdcf68443349abaa089f1dd5f6558c |
| institution | Directory Open Access Journal |
| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-12-12T06:00:28Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-18fdcf68443349abaa089f1dd5f6558c2022-12-22T00:35:25ZengMDPI AGGeosciences2076-32632017-07-01735510.3390/geosciences7030055geosciences7030055Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal ConditionsAli Nadir Arslan0Cemal Melih Tanis1Sari Metsämäki2Mika Aurela3Kristin Böttcher4Maiju Linkosalmi5Mikko Peltoniemi6Finnish Meteorological Institute (FMI), Erik Palménin aukio 1, FI-00560 Helsinki, FinlandFinnish Meteorological Institute (FMI), Erik Palménin aukio 1, FI-00560 Helsinki, FinlandFinnish Environment Institute Finland (SYKE), Mechelininkatu 34a, FI-00251 Helsinki, FinlandFinnish Meteorological Institute (FMI), Erik Palménin aukio 1, FI-00560 Helsinki, FinlandFinnish Environment Institute Finland (SYKE), Mechelininkatu 34a, FI-00251 Helsinki, FinlandFinnish Meteorological Institute (FMI), Erik Palménin aukio 1, FI-00560 Helsinki, FinlandNatural Resources Institute Finland (LUKE), Viikinkaari 4, FI-00790 Helsinki, FinlandFractional snow cover (FSC) is an important parameter to estimate snow water equivalent (SWE) and surface albedo important to climatic and hydrological applications. The presence of forest creates challenges to retrieve FSC accurately from satellite data, as forest canopy can block the sensor’s view of snow cover. In addition to the challenge related to presence of forest, in situ data of FSC—necessary for algorithm development and validation—are very limited. This paper investigates the estimation of FSC using digital imagery to overcome the obstacle caused by forest canopy, and the possibility to use this imagery in the validation of FSC derived from satellite data. FSC is calculated here using an algorithm based on defining a threshold value according to the histogram of an image, to classify a pixel as snow-covered or snow-free. Images from the MONIMET camera network, producing a continuous image series in Finland, are used in the analysis of FSC. The results obtained from automated image analysis of snow cover are compared with reference data estimated by visual inspection of same images. The results show the applicability and usefulness of digital imagery in the estimation of fractional snow cover in forested areas, with a Root Mean Squared Error (RMSE) in the range of 0.1–0.3 (with the full range of 0–1).https://www.mdpi.com/2076-3263/7/3/55image processingwebcam monitoringdigital imagessnow coverboreal forests |
| spellingShingle | Ali Nadir Arslan Cemal Melih Tanis Sari Metsämäki Mika Aurela Kristin Böttcher Maiju Linkosalmi Mikko Peltoniemi Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions Geosciences image processing webcam monitoring digital images snow cover boreal forests |
| title | Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions |
| title_full | Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions |
| title_fullStr | Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions |
| title_full_unstemmed | Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions |
| title_short | Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions |
| title_sort | automated webcam monitoring of fractional snow cover in northern boreal conditions |
| topic | image processing webcam monitoring digital images snow cover boreal forests |
| url | https://www.mdpi.com/2076-3263/7/3/55 |
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