Estimation of snow covered area for an urban catchment using image processing and neural networks
This paper presents a method to estimate the snow covered area (SCA) for small urban catchments. The method uses images taken with a digital camera positioned on top of a tall building. The camera is stationary and takes overview images of the same area every fifteen minutes throughout the winter se...
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| Veröffentlicht in: | Water science and technology 2003-01, Vol.48 (9), p.155-164 |
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| creator | Matheussen, B V Thorolfsson, S T |
| description | This paper presents a method to estimate the snow covered area (SCA) for small urban catchments. The method uses images taken with a digital camera positioned on top of a tall building. The camera is stationary and takes overview images of the same area every fifteen minutes throughout the winter season. The images were read into an image-processing program and a three-layered feed-forward perceptron artificial neural network (ANN) was used to calculate fractional snow cover within three different land cover types (road, park and roofs). The SCA was estimated from the number of pixels with snow cover relative to the total number of pixels. The method was tested for a small urban catchment, Risvollan in Trondheim, Norway. A time series of images taken during spring of 2001 and the 2001-2002 winter season was used to generate a time series of SCA. Snow covered area was also estimated from aerial photos. The results showed a strong correlation between SCA estimated from the digital camera and the aerial photos. The time series of SCA can be used for verification of urban snowmelt models. |
| doi_str_mv | 10.2166/wst.2003.0515 |
| format | Article |
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The method uses images taken with a digital camera positioned on top of a tall building. The camera is stationary and takes overview images of the same area every fifteen minutes throughout the winter season. The images were read into an image-processing program and a three-layered feed-forward perceptron artificial neural network (ANN) was used to calculate fractional snow cover within three different land cover types (road, park and roofs). The SCA was estimated from the number of pixels with snow cover relative to the total number of pixels. The method was tested for a small urban catchment, Risvollan in Trondheim, Norway. A time series of images taken during spring of 2001 and the 2001-2002 winter season was used to generate a time series of SCA. Snow covered area was also estimated from aerial photos. The results showed a strong correlation between SCA estimated from the digital camera and the aerial photos. The time series of SCA can be used for verification of urban snowmelt models.</description><identifier>ISSN: 0273-1223</identifier><identifier>ISBN: 9781843394587</identifier><identifier>ISBN: 1843394588</identifier><identifier>EISSN: 1996-9732</identifier><identifier>DOI: 10.2166/wst.2003.0515</identifier><identifier>PMID: 14703149</identifier><language>eng</language><publisher>England: IWA Publishing</publisher><subject>Artificial neural networks ; Cameras ; Catchments ; Cities ; Digital cameras ; Digital imaging ; Environment Design ; Environmental Monitoring - methods ; Image processing ; Land cover ; Neural networks ; Neural Networks (Computer) ; Photography ; Pixels ; Roofs ; Seasons ; Snow ; Snow cover ; Snowmelt ; Tall buildings ; Time series ; Urban catchments ; Water Movements ; Water Supply ; Winter</subject><ispartof>Water science and technology, 2003-01, Vol.48 (9), p.155-164</ispartof><rights>Copyright IWA Publishing Nov 2003</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-e0cee29098d8fe1ab762a875182a160bb80b11571abb99c646f1b5c2d0a9193f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,780,784,789,790,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/14703149$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Viklander, M</contributor><contributor>Marsalek, J</contributor><contributor>Malmqvist, PA</contributor><contributor>Watt, WE (eds)</contributor><creatorcontrib>Matheussen, B V</creatorcontrib><creatorcontrib>Thorolfsson, S T</creatorcontrib><title>Estimation of snow covered area for an urban catchment using image processing and neural networks</title><title>Water science and technology</title><addtitle>Water Sci Technol</addtitle><description>This paper presents a method to estimate the snow covered area (SCA) for small urban catchments. The method uses images taken with a digital camera positioned on top of a tall building. The camera is stationary and takes overview images of the same area every fifteen minutes throughout the winter season. The images were read into an image-processing program and a three-layered feed-forward perceptron artificial neural network (ANN) was used to calculate fractional snow cover within three different land cover types (road, park and roofs). The SCA was estimated from the number of pixels with snow cover relative to the total number of pixels. The method was tested for a small urban catchment, Risvollan in Trondheim, Norway. A time series of images taken during spring of 2001 and the 2001-2002 winter season was used to generate a time series of SCA. Snow covered area was also estimated from aerial photos. The results showed a strong correlation between SCA estimated from the digital camera and the aerial photos. The time series of SCA can be used for verification of urban snowmelt models.</description><subject>Artificial neural networks</subject><subject>Cameras</subject><subject>Catchments</subject><subject>Cities</subject><subject>Digital cameras</subject><subject>Digital imaging</subject><subject>Environment Design</subject><subject>Environmental Monitoring - methods</subject><subject>Image processing</subject><subject>Land cover</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Photography</subject><subject>Pixels</subject><subject>Roofs</subject><subject>Seasons</subject><subject>Snow</subject><subject>Snow cover</subject><subject>Snowmelt</subject><subject>Tall buildings</subject><subject>Time series</subject><subject>Urban catchments</subject><subject>Water Movements</subject><subject>Water Supply</subject><subject>Winter</subject><issn>0273-1223</issn><issn>1996-9732</issn><isbn>9781843394587</isbn><isbn>1843394588</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kc9LHDEUx4O16Go99loCQull1veSTH4cRewPELzoechk3uja3cQmM13875utC0IPveRB8nkfXt6XsY8IS4FaX2zLtBQAcgkttgdsgc7pxhkp3rEzZyxaJaVTrTWHbAHCyAaFkMfspJQnADBSwRE7RmVAonIL5q_LtNr4aZUiTyMvMW15SL8p08B9Js_HlLmPfM59PYOfwuOG4sTnsooPvHY-EH_OKVD5e-HjwCPN2a9rmbYp_ywf2PvRrwud7espu_96fXf1vbm5_fbj6vKmCdLi1BAEIuHA2cGOhL43WnhrWrTCo4a-t9Ajtqa-9M4FrfSIfRvEAN6hk6M8ZZ9fvXWcXzOVqdusSqD12kdKc-nQCe3AiAp--T8IQlW9E1DR83_QpzTnWL9RdUoqRKHbSjWvVMiplExj95zrZvJLVXW70LoaWrcLrduFVvlPe-vcb2h4o_epyD-A44-s</recordid><startdate>20030101</startdate><enddate>20030101</enddate><creator>Matheussen, B V</creator><creator>Thorolfsson, S T</creator><general>IWA Publishing</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7UA</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H96</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L.G</scope><scope>L6V</scope><scope>M0S</scope><scope>M1P</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20030101</creationdate><title>Estimation of snow covered area for an urban catchment using image processing and neural networks</title><author>Matheussen, B V ; 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The method uses images taken with a digital camera positioned on top of a tall building. The camera is stationary and takes overview images of the same area every fifteen minutes throughout the winter season. The images were read into an image-processing program and a three-layered feed-forward perceptron artificial neural network (ANN) was used to calculate fractional snow cover within three different land cover types (road, park and roofs). The SCA was estimated from the number of pixels with snow cover relative to the total number of pixels. The method was tested for a small urban catchment, Risvollan in Trondheim, Norway. A time series of images taken during spring of 2001 and the 2001-2002 winter season was used to generate a time series of SCA. Snow covered area was also estimated from aerial photos. The results showed a strong correlation between SCA estimated from the digital camera and the aerial photos. The time series of SCA can be used for verification of urban snowmelt models.</abstract><cop>England</cop><pub>IWA Publishing</pub><pmid>14703149</pmid><doi>10.2166/wst.2003.0515</doi><tpages>10</tpages></addata></record> |
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| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Artificial neural networks Cameras Catchments Cities Digital cameras Digital imaging Environment Design Environmental Monitoring - methods Image processing Land cover Neural networks Neural Networks (Computer) Photography Pixels Roofs Seasons Snow Snow cover Snowmelt Tall buildings Time series Urban catchments Water Movements Water Supply Winter |
| title | Estimation of snow covered area for an urban catchment using image processing and neural networks |
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