Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection
Sky View Factor (SVF, a dimensionless value between 0 and 1 representing obstructed and unobstructed sky, respectively) has an important influence on urban energy balance, and is a key contributor to the Urban Heat Island (UHI) effect experienced by heavily built up regions. Continuous urban SVF map...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-07-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/8/7/568 |
| _version_ | 1819291447860396032 |
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| author | Matus Hodul Anders Knudby Hung Chak Ho |
| author_facet | Matus Hodul Anders Knudby Hung Chak Ho |
| author_sort | Matus Hodul |
| collection | DOAJ |
| description | Sky View Factor (SVF, a dimensionless value between 0 and 1 representing obstructed and unobstructed sky, respectively) has an important influence on urban energy balance, and is a key contributor to the Urban Heat Island (UHI) effect experienced by heavily built up regions. Continuous urban SVF maps used in modeling the spatial distribution of UHI can be derived analytically using Lidar data; however, Lidar data are costly to obtain and often lack complete coverage of large cities or metropolitan areas. This study develops and validates a method for estimating continuous urban SVF from globally available Landsat TM data, based on the presence of shadows cast by SVF-reducing urban features. SVF and per-pixel shadow proportion (SP) were first calculated for synthetic grid cities to confirm a logarithmic relationship between the two properties; then Lidar data from four US cities were used to determine an empirical regression relating SP to SVF. Spectral Mixture Analysis was then used to estimate per-pixel SP in a Landsat 5 TM image covering the Greater Vancouver Area, Canada, and the empirical regression was used to calculate SVF from per-pixel SP. The accuracy of the resulting SVF map was validated using independent Lidar-derived SVF data (R2 = 0.78; RMSE = 0.056). |
| first_indexed | 2024-12-24T03:38:47Z |
| format | Article |
| id | doaj.art-e22eb1e87d5d4755a91ec69ab7359832 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:38:47Z |
| publishDate | 2016-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e22eb1e87d5d4755a91ec69ab73598322022-12-21T17:16:59ZengMDPI AGRemote Sensing2072-42922016-07-018756810.3390/rs8070568rs8070568Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow DetectionMatus Hodul0Anders Knudby1Hung Chak Ho2Department of Geography, Environment and Geomatics, University of Ottawa, Ottawa, ON K1N 6N5, CanadaDepartment of Geography, Environment and Geomatics, University of Ottawa, Ottawa, ON K1N 6N5, CanadaDepartment of Geography, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaSky View Factor (SVF, a dimensionless value between 0 and 1 representing obstructed and unobstructed sky, respectively) has an important influence on urban energy balance, and is a key contributor to the Urban Heat Island (UHI) effect experienced by heavily built up regions. Continuous urban SVF maps used in modeling the spatial distribution of UHI can be derived analytically using Lidar data; however, Lidar data are costly to obtain and often lack complete coverage of large cities or metropolitan areas. This study develops and validates a method for estimating continuous urban SVF from globally available Landsat TM data, based on the presence of shadows cast by SVF-reducing urban features. SVF and per-pixel shadow proportion (SP) were first calculated for synthetic grid cities to confirm a logarithmic relationship between the two properties; then Lidar data from four US cities were used to determine an empirical regression relating SP to SVF. Spectral Mixture Analysis was then used to estimate per-pixel SP in a Landsat 5 TM image covering the Greater Vancouver Area, Canada, and the empirical regression was used to calculate SVF from per-pixel SP. The accuracy of the resulting SVF map was validated using independent Lidar-derived SVF data (R2 = 0.78; RMSE = 0.056).http://www.mdpi.com/2072-4292/8/7/568Sky View Factorurban remote sensingurban heat islandLandsat TMshadow detectionspectral mixture analysisshadow proportionVancouver |
| spellingShingle | Matus Hodul Anders Knudby Hung Chak Ho Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection Remote Sensing Sky View Factor urban remote sensing urban heat island Landsat TM shadow detection spectral mixture analysis shadow proportion Vancouver |
| title | Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection |
| title_full | Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection |
| title_fullStr | Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection |
| title_full_unstemmed | Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection |
| title_short | Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection |
| title_sort | estimation of continuous urban sky view factor from landsat data using shadow detection |
| topic | Sky View Factor urban remote sensing urban heat island Landsat TM shadow detection spectral mixture analysis shadow proportion Vancouver |
| url | http://www.mdpi.com/2072-4292/8/7/568 |
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