Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection

Abstract

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 (R 2 = 0.78; RMSE = 0.056).

Monitoring and Evaluating Nature-Based Solutions Implementation in Urban Areas by Means of Earth Observation

“…that can be deployed for monitoring and evaluation of NBS implementation. Focusing on spaceborne observations, they are capable of identifying land cover/use change on local scale [27], providing meaningful urban planning indicators for NBS implementation monitoring, retrieving spatial data of urban surface structure [28,29]; estimating urban change and sprawl [30]; and mapping the state of the urban surface before and after an intervention (e.g., green roofs) [31]. The spatial and temporal characteristics of the Copernicus Sentinels, designed to provide routine observations for multidisciplinary operational services, are suitable for addressing challenges associated with planning interventions in the urban environment, such as NBS.…”

Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach

“…The SVF is a ratio to measure the openness of a particular area within an urban setting and in general a terrestrial landscape, which has significant implications for the incoming and outgoing radiation [9]. SVF has been widely used in urban climate research [15, 21, 25, 28, 35, 45, 56], especially to improve spatial models of air pollution prediction [18, 39]. In this study, the SVF was used to locate high-density environments that can potentially trap air pollutants and prohibit air ventilation during prolonged dust events.…”

Spatiotemporal Prediction of Increasing Winter Perceived Temperature across a Sub-Tropical City for Sustainable Planning and Climate Change Mitigation

“…In brief, data of green coverage were derived from the vegetation map based on the focal statistics function of ArcGIS, and the derived data indicated the percentage of green coverage within radii of 100 m, 200 m, 300 m, 400 m, and 500 m, respectively. Public open spaces and road networks were retrieved from the “open space” and “road” GIS-based land use data, and these data subsets were also spatially averaged by focal statistics for estimating the percentage of public open spaces or road networks within a radii of 100 m, 200 m, 300 m, 400 m, and 500 m. Building density was demonstrated by SVF, where higher SVF indicated lower building density and lower SVF indicated higher building density [34,35]. These data were also spatially averaged by focal statistics as stated above.…”