Global Distribution and Density of Constructed Impervious Surfaces

Abstract

We present the first global inventory of the spatial distribution and density of constructed impervious surface area (ISA). Examples of ISA include roads, parking lots, buildings, driveways, sidewalks and other manmade surfaces. While high spatial resolution is required to observe these features, the new product reports the estimated density of ISA on a one-km2 grid based on two coarse resolution indicators of ISA – the brightness of satellite observed nighttime lights and population count. The model was calibrated using 30-meter resolution ISA of the USA from the U.S. Geological Survey. Nominally the product is for the years 2000-01 since both the nighttime lights and reference data are from those two years. We found that 1.05% of the United States land area is impervious surface (83,337 km2) and 0.43 % of the world's land surface (579,703 km2) is constructed impervious surface. China has more ISA than any other country (87,182 km2), but has only 67 m2 of ISA per person, compared to 297 m2 per person in the USA. The distribution of ISA in the world's primary drainage basins indicates that watersheds damaged by ISA are primarily concentrated in the USA, Europe, Japan, China and India. The authors believe the next step for improving the product is to include reference ISA data from many more areas around the world.

Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO2 and PM10

“…33 On the 34,35 We use the 1:10,000 EuroStreets digital road network (version 3.1, based on TeleAtlas MultiNet TM for year-2008) to derive road density variables. EuroStreets includes 9 road classes, which we aggregate into major roads (motorways, main roads, and other major roads) and minor roads (secondary and four types of local roads).…”

Detecting change in urban areas at continental scales with MODIS data

“…Several ca 2000 global maps of urban areas have been produced in the past decade (Bhaduri et al, 2002;Schneider et al, 2003;CIESIN, 2004;Elvidge et al, 2007). Although these maps are static depictions of urban areas largely dependent on the input data sources (e.g., remote sensing, nighttime lights, census data), they have shown the potential for largearea maps of urban extent/expansion for a large number of applications, including: assessment of arable land (Tan et al, 2005;Avellan et al, 2012), water quality/availability (McDonald et al, 2011), natural resources (Lambin &,Meyfroidt, 2011), habitat loss (Radeloff et al, 2005) and biodiversity (Guneralp et al, 2013); air pollution monitoring and associated impacts to human health (Grimm et al, 2008;Cassiani et al, 2013); and regionalglobal modeling of climate (Oleson et al, 2008), hydrological (McGrane et al, 2014), and biogeochemical cycles (Nordbo et al, 2012;Zhao et al, 2013).…”

“…The second is whether the adopted definition is congruent with how the maps are produced, such that the input datasets, classification method, and thematic classes align with how urban areas are defined. Nearly all recent efforts exploit optical data or nighttime lights data, and all utilize either semi-automated classification algorithms (Schneider et al, 2003;Bartholome & Belward, 2005;ESA, 2008) or data fusion methods that draw on a combination of satellite imagery, census data, and GIS datasets (Bhaduri et al, 2002;CIESIN, 2004;Goldewijk, 2005;Elvidge et al, 2007).…”

“…The definitional problems are twofold. First, any number of operational definitions of 'urban' are employed, ranging from functional definitions related to human land use (Balk et al, 2004;Zhang & Seto, 2011), administrative boundaries (Deichmann et al, 2001), or population size and density (Bhaduri, 2007), to physical ones based on land cover (Schneider et al, 2003;Elvidge et al, 2007) or anthropogenic light (NGDC, 2007). The second is whether the adopted definition is congruent with how the maps are produced, such that the input datasets, classification method, and thematic classes align with how urban areas are defined.…”

Airing ‘clean air’ in Clean India Mission

“…Most of the metropolitan cities in India are facing similar types of experiences with a major share of impervious land. India stands 3rd in the total impervious surface area after China and United States with 81,221 Km 2 of impervious surface area (Elvidge et al 2007). In this situation, roof top greening can provide better results for controlling airborne particulates, precursor gases and other air toxics.…”