Satellite scatterometer estimation of urban built-up volume: Validation with airborne lidar data

Mathews, Adam J.; Frazier, Amy E.; Nghiem, S. V.; Neumann, Gregory A.; Yun, Zhao

doi:10.1016/j.jag.2019.01.004

Cited by 22 publications

(17 citation statements)

References 17 publications

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“…Another source of data to measure urban structure come from scatterometers. For example, the SeaWinds scatterometer onboard the QuikSCAT satellite launched in 1999, was designed to measure the speed and direction of winds that cause ocean waves but also have been used to characterize building volumes and other human-made structures (Hardin et al 1997, Frolking et al 2013, Nguyen et al 2018, Mathews et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Building up or spreading out? Typologies of urban growth across 478 cities of 1 million+

Mahtta

Mahendra

Seto

2019

Environ. Res. Lett.

140

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Urban form in both two-(2D) and three-dimensions (3D) has significant impacts on local and global environments. Here we developed the largest global dataset characterizing 2D and 3D urban growth for 478 cities with populations of one million or larger. Using remote sensing data from the SeaWinds scatterometer for 2001 and 2009, and the Global Human Settlement Layer for 2000 and 2014, we applied a cluster analysis and found five urban growth typologies: stabilized, outward, mature upward, budding outward, upward and outward. Budding outward is the dominant typology worldwide, per the largest total area. Cities characterized by upward and outward growth are few in number and concentrated primarily in China and South Korea, where there has been a large increase in high-rises during the study period. With the exception of East Asia, cities within a geographic region exhibit remarkably similar patterns of urban growth. Our results show that every city exhibits multiple urban growth typologies concurrently. Thus, while it is possible to describe a city by its dominant urban growth typology, a more accurate and comprehensive characterization would include some combination of the five typologies. The implications of the results for urban sustainability are multifold. First, the results suggest that there is considerable opportunity to shape future patterns of urbanization, given that most of the new urban growth is nascent and low magnitude outward expansion. Second, the clear geographic patterns and wide variations in the physical form of urban growth, within country and city, suggest that markets, national and subnational policies, including the absence of, can shape how cities grow. Third, the presence of different typologies within each city suggests the need for differentiated strategies for different parts of a single city. Finally, the new urban forms revealed in this analysis provide a first glimpse into the carbon lock-in of recently constructed energy-demanding infrastructure of urban settlements.

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Section: Introductionmentioning

confidence: 99%

Building up or spreading out? Typologies of urban growth across 478 cities of 1 million+

Mahtta

Mahendra

Seto

2019

Environ. Res. Lett.

140

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“…Beyond the capability of delineating boundaries demarcating DSM-based urban and rural/natural areas, σ 0 values can also be used to (i) distinguish among different intra-urban typologies, such as city cores (including commercial and industrial centers) with larger building volume and residential areas with lower building volume, and (ii) monitor their changes over time (see Section 4.2). This is because the value of σ 0 in urban areas is dependent on the 3D building volume, with a consistent linear relationship over a large dynamic range without a saturation problem [27]. It is noted that the capability of DSM data to capture physical urban patterns has been validated both in two-dimensional (2D) extent [46] and in three-dimensional (3D) building volume [27].…”

Section: Qscat Data and Dense Sampling Methods For Urban Observationsmentioning

confidence: 99%

“…This is because the value of σ 0 in urban areas is dependent on the 3D building volume, with a consistent linear relationship over a large dynamic range without a saturation problem [27]. It is noted that the capability of DSM data to capture physical urban patterns has been validated both in two-dimensional (2D) extent [46] and in three-dimensional (3D) building volume [27].…”

Section: Qscat Data and Dense Sampling Methods For Urban Observationsmentioning

confidence: 99%

“…To identify the optimal threshold values for σ 0 and decadal IV, both thresholds were iteratively adjusted by comparing the result from each iteration with high-resolution Landsat images of the city of Wuhan (China)-used as a test case for calibrating and validating the urban classification algorithm [27,43]. The annual results ( Figure 6), obtained using an optimal σ 0 threshold of -8.0 dB and decadal IV threshold of 2.0, show that the Beijing infrastructure-based urban area (i) is much smaller than the 1994-1995 stable night light extent in the early 2000s (i.e., [2000][2001][2002][2003] due to the well-known blooming effect [22], (ii) becomes comparable to it in the mid-2000s (i.e., [2004][2005][2006], and (iii) finally sprawls well beyond it in the late 2000s (i.e., [2007][2008][2009].…”

Section: Beijing Urban Extentmentioning

confidence: 99%

“…Synthetic aperture radar (SAR) data, which could potentially be used to consistently map infrastructure-based urban areas [23], are generally not collected globally over regular time intervals-with partial exceptions represented by the Shuttle Radar Topography Mission (SRTM) [24], which collected global SAR data in February 2000, and the TanDEM-X satellite, which collected global SAR data in 2011-2012 [25]. Very high-resolution spotlight SAR [26] and Lidar data [27] have been successfully used to map infrastructure-based urban areas at a meter or even sub-meter resolution, but their coverage is extremely limited in both space and time [27].…”

Section: Introductionmentioning

confidence: 99%

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Transformative Urban Changes of Beijing in the Decade of the 2000s

et al. 2020

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The rapid economic growth, the exodus from rural to urban areas, and the associated extreme urban development that occurred in China in the decade of the 2000s have severely impacted the environment in Beijing, its vicinity, and beyond. This article presents an innovative approach for assessing mega-urban changes and their impact on the environment based on the use of decadal QuikSCAT (QSCAT) satellite data, acquired globally by the SeaWinds scatterometer over that period. The Dense Sampling Method (DSM) is applied to QSCAT data to obtain reliable annual infrastructure-based urban observations at a posting of ~1 km. The DSM-QSCAT data, along with different DSM-based change indices, were used to delineate the extent of the Beijing infrastructure-based urban area in each year between 2000 and 2009, and assess its development over time, enabling a physical quantification of its urbanization which reflects the implementation of various development policies during the same time period. Eventually, as a proxy for the impact of Beijing urbanization on the environment, the decadal trend of its infrastructure-based urbanization is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities as observed from the Global Ozone Monitoring Experiment (GOME) instrument aboard the second European Remote Sensing satellite (ERS-2) between 2000 and 2002, and from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY aboard of the ESA’s ENVIronmental SATellite (SCIAMACHY /ENVISAT) between 2003 and 2009. Results reveal a threefold increase of the yearly tropospheric NO2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.

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Examining Urban Built‐up Volume

Mathews

Nghiem

2021

Urban Remote Sensing

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Satellite scatterometer estimation of urban built-up volume: Validation with airborne lidar data

Cited by 22 publications

References 17 publications

Building up or spreading out? Typologies of urban growth across 478 cities of 1 million+

Building up or spreading out? Typologies of urban growth across 478 cities of 1 million+

Transformative Urban Changes of Beijing in the Decade of the 2000s

Examining Urban Built‐up Volume

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