2020
DOI: 10.3390/rs12040652
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Transformative Urban Changes of Beijing in the Decade of the 2000s

Abstract: 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 ap… Show more

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Cited by 7 publications
(5 citation statements)
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“…To supplement climate data, we included four habitat variables derived from orbiting satellites over the years 2001–2005 as potential measures of vegetation required for foraging, nesting, and roosting of parrots: (1) the normalized difference vegetation index (NDVI), which describes vegetation greenness; (2) NDVI annual standard deviation (NDVI_std), which describes greenness seasonality; (3) percentage tree cover (tree) from the Moderate Resolution Imaging Spectroradiometer (MODIS); (4) and a variable collected by the Quick Scatterometer (QuikSCAT), a low Earth‐orbiting satellite designed to measure sea winds from microwave backscatter. Terrestrial QuikSCAT data can relate to tree canopy structure and moisture content (Frolking et al., 2006), but in the context of this study QuikSCAT most likely relates to urbanization, with larger values (more backscatter) in urban areas compared to natural areas (Nghiem et al., 2009; Sorichetta et al., 2020).…”
Section: Methodsmentioning
confidence: 88%
See 1 more Smart Citation
“…To supplement climate data, we included four habitat variables derived from orbiting satellites over the years 2001–2005 as potential measures of vegetation required for foraging, nesting, and roosting of parrots: (1) the normalized difference vegetation index (NDVI), which describes vegetation greenness; (2) NDVI annual standard deviation (NDVI_std), which describes greenness seasonality; (3) percentage tree cover (tree) from the Moderate Resolution Imaging Spectroradiometer (MODIS); (4) and a variable collected by the Quick Scatterometer (QuikSCAT), a low Earth‐orbiting satellite designed to measure sea winds from microwave backscatter. Terrestrial QuikSCAT data can relate to tree canopy structure and moisture content (Frolking et al., 2006), but in the context of this study QuikSCAT most likely relates to urbanization, with larger values (more backscatter) in urban areas compared to natural areas (Nghiem et al., 2009; Sorichetta et al., 2020).…”
Section: Methodsmentioning
confidence: 88%
“…Many organisms are limited by temperature extremes, especially outside the tropics (Khaliq et al, 2017), and prior field studies have suggested an important role for seasonality of rainfall to the ecology of these species (Renton & Salinas-Melgoza, 2004) 4) and a variable collected by the Quick Scatterometer (QuikSCAT), a low Earthorbiting satellite designed to measure sea winds from microwave backscatter. Terrestrial QuikSCAT data can relate to tree canopy structure and moisture content (Frolking et al, 2006), but in the context of this study QuikSCAT most likely relates to urbanization, with larger values (more backscatter) in urban areas compared to natural areas (Nghiem et al, 2009;Sorichetta et al, 2020).…”
Section: Geospatial Environmental Datamentioning
confidence: 85%
“…These included: 19 temperature and precipitation variables collected and extrapolated from weather stations (http://www.worldclim.org) plus 4 habitat variables derived from orbiting satellites: (1) normalized difference vegetation index (NDVI), which describes vegetation greenness; (2) NDVI annual standard deviation (NDVI_std), which describes greenness seasonality; (3) percentage tree cover (tree) from the Moderate Resolution Imaging Spectroradiometer (MODIS); (4) and a variable collected by the Quick Scatterometer (QuickSCAT), a low Earth-orbiting satellite designed to measure sea winds from microwave backscatter. Terrestrial QuickSCAT data can relate to tree canopy structure and moisture content (Frolking et al, 2006), but in the context of this study QuickSCAT most likely relates to urbanization, with larger values (more backscatter) in urban areas compared to natural areas (Nghiem et al, 2009; Sorichetta et al, 2020).…”
Section: Methodsmentioning
confidence: 89%
“…Currently, fossil fuel CO 2 (FFCO2) emission is Remote Sens. 2021, 13, 2439 2 of 22 estimated with nighttime light (NTL) data as a proxy for human settlements, which can be improved by maps of physically defined building structures [5].…”
Section: Introductionmentioning
confidence: 99%
“…A demonstration of the ability of radar backscatter signatures to detect building structures is founded on radar responses to true physical structures of buildings [5,6], rather than optical colors or spectral appearances of land cover types. As our method is based on radar signatures of physical building structures, it can successfully capture the characteristics of urban building patterns corresponding to different urban development classes and socioeconomic status (see Tables 1 and 2), and in differ-ent rural-urban landscapes in both inland and coastal regions with wet and arid environmental conditions, or over sea surfaces under different wind and wave effects.…”
mentioning
confidence: 99%