China’s urban expansion from 1990 to 2010 determined with satellite remote sensing

Wang, Lei; Li, CongCong; Ying, Qing; Cheng, Xiao; Wang, Xiaoyi; Li, Xueyan; Hu, Luanyun; Lu, Liang; Yu, Le; Huang, Huabing; Gong, Peng

doi:10.1007/s11434-012-5235-7

Cited by 294 publications

(162 citation statements)

References 38 publications

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“…By taking the LAD result as examples with the zooming in over 4 parts of the significant regions of change in China, we can see that surface cover greenness of China increased most significantly in central Shaanxi and Shanxi, part of Qinghai, Gansu, and Ningxia (Figure 3(a)). In contrast, the most significant regions of greenness decrease appeared in northern Inner Mongolia (Figure 3(b)), and most parts of southern Tibet (Figure 3 [30]. Between 2000 and 2010, another study on the changing trend of forest leaf area index over China reported an obvious reduction trend of forest in parts of Jiangsu and Zhejiang and significant increase in Shaanxi and Shanxi [31].…”

Section: Change Trendmentioning

confidence: 96%

Change of surface cover greenness in China between 2000 and 2010

Liu

Gong

2012

Chin. Sci. Bull.

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Surface greenness reflects the situation of vegetation cover. Vegetation index calculated from the Red and Near Infrared bands of remote sensing images, whose values indicate the level of photosynthetic activity, is monotonically related to surface greenness when vegetation canopy does not fully cover the background soil. Especially for desert regions, vegetation index is positively correlated with vegetation coverage. Therefore, vegetation index can be used to study the change in greenness of desert areas. This study collected MODIS Normalized Difference Vegetation Index (NDVI) data from 2000 to 2010 and analyzed their change over China in this period. The results showed that an increasing trend of NDVI occurred over 66.84% (OLS fitting) or 64.27%

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Section: Change Trendmentioning

confidence: 96%

Change of surface cover greenness in China between 2000 and 2010

Liu

Gong

2012

Chin. Sci. Bull.

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“…A meta-analysis of urban expansion indicates that localto regional-scale studies are geographically biased, leaving even many large cities unstudied (Seto et al, 2011). Detailed maps on regional-to global-scale changes in urban land do not exist; previous efforts have been sample-based (Angel et al, 2005;Schneider & Woodcock, 2008), focused on one country (Homer et al, 2004;Wang et al, 2012), or drawn conclusions from datasets with substantial temporal and spatial mismatch and variability in how cities are defined (Seto et al, 2010). Routine monitoring of urban expansion across large areas could therefore provide the spatial information on patterns of urban growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social and environmental impacts that result (Deuskar et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

153

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Abstract. Urbanization is one of the most important components of global environmentalchange, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a new methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify ca 2010 urban extent; and (3) mapping newly built areas (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250 m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East-Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socicoeconomic/political and ecological/climatic conditions with good accuracy (70-91% overall accuracy by country, 69-89% by biome). The 250 m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of ii the error in the change detection is related to human decision making or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as provide a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.

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“…However, what is the rate and spatial distribution of China's urbanization during the past 2 decades, particularly the first 10 years of the 21st century remains to be answered. Wang et al [49] …”

Section: Progress In Remote Sensing Of Environmental Change Over Chinamentioning

confidence: 99%

“…Desert areas have a shrinking trend. However, greenness over some agricultural areas in eastern China is experiencing a decline possibly due to urban expansion [49].…”

Section: Progress In Remote Sensing Of Environmental Change Over Chinamentioning

confidence: 99%