Abstract

Built-up area supports human settlements and activities, and its spatial distribution and temporal dynamics have significant impacts on ecosystem services and global environment change. To date, most of urban remote sensing has generated the maps of impervious surfaces, and limited effort has been made to explicitly identify the area, location and density of built-up in the complex and fragmented landscapes based on the freely available datasets. In this study, we took the lower Yangtze River Delta (Landsat Path/Row: 118/038), China, where extensive urbanization and industrialization have occurred, as a case study site. We analyzed the structure and optical features of typical land cover types from (1) the HH and HV gamma-naught imagery from the Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and (2) time series Landsat imagery. We proposed a pixel- and rule-based decision tree approach to identify and map built-up area at 30-m resolution from 2007 to 2010, using PALSAR HH gamma-naught and Landsat annual maximum Normalized Difference Vegetation Index (NDVImax). The accuracy assessment showed that the resultant annual maps of built-up had relatively high user (87–93%) and producer accuracies (91–95%) from 2007 to 2010. The built-up area was 2805km2 in 2010, about 16% of the total land area of the study site. The annual maps of built-up in 2007–2010 show relatively small changes in the urban core regions, but large outward expansion along the peri-urban regions. The average annual increase of built-up areas was about 80km2 per year from 2007 to 2010. Our annual maps of built-up in the lower Yangtze River Delta clearly complement the existing maps of impervious surfaces in the region. This study provides a promising new approach to identify and map built-up area, which is critical to investigate the interactions between human activities and ecosystem services in urban-rural systems.

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