Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery

Dawei Wen,Anlu Zhang,Xinli Ke,Xin Huang

doi:10.3390/rs11070763

Abstract

Inner-city redevelopment is regarded as an effective way to promote land-use efficiency and optimize land-use structure, especially with the establishment of urban growth boundaries in Chinese cities. However, inner-city redevelopment as compared to urban sprawl has been rarely monitored in 2D space, let alone in 3D space. Therefore, in this paper, a novel approach to generate time-series 3D building maps (i.e., building footprint and height) based on high-resolution (2 m) multi-view ZY-3 satellite imagery was proposed. In the proposed method, the building footprint was updated by an object-based image-to-map change detection method, which employed spectral (i.e., HSV and NDVI) and structural features (i.e., morphological building index) to extract non-building and building objects, respectively; building height was estimated automatically through semi-global matching of multi-view images. We applied the proposed method to four representative Chinese megacities, i.e., Beijing, Xi’an, Shanghai, and Wuhan, for the period 2012–2017, and detected building footprints with overall accuracies ranging from 84.84% to 97.60%. The building height estimation was also relatively accurate, with the bias, slope, and root-mean-square error being −0.49–2.30 m, 0.93–1.10 m, and 4.94–7.31 m, respectively. Our results show that the total building coverage decreased over the study period, accompanied by an increase in both area-weighted building height and floor area ratio. In addition, compact low-rise buildings have been replaced by open high-rise buildings in the urban redevelopment process. Moreover, due to the scattered spatial distribution of the redevelopment sites, the local spatial aggregation patterns of building density are unlikely to shift between hotspots (i.e., spatial aggregation of high values) and coldspots (i.e., spatial aggregation of low values).

Highlights

The developing world has experienced unprecedentedly rapid urbanization in recent decades
The urban expansion elasticity coefficient, which is defined as the ratio of growth rate of urban land to that of urban population, was 1.85 for China during 2000–2010, which was almost double the “reasonable value” (1.12) suggested by the China Academy of Urban Planning and Design [2]
An example of a 2D image and the corresponding 3D building map is shown in Figure 4 for a visAuanleixnasmpepclteioonf,afr2oDmimwahgicehanitdctahnebceororbesperovneddintgha3tDthbeuihldeignhgtmdaisptiinscstihoonwbnetinweFeignubreui4ldfionrgas vainsduaglrionuspnedctoiobnje,cftrso(msuwchhiacshriitvcearns abnedobroseardvse)disthaaptptahreenhte.igIhnt addisdtinticotnio,ndbifefetwreenetnbbuuilidldininggsssauncdh garsosuknydscorabpjeecrtss,nids oafpfipcaerebnuti.ldIningasddariteioenff,edctiifvfeerleyndt ebmuoilndsitnrgastedsuicnh thase s3kDysucrbaapnerds,amta.alAls,qruesaindteitnattiiavlehaocucsuersa,caynadssoefsfiscme ebnutilodfinthges tairmeee-fsfeerciteisve3lDy dbeumildoinnsgtrmateadpsinwtahsea3lsDo ucorbnadnucdteadta

Summary

Introduction

The developing world has experienced unprecedentedly rapid urbanization in recent decades. Urban expansion can lead to widespread ecological and land degradation, and issues such as farmland occupation [3], lake shrinkage [4], vegetation loss [5,6], food security [7,8], air pollution [9], and the urban heat island effect [10], which have raised serious concerns for both government and the general public Under this background, the government of China is starting to delimit “ecological control lines” [11], “red lines” for arable land [12], and “urban growth boundaries” [13] to protect China’s limited ecological land and arable land and avoid blind expansion (i.e., blind pursuit of large-scale city construction without considering practical conditions). Under the restriction of urban expansion, redevelopment of existing urban land becomes a good alternative to create more urban space

Methods

Results

Conclusion