A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data

Li, Xiaoping; Xia, Li; Chen, Yi-Min; Tan, Zhangzhi; Li, Shaoying; Ai, Bin

doi:10.1007/s10980-010-9454-5

Cited by 375 publications

(271 citation statements)

References 31 publications

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“…The increase in the size of built-up patches was due to the process of extension or expansion, while the decrease in the mean distances between built-up patches was due to the combined effect of diffusion, expansion, and the development of new patches near or in between patches, which is also a type of infill urban development. In general, these results show evidence to support the diffusion-coalescence urban growth theory [28,74,75]: "Diffusion is a process in which new urban areas are dispersed from the origin point or 'seed' location, while coalescence is the union of individual urban patches, or the growing together of the individual urban patches into one form or group" [1]. Based on the results, we can say that the CMA is still in the early stages of this oscillating process; i.e., it is experiencing more diffusion and less coalescence.…”

Section: Spatial Patterns Of Ulcs and Their Implicationssupporting

confidence: 65%

Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka

Subasinghe

Estoque

Murayama

2016

IJGI

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Abstract:Understanding urban growth spatiotemporally is important for landscape and urban development planning. In this study, we examined the spatiotemporal pattern of urban growth of the Colombo Metropolitan Area (CMA)-Sri Lanka's only metropolitan area-from 1992 to 2014 using remote sensing data and GIS techniques. First, we classified three land-use/cover maps of the CMA (i.e., for 1992, 2001, and 2014) using Landsat data. Second, we examined the temporal pattern of urban land changes (ULCs; i.e., land changes from non-built-up to built-up) across two time intervals (1992-2001 and 2001-2014). Third, we examined the spatial pattern of ULCs along the gradients of various driver variables (e.g., distance to roads) and by using spatial metrics. Finally, we predicted the future urban growth of the CMA (2014-2050). Our results revealed that the CMA's built-up land has increased by 24,711 ha (221%) over the past 22 years (11,165 ha in 1992 to 35,876 ha in 2014), at a rate of 1123 ha per year. The analysis revealed that ULC was more intense or faster during the 2000s (1268 ha per year) than in the 1990s (914 ha per year), coinciding with the trends of population and economic growth. The results also revealed that most of the ULCs in both time intervals occurred in close proximity to roads and schools, while also showing some indications of landscape fragmentation and infill urban development patterns. The ULC modeling revealed that by 2030 and 2050, the CMA's built-up land will increase to 42,500 ha and 56,000 ha, respectively. Most of these projected gains of built-up land will be along the transport corridors and in proximity to the growth nodes. These findings are important in the context of landscape and urban development planning for the CMA. Overall, this study provides valuable information on the landscape transformation of the CMA, also highlighting some important challenges facing its future sustainable urban development.

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Section: Spatial Patterns Of Ulcs and Their Implicationssupporting

confidence: 65%

Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka

Subasinghe

Estoque

Murayama

2016

IJGI

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“…The landscape expansion index (LEI) was calculated to define these three urban growth types using the following equation according to Liu et al [55]:…”

Section: Urban Expansion Patternmentioning

confidence: 99%

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

Liu

Chen

2017

Remote Sensing

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Detailed studies on the spatiotemporal patterns of urban agglomeration in the Middle Yangtze River Basin (MYRB) are rare. This paper analyzed the spatiotemporal patterns of urbanization in the MYRB using multi-temporal remote sensing data circa 2000, 2008 and 2016 integrated with geographic information system (GIS) techniques and landscape analysis approaches. A multi-level analysis of the rate and intensity, type as well as the landscape changes of urban expansion at regional, prefectural and inner-city levels was performed. Results show that the MYRB experienced rapid urban expansion with an annual expansion rate of 3.199%, especially in the Chang-Zhu-Tan and Poyang Lake metropolitan areas. The small and medium cities presented faster urban expansion than the larger cities with annual growth rates three times the average level. Urban expansion within the three capital cities was further analyzed in detail. It is found that outlying expansion and edge-expansion were the dominant growth patterns at all the three levels. Although urbanization in the MYRB has a remarkable increase in the past sixteen years, its annual growth rate of urban land expansion has fallen behind the three other large urban agglomerations in China as a result. Finally, the spatial evolution of the socioeconomic structure of the MYRB was further explored. It indicated that urban land was distributed mainly along the "northwest-southeast" direction and that the economic spatial interactions among cities showed a pattern of "multi-polarization and fragmentation", which illustrates the weak radiative driving forces of the central cities. The MYRB urban agglomeration faces a great challenge to manage trades-offs between narrowing the intra-regional disparity and maintaining synergetic development among cities.

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“…The LPIs could be calculated using FRAGSTATS, which is an outstanding software developed to quantify landscape structure (Liu et al 2010). …”

Section: Landscape Pattern Indicesmentioning

confidence: 99%

“…LPIs, the embodiment of the spatial heterogeneity of land use, were often used for describing the LUC (Seto & Fragkias 2005;Serra et al 2008;Liu et al 2010;Feng et al 2011). Researches have shown that LPIs can influence a variety of ecological phenomena.…”

Section: Introductionmentioning

confidence: 99%

Simulating land use change by integrating ANN-CA model and landscape pattern indices

Yang

Chen

Zheng

2015

Geomatics, Natural Hazards and Risk

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Artificial neural networkÀcellular automata model has been applied successfully in land use change simulation. However, it has rarely been integrated with landscape pattern indices (LPIs), the embodiment of the spatial heterogeneity of land use. This paper proposed to integrate LPIs as the parameters of artificial neural networkÀcellular automata model. Subsequent to a description of the principles and implementation of the model, a case study was presented in Changping district. In the case study, two LPIs, the landscape similarity index and patch density, along with 10 other spatial variables, were selected as the influencing factors of land use change. Based on land use maps in years 1988 and 1998, a land use map in 2008 was simulated by the proposed model. Comparing with the actual land use map in 2008 and the simulated result of artificial neural networkÀcellular automata model, the results showed that the proposed model is more applicable for simulating land use change in the study area; the limitation of this model is the spatial scale and calculation method of LPIs.

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A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data

Cited by 375 publications

References 31 publications

Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka

Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

Simulating land use change by integrating ANN-CA model and landscape pattern indices

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