Gradient analysis of landscape spatial and temporal pattern changes in Beijing metropolitan area

Ye, Yang; Zhou, Qiming; Gong, Jian; Wang, Xiaocong

doi:10.1007/s11431-010-3206-2

Cited by 28 publications

(22 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The urban-rural gradient paradigm is commonly used to investigate how urbanisation is changing the ecological patterns and related processes across the landscape (McDonnell and Pickett, 1990;Luck and Wu, 2002;Hahs and 2006; McDonnell and Hahs, 2008;Yang et al, 2010;Vizzari, 2011a). One effective method for representing and analysing the urban-rural gradient and the urban sprawl is using Kernel Density Estimation (KDE) techniques applied on locations of buildings (Torrens and Alberti, 2000;Vizzari, 2011a).…”

Section: G Modica Et Almentioning

confidence: 99%

See 1 more Smart Citation

Spatio-temporal analysis of the urban–rural gradient structure: an application in a Mediterranean mountainous landscape (Serra San Bruno, Italy)

et al. 2012

View full text Add to dashboard Cite

Abstract. The most recent and significant transformations of European landscapes have occurred as a consequence of a series of diffused, varied and often connected phenomena: urban growth and sprawl, agricultural intensification in the most suitable areas and agricultural abandonment in marginal areas. These phenomena can affect dramatically ecosystems' structure and functioning, since certain modifications cause landscape fragmentation while others tend to increase homogeneity. Thus, a thorough comprehension of the evolution trends of landscapes, in particular those linked to urban-rural relations, is crucial for a sustainable landscape planning.In this framework, the main objectives of the present paper are: (a) to investigate Land Use/Land Cover (LULC) transformations and dynamics that occurred over the period in the municipality of Serra San Bruno (Calabria, Italy), an area particularly representative of the Mediterranean mountainous landscape; (b) to compare the settlement growth with the urban planning tools in charge in the study area; (c) to examine the relationship between urbanrural gradient, landscape metrics, demographic and physical variables; (d) to investigate the evolution of urban-rural gradient composition and configuration along significant axes of landscape changes.Data with a high level of detail (minimum mapping unit 0.2 ha) were obtained through the digitisation of historical aerial photographs and digital orthophotos identifying LULC classes according to the Corine Land Cover legend. The investigated period was divided into four significant time intervals, which were specifically analysed to detect LULC changes.Differently from previous studies, in the present research the spatio-temporal analysis of urban-rural gradient was performed through three subsequent steps: (1) kernel density analysis of settlements; (2) analysis of landscape structure by means of metrics calculated using a moving window method; (3) analysis of composition and configuration of the urbanrural gradient within three landscape profiles located along significant axes of LULC change.The use of thematic overlays and transition matrices enabled a precise identification of the LULC changes that had taken place over the examined period. As a result, a detailed description and mapping of the landscape dynamics were obtained. Furthermore, landscape profiling technique, using continuous data, allowed an innovative and valuable approach for analysing and interpreting urban-rural gradient structure over space and time.

show abstract

Section: G Modica Et Almentioning

confidence: 99%

“…Landscape metrics find a very effective application in the analysis of urban to rural gradient composition and configuration (e.g. Luck and Wu, 2002;Hahs and McDonnell, 2006;Weng, 2007;Yang et al, 2010).…”

Section: G Modica Et Almentioning

confidence: 99%

Spatio-temporal analysis of the urban–rural gradient structure: an application in a Mediterranean mountainous landscape (Serra San Bruno, Italy)

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Yang et al [18] have discovered the trend of spatiotemporal changes in Beijing metropolitan area during the past 15 years, by computing several landscape metrics using a moving window along a 96-km-long transect across Beijing metropolitan area from west to east. The results not only show the spatial and temporal landscape characteristics of Beijing City hypothetical framework but also reveal some new patterns.…”

Section: Geospatial Change Detectionmentioning

confidence: 99%

Methodologies and applications in Digital China

2012

View full text Add to dashboard Cite

show abstract

“…Land cover change detection (LCCD), which is a classic problem, has recently been a hot topic in remote sensing [1][2][3][4][5][6][7][8], because LCCD plays an increasingly important role in making decisions to promote sustainable urban development, such as urban expansion [9,10], city temperature change analysis [11,12], urban air quality analysis [13], man-made target change detection [14], and urban landscape change [15][16][17], etc. In addition, LCCD has a positive effect in natural resource management on Earth's surface, for example, in forest deformation monitoring [18,19] and land use monitoring [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Liu

Zhang

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy.

show abstract

Gradient analysis of landscape spatial and temporal pattern changes in Beijing metropolitan area

Cited by 28 publications

References 26 publications

Spatio-temporal analysis of the urban–rural gradient structure: an application in a Mediterranean mountainous landscape (Serra San Bruno, Italy)

Spatio-temporal analysis of the urban–rural gradient structure: an application in a Mediterranean mountainous landscape (Serra San Bruno, Italy)

Methodologies and applications in Digital China

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Contact Info

Product

Resources

About