Effects of Changing Scales on Landscape Patterns and Spatial Modeling Under Urbanization

Yang, Jinming; Li, Shimei; Wang, Xiaojie; Zhang, Xiaoguang

doi:10.3846/jeelm.2020.12081

Cited by 7 publications

(6 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Landscape change analysis can reflect effectively land use spatial pattern its impacts on landscape ecology based on geometrical features (Jin et al, 2020;Yang et al, 2020;Aman et al, 2021). Usually, landscape patterns have been analyzed by landscape metrics (McGarigal & Marks, 1995), which are classified into the three levels, namely patch, class, and landscape to quantify many aspects of landscape pattern, such as the fragmentation, heterogeneity, and connectivity (Cabral & Costa, 2017).…”

Section: Analysis Of Landscape Patternmentioning

confidence: 99%

Landscape Change of Land Use in the Karst Region of Jinan City, North China

Heng

2023

Journal of Environmental Engineering and Landscape Management

View full text Add to dashboard Cite

With the rapid development of urbanization, land use change has occurred in most karst mountain regions of North China over the last decade, so studying landscape pattern changes induced by urbanization would have implicational significance to regional planning and sustainability. Based on RS, GIS, and field investigation, land use change induced by urbanization in Jinan city belonging to the karst mountainous area of North China was analyzed over 30 years from 1987 to 2018, and further the landscape response of these changes was explored. The results indicate that (1) the most obvious changes have occurred in both urban/built-up land area and cropland area with rapid urbanization development in Jinan’s karst area, and the former increased by 246.4 km2 but the latter decreased by 343.3 km2 from 1987 to 2018; (2) landscape pattern of land use is profoundly changed by quick urbanization in the period from 2000 to 2018, but does not siginificently from 1987 to 2000; and (3) in the monitoring period, the cropland’s shape inclines to fragmentation and regularization, and the shapes of urban/built-up land and barren land have become increasingly distinct from the patch class level; from patch landscape level, the artificial landscape type (urban/built-up land) is increasingly dominant but the natural landscape type (grassland) is decreasingly dominant, thereby resulting in the disturbance of urban karst environment of Jinan city. Therefore, a protection policy should be taken to achieve strong urban karst sustainable development of North China.

show abstract

Section: Analysis Of Landscape Patternmentioning

confidence: 99%

Landscape Change of Land Use in the Karst Region of Jinan City, North China

Heng

2023

Journal of Environmental Engineering and Landscape Management

View full text Add to dashboard Cite

show abstract

“…Therefore, land use changes should be detected through Landscape pattern analysis [7]. Recently, research on urban growth and landscape changes has attracted increasing attention [7,9,11,12,15,19,[21][22][23][24][25][26][27][28][29][30][31]. Remote sensing (RS) data which are processed by geographic information system (GIS) software have been known as a very useful tool in studies on land use, especially for detection, mapping and modelling land cover patterns in a given area over a specific period of time [32].…”

Section: Introductionmentioning

confidence: 99%

“…( 8)) and Shannon's diversity index (SHDI) that reflects the heterogeneity of the landscape (Eq. ( 9)) [31]:…”

mentioning

confidence: 99%

See 1 more Smart Citation

Modelling of Expansion Changes of Vilnius City Area and Impacts on Landscape Patterns Using an Artificial Neural Network

Mirsanjari

Visockienė

Mohammadyari

et al. 2021

Ecological Chemistry and Engineering S

View full text Add to dashboard Cite

The present study aimed to analyse changes in the land cover of Vilnius city and its surrounding areas and propose a scenario for their future changes using an Artificial Neural Network. The land cover dynamics modelling was based on a multilayer perceptron neural network. Landscape metrics at a class and landscape level were evaluated to determine the amount of changes in the land uses. As the results showed, the Built-up area class increased, while the forest (Semi forest and Dense forest) classes decreased during the period from 1999 to 2019. The predicted scenario showed a considerable increase of about 60 % in the Built-up area until 2039. The vegetation plant areas consist about 47 % of all the area in 2019, but it will be 36 % in 2039, if this trend (urban expansion) continues in the further. The findings further indicated the major urban expansion in the vegetation areas. However, Built-up area would expand over Semi forest land and Dense forest land, with a large part of them changed into built- up areas.

show abstract

“…Three levels for landscape analyses, i.e., patch level-for each patch in the landscape mosaic, class level-for each patch class in the landscape, and landscape level-for the landscape mosaic as a whole can be performed [22]. For vegetation cover expressed in raster data model, a local or neighborhood-based fragmentation analysis measures fragmentation of vegetation cover at local scales defined by varied neighborhood windows; such local fragmentation indicators are statistically computed from the spatial arrangement of the vegetated and non-vegetated pixels within one or multiple vegetated patches overlapped by a moving window [23,24]. The main advantage of local indicators to vegetation cover fragmentation is that they are capable of reflecting the spatial heterogeneity and thus more preferable to reveal spatial variations in the vegetation cover patterns [25,26].…”

Section: Introductionmentioning

confidence: 99%

Assessing Urban Greenness Fragmentation and Analysis of Its Associated Factors: A Case Study in Wuhan Metropolitan Area, China

Fang

Sha

Wang

et al. 2021

IJGI

View full text Add to dashboard Cite

Green vegetation plays a vital role in urban ecosystem services. Rapid urbanization often tends to induce urban vegetation cover fragmentation (UVCF) in cities and suburbs. Mapping the changes in the structure (aggregation) and abundance of urban vegetation cover helps to make improved policies for sustainable urban development. In this paper, a new distance-based landscape indicator to UVCF, Frag, was proposed first. Unlike many other landscape indicators, Frag measures UVCF by considering simultaneously both the structure and abundance of vegetation cover at local scales, and thus provides a more comprehensive perspective in understanding the spatial distribution patterns in urban greenness cover. As a case study, the urban greenness fragmentation indicated by Frag was demonstrated in Wuhan metropolitan area (WMA), China in 2015 and 2020. Support vector machine (SVM) was then designed to examine the impact on the Frag changes from the associated factors, including urbanization and terrain characteristics (elevation and slope). The Frag changes were mapped at different scales and modeled by SVM from the selected factors, which reasonably explained the Frag changes. Sensitivity analysis for the SVM model revealed that urbanization showed the most dominant factor for the Frag changes, followed by terrain elevation and slope. We conclude that Frag is an effective scale-dependent indicator to UVCF that can reflect changes in the structure and abundance of urban vegetation cover, and that modeling the impact of the associated factors on UVCF via the Frag indicator can provide essential information for urban planners.

show abstract

Effects of Changing Scales on Landscape Patterns and Spatial Modeling Under Urbanization

Cited by 7 publications

References 52 publications

Landscape Change of Land Use in the Karst Region of Jinan City, North China

Landscape Change of Land Use in the Karst Region of Jinan City, North China

Modelling of Expansion Changes of Vilnius City Area and Impacts on Landscape Patterns Using an Artificial Neural Network

Assessing Urban Greenness Fragmentation and Analysis of Its Associated Factors: A Case Study in Wuhan Metropolitan Area, China

Contact Info

Product

Resources

About