scholarly journals Directional and Zonal Analysis of Urban Thermal Environmental Change in Fuzhou as an Indicator of Urban Landscape Transformation

2019 ◽  
Vol 11 (23) ◽  
pp. 2810 ◽  
Author(s):  
Youshui Zhang ◽  
Xiaoqin Wang ◽  
Heiko Balzter ◽  
Bingwen Qiu ◽  
Jingyuan Cheng
Keyword(s):  
Landscape Pattern ◽  
Urban Landscape ◽  
Thermal Environment ◽  
Urban Expansion ◽  
Random Noise ◽  
Spectral Unmixing ◽  
Landscape Patterns ◽  
Sustainable Urban Development ◽  
City Centre ◽  
Hotspot Analysis

Urban expansion results in landscape pattern changes and associated changes in land surface temperature (LST) intensity. Spatial patterns of urban LST are affected by urban landscape pattern changes and seasonal variations. Instead of using LST change data, this study analysed the variation of LST aggregation which was evaluated by hotspot analysis to measure the spatial dependence for each LST pixel, indicating the relative magnitudes of the LST values in the neighbourhood of the LST pixel and the area proportion of the hotspot area to gain new insights into the thermal effects of increasing impervious surface area (ISA) caused by urbanization in Fuzhou, China. The spatio-temporal relationship between urban landscape patterns, hotspot locations reflecting urban land cover change in space and the thermal environment were analysed in different sectors. The linear spectral unmixing method of fully constrained least squares (FCLS) was used to unmix the bi-temporal Landsat TM/OLI imagery to derive subpixel ISA and the accuracy of the percent ISA was assessed. Then, a minimum change threshold was chosen to remove random noise, and the change of ISA between 2000 and 2016 was analysed. The urban area was divided into three circular consecutive urban zones in the cardinal directions from the city centre and each circular zone was further divided into eight segments; thus, a total of 24 spatial sectors were derived. The LST aggregation was analysed in different directions and urban segments and hotspot density was further calculated based on area proportion of hotspot areas in each sector. Finally, variations of mean normalized LST (NLST), area proportion of ISA, area proportion of ISA with high LST, and area proportion of hotspot area were quantified for all sectors for 2000 and 2016. The four levels of hotspot density were classified for all urban sectors by proportional ranges of 0%–25%, 25%–50%, 50%–75% and 75%–100% for low-, medium-, sub-high, and high density, and the spatial dynamics of hotspot density between the two dates showed that urbanization mainly dominated in sectors south–southeast 2 (SSE2), south–southwest 2 (SSW2), west–southwest 2 (WSW2), west–northwest 2 (WNW2), north–southwest 2 (NSW2), south–southeast 3 (SSE3) and south–southwest 3 (SSW3). This paper suggests a methodology for characterizing the urban thermal environment and a scientific basis for sustainable urban development.

Assessment of landscape changes under different urban dynamics based on a multiple-scenario modeling approach

2020 ◽  
Vol 47 (8) ◽  
pp. 1361-1379
Author(s):  
Chao Xu ◽  
Dagmar Haase ◽  
Meirong Su ◽  
Yutao Wang ◽  
Stephan Pauleit
Keyword(s):  
Influencing Factors ◽  
Landscape Pattern ◽  
Urban Landscape ◽  
Diversity Index ◽  
Housing Demand ◽  
Landscape Patterns ◽  
Urban Region ◽  
Aggregation Index ◽  
Rapid Urbanization ◽  
Urban Dynamics

In the context of rapid urbanization, it remains unclear how urban landscape patterns shift under different urban dynamics, in particular taking different influencing factors of urban dynamics into consideration. In the present study, three key influencing factors were considered, namely, housing demand, spatial structure, and growth form. On this basis, multiple urban dynamic scenarios were constructed and then calculated using either an autologistic regression–Markov chain–based cellular automata model or an integer programming-based urban green space optimization model. A battery of landscape metrics was employed to characterize and quantitatively assess the landscape pattern changes, among which the redundancy was pre-tested and reduced using principal component analysis. The case study of the Munich region, a fast-growing urban region in southern Germany, demonstrated that the changes of the patch complexity index and the landscape aggregation index were largely similar at sub- and regional scales. Specifically, low housing demand, monocentric and compact growth scenarios showed higher levels of patch complexity but lower levels of landscape aggregation, compared to high housing demand, polycentric and sprawl growth scenarios, respectively. In contrast, the changes in the landscape diversity index under different scenarios showed contrasting trends between different sub-regional zones. The findings of this study provide planners and policymakers with a more in-depth understanding of urban landscape pattern changes under different urban planning strategies and its implications for landscape functions and services.

scholarly journals Temporal and Spatial Analyses of the Landscape Pattern of Wuhan City Based on Remote Sensing Images

2018 ◽  
Vol 7 (9) ◽  
pp. 340 ◽  
Author(s):  
Jianjun Lv ◽  
Teng Ma ◽  
Zhiwen Dong ◽  
Yao Yao ◽  
Zehao Yuan
Keyword(s):  
Landscape Pattern ◽  
Urban Areas ◽  
Agricultural Landscape ◽  
Urban Landscape ◽  
Landscape Heterogeneity ◽  
Central City ◽  
National Level ◽  
Landscape Patterns ◽  
Wuhan City ◽  
Entire Area

With the acceleration of the process of building a national-level central city in Wuhan, the landscape pattern of the city has undergone tremendous changes. In this paper, remote images are classified through the neural network classification method, based on texture extraction, and the evolution of landscape patterns was quantitatively analyzed, based on the method of moving windows, landscape metrics and urban density calculation, in order to accurately extract landscape types and perform quantitative analyses. Wuhan City is taken as an example. The surface coverage of Wuhan City from 1989 to 2016 is divided into four types: agricultural landscape clusters, forest landscape clusters, water landscape clusters, and urban landscape clusters. It was concluded that, during the study period, the landscape heterogeneity of the entire area in Wuhan has increased, but the central urban area in Wuhan has decreased. The development of urban areas has compacted inwards but expanded outwards. In addition, the western part of Wuhan City developed better than the eastern part.

scholarly journals Analyzing the interpretative ability of landscape pattern to explain thermal environmental effects in the Beijing-Tianjin-Hebei urban agglomeration

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7874
Author(s):  
Dongchuan Wang ◽  
Zhichao Sun ◽  
Junhe Chen ◽  
Xiao Wang ◽  
Xian Zhang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Spatial Heterogeneity ◽  
Landscape Pattern ◽  
Thermal Environment ◽  
Hot Spot ◽  
Pearson Correlation ◽  
Ordinary Least Squares ◽  
Urban Agglomeration ◽  
Landscape Patterns ◽  
Anthropogenic Heat

The development of the urban agglomeration has caused drastic changes in landscape pattern and increased anthropogenic heat emission and lead to the urban heat island (UHI) effect more serious. Therefore, understanding the interpretation ability of landscape pattern on the thermal environment has gradually become an important focus. In the study, the spatial heterogeneity of the surface temperature was analyzed using the hot-spot analysis method which was improved by changing the calculation of space weight. Then the interpretation ability of a single landscape and a combination of landscapes to explain surface temperature was explored using the Pearson correlation coefficient and ordinary least squares regression from different spatial levels, and the spatial heterogeneity of the interpretation ability was explored using geographical weighted regression under the optimal granularity (5 × 5 km). The results showed that: (1) The hot spots of surface temperature were distributed mainly in the plains and on the southeast hills, where the landscapes primarily include artificial landscape (ArtLS) and farmland landscape (FarmLS). The cold spots were distributed mainly in the northern hills, which are dominated by forest landscape (ForLS). (2) On the whole, the interpretative ability of ForLS, FarmLS, ArtLS, green space landscape pattern, and ecological landscape pattern to explain surface temperature was stronger, whereas the interpretative ability of grassland landscape and wetland landscape to explain surface temperature was weaker. The interpretation ability of landscape pattern to explain surface temperature was obviously different in different areas. Specifically, the ability was stronger in the hills than in the plain and plateau. The results are intended to provide a scientific basis for adjusting landscape structural, optimizing landscape patterns, alleviating the UHI effect, and coordinating the balance among cities within the urban agglomeration.

scholarly journals Quantifying the Spatial Integration Patterns of Urban Agglomerations along an Inter-City Gradient

2019 ◽  
Vol 11 (18) ◽  
pp. 5000 ◽  
Author(s):  
Yiliang Wan ◽  
Chuxiong Deng ◽  
Tao Wu ◽  
Rui Jin ◽  
Pengfei Chen ◽  
...  
Keyword(s):  
Urban Landscape ◽  
Landscape Planning ◽  
Urban Expansion ◽  
Landscape Composition ◽  
Urban Agglomeration ◽  
Metropolitan Region ◽  
Sustainable Urban Development ◽  
Spatial Integration ◽  
Urban Agglomerations ◽  
Urban Rural

Understanding the integration process of urban agglomeration is essential for sustainable regional development and urban planning. However, few studies have analyzed the spatial integration patterns of metropolitan regions according to the impacts of landscape ecology along rail transit corridors. This study performed a comprehensive inter-city gradient analysis using landscape metrics and radar charts in order to determine the integration characteristics of an urban agglomeration. Specifically, we analyzed the evolution of spatial heterogeneity and functional landscapes along gradient transects in the Changsha–Zhuzhou–Xiangtan (CZT) metropolitan region during the period of 1995–2015. Four landscape functional zones (urban center, urban area, urban–rural fringe, and green core) were identified based on a cluster analysis of landscape composition, connectivity, and fragmentation. The landscape metric NP/LPI (number of patches/largest patch index) was proposed to identify the urban–rural fringe, which revealed that the CZT region exhibited a more aggregated form, characterized by a single-core, continuous development, and the compression of green space. The integration of cities has resulted in continued compression and fragmentation of ecological space. Therefore, strategies for controlling urban expansion should be adopted for sustainable urban development. The proposed method can be used to quantify the integration characteristics of urban agglomerations, providing scientific support for urban landscape planning.

The Reconstitution of Landscape Pattern at Master Planning Level in Ankang City

2011 ◽  
Vol 368-373 ◽  
pp. 1724-1731
Author(s):  
Ming Hua Huang ◽  
Yang Wang ◽  
Xiao Nan Shi
Keyword(s):  
Urban Space ◽  
Landscape Pattern ◽  
Urban Form ◽  
Green Space ◽  
Urban Landscape ◽  
Landscape Patterns ◽  
Master Planning ◽  
Land Resources ◽  
Space System ◽  
Natural Landscape

The author’s observation on the new urban form of Ankang city grounded on the landscape pattern by analyzing and evaluating the present conditions of land resources, historic cultural attractions, and natural landscape from ecological perspective, finding out the problems in the current construction of urban landscape environment with consideration on local special landscape theme. Besides, the authors emphasized and constructed the landscape patterns at master planning level by layout of city green corridor, preservation of the wetland as the urban corn and improvement of the urban green space system in combination of history, culture and natural environment, urban space and urban spirit, as well as history and future construction.

scholarly journals Influence of Impervious Surface Area and Fractional Vegetation Cover on Seasonal Urban Surface Heating/Cooling Rates

2021 ◽  
Vol 13 (7) ◽  
pp. 1263
Author(s):  
Youshui Zhang ◽  
Heiko Balzter ◽  
Yu Li
Keyword(s):  
Vegetation Cover ◽  
Urban Landscape ◽  
Thermal Environment ◽  
Impervious Surface ◽  
Landscape Patterns ◽  
Surface Heating ◽  
Heating Effect ◽  
Four Seasons ◽  
Cooling Effects ◽  
Urban Thermal Environment

The urban thermal environment is impacted by changes in urban landscape patterns resulting from urban expansion and seasonal variation. In order to cope effectively with urban heat island (UHI) effects and improve the urban living environment and microclimate, an analysis of the heating effect of impervious surface areas (ISA) and the cooling effects of vegetation is needed. In this study, Landsat 8 data in four seasons were used to derive the percent ISA and fractional vegetation cover (FVC) by spectral unmixing and to retrieve the land surface temperature (LST) from the radiative transfer equation (RTE). The percent ISA and FVC were divided into four different categories based on ranges 0–25%, 25–50%, 50–75%, and 75–100%. The LST with percent ISA and FVC were used to calculate the surface heating rate (SHR) and surface cooling rate (SCR). Finally, in order to analyze the heating effect of ISA and the cooling effect of vegetation, the variations of LST with SHR and SCR were compared between different percent ISA and FVC categories in the four seasons. The results showed the following: (1) In summer, SHR decreases as percent ISA increases and SCR increases as FVC increases in the study area. (2) Unlike the dependence of LST on percent ISA and FVC, the trends of SHR/SCR as a function of percent ISA/FVC are more complex for different value ranges, especially in spring and autumn. (3) The SHR (heating capacity) decreases with increasing percent ISA in autumn. However, the SCR (cooling capacity) decreases with increasing FVC, except in summer. This study shows that our methodology to analyze the variation and change trends of SHR, SCR, and LST based on different ISA and FVC categories in different seasons can be used to interpret urban ISA and vegetation cover, as well as their heating and cooling effects on the urban thermal environment. This analytical method provides an important insight into analyzing the urban landscape patterns and thermal environment. It is also helpful for urban planning and mitigating UHI.

scholarly journals Quantitative Influence of Land-Use Changes and Urban Expansion Intensity on Landscape Pattern in Qingdao, China: Implications for Urban Sustainability

2019 ◽  
Vol 11 (21) ◽  
pp. 6174 ◽  
Author(s):  
Jinming Yang ◽  
Shimei Li ◽  
Huicui Lu
Keyword(s):  
Land Use ◽  
Landscape Pattern ◽  
Urban Areas ◽  
Urban Expansion ◽  
Land Use Changes ◽  
Urban Sustainability ◽  
Urban Ecosystems ◽  
Landscape Patterns ◽  
Plain Area ◽  
Suburban Areas

The spatial structure and configuration of land-use patches, i.e., landscape patterns could affect the flow of energy and materials in inner-urban ecosystems, and hence the sustainable development of urban areas. Studying landscape pattern changes under the process of urbanization would have implicational significance to urban planning and urban sustainability. In this paper, land-use change and urban expansion intensity (UEI) were treated as the inducement factors for changes in landscape patterns, and stepwise regression and geographically weighted regression (GWR) were adapted to quantify their integrated and distributed magnitude effects on landscape patterns, respectively. The findings suggested that land-uses have different contributions to changes in landscape patterns at different urban development zones (downtown, suburban plain area and mountainous suburban areas). Furthermore, the GWR analysis results indicated that the effect of UEI on landscape patterns has spatial and temporal heterogeneity. From 1987 to 2000, the UEI had great explanatory capacity on changes in landscape patterns and helped the landscape assemble faster in the downtown and adjacent areas. However, with the shifting of the center of urban construction from downtown to the suburbs, the high explanatory ability was oriented towards suburban areas during 2000–2016 and the magnitude of influence spatially changed. Therefore, a compact city and protection policy should be adapted to different regions in the study area to achieve strong urban sustainability.

scholarly journals Variations in the Effects of Landscape Patterns on the Urban Thermal Environment during Rapid Urbanization (1990–2020) in Megacities

2021 ◽  
Vol 13 (17) ◽  
pp. 3415
Author(s):  
Haipeng Ye ◽  
Zehong Li ◽  
Ninghui Zhang ◽  
Xuejing Leng ◽  
Dan Meng ◽  
...  
Keyword(s):  
Green Space ◽  
Thermal Environment ◽  
Urban Expansion ◽  
Urban Green Space ◽  
Landscape Patterns ◽  
Cooling Effect ◽  
Landscape Configuration ◽  
Rapid Urbanization ◽  
Urban Green ◽  
Urban Thermal Environment

Deterioration of the urban thermal environment, especially in megacities with intensive populations and high densities of impervious surfaces, is a global issue resulting from rapid urbanization. The effects of landscape patterns on the urban thermal environment within a single area or single period have been well documented. Few studies, however, have explored whether the effects can be adapted to various cities at different urbanization stages. This paper investigated the variations of these effects in the five largest and highly urbanized megacities of China from 1990 to 2020 using various geospatial approaches, including concentric buffer analysis, correlation analysis, and hierarchical ridge regression models. The results indicated that the effects of landscape patterns on the urban thermal environment were greatly variable at different urbanization stages. Although landscape composition was more important than landscape configuration in determining the urban thermal environment, the standard coefficients of composition metrics continuously decreased from 1990 to 2020. However, configuration metrics, such as patch density, edge density, and shape complexity, could affect the land surface temperature (LST) to a larger extent at the highly urbanized stage. The urbanization process could also affect the cooling effect of urban green space. At the initial stage of rapid urban expansion in approximately 2000, urban green space explained the most variation in LST, with a value as high as 10%. To maximize the cooling effect, the spatial arrangement of urban green space should be highlighted in the region that was 10–15 km from the city center, where the mean LST experienced a significant decline. These results may provide deeper insights into improving the urban thermal environment by targeted strategies in optimizing landscape patterns for areas at different urbanization stages.

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