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Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 42547

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Special Issue Editors

Dr. Mehebub Sahana

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Guest Editor

Spatial Policy and Analysis Laboratory, School of Environment, Education and Development, The University of Manchester, Oxford Rd, Manchester M13 9PL, UK
Interests: geographical information systems; environmental geography; remote sensing; landscape ecology; land-use change; peri-urban environment; biodiversity conservation

Dr. Hashem Dadashpoor

E-Mail Website
Guest Editor

Associate Professor of Urban and Regional Planning, Tarbiat Modares University, Tehran, Iran
Interests: urban/peri-urban land use dynamics; landscape patterns; metropolitan planning; planning theory and practice; land use policy; spatial justice/injustice

Dr. Priyank Patel

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Guest Editor

Department of Geography, Presidency University, Kolkata 700073, India
Interests: geoinformatics; cartography; cultural landscapes; landscape change; geomorphology

Dr. Alexander Follmann

E-Mail Website
Guest Editor

Institute of Geography & Global South Studies Center, University of Cologne, Cologne, Germany
Interests: human-environment geography; urban geography; urban/peri-urban land use dynamics; urban governance

Special Issue Information

Dear Colleagues,

The journal Remote Sensing (ISSN 2072-4292, IF 5.001) is currently running a Special Issue entitled "Remote Sensing Approaches to Landscape Ecology Analysis of Urban and Peri-Urban Environments.” Dr. Mehebub Sahana, Dr. Hashem Dadashpoor, Dr. Priyank Pravin Patel, and Dr. Alexander Follmann are serving as Guest Editors for this issue. We think you could make an excellent contribution based on your expertise in the particular field.

The main objective of this Special Issue shall be to provide a scientific forum for advancing the successful implementation of Remote Sensing (RS) technologies and Geographic Information System (GIS)-based methods towards landscape ecology analysis in urban areas and the peri-urban environment, and to foster informed debates among scientists and stakeholders on the environmental issues prevalent therein, relating these to city growth dynamics.

With unprecedented and in many cases unregulated growth occurring in the urban and peri-urban areas across the world, the land use character within and around such places are extremely dynamic, with fast-changing ecological characteristics and functions. Remote sensing and GIS techniques are considered an ideal tool to analyze such urban and peri-urban environments, their dynamicity, and the altered nature of the neighborhoods in such areas, relating these aspects with arising issues concerning urban development and environmental change monitoring and modeling. The use of large spatio-temporal geospatial datasets and Big Data to gauge city growth and sprawl effects, together with their effect on the urban environment's sustainability and livability, are thus important considerations to frame better policies to manage such locales.

Apart from the outlines stated above, prospective contributions to the Special Issue may also examine the influence of changing demographics, socio-cultural attributes, and climate change aspects on the urban and peri-urban ecology. This special issue aims to provide an outlet for high quality peer-reviewed publications that implement state-of-the-art methods and techniques incorporating geoinformatics-based methods to map, evaluate, and model urban landscape attributes, its changes, and their ecological implications together with the framing of newer hypotheses that can further understandings of the operative processes. The tentative Special Issue may encompass (without being limited to) the following themes:

Urban and peri-urban land use dynamics
Application of remote sensing and GIS on urban environmental change, sustainability and livability (climate, biodiversity, green space, etc.)
Remote sensing and GIS analysis informing/supporting urban and peri-urban governance and planning
Landscape ecological analysis
Monitoring, mapping, and assessing the urban spatial patterns and land use changes
Urban growth and fringe development
Rural-urban linkages
Relations between urban growth and climate change

Given your expertise in this domain, we invite you to contribute a paper on the above or any related issues.

With best regards,

Dr. Mehebub Sahana
Dr. Hashem Dadashpoor
Dr. Priyank Patel
Dr. Alexander Follmann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Rural-urban linkage
Landscape Ecology Analysis
Urban growth and fringe development
Built environment and climate change
Application of remote sensing and GIS
Urban and peri-urban land use dynamics
Metropolitan spatial structure and landscape patterns
Urban dynamics

Published Papers (13 papers)

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Research

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18 pages, 8624 KiB

Open AccessArticle

Exploring Spatial Network Structure of the Metropolitan Circle Based on Multi-Source Big Data: A Case Study of Hangzhou Metropolitan Circle

by Jing Zhang, Qi Hao, Xinming Chen, Congmou Zhu, Ling Zhang, Mengjia Hong, Jiexia Wu and Muye Gan

Remote Sens. 2022, 14(20), 5266; https://doi.org/10.3390/rs14205266 - 21 Oct 2022

Cited by 3 | Viewed by 1671

Abstract

The metropolitan circle is the basic unit of regional competition. Enhancing the connection between cities in the metropolitan circle and optimizing the spatial layout of the metropolitan circle is one of the goals of regional high-quality development in the new era. Therefore, it is of great significance to analyze the spatial network structure of the metropolitan circle. Taking Hangzhou metropolitan circle as an example, this study used web crawler technology to obtain data in multiple Internet big data platforms; used centrality analysis, flow data model, and social network analysis to construct the network connection matrix of human flow, goods flow, capital flow, information flow, and traffic flow; and explored the spatial network structure of the metropolitan circle. The results showed that the node intensity of the metropolitan circle presented a distribution pattern of strong in the east and weak in the west. The network connections of each county under the action of different element flows were different, and the skeleton of the integrated flow network connections showed a starfish-shaped feature. Hangzhou, Jiaxing, Huzhou, and Shaoxing cities had strong group effects in goods flow and traffic flow, while Quzhou and Huangshan cities had relatively independent cohesive subgroups in human flow and information flow. This study can provide useful references for regional development and spatial planning implementation. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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17 pages, 6279 KiB

Open AccessArticle

Exploring Dynamic Street Vendors and Pedestrians through the Lens of Static Spatial Configuration in Yuncheng, China

by Ziwen Sun, Iain Scott, Simon Bell, Yufeng Yang and Zichu Yang

Remote Sens. 2022, 14(9), 2065; https://doi.org/10.3390/rs14092065 - 26 Apr 2022

Cited by 6 | Viewed by 2411

Abstract

Extensive studies have demonstrated the relationship between static street networks and dynamic pedestrian movement. Street vendors temporarily appear in a vibrant space, closely engaging with numerous pedestrians. Is street vending distribution related to street network configuration? And if so, in what ways? This study investigates the heterogeneous relationships between pedestrians, vendors and the urban and suburban places they inhabit in Yuncheng by contrasting two different types of pedestrian-driven maps. The vending distribution map was identified via semi-structured interviews with urban managers (n = 11) in 2017. The spatial network configuration was examined through space syntax segment angular analysis, containing Integration, Choice and Combined Centrality properties at macro-, meso- and micro-scales. By overlapping the distribution map to the syntactic maps in the Quantum Geographic Information System (QGIS) platform, our results show that street vending distribution is most related to the Choice property at the meso-scale. Using semi-structured interviews (n = 53) and observations at two specific sites, we further found relationships tend to be opposed in urban and suburban areas. The study provides an alternative approach to integrating quantitative and qualitative data, expanding the theoretical and empirical understanding of spatial configuration and urban street activities related to walking and vending. This understanding has the potential to contribute to the design of more liveable street spaces. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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24 pages, 15502 KiB

Open AccessArticle

On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

by Qiong Wu, Zhaoyi Li, Changbao Yang, Hongqing Li, Liwei Gong and Fengxiang Guo

Remote Sens. 2022, 14(2), 279; https://doi.org/10.3390/rs14020279 - 07 Jan 2022

Cited by 15 | Viewed by 2516

Abstract

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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17 pages, 12409 KiB

Open AccessArticle

The Use of Remote Sensing to Quantitatively Assess the Visual Effect of Urban Landscape—A Case Study of Zhengzhou, China

by Chaofan Xi, Yulong Guo, Ruizhen He, Bo Mu, Peixuan Zhang and Yuan Li

Remote Sens. 2022, 14(1), 203; https://doi.org/10.3390/rs14010203 - 03 Jan 2022

Cited by 9 | Viewed by 2309

Abstract

The landscape visual effect of a city, which is generated by its long-term development, is an important index in city planning. In this study, we build a quantitative evaluation and remote sensing estimation scheme of landscape visual effect. The study contains two main steps. First, utilizing the Elo rating system and in situ sampled panoramic pictures, the quantitative assessment of the city landscape visual effect was carried out. Then, the landscape visual effect estimation model was built and applied to Landsat remote sensing image to generate the spatial distribution of landscape visual effect in Zhengzhou city, 2017. At last, the effect of different combination of land use and elevation to the landscape visual effect was discussed. The results showed the following: (1) the Elo rating system is an effective method to quantitatively evaluate the city landscape visual effect; (2) the landscape visual effect remote sensing estimation model had a good performance, with the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the model are less than 0.05 and 80, respectively; (3) the landscape visual effect score of Zhengzhou city, 2017, was high in the southwest and low in the northeast; (4) different land use situation and average surface elevation had a complex influence on the landscape visual effect. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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33 pages, 20233 KiB

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Monitoring Metropolitan Growth Dynamics for Achieving Sustainable Urbanization (SDG 11.3) in Kolkata Metropolitan Area, India

by Sk Mithun, Mehebub Sahana, Subrata Chattopadhyay, Brian Alan Johnson, Khaled Mohamed Khedher and Ram Avtar

Remote Sens. 2021, 13(21), 4423; https://doi.org/10.3390/rs13214423 - 03 Nov 2021

Cited by 11 | Viewed by 2960

Abstract

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon’s entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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23 pages, 9089 KiB

Open AccessArticle

Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion

by Xiong He, Yongwang Cao and Chunshan Zhou

Remote Sens. 2021, 13(18), 3639; https://doi.org/10.3390/rs13183639 - 12 Sep 2021

Cited by 33 | Viewed by 3060

Abstract

The rapid development of the urban city has led to great changes in the urban spatial structure. Thus, analyses of polycentric urban spatial structures are important for understanding these kinds of structures. In order to accurately evaluate the polycentric spatial structure of urban agglomerations and judge the differences between the actual development situation and overall planning of urban agglomerations, this study proposes a new method to identify the polycentric spatial structure of urban agglomerations in the Pearl River Delta based on the fusion of nighttime light (NTL) data, point of interest (POI) data, and Tencent migration data (TMG). In the first step, the NTL, POI, and TMG data are fused via wavelet transform; in the second step, Anselin local Moran’s I (LMI) and geographically weighted regression (GWR) were used to identify the main centers and subcenters, respectively. In the third step, the accuracy of the results of this study was further verified and discussed in the context of overall planning. The results show that the accuracy of urban polycenter identification via LMI and GWR after data fusion was 92.84%, and the Kappa value was 0.8971, which was higher than the results of polycenter identification via the traditional relative threshold. After comparing the identification results with the overall planning, firstly, we see that the fusion of multi-source big data can help to accurately evaluate the polycentric spatial structure within the urban agglomeration. Secondly, the fusion of dynamic data and static data can help identify the polycentric spatial structure of urban space more accurately. Therefore, this study can provide a new design for urban polycentric spatial structures, and further provide a reliable reference for the spatial optimization of urban agglomeration and the formulation of regional spatial development policies. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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22 pages, 44591 KiB

Open AccessArticle

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

by Haipeng Ye, Zehong Li, Ninghui Zhang, Xuejing Leng, Dan Meng, Ji Zheng and Yu Li

Remote Sens. 2021, 13(17), 3415; https://doi.org/10.3390/rs13173415 - 27 Aug 2021

Cited by 21 | Viewed by 2896

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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30 pages, 14724 KiB

Open AccessArticle

Impacts of Landscape Patterns on Ecosystem Services Value: A Multiscale Buffer Gradient Analysis Approach

by Wanxu Chen, Jie Zeng, Yumei Chu and Jiale Liang

Remote Sens. 2021, 13(13), 2551; https://doi.org/10.3390/rs13132551 - 29 Jun 2021

Cited by 29 | Viewed by 3825

Abstract

In recent decades, substantial changes have occurred in the spatial structure and form of landscapes in metropolises; these have greatly impacted ecosystem provision capacities. Clarifying the impact mechanism of landscape patterns on ecosystem services can provide insights into regional ecological conservation and sustainable development measures. Although previous studies have explored the impacts of landscape patterns on ecosystem services at multiple scales, few studies have been conducted using the buffer gradient analysis approach. Using land-use/cover change data, this study measured the evolution of spatiotemporal features of landscape patterns and ecosystem services value (ESV) with 1, 2, and 3 km buffer-zone scales in Wuhan, China. Econometric models were then used to analyze the impacts of landscape patterns on ecosystem services at different buffer-zone scales. The results demonstrated that rapid urbanization in Wuhan has led to significant changes in landscape patterns, and the landscape pattern metrics exhibited significant spatial heterogeneity. The ESV in Wuhan exhibited a steady decline during the study period. Hydrological regulations and waste treatment functions contributed to the largest proportion of ESV, and raw material production functions contributed to the lowest proportion. Landscape pattern metrics exerted a significant influence on ESV; however, this influence varied greatly. The results of this study provide a new understanding of the influence mechanism of landscape patterns on ecosystem services at 1, 2, and 3 km buffer-zone scales. These findings are critical for facilitating landscape planning and regional sustainable development. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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17 pages, 25245 KiB

Open AccessArticle

Potential Land Use Conflict Identification Based on Improved Multi-Objective Suitability Evaluation

by Wenli Jing, Kanhua Yu, Lian Wu and Pingping Luo

Remote Sens. 2021, 13(12), 2416; https://doi.org/10.3390/rs13122416 - 20 Jun 2021

Cited by 25 | Viewed by 3684

Abstract

Accurately identifying potential land use conflicts (LUCs) is critical for alleviating the ever-intensifying contradictions between humans and nature. The previous studies using the method of suitability analysis did not take full advantage of the current land use and multi-function characteristics of land resources. In this study, an improved model of suitability analysis was realized. In order to explore the LUCs status, including the types, intensity and distribution, a multi-objective suitability evaluation model was constructed from the perspective of production-living-ecological functions. And it was applied to Hengkou District, a typical region of the Qin-Ba mountainous area in the central part of China. The results show that the suitability distribution of living- production-ecological functions vary widely from the center to the periphery with altitude in Hengkou District; 22.03% of the land is at a risk of land use conflict. Among them, the high potential conflict areas account for 55.32%, and the conflicts between production and ecological lands (L2P1E1, L3P1E1) are the largest, which are located at the fringe of the central urban and ecologically dominant area. Therefore, it is necessary to adopt effective strategies to achieve a balance between the differential demands of land use. This research could better reflect the true situation of land use in ecologically sensitive mountainous areas and would provide theoretical and methodological support for the identification and prevention of potential LUCs. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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26 pages, 54684 KiB

Open AccessArticle

Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data

by Claire Teillet, Benjamin Pillot, Thibault Catry, Laurent Demagistri, Dominique Lyszczarz, Marc Lang, Pierre Couteron, Nicolas Barbier, Arsène Adou Kouassi, Quentin Gunther and Nadine Dessay

Remote Sens. 2021, 13(12), 2398; https://doi.org/10.3390/rs13122398 - 19 Jun 2021

Cited by 1 | Viewed by 2579

Abstract

Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the “neighborhood” scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us to propose a single fast unsupervised method for the characterization of urban areas. With the FOTOTEX algorithm, this paper introduces a texture-based method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale (“neighbourhoods”) and micro-scale (objects). FOTOTEX combines a Fast Fourier Transform and a Principal Component Analysis to convert texture into frequency signal. Several parameters were tested over Sentinel-2 and Pleiades imagery on Bouake and Brasilia. Results showed that a single Sentinel-2 image better assesses the urban footprint than the global products. Pleiades images allowed discriminating neighbourhoods and urban objects using texture, which is correlated with metrics such as building density, built-up and vegetation proportions. The best configurations for each scale of analysis were determined and recommendations provided to users. The open FOTOTEX algorithm demonstrated a strong potential to characterize the three nested scales of urban areas, especially when training and validation data are scarce, and computing resources limited. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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17 pages, 9680 KiB

Open AccessArticle

Understanding the Diversity of Urban–Rural Fringe Development in a Fast Urbanizing Region of China

by Guoyu Li, Yu CAO, Zhichao He, Ju He, Yu Cao, Jiayi Wang and Xiaoqian Fang

Remote Sens. 2021, 13(12), 2373; https://doi.org/10.3390/rs13122373 - 17 Jun 2021

Cited by 20 | Viewed by 3384

Abstract

The territories between urban and rural areas, also called urban–rural fringe, commonly present inherent instability and notable heterogeneity. However, investigating the multifaceted urban–rural fringe phenomenon based on large-scale identification has yet to be undertaken. In this study, we adopted a handy clustering-based method by incorporating multidimensional urbanization indicators to understand how the urban–rural fringe development vary across space and shift over time in the Yangtze River Delta Urban Agglomeration, China. The results show that (1) the growth magnitude of urban–rural fringe areas was greater than urban areas, whereas their growth rate was remarkably lower. (2) The landscape dynamics of urban–rural fringe varied markedly between fast-developing and slow-developing cities. Peripheral sprawl, inter-urban bridge, and isolated growth were the representative development patterns of urban–rural fringe in this case. (3) Urban–rural fringe development has predominantly occurred where cultivated land is available, and significant inter-provincial differences and tendency shifts have been found in the land cover change processes therein. Our findings indicate that the urban–rural fringe development should be considered as a stage-dependent and place-specific process. This work extends previous knowledge by unraveling the diversity of urban–rural fringe in a fast urbanizing region, and can provide insights into reorienting the spatial planning and land use management within the urban–rural interface. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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Review

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30 pages, 5192 KiB

Open AccessReview

Where Is the Peri-Urban? A Systematic Review of Peri-Urban Research and Approaches for Its Identification and Demarcation Worldwide

by Mehebub Sahana, Joe Ravetz, Priyank Pravin Patel, Hashem Dadashpoor and Alexander Follmann

Remote Sens. 2023, 15(5), 1316; https://doi.org/10.3390/rs15051316 - 27 Feb 2023

Cited by 11 | Viewed by 7660

Abstract

Metropolitan areas worldwide have grown rapidly and are usually surrounded by peri-urban zones that are neither urban nor rural. Despite widespread use of the term ‘peri-urban’, physical determination of these spaces is difficult due to their transient nature and multiple definitions. While many have identified peri-urban areas regionally or globally, questions persist on where exactly the peri-urban is located, and what are the most apt methods to delineate its boundaries. The answers are pertinent towards framing targeted policies for governing the dynamic socio-spatial transformations in these zones. This paper reviews peri-urban research over the last 50-plus years to discern the existing methodologies for its identification/demarcation and their applications. For this, a total of 3124 documents on peri-urban studies were identified through keyword searches in Scopus and Google Scholar databases. Thereafter, 56 documents were examined that explicitly dealt with demarcating peri-urban zones. Results reveal that there is no standout/generalized method for peri-urban demarcation. Rather, these approaches are geographically specific and vary across developed and developing countries, due to differences in land-use patterns, socioeconomic drivers, and political systems. Thus, we recommend developing a ‘pluralistic’ framework for determining peri-urban boundaries at the regional–global scale to enable better framing of relevant policies. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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13 pages, 2900 KiB

Open AccessReview

Impacts of Transportation Networks on the Landscape Patterns—A Case Study of Shanghai

by Xueli Chang, Xiaoyu Huang, Xuchen Jiang and Rui Xiao

Remote Sens. 2022, 14(16), 4060; https://doi.org/10.3390/rs14164060 - 19 Aug 2022

Cited by 6 | Viewed by 1642

Abstract

During 1995–2010, a comparison of the comprehensive effects of four different types of urban roads on landscape diversity in Shanghai was performed. Remote sensing images from 1995, 2000, 2005, and 2010 and the transfer matrix mathematical model were used to conduct the research, which focused on studying the rule of land use change in the study area over the past 15 years. Seven different landscape indices (SHDI, SHEI, ED, NP, TE, AWMSI, and MSI) were also selected to analyze the landscape pattern of Shanghai in three different periods and to explore the regularity of its dynamic changes and influential factors. Buffer analysis and statistical analyses were applied to represent the relationship between the distance from different roads quantitatively (e.g., railway, highway, national highway, and provincial highway) and a series of landscape pattern metrics (e.g., SHDI, PD, ED, and IJI) thus exploring the influence of transportation routes on the surrounding ecological environment. The results showed that (1) each type of landscape in the study area changed considerably during the research periods, a great amount of cultivated land was transformed into construction land, and the water area largely decreased. (2) The transportation routes exerted different impacts on the road landscape during different periods, and the effects were more obvious at the early stage of road construction. (3) The national way and the highway influenced the patch density (PD) index and edge density (ED) index more clearly, while the national way and the railway had greater effects on the landscape diversity indices. This study presents an approach that can be applied to quantitatively describe the impacts of transportation routes on landscape patterns and has the potential to facilitate route network planning. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Landscape Analysis of Urban and Peri-Urban Environments)

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