Ongoing urbanization has led to the continuous expansion of built-up areas; as a result, open space is under great threat. Despite the wealth of studies conducted on open spaces, there is still a further need to further investigate the morphology of open space, particularly in an effort to understand the trends and drivers of open space morphological transformation that remain under-researched.
Year | Types of Settings | Authors | Scale | Research Concerns and Objectives | Identified Drivers of Space Morphology | Methods (Data Collection and Data Analysis) |
---|---|---|---|---|---|---|
2022 | Urban green spaces | [22][17] | City | Investigating the distribution patterns and drivers of UGS. | Wealth and land use | Using a combination of remote sensing data and fieldwork. Calculating the proportion of UGS in different urban functional units. |
2022 | Green spaces | [23][18] | District | Exploring the local spatial evolution and analyzing the influence factors of its transformation. | Social and economic development. | The remote sensing image data was used as the basic data. Extracting the green space area conversion analysis information. |
2022 | Green spaces | [24][19] | City | Investigating the changing pattern of green spaces and how the topographic gradients of elevation and slope influence changes. | Topographic gradients of elevation and slope. | Applied for land use/land cover classification using GIS. Using overlay analysis. |
2022 | Parks and green spaces | [25][20] | City | Analyzing the correlation between urban development and parks and green spaces policy. | Green spaces policy. | Literature review, including theses, academic journal papers, research reports, and newspaper materials. |
2022 | Green spaces | [26][21] | City | Analyzing the green spaces of Dhaka over a 30-year period using GIS and remote sensing. | High population density and accelerated infrastructure development. | Using GIS and Remote Sensing to collect images. Using normalized difference vegetation index (NDVI) to calculate the total changes. |
2022 | Urban green spaces | [27][22] | Downtown | Comparing the changes of greening policies for UGS evolution in the two cities. | Several urban greening policies. | Using GIS and Remote Sensing to collect images. Statistical Yearbooks and document planning. Using the area change index, spatial morphological dimension, and spatial aggregation dimension. |
2021 | Urban green spaces | [28][23] | City | Exploring the spatial-temporal dynamics of UGS and its influences on urban eco-environments in developing cities. | Rapid urbanization and population growth. | Landsat images and MODIS products; maps; statistical data. Landscape pattern analysis. |
2021 | Green spaces | [29][24] | Not available | Identifying the relevant issues to address the challenges facing China’s Green spaces planning system. | Policy regulations. | Literature review and policy analysis. |
2021 | Green spaces | [30][25] | City | Evaluating the impact of changes on the structure of green spaces, and exploring the impact of different types of urban expansion and planning policies on changes to green space structure. | Rapid urban expansion. | Land use land cover (LULC) maps of the cities were developed based on satellite images. Landscape metrics and statistical analysis. |
2021 | Urban green spaces | [31][26] | City | Assessing the magnitude, directions of urban expansion and UGS change, as well as spatial variations. | The spatio-temporal pattern of urban expansion. | RS and GIS and Landscape Expansion Index (LEI) were used to extract Land Use Land Cover (LULC) data. Measuring urban expansion and UGS change and analyze urban growth patterns. |
2021 | Green spaces | [32][27] | Regional | Revealing the spatial-temporal change and driving factors of green spaces. | Anthropogenic activities and geographical environmental factors. |
Remote sensing imageries. Landscape pattern index. |
2021 | Urban green spaces | [33][28] | Cities | Exploring the effect of different levels of urbanization on changes in green spaces. | Economic development. | Using a time-series of remote sensing data. Indexes analysis. |
2021 | Urban green spaces | [34][29] | City | Employing integrated approaches to characterize the changing patterns and intensities of green spaces. | Rapid urbanization and greening policies. | Landsat images to interprete land use datasets. Landscape metrics. |
2020 | Urban green spaces | [35][30] | City | Assessing the present status of green cover and evaluating the spatio-temporal changes in the land use/land cover composition. | Rapid and unplanned urbanization. | Field visits, the Office of the Asansol Municipal Corporation. Calculation of NDVI. |
2020 | Urban green spaces | [36][31] | District | Analyzing the dynamic changes in landscape patterns, quantitatively evaluating the eco-service value of urban green spaces, and discussing how they mutually influence each other. | The rapid development of urban-rural integration and human factors. | Remote sensing image. Landscape pattern index. |
2020 | Urban green spaces | [37][32] | City | How political circumstances of municipal governance and the pursuit of development can precipitate losses. | The political circumstances of an urban area. | Using a time series of satellite images. Using the area calculation function in ArcMap 10.5. |
2020 | Green spaces | [38][33] | City | Analyzing and assessing the changing scale and spatial layout of the urban green spaces. | The expansion of urban and built-up areas, and the influx of migrants. | Using the landsat thematic mapper (TM) and OLI/TIRS remote sensing image data. Assessment using various indices. |
2019 | Urban green spaces | [39][34] | Town | Analyzing urban green space changes and their drivers. | Physical expansion of the built-up area, population growth, high land value, and laxity in the enforcement of planning regulations. | Using series Landsat images, land inventory, interview, focus group discussion, and field observation for data collection, and a combination of techniques, including pixel based image classification, qualitative descriptive and GIS-based processing for data analyses. |
2019 | Green spaces | [40][35] | City | An analysis of fragmented green spaces has been conducted. | Urbanization. | High-resolution satellite images. Using ENVI software computing. |
2019 | Urban green spaces | [41][36] | City | Analyzing temporal and spatial changes in urban green spaces and exploring the driving forces underlying the observed changes. | Different districts’ geographical locations. | The Earth System Science Data Sharing Platform. Remote sensing images. Calculating landscape indices. |
2019 | Urban open-green spaces | [42][37] | City | Investigating the changes that have occurred in urban open-green spaces in Nevsehir. | Urbanization. | Analyses consist of satellite image classification, plant index production, and GIS-based analyses methods. |
2019 | Green spaces | [43][38] | City | Understanding the factors that determine an increase or decrease of urban green spaces in a post-socialist city. | Different regimes. | Historical maps and aerial images. Temporal analysis, proximity analysis. |
2019 | Urban green spaces | [44][39] | City | Focusing on urban GS at a neighborhood scale to analyze GS in more granular detail. | Compact urbanization. | Urban GS was extracted using the normalized difference vegetation index based on GF-1 remote sensing images. Overlay. |
2019 | Urban green spaces | [45][40] | City center | Developing an understanding of how urbanization influences the fragmentation of urban green spaces, and offers insights into the planning of urban green spaces from the perspective of promoting sustainability. | Rapid urbanization and planning policies. | Landsat images. Landscape metrics. |
2018 | Urban green spaces | [46][41] | Cities | Determining the appropriate proportion of public greenery to built-up areas in cities. | Urbanization. | The Local Data Bank. Surveys. |
2018 | Green spaces | [47][42] | Regional | Investigating green space types of the Beijing–Tianjin–Hebei region based on the elevation data and land use/cover for those years. | Urbanization and greening policies. | Landsat images. Using ENVI software computing. |
2018 | Urban public spaces | [48][43] | City | Identifying the major environmental challenges associated with the continued destruction of public urban space. | Rapid population increase. | Literature review. |
2017 | Urban green spaces | [49][44] | Regional | Developing a systematic approach to monitoring changes in the urban landscape and assessing the conditions of UGS in the Klang Valley. | Urbanization. | Remote sensing processing techniques were used to extract meaningful data from mid-resolution Landsat satellite images. Analyse using landscape metrics. |
2017 | Urban green spaces | [50][45] | City | Studying the distribution of various types of urban green space in Shanghai. | Rapid urbanization. | High satellite image data. Landscape pattern index and gradient analysis. |
2017 | Public open spaces | [51][46] | City | Discerning the influence of factors on open space planning and development in Hong Kong. | Government planning and development strategies. | Government’s latest planning and development strategies. |
2017 | Urban green spaces | [52][47] | Cities | Identifying general patterns relating to the quantity and structure of urban green space, and the demographic and economic characteristics of the cities in the study. | Population density and economic level. | Using remote sensing analysis of Landsat 7 data. Calculating landscape indices. |
2016 | Green spaces | [53][48] | City | Exploring the change of green space in Suzhou and revealing the spatial characteristics, ecological benefits, and its impact mechanism. | Different districts’ geographical locations. | Landsat remote-sensing image data. Analyse using landscape metrics. |
2015 | Green spaces | [54][49] | City | Assessment of changes in green spaces of Nanjing in terms of scale and structure. | Population density. | Landsat Satellite Data. Analyse using landscape metrics. |
2015 | Urban green-spaces | [55][50] | Cities | Identifying problems, challenges and strategies of urban green space planning during the densification processes. | Urban densification. | Literature review. |
2013 | Urban green spaces | [56][51] | City | Investigating land use/land cover changes in Dehradun city and associated changes in urban green cover between 2004 and 2009. | Urbanization. | Remote Sensing to obtain detailed. Using Image Derived Parameters. |
2013 | Green spaces | [57][52] | Cities | Investigating the temporal trend in green space coverage and its relationship with urbanization. | Urbanization. | The Statistics Yearbook, green space coverage of cities were calculated through least square linear regressions. |
2013 | Urban green spaces | [58][53] | City | Analyzing the environmental quality based on green spaces to provide appropriate recommendations to elevate the environmental quality to international standards. | Population density. | Green space areas were extracted from Thailand Earth Observation System (THEOS) satellite imagery using Normalized Difference Vegetation Index (NDVI). Extracted green space areas were further analysed quantitatively with air quality indicators and population density utilizing deductive indexing method. |
2011 | Green spaces | [59][54] | City | To develop a comprehensive plan of green spaces development both at the municipal and regional levels. | Geography. | Using GIS and FRAGSTATS 3.3. Overlaying the two green space distribution maps and calculating the changing area, and the variation values of each green space type were obtained from the data of the land use change survey. |
2011 | Green spaces | [60][55] | City | Using landscape metrics to assess green spaces fragmentation. | Different districts’ geographical locations. | The original orthophoto maps and land use digital maps with 0.5 m resolution used in this study were purchased from the Hong Kong government. Green spaces and different land uses were extracted from the maps and transferred to raster maps, assisted by “3S” techniques. Calculating different landscape metrics. |
2011 | Urban green spaces | [61][56] | City | Using landscape pattern metrics to characterize shifting green space patterns. | Rapid urbanization and greening policies. | Remote-sensing image data. Landscape metrics analysis. |
2009 | Urban green spaces | [62][57] | City | To detect changes in the extent and pattern of green areas of Mashad and analyze the results of landscape ecology principles and functioning of the green spaces. | Open lands for housing development. | Combination of remote sensing image classification, landscape metrics assessment and vegetation indices. |
2008 | Open spaces | [1] | City | Evaluating the land-use zoning and development of open spaces. | Different districts’ geographical locations. | The land-use planning and statutory zoning for open space. |
2007 | Urban green spaces | [11] | Downtown | Identifying green space changes and their drivers. | Economic growth, population increases, urbanization, and weaknesses in the planning and management of urban development. | Graph theory, landscape metrics, GIS and FRAGSTATS 3.3. |
2007 | Greenbelt | [63][58] | City | Documenting the spatial and temporal changes of greenbelts over the past decade by analyzing satellite images. | Urban containment policy. | Remote Sensing, analysis of archived documents. |
2006 | Urban green spaces | [64][59] | City | Presenting a new method for quantifying and capturing changes in green space patterns. | Government policy. | GIS and remote sensing. Landscape metrics. |
2006 | Urban open spaces | [65][60] | City | Exploring the revitalization of existing traditional open spaces. | City Planning Act. | Case study. |
2003 | Green spaces | [66][61] | City | Examining the issues, obstacles, and processes involved in remediating potentially contaminated urban brownfield sites. | Urban planning policy. | Case studies and personal interviews. |