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(a) Localization and position of the city of Athens in the urban hierarchy of Greece and (b) geophysical map of Attika and zone of study. Source: data of the Hellenic National Statistical Office (ESYE) and Minister of Space Planning and Environment (YPEHODE).
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In this study, a set of multi‐spectral satellites images were used to locate and identify the irregular settlements zones in the Athens metropolitan area. To achieve this goal, indexes—Brightness Index and Normalized Difference Vegetation Index—and supervised classifications are computed and applied to the images. In order to locate and identify th...
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... some terrestrial and space-borne information, a selection was applied to statistical data and reference units, on a series of aerial photographs and satellite images. The limits of the studied area are defined by a square ( figure 1(b)), which includes the continuous urban zone of Athens, parts of the mountains in its vicinity and other surrounding areas. The administrative limits of the agglomeration of Athens and the close urban zones were defined by the National Statistical Office (ESYE) in 1991 and their spatial unit polygons are provided by YPEHODE (Ministry of Regional Planning and Environment). ...
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Resulting from the society shift of the postwar period, urban growth and urban sprawl of infrastructure and settlements of individual housing are today at the centre of public policies' focus, in particular due to their widely criticized environmental impacts. If sustainable urban planning strategies aim at avoiding the construction of new peripher...
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... The extent of soil sealing all around the world is so intense that it deserves specific monitoring actions, as documented in Figure 3.1. The continuous monitoring of impervious land across various spatial scales emerges as a crucial tool for assessing natural resources and shaping urban containment strategies (Weber et al. 2005;Christopoulou et al. 2007;Doygun et al. 2008). ...
Departing from conventional narratives centered on economic stagnation and social secularism, this book offers a fresh perspective on Mediterranean urbanities. It posits their correlation with housing and welfare regimes, societal transformations, local governance structures, and deficiencies in spatial planning. The analysis within delves into the neglected potential for mitigating regional disparities, conducting a meticulous examination of environmental disparities, economic imbalances, and overarching social inequalities in Southern European regions. The outcome aims to furnish an integrated, and potentially holistic, understanding of spatial divisions between cities and their surrounding territories.
... Metropolitan Kedungsepur, in this study, refers to Presidential Regulation Number 78 of 2017 concerning Spatial Plans for the Urban Areas of Kendal, Demak, Ungaran, Salatiga, Semarang, and Purwodadi. The data collection methods in this study involve interpreting satellite imagery [11,12], considering elements of interpretation, i.e., hue or colour, size, shape, texture, pattern, height, shadow, site and association. Sutanto [13] and Somantri [14] stated that interpreting temporal images could identify changes in urban land use. ...
... There are several studies comparing the UGS extraction from the functional perspective to quantify multiple ecosystem services [46,47], while few studies pay further attention to the block perspective. On the one hand, the block is the basic unit of various functional zones in urban planning and design [48]. ...
The appropriate resolution has been confirmed to be crucial to the extraction of urban green space and the related research on ecosystem services. However, the factors affecting the differences between various resolutions of data in certain application scenarios are lacking in attention. To fill the gap, this paper made an attempt to analyze the differences of various resolutions of data in green space extraction and to explore where the differences are reflected in the actual land unit, as well as the factors affecting the differences. Further, suggestions for reducing errors and application scenarios of different resolutions of data in related research are proposed. Taking a typical area of Nanjing as an example, data taken by DJI drone (0.1 m), GaoFen-1 (2 m) and Sentinel-2A (10 m) were selected for analysis. The results show that: (1) There were minimal differences in the green space ratio of the study area calculated by different resolutions of data on the whole, but when subdivided into each land use type and block, the differences were obvious; (2) The function, area and shape of the block, as well as the patch density and aggregation degree of the internal green space, had a certain impact on the differences. However, the specific impact varied when the block area was different; and (3) For the selection of the data source, the research purpose and application scenarios need to be comprehensively considered, including the function and attributes of the block, the distribution characteristics of green space, the allowable error limits and the budget. The present study highlighted the reasons of differences and hopefully it can provide a reference for the data selection of urban green space in the practical planning and design.
... En el segundo de ellos, denominado flujo RGB, se emplearon las bandas rojo, verde y azul, y adicionalmente el MDV y el índice de suelo BI (tabla 1). En ambos flujos, los mencionados índices espectrales se emplearon como umbral de reclasificación para discriminar arbolado del resto de cubiertas, lo cual ha sido llevado a cabo con anterioridad en numerosos estudios de clasificación de imágenes (Weber et al., 2005;Kumar et al., 2015;Fragoso-Campón et al., 2020). -Multiresolution segmentation: Algoritmo de segmentación de imágenes que minimiza localmente la heterogeneidad media de los objetos de la imagen para una resolución determinada. ...
... Many studies focus on the mapping of urban land use/land cover, yet, in the majority of these works, the focus does not lie on the mapping of urban vegetation, but on characterizing built-up areas with different functionalities (residential, commercial, etc.) or morphology [33][34][35]. In these studies, vegetation is usually represented by only one or two classes (e.g., high versus low vegetation, woody versus herbaceous). ...
Green space is increasingly recognized as an important component of the urban environment. Adequate management and planning of urban green space is crucial to maximize its benefits for urban inhabitants and for the urban ecosystem in general. Inventorying urban vegetation is a costly and time-consuming process. The development of new remote sensing techniques to map and monitor vegetation has therefore become an important topic of interest to many scholars. Based on a comprehensive survey of the literature, this review article provides an overview of the main approaches proposed to map urban vegetation from high-resolution remotely sensed data. Studies are reviewed from three perspectives: (a) the vegetation typology, (b) the remote sensing data used and (c) the mapping approach applied. With regard to vegetation typology, a distinction is made between studies focusing on the mapping of functional vegetation types and studies performing mapping of lower-level taxonomic ranks, with the latter mainly focusing on urban trees. A wide variety of high-resolution imagery has been used by researchers for both types of mapping. The fusion of various types of remote sensing data, as well as the inclusion of phenological information through the use of multi-temporal imagery, prove to be the most promising avenues to improve mapping accuracy. With regard to mapping approaches, the use of deep learning is becoming more established, mostly for the mapping of tree species. Through this survey, several research gaps could be identified. Interest in the mapping of non-tree species in urban environments is still limited. The same holds for the mapping of understory species. Most studies focus on the mapping of public green spaces, while interest in the mapping of private green space is less common. The use of imagery with a high spatial and temporal resolution, enabling the retrieval of phenological information for mapping and monitoring vegetation at the species level, still proves to be limited in urban contexts. Hence, mapping approaches specifically tailored towards time-series analysis and the use of new data sources seem to hold great promise for advancing the field. Finally, unsupervised learning techniques and active learning, so far rarely applied in urban vegetation mapping, are also areas where significant progress can be expected.
... By linking (urban) form and (ecological) functions in peri-urban areas, a long-term analysis of per-capita land-use indicators provides the necessary informative base to any policy aimed at containing urban expansion [51][52][53][54]. In this direction, spatial planning is urgently required to finely tune urban expansion with population increase and growth in economic activities, limiting settlement diffusion (e.g., in shrinking cities) [55]. ...
The spatial mismatch between population growth and settlement expansion is at the base of current models of urban growth. Empirical evidence is increasingly required to inform planning measures promoting urban containment in the context of a stable (or declining) population. In these regards, per-capita indicators of land-use change can be adopted with the aim at evaluating long-term sustainability of urbanization processes. The present study assesses spatial variations in per-capita indicators of land-use change in Rome, Central Italy, at five years (1949, 1974, 1999, 2008, and 2016) with the final objective of quantifying the mismatch between urban expansion and population growth. Originally specialized in agricultural productions, Rome’s metropolitan area is a paradigmatic example of dispersed urban expansion in the Mediterranean basin. By considering multiple land-use dynamics, per-capita indicators of landscape change delineated three distinctive waves of growth corresponding with urbanization, suburbanization, and a more mixed stage with counter-urbanization and re-urbanization impulses. By reflecting different socioeconomic contexts on a local scale, urban fabric and forests were identified as the ‘winner’ classes, expanding homogeneously over time at the expense of cropland. Agricultural landscapes experienced a more heterogeneous trend with arable land and pastures declining systematically and more fragmented land classes (e.g., vineyards and olive groves) displaying stable (or slightly increasing) trends. The continuous reduction of per-capita surface area of cropland that’s supports a reduced production base, which is now insufficient to satisfy the rising demand for fresh food at the metropolitan scale, indicates the unsustainability of the current development in Rome and more generally in the whole Mediterranean basin, a region specialized traditionally in (proximity) agricultural productions.
... 59 -7Ι based οη the integration of statistical surveys, remote sensing, and Γιeld mapping (e.g. Weber et αl., [18]). The present study is α preliminary contribution to this issue and confirms the cruoial role of official statistios databases (e.g. ...
... For instance, Yang & Lo (2002) performed a detailed LULC analysis of Atlanta (Georgia) metropolitan area using Landsat MSS and TM data and an unsupervised clustering method; their study revealed the loss of vegetation and occurrence of urban sprawl in the city. Weber et al. (2005) conducted the research considering SPOT (Satellite Pour l'Observation de la Terre) imagery and adopted parallelepiped classification along with a brightness index and a Normalized Difference Vegetation Index (NDVI) to identify the urban green space of Athens metropolitan. The multi-temporal trends of urban expansion have been characterized and its impact on alteration of land transformation has been studied by Xiao et al. (2006). ...
The emergence of alienated patch in the periphery of the city or fragmentation of the main city are the results of irresponsible and poor planning. This global problem of sprawl is strengthening even more with the hasty pace of urbanization. Despite the existing policies and regulations, it is a huge failure to control the sprawl. Hence, city planners and policy makers need to be more efficient in designing the cities to achieve sustainable development goals. For that purpose, adequate and informative data of the urban morphology, growth pattern, sprawl characteristics are required. Geospatial technology is a cost-effective measure and best among currently available techniques for collecting real-time/near real-time geographical data of the entire globe. The geographic information system (GIS) provides numerous tools for assessment of multidimensionality of urban sprawl. This chapter discusses various urban models, different forms of urban expansion, and a few existing methods to quantify sprawl.
... In recent years, comparative analysis aimed at capturing underlying patterns and trends from land-use databases has been fed by a continuous demand for high-resolution data as well as reliable indicators and new methodologies [60][61][62][63][64][65][66][67]. This work focuses on land-use changes occurring in Rome, which has grown very fast over the last seventy years, to infer different means of urban expansion using exploratory data analysis. ...
This study investigates long-term landscape transformations (1949-2016) in urban Rome, Central Italy, through a spatial distribution of seven metrics (core, islet, perforation, edge, loop, bridge, branch) derived from a Morphological Spatial Pattern Analysis (MSPA) analyzed separately for seven land-use classes (built-up areas, arable land, crop mosaic, vineyards, olive groves, forests, pastures). A Principal Component Analysis (PCA) has been finally adopted to characterize landscape structure at 1949 and 2016. Results of the MSPA demonstrate how both natural and agricultural land-uses have decreased following urban expansion. Moreover, the percent 'core' area of each class declined substantially, although with different intensity. These results clearly indicate ‘winners’ and ‘losers’ after long-term landscape transformations: urban settlements and forests belong to the former category, the remaining land-use classes (mostly agricultural) belong to the latter category. Descriptive statistics and multivariate exploratory techniques finally documented the intrinsic complexity characteristic of actual landscapes. The findings of this study also demonstrate how settlements have expanded chaotically over the study area, reflecting a progressive 'fractalization' and inhomogeneity of fringe landscapes, with negative implications for metropolitan sustainability at large. These transformations were unable to leverage processes of settlement and economic re-agglomeration around sub-centers typical of polycentric development in the most advanced socioeconomic contexts.
... In 1931, Fawcett (cited by [2]) introduced an administrative boundary-based classification to define urban agglomeration but was severely criticized by John Friedmann and Weber who stated, "Every city does not perform the same cognitive interpretation of urban agglomeration as they differ from the administrative demarcations, population concentration, economy and services" [15,16]. In order to mitigate such limitations, Zipf applied the Gravity Model to explain urban agglomeration [17]. ...
... Also, it is bounded to a given threshold distance which could compute the intensity of agglomeration through a nodal interpretation but has no facility to model its actual expanse [20]. In domain of remote sensing, in order to capture the expanse of the urban agglomeration, Weber [16] introduced a satellite-image processing approach as a constraints-free and globally applicable method [25]. This method was initially lacking in the applicability in the resource-constrained contexts due to lack of access to satellite imagery data with high accuracy [26,27]. ...
The configuration of urban areas depicts the pattern of urban agglomeration that drives a country's economy. Since the era of the early-industrial revolution, many scholars have been continuingly attempted to recognize the pattern of urban agglomeration in cities and regions. Nevertheless, such efforts had been in completed in capturing the complex nature of the agglomerations in modern cities. In such context, this study proposes a novel modeling approach to capture the pattern of urban agglomeration of a given region. In the proposed model, which is named as the intersection based clustered network model (iCN Model), the centrality of the road network is considered as the primary indicator in capturing the urban agglomeration pattern. The model was developed on the basis of the percolation theory and fractal geometry. The model distinguishes the agglomerated urban clusters and measures the self-similarity of the clusters of urban agglomeration. The results revealed that the urban agglomeration pattern derived from the iCN model corresponds with the satellite imagery derived the urban agglomeration pattern with an acceptable level of accuracy (> 71% of KAPPA) and it is proposed to be applied in spatial planning and transport planning practice.
