Assessment of Urban Dynamics to Understand Spatiotemporal Differentiation at Various Scales Using Remote Sensing and Geospatial Tools

Abstract

Analysis of urban dynamics is a pivotal step towards understanding landscape changes and developing scientifically sound urban management strategies. Delineating the patterns and processes shaping the evolution of urban regions is an essential part of this step. Utilizing remote-sensing techniques and Geographic Information System (GIS) tools, we performed an integrated analysis on urban expansion in Srinagar city and surrounding areas from 1999 to 2017 at multiple scales in order to assist urban planning initiatives. To capture various spatial indicators of expansion, we analysed (i) land use/land cover (LULC) changes, (ii) rate and intensity of changes to built-up areas, (iii) spatial differentiation in landscape metrics (at 500, 1000 and 2000 m cell-size), and (iv) growth type of the urban expansion. Global Moran’s I statistics and local indicators of spatial association (LISA) were also employed to identify hotspots of change in landscape structure. Our methodology utilizes a range of geovisualization tools which are capable of appropriately addressing various elements required for strategic planning in growing cities. The results highlight aggregation and homogenization of the urban core as well as irregularity and fragmentation in its periphery. A combination of spatial metrics and growth type analysis supports the supposition that there is a continuum in the diffusion-coalescence process. This allows us to extend our understanding of urban growth theory and to report deviations from accepted stages of growth. As our results show, each dominating growth phase of the city—both diffusion (1999) and coalescence (2009 and 2017)—is interspersed with features from the other type. An improved understanding of spatial differentiation and the identification of hotspots can serve to make urban planning more tailored to such local conditions. An important insight derived from the results is the applicability of remote-sensing data in urban planning measures and the usefulness of freely available medium resolution data in gaining a comprehensive understanding of the evolution of cities.

Urban land use land cover mapping in tropical savannah using Landsat-8 derived normalized difference vegetation index (NDVI) threshold

“…Quantitative assessment of the land cover maps is done using unbiased error matrix and the details are presented in Table 2. In error analysis, it is important to know which class(es) have the greatest error by examining individual class accuracy using the user's and producer's accuracy, which measure the correctly classified pixel in the reference data (Anees et al 2020;Jin et al 2018). For this study, the user's accuracy for the individual class ranges between 55% and 100% for the first map (Fig 4a), 24% and 100% for the second map (except for the soil) (Fig 4b), and between 60% to 100% for the third map (Fig 4c).…”

Detection of Urban and Environmental Changes via Remote Sensing

“…The objective is to detect and express significant data records as a set of primary indices or main components [51,53]. Anees et al [54] used remote sensing with geographic information systems (GIS) change detection techniques to map the spatial dynamics of urban land modifications.…”

Special Issue “Remote-Sensing-Based Urban Planning Indicators”

“…The contributions and their EO-based indicators cover different planning-related sectors and domains (Table 1). The majority of indicators relate to land and environmental issues (e.g., [34,35]), while only a few indicators provide information that is more complex to derive from EO data, e.g., information on urban services or socio-economic conditions (e.g., [36,37]). In these sectors, there is still much scope for EO data to fill information gaps, e.g., with the recent advances in machine learning to provide data on complex urban classification problems.…”