MODELLING OF URBAN DYNAMICS USING URBAN GREEN SPACE AND MULTI-SENSOR DATA

Abstract

The process of urbanisation, particularly in emerging nations, like India, is a serious problem because of its profound impact on resources, environment, and human health. Rapid population increase, industrialization, and socio-economic considerations, all contribute to the expansion of urban areas, resulting in challenges, like urban sprawl and the establishment of Urban Heat Island (UHI). Addressing these difficulties demands a thorough understanding of the urban growth dynamics, the importance of Urban Green Spaces (UGS), and UHI mitigation. This study investigates the complex interaction between the urbanisation, UGS, and UHI using modern geospatial technologies such as Geographic Information Systems (GIS) and remote sensing. The study investigates the cooling intensity of UGS and its spatial attributes, focusing on Indian cities, using land use (LU) classes, land cover (LC) indices, Land Surface Temperature (LST) data, and Landscape Metrics (LSM). By analysing existing literature and research approaches, the study emphasises the importance of UGS in promoting healthy urban living, minimising the negative effects of urbanisation, and advancing sustainable development. Furthermore, it emphasises the importance of multidisciplinary approaches that combine ecological, social, and technical perspectives in order to successfully manage UGS and improve urban resilience. This comprehensive study sheds light on the current state of UGS research, its methodology, and its implications for urban planning and sustainability in Indian cities. Objectives of the study are to incorporate UGS into the analysis of urban dynamics in an Indian city utilising remote sensing data. The objectives include characterising urban dynamics and undertaking multi-scale analysis with UGS integration. Sub-objectives include analysing built up features, spatio-temporal changes in urban dynamics, and the urban thermal environment, as well as optimising urban parks. The study looks at spatial attributes of built-up LU class, population correlations, and LST trends in Indian city. It also assesses the impact of UGS on urbanisation and local thermal environment, with a special focus on Lucknow city, India. Chapter 2 presents a spatio-temporal analysis of urban dynamics in Indian cities, with a focus on land use/land cover (LULC) components. Open-source data, including Decadal Land Use Data by ORNL DAAC for India from year 1985 to 2005 and Copernicus Global Land Service Dynamic Land Cover (CGLS-LC100) for year 2015, were used. GIS tools and the RStudio® landscapemetrics library were used to compute LSM for built-up and shrub LU classes in 694 districts. In addition, MODIS LST maps were utilised to analyse temperature patterns, and population data from the year 2001 and 2011 Indian censuses were incorporated to correlate with urban metrics. Results of the chapter focuses on the thorough spatio-temporal analysis i undertaken across India's heterogeneous environment, specifically on LULC changes and their impact on LST in 694 districts. The study used open-source data and GIS technologies to identify substantial correlations between spatial attributes of urbanisation and LST patterns, emphasising the importance of LSM such as Perimeter-Area Ratio (PARA), Fractal Dimension Index (FRAC), Contiguity Index (CONITG), and Core Area Index (CAI). The findings highlight the importance of prioritising UGS in optimising urban sprawl and achieving sustainability. Furthermore, the study clarifies the significance of population dynamics in creating urbanisation patterns, providing important insights for future city planning efforts. Chapter 3 explores urban sprawl in study area of Lucknow Development Authority (LDA), India, using magnitude and direction techniques. It examines the qualitative and quantitative features of urban expansion patterns, classifying them into three types: infilling, edge-expansion, and outlying growth. The study uses LSM over Landsat series data from 1990 to 2020 to investigate the spatio-temporal changes in LDA's urban landscape structure. Concentric buffer analysis is used to comprehend urbanisation patterns across four directional zones. Furthermore, the study employs approaches such as urban growth type classification and LSM calculation to provide a thorough analysis of LDA's changing urban dynamics. Results indicate significant growth of built-up LU in LDA, particularly in the North-East (NE) and South-East (SEE) direction, that too at the expense of vegetation and agricultural loss. Urban sprawl is found to be majorly of edge-expansion type. LSM show dispersion away from the city centre towards the outskirts, with increasing complexity, depicted by Landscape Shape Index (LSI). Shannon's entropy (Hn) suggests dispersed growth, with NE direction having the most dispersion. These findings highlight the importance of comprehensive urban planning in managing urban expansion efficiently in rapidly urbanising places such as LDA. Chapter 4 investigates the impact of UHI on LDA, with a focus on directional buffers from city centre. The study uses same data from chapter 3 to connect LST with spatial attributes of UGS. LC indices such as NDVI, NDBI, NDBaI, and NDWI are used to highlight their correlation with LST trends. Normalisation of LST (NLST) is done to account for seasonal differences. LSM are used to assess how spatial attributes of UGS affect built-up and LST at built-up in neighbourhood. Categories for UGS size, distance from built-up regions, and LST levels have been defined for analysis. Results show that NLST exhibits consistent negative correlation with NDVI and NDWI but positive correlation with the NDBI and NDBaI over time. Spatial analysis unveils urbanization's influence on local climate, with higher temperatures and UHI effects concentrated at the city center. Surface Urban Heat Intensity (SUHI) has escalated due to urban expansion, albeit partially mitigated by UGS initiatives. Dynamic LSM variations from 1990 to 2020 ii indicate shifts possibly due to LU, urbanization, or environmental changes. Notably, UGS impacts built-up areas at close distance only, influenced by UGS size and LSM like Aggregation Index (AI) and LSI. These findings offer valuable insights for policymakers and urban planners to address UHI impacts and safeguard natural landscapes amidst ongoing urbanization trends. Chapter 5 examines the cooling capacity of urban parks in city of Lucknow, India, in the context of rising urbanisation and climate change problems. The study uses satellite images such as Landsat- 8 (30 m) and PlanetScope (3 m) and urban park inventories to better understand how the spatial attributes of these parks affect their cooling impact on neighbouring built-up patches. The study evaluates the cooling distance and intensity of parks using advanced methodologies such as downscaling Landsat-8-derived LST up to the resolution of Planet data (3 m) and analysing LSM, which is critical for analysing impact of urban cooling by urban parks. The process entails digitising parks, categorising LU, downscaling LST, and urban parks’ spatial attributes which culminates in a full analysis of urban parks' contribution to thermal comfort and sustainable urbanisation. In results, urban parks were found to considerably reduce LST within a distance of 18 m from their boundaries, with an average cooling of 2.55 °C, at built-up in neighbourhood. CONTIG, CAI, PARA, and FRAC are spatial attributes associated with urban park cooling. Smaller in size and less complex parks produced larger cooling benefits. The study countered earlier findings by emphasising the small effect of urban park size on cooling the neighbourhood. The findings imply that ideal park designs provide cooling benefits, with implications for urban planning to reduce the UHI effect. The study also emphasises the necessity of taking into account ecological and cultural benefits, as well as cooling impacts, when designing urban parks. The research uses modern geospatial techniques to analyse the complex interaction between the urbanisation, UGS, and UHI in Indian cities. It analyses spatio-temporal remote sensing data and finds that urban sprawl has a major impact on LULC classes, LST trends, and the effectiveness of UGS in mitigating UHI impacts. LSM such as PARA, FRAC, CONITG, and CAI are found to be relevant for analysis of urban dynamic by remote sensing data of various resolutions. The findings highlight the crucial role of UGS in supporting sustainable development and improving urban resilience. Insights on the cooling capability of urban parks (up to 18 m) with specific spatial attributes, highlight the necessity of strategic planning of UGS in combating the negative effects of growing urbanisation and UHI.