Integrating remote sensing and geospatial big data on urban land use mapping and impact analysis of urban land use changes on green space distribution

Student thesis: PhD Thesis

Abstract

Urban land use significantly affects the urban environment, especially urban green space. Urban green spaces, such as parks, farmland, and gardens, can provide extensive benefits. Previous studies have proved that the ecological and socio-economic benefits of urban green spaces are highly dependent on their spatial patterns. The urban green landscape can act as a planning strategy to improve the urban environment and make the city achieve the Sustainable Development Goals (SDGs). However, the impact of urban land use changes on urban green space distributions has largely been ignored due to the lack of high-quality urban land use maps and impact analysis methods. Therefore, this Ph.D. thesis aims to improve the existing urban land use maps for investigating the dynamics of urban land use and assess the impact of urban land use changes on urban green space distribution. It is of great significance to study the urban functional patterns and urban green space distribution under the background of the transition from urban land cover to urban land use. The research uses Hangzhou as a case study, as it is one of the most typical cities in terms of urbanization, population growth, economic development, and land use changes.
Chapter 1 described the background of the study and introduces the research aims, objectives, main definitions, and structures in this thesis. In Chapter 2, I summarized the relevant literature, focusing on the urban land use mapping as well as the impact of urban land use on urban green space distribution. Chapter 3 introduced the methods mainly used in this thesis. The results in Chapter 4 (research chapter 1) demonstrated that integrating remote sensing (RS) and geospatial big data (GBD) provides new opportunities for urban land use classification. The integration strategies were categorized into decision-level integration (DI) and feature-level integration (FI) according to the fusion mode and process. Chapter 5 (research chapter 2) tested the DI and FI methods for mapping urban land use in Hangzhou city and concluded that the differences in data sources, features, classifiers, training samples, and land use types might lead to the different classification results according to the process of integration methods. Chapter 6 (research chapter 3) investigated the spatial-temporal dynamics of urban land use changes from 2017 to 2021, indicating the increase of institution and residence parcels increased, and the decrease in business and open space parcels. Chapter 7 (research chapter 4) found that the urban green space distribution was affected significantly by urban land use changes in Hangzhou city. The area of urban green space has increased from 2017 to 2021 totally, which was mainly concentrated in the urban core regions, indicating Hangzhou city has made remarkable achievements in green space planning within the urban center. In addition, the residence parcels have the largest increase in urban green space areas, while the areas of urban green space in the open space parcels have decreased. There are certain differences in the fragmentation, complexity, aggregation, and uniformity of urban green space patches within different urban land use change types. Chapter 8 synthesized the results obtained from this thesis with each of the research chapters. An outlook and recommendations for future research are also presented.
Date of AwardJul 2022
Original languageEnglish
Awarding Institution
  • University of Nottingham
SupervisorPing Fu (Supervisor) & Ali Cheshmehzangi (Supervisor)

Keywords

  • remote sensing
  • geospatial big data
  • urban land use
  • urbanization
  • urban green space
  • mapping
  • POI

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