Abstract
Abstract. Urban built-up area change information in multiple periods is a pivotal factor in global climate change application and sustainable development research. Due to spatial-temporal expression of land cover types, processing speed and operability, built-up area change information extraction using Landsat time series data is still a challenging task. To provide insights into the inter-annual dynamic of land use change, focusing on how time series characteristics improves recognition of urban change and how much online extraction convenience is facilitated, this paper presents a new methodology to built-up change area extraction using inter-annual time series of Landsat images. The central premise of the approach is that time series characteristics are firstly expressed by spectral index. The logistic algorithm is then used in time series trajectory modelling of land cover types for annual urban built-up change area extraction. Finally, the individual steps of the whole process, including image selection, time series trajectory modelling and results display, are converted to web service for online processing. The further comparison is also conducted between the proposed method and post-classification comparison method. Results show that the online processing mode has strengths regarding the provision of functionality to user-end, the automation of recurring tasks or the sharing of workflows. Results also demonstrate that the proposed method improves the accuracy of annual urban built-up change area extraction.
Highlights
Urban built-up area change information in multiple periods is pivotal to understand the complex drivers and mechanisms in global climate change and to forecast future land use trends in sustainable development (Arsanjani, et al, 2016; Aburas, et al, 2018 )
In this paper, we proposed a new methodology of mapping urban built-up area growth dynamics from Landsat time series data
The whole process of the extraction is converted to web service for online processing
Summary
Urban built-up area change information in multiple periods (i.e. three or more) is pivotal to understand the complex drivers and mechanisms in global climate change and to forecast future land use trends in sustainable development (Arsanjani, et al, 2016; Aburas, et al, 2018 ). Many studies on urban area and change extraction with Landsat time series data have already been carried out (Landsat time series data refers to annual or monthly Landsat images, or all available Landsat images during a long period time like ten or more years). Most existing urban land change studies only use few multi-temporal Landsat data (e.g. images of each five years) in urban expansion analysis. Few existing studies adopted the post-classification method for extraction of annual urban expansion from Landsat time series data, which first classifies Landsat images of each year separately and compares classification results to obtain urbanization dynamic (Zhang, et al, 2016; Sumari, et al, 2017; Yu, et al, 2018)
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