The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 735–739, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-735-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 735–739, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-735-2020

  25 Aug 2020

25 Aug 2020

ANNUAL URBAN BUILT-UP CHANGE AREA ONLINE EXTRACTION USING LANDSAT TIME SERIES DATA

J. Zhang1, H. Wu1, and C. Cai2 J. Zhang et al.
  • 1National Geomatics Center of China, Beijing, 100830, China
  • 2Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing Institute of Surveying and Mapping, China

Keywords: Information extraction, Online processing, Urban built-up area, Time series trajectory model, Landsat

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.