Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data - Tengyun Hu; Meng Zhang; Xuecao Li; Tinghai Wu; Qiwei Ma; Jianneng Xiao; Xieqin Huang; Jinchen Guo; Yangchun Li; Donglie Liu (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Extracting building construction time is crucial for effective land resource management and sustainable urban development, particularly in fast-growing urban areas. However, acquiring building construction time remains challenging due to limited observations with multiple changes. To address this issue, we applied a monthly time series of remote sensing images and the LandTrendr change detection algorithm to extract building construction times. We identified the sensitive index of short wavelength infra-red (SWIR) from satellite observations for detecting changes in building construction, demolition, and reconstruction. Comparing composite results at different temporal intervals revealed that monthly data is more effective in accurately characterizing building changes compared to daily and yearly intervals. Additionally, our improved algorithm in Google Earth Engine identified the maximum change time as the construction turning point at the pixel level. We then revealed Beijing's construction time from 1990 to 2020 by overlaying building footprint data with extracted year information from Landsat images. Our results achieved an 82.32% agreement on identified construction time of buildings with a two-year tolerance strategy using 560 randomly collected building samples. Our results outperformed traditional methods such as annual time series composition with the same LandTrendr algorithm, historical surveying map, and building change time of social big data monitoring, with derived overall accuracies of 68.75%, 74.64%, and 67.47%, respectively, suggesting the good performance of the adopted approach. This study offered a potential avenue for detailed monitoring of urban building changes at a fine-grained spatial scale, with far-reaching implications for sustainable urban development practices.