Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing

Impervious surfaces are important indicators of human activity, and finding ways to quantify the gain and loss of impervious surfaces is important for sustainable urban development. However, most relevant studies assume that the transformation of natural surfaces to impervious surfaces is irreversible; thus, the losses of impervious surfaces are often ignored. Here, we propose a novel framework taking advantage of continuous change detection, multitemporal classification, and LandTrendr optimization to track the annual gains and losses in impervious surfaces. It may be the first study to focus on both loss and gain of impervious surfaces using time-series Landsat imagery. Specifically, we built dual continuous-change-detection models to pursue lower commission and omission errors for generating time-series training samples. Then, we adopted time-series classifications from multisource information and derived training samples to develop annual impervious-surface maps from 1985 to 2022 in Beijing. Afterwards, a novel optimization algorithm considering spatial heterogeneity and taking advantage of the LandTrendr algorithm was also proposed to optimize the spatiotemporal consistency of these impervious-surface maps. We further calculated accuracy metrics for the proposed method using time-series validation points, finding overall accuracies of 92.91 %±0.97 % and 93.17 %±1.26 % for gains and losses in impervious surfaces, respectively, using a one-year tolerance. Lastly, we revealed the gains and losses of impervious surfaces in Beijing during 1985–2022. The gained area of impervious surfaces was found to be 1996.21 km² ± 18.58 km², and there was a rapid increase during 2000–2010; the total lost area of impervious surfaces was 898.60 km² ± 4.58 km², of which 564.85 km² ± 2.21 km² first increased and was then lost. Therefore, the proposed method provides a new way of tracking the gain and loss of impervious surfaces, and it offers new possibilities for monitoring urban regreening.