Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

Abstract

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.

GWL_FCS30: global 30 m wetland map with fine classification system using multi-sourced and time-series remote sensing imagery in 2020

“…For example, the tidal flat was the status of seawater at the high tidal stage and 345 mud or sand flats at low tidal stages (Wang et al, 2021); therefore, it was necessary to extract the high-and low-water-level features to completely capture these water-level sensitive wetlands. Over the past several years, the time-series compositing strategy has been widely used to capture phenological and cloud-free composites (Jia et al, 2020;Ludwig et al, 2019;Murray et al, 2019;Zhang et al, 2021a). For example, Murray et al (2019) used the quantile compositing method to extract different tidal stage information, and successfully produced the 350 global distribution of tidal flats.…”

“…There were usually two options for capturing phenological features from time-series Landsat imagery. These included seasonal-based compositing (Zhang et al, 2021a;Zhang et al, 2022a) and percentile-based compositing (Hansen et al, 2014;Zhang and Roy, 2017;Zhang et al, 2021b). The former used the phenological 385 calendar for selecting time-matched imagery.…”

Long-term water clarity patterns of lakes across China using Landsat series imagery from 1985 to 2020

“…Since BAP compiles cloud-free images by selecting the best available observation based on user-defined criteria (Gomez et al, 2016;Griffiths et al, 2013), the BAP composites can retain the source image information from which they came. In addition, since BAP can ensure phenological consistency between multitemporal BAP composites by setting the acquisition day-of-year (DOY) criteria (Griffiths et al, 2014;Chen et al, 2021), it is suitable for multiyear change detection and assessment (Griffiths et al, 2014;Gomez et al, 2016;Hermosilla et al, 2015;Zhang et al, 2021a). Accordingly, the BAP method was used to generate cloud-free composites.…”

Loss of Relict Oak Forests along Coastal Louisiana: A Multiyear Analysis Using Google Earth Engine

“…A greater mixture of classes allows more accurate measurements for those interested in landscapescale ecological processes. However, VHR can be used to map stands at the species level, which allows forest managers an unparalleled insight into the dynamics of recruitment, disease, or resource availability [55].…”