A Scheme for the Long-Term Monitoring of Impervious−Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 16; p. 1891
• Main Authors: Xu, Hanzeyu, Wei, Yuchun, Liu, Chong, Li, Xiao, Fang, Hong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2019
• Subjects: Algorithms; Archives & records; Classification; Datasets; Disturbances; Earth; Ecosystems; Geography; Google Earth Engine; High frequencies; high frequency Landsat archives; Human settlements; impervious surface dynamics; Inspection; Laboratories; land disturbance; Landsat; Landsat satellites; LandTrendr; Monitoring; Remote sensing; Surface dynamics; Time series; Trends; Urban areas; Urbanization; Vegetation; United States > US; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11161891

Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious−relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal−spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is −1.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious−relevant land disturbances with high frequency Landsat archives in an efficient way.