Characterizing the Up-To-Date Land-Use and Land-Cover Change in Xiong’an New Area from 2017 to 2020 Using the Multi-Temporal Sentinel-2 Images on Google Earth Engine

Abstract

Land use and land cover (LULC) are fundamental units of human activities. Therefore, it is of significance to accurately and in a timely manner obtain the LULC maps where dramatic LULC changes are undergoing. Since 2017 April, a new state-level area, Xiong’an New Area, was established in China. In order to better characterize the LULC changes in Xiong’an New Area, this study makes full use of the multi-temporal 10-m Sentinel-2 images, the cloud-computing Google Earth Engine (GEE) platform, and the powerful classification capability of random forest (RF) models to generate the continuous LULC maps from 2017 to 2020. To do so, a novel multiple RF-based classification framework is adopted by outputting the classification probability based on each monthly composite and aggregating the multiple probability maps to generate the final classification map. Based on the obtained LULC maps, this study analyzes the spatio-temporal changes of LULC types in the last four years and the different change patterns in three counties. Experimental results indicate that the derived LULC maps achieve high accuracy for each year, with the overall accuracy and Kappa values no less than 0.95. It is also found that the changed areas account for nearly 36%, and the dry farmland, impervious surface, and other land-cover types have changed dramatically and present varying change patterns in three counties, which might be caused by the latest planning of Xiong’an New Area. The obtained 10-m four-year LULC maps in this study are supposed to provide some valuable information on the monitoring and understanding of what kinds of LULC changes have taken place in Xiong’an New Area.

Keywords

• land use and land cover
• Xiong’an New Area
• Google Earth Engine
• multi-temporal classification