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.
Highlights
Land-use and land-cover (LULC) maps, such as impervious surface, vegetation, and waterbody, provide some fundamental description of the Earth’s land surface [1,2,3]
Machine-learning methods have played a significant role in these LULC studies, especially in accelerating the processing of a large volume of image stacks and in mining the unique patterns and knowledge related to the subsequent LULC changes
Based on the derived LULC maps, we first analyzed the spatio-temporal changes of LULC in Xiong’an New Area, and we further evaluated the different development patterns in three counties and found that the LULC maps in three counties presented some varying characteristics
Summary
Land-use and land-cover (LULC) maps, such as impervious surface, vegetation, and waterbody, provide some fundamental description of the Earth’s land surface [1,2,3]. The annual LULC maps in the conterminous United States from 1973 to 2000 was generated, and the changing patterns were summarized in that an almost 8.6% of the United States’ land area experienced a change in LULC at least one time [7]. It can be noted from these case studies that long time-series LULC maps were generated using RS images and applied to unravel the mysterious masks of what the Earth’s land surface had undergone and what potential impact these changes might subsequently bring. In order to obtain the LULC maps with more details and higher accuracy, the satellite images with better spectral, spatial, and temporal resolutions have been more and more applied, and a typical case of this obvious progress is to substitute the 500- or 250-m MODIS images with 30-m Landsat images and 10-m Sentinel-2 images
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