Rapid Land Cover Classification Using a 36-Year Time Series of Multi-Source Remote Sensing Data

Xingguang Yan, Jing Li (), Andrew R. Smith, Di Yang, Tianyue Ma and Yiting Su
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Xingguang Yan: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Jing Li: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Andrew R. Smith: School of Environmental and Natural Sciences, Bangor University, Bangor LL57 2UW, UK
Di Yang: Wyoming Geographic Information Science Center, University of Wyoming, Laramie, WY 82071, USA
Tianyue Ma: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Yiting Su: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Land, 2023, vol. 12, issue 12, 1-14

Abstract: Long time series land cover classification information is the basis for scientific research on urban sprawls, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the random forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of the sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of the sample points without land class change, determined by counting the sample points of each band of the Landsat time series from 1986 to 2022, was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of the TM and ETM+ sensor data from 2013 to 2022; and (iii) the addition of a mining land cover type increases the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and forest area. Among the land classifications via multi-source remote sensing, the combined variables of Spectral band + Index + Terrain + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and the use of sensors under complex terrain conditions. The use of the GEE cloud computing platform enabled the rapid analysis of remotely sensed data to produce land cover maps with high accuracy and a long time series.

Keywords: Google Earth Engine; sample migration; land classification; multi-source remote sensing; spontaneous forest; machine learning; AI Earth (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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