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

被引:6
作者
Yan, Xingguang [1 ,2 ,3 ]
Li, Jing [1 ]
Smith, Andrew R. [2 ,3 ]
Yang, Di [4 ]
Ma, Tianyue [1 ]
Su, Yiting [1 ,2 ,3 ]
机构
[1] China Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China
[2] Bangor Univ, Sch Environm & Nat Sci, Bangor LL57 2UW, Wales
[3] Bangor Univ, Environm Ctr Wales, Bangor LL57 2UW, Wales
[4] Univ Wyoming, Wyoming Geog Informat Sci Ctr, Laramie, WY 82071 USA
基金
美国国家航空航天局;
关键词
Google Earth Engine; sample migration; land classification; multi-source remote sensing; spontaneous forest; machine learning; AI Earth; SUPPORT VECTOR MACHINES; RANDOM FOREST; LEARNING-METHODS; IMAGES; TREES; FRAMEWORK;
D O I
10.3390/land12122149
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:14
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