Abstract
Abstract. Remote sensing is going through a basic transformation, in which a wide array of data-rich applications is gradually taking the place of methods interpreting one or two imageries. These applications have been greatly facilitated by Google Earth Engine (GEE), which provides both imagery access and a platform for advanced analysis techniques. Within the field of land cover classification, GEE provides the ability to create fast new classifications, particularly at global extents. Despite the role of indices and other ancillary data in classification, GEE platform pixel-based supervised classification (GEE-PBSC), as a relatively fast and common classification method in remote sensing, was not directly analysed and assessed about accuracy in current researches. We ask how high the classification accuracy of GEE-PBSC is, and which type of land cover is more suitable to be classified by GEE-PBSC method with a credible accuracy. Here we adopt GEE-PBSC method to classify Landsat 5 TM imageries in Shandong province in 2010, and compare the result with GlobeLand30 product in 2010 from three aspects: type composition, type confusion and spatial consistency to assess the classification accuracy. Before the comparison, multiple cross-validation, which shows that the overall average test accuracy is about 74%, is required to ensure the reliability. The comparison experiment shows that the spatial consistency ratio of artificial surface, cultivated land and water is about 99.30%, 85.78% and 73.02% respectively. The pixel purity of artificial surface and cultivated land is about 90.26% and 81.45% respectively. The overall spatial consistency ratio is about 82.04%. Although the GEE-PBSC method can achieve high test accuracy, the result is still far from GlobeLand30 product in 2010. Because the GEE-PBSC only uses the pixel information of imageries and does not integrate other multi-source data to assist classification. In addition, classification result also shows that using GEE-PBSC to classify artificial surface and cropland has obvious advantages over other land classes, and their classification results is close to GlobeLand30.
Highlights
Land cover refers to the material synthesis with different natural properties and characteristics on the earth's surface, which represents the differences in surface hydrothermal and material balance, biogeochemical circulation and other processes
We identify seven fundamental land cover categories: Cropland; Forest; Grassland; Wetland; Water; Artificial surface and Bare land
In order to minimize the impact of training sample differences on classification accuracy, 60% of the samples are randomly selected each time as the training set, and the remaining 40% as the test set for 10 times of cross-validation experiments
Summary
Land cover refers to the material synthesis with different natural properties and characteristics on the earth's surface, which represents the differences in surface hydrothermal and material balance, biogeochemical circulation and other processes. Land cover change alters the land-atmosphere moisture, energy and carbon cycle, contributing to global climate change (Foley et al, 2005). The change of land cover will significantly change the characteristics such as surface albedo and emissivity, so as to affect the surface hydrothermal process budget and generate strong feedback on the climate system (Weng et al, 2007). A scientific and accurate measurement of the spatial distribution and dynamic change of global land cover is of great significance for the study of energy balance, carbon cycle and other biogeochemical cycles, climate change and biodiversity of the earth system
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