Abstract
We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA’s accuracy, and PlanetScope’s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1–78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.
Highlights
The spectral bands of different satellites had significant differences (p < 0.05 according to Tukey’s test), i.e., Corine Land Cover (CLC) polygons of the same category were represented by different statistical parameters
We analyzed the polygons of the Corine Land Cover 2018 (CLC2018) database with respect to pixel information derived from PlanetScope, Sentinel-2 and Landsat-8 images
Wetlands and water bodies categories were the most frequently mixing categories of CLC based on reflectance values; Bivariate statistical tests cannot provide enough information to conclude on the spectral separability of LC classes, but classification algorithms involving several variables can be efficient techniques
Summary
Land use/land cover (LULC) mapping is one of the most important tasks of environmental assessments and environmental monitoring [1,2]. LULC mapping is based on remotely sensed images and has a history stretching back about 40 years [3]. Land cover categories can be identified with automatic image classification using machine learning algorithms using training data to develop a model, which provides the best fit for the known data points; another alternative is a visual interpretation and land cover patch. Image classification can use non-supervised or supervised techniques, which have extensive literature: the various types of LULC classification of remotely sensed images is a very popular research topic, e.g., [5,6,7]. Accuracy depends on the interpreter’s expertise and local knowledge of the area, and as there are many interpreters with different skills, the product quality is heterogeneous
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