User-Aware Evaluation for Medium-Resolution Forest-Related Datasets in China: Reliability and Spatial Consistency - Xueli Peng; Guojin He; Guizhou Wang; Tengfei Long; Xiaomei Zhang; Ranyu Yin (Remote Sensing, 2023)
Forest cover data are fundamental to sustainable forest management and conservation. Available medium-resolution publicly shared forest-related datasets provide primary information on forest distribution. The evaluation of relevant datasets is of great importance to learn about the differences, characterize the accuracy, and provide a reference for rational use. This study presents an evaluation and analysis of the forest-related datasets in China around 2020, including TreeCover and the forest-related layer (latter referred to as the forest datasets) in WorldCover, Esri land cover, FROM-GLC10, GlobeLand30, and GLC_FCS30. These forest datasets, that are obtained by aggregating forest-related lasses based on the classification schemes, are analyzed from spatial consistency and accuracy comparison. The results illustrate that forest datasets with 10m resolution are generally more precise than those with 30m resolution in China. WorldCover shows the highest accuracy, with producer accuracy and user accuracy of 91.4% and 87.09%, respectively. These datasets exhibit high accuracy but great spatial inconsistency. The more consistent the regions are, the more accurate the accuracy is. High consistency (≥5, i.e., classified into forests by five datasets) areas account for 56.49% of areas of forest classified (AFC), while the area of low consistency (≤2) reach 25.51% of AFC. The analysis delves into the datasets, offering a reliable reference for the usage of these datasets.