Accuracy and consistency assessment of forest cover datasets: a comparative study of two provinces in China - Zhu, Kailin; Lin, Dongfang; Fu, Haiqiang; Zhang, Tao; Li, Defang; Wang, Xuan; Liu, Yongsheng (Frontiers in Forests and Global Change, 2025)
Forests play a crucial role in maintaining the ecological balance of the Earth. While existing publicly available datasets typically offer high accuracy in identifying large-scale forest concentrations, discrepancies arise in forest extraction within smaller regions. This variation complicates the selection of appropriate forest cover datasets for specific areas. This study focuses on the southern and northern regions of China, represented by Hunan Province and Heilongjiang Province, respectively. It systematically evaluates the performance of eight forest cover datasets from 2020 in terms of forest area estimation, spatial consistency, and classification accuracy. Through confusion analysis of classification in low-consistency areas, the study identifies the confusion patterns between forests and other land cover types in different regions. Additionally, the study explores the causes of discrepancies between datasets by considering topographic factors and human activities. The results show that the CRLC 2020 outperforms others in terms of both area estimation and classification accuracy, achieving classification accuracies of 90.88% in Hunan Province and 91.69% in Heilongjiang Province. High-consistency areas (levels 6–8) in Hunan account for 69.4%, lower than Heilongjiang’s 77%. This comprehensive analysis provides valuable insights for forestry practitioners in selecting appropriate forest cover datasets in areas with complex land cover, offering reliable recommendations for forest cover mapping and the formulation of sound mapping strategies.