# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 4 Discussion  
### Discrepancies among Datasets  
Our study also screened regions with levels 1-4 in the forest consistency map, compared to sample points in the corresponding regions to investigate the misclassification of forests in each dataset. Figure 12 shows the correct classification of forests and specific misclassification forms for 2015, 2017, and 2020, respectively. The agreement rate between classification and  
Figure 11: The proportion of eight spatial consistency levels in 6 climate zones. Figure 10: Spatial consistency of eight datasets with K�PPEN-Geiger climate classification. sample points for all three datasets in 2015 is higher than 50%, and the highest correct rate is FROM-GLC at 62.26%. Among the misclassification cases of forests, FROM-GLC mainly misclassified non-forest as forest, while CCI-LC and GLC-FCS more often misclassified forest as non-forest, their percentages are both higher than 30%. In a comprehensive analysis based on the year, the proportion of correctly classified datasets in 2015 is 58.28%. The proportion of forest misclassified as non-forest is 25.58%, which is higher than the proportion of non-forest misclassified as forest at 16.14%. In 2017, the difference is generally lower than in 2015 and 2020 because these two datasets are essentially homogeneous. FROM-GLC has a slightly higher percentage of the misclassified forest as non-forest at 18.24%. FROM-GLC10, on the other hand, has a slightly higher percentage of misclassified non-forest as forest at 23.90%. In 2020, GlobeLand30 has a correct classification of 66.67%, which is also the highest in all datasets. In contrast, GLC-FCS has a lower percentage of correct classifications, and both misclassifications are higher than GlobeLand30, where the misclassification of forest as non-forest is as high as 34.59%. Several reasons account for the discrepancies among different forest datasets. Firstly, different datasets have their classification schemes [40; 41], directly leading to discrepancies. For example, in the classification of low trees, some datasets consider them as forest, as  
Figure 12: Proportion of forest datasets correctly classified and two incorrectly classified in 2015/2017/2020. (**a**) Three datasets in 2015; (**b**) Two datasets in 2017; (**c**) Two datasets in 2020.  
in the case of FROM-GLC, which overestimates its forest area. While some products classify them as shrubs, as in the CCI-LC and GLC-FCS datasets, the forest area is slightly underestimated. There is also a complex definition of \"Transitional Woodland\" [42]. There is a wide divergence in the interpretation of land types in transition areas across datasets, which may lead to confusion between forest and other vegetation types. Secondly, different datasets use different satellite images, produced in different ways, and with different spatial resolutions, which will inevitably lead to discrepancies. Furthermore, due to the difference in topography, the spectral response of similar features on the sunny side and the backside of the mountainous area differs greatly. Furthermore, different types of features may reflect similar spectral characteristics, resulting in the phenomenon of \"different spectra for the same thing and different things for the same spectra\". In forest mapping, the same tree species may show different spectral reflectance phenomena, presenting different colors and tones in the image. Different tree (shrubs/other vegetation, etc.) species may have similar spectral reflectance properties; they show almost the same color and hue in the image, resulting in interpretation difficulties and leading to confusion or misclassification of forests. Thirdly, it was found that both terrain and climate can affect the distribution of forests. Most of Myanmar is dominated by a tropical climate, including tropical monsoon climate and savanna climate. The climate is above 18 \({}^{\circ}\)C per month throughout the year and precipitation is abundant, providing strong natural conditions for forest growth.