# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 2 Materials and Methods  
### Global Land Cover Datasets  
The detailed information of used land cover datasets is summarized and shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Time** & **Satellite** & **Spatial Resolution** & \begin{tabular}{c} **Classification** \\ **Scheme** \\ \end{tabular} \\ \hline Hansen & 2010 & Landsat 7 ETM+ & 30 m & \\ \hline CCI-LC & 2015 & \begin{tabular}{c} ENVISAT/MERIS/ \\ SPOT-VGT/PROBA-V \\ \end{tabular} & 300 m & LCCS \\ \hline \multirow{4}{*}{FROM-GLC} & 2015 & \multirow{4}{*}{\begin{tabular}{c} Landsat TM/ETM+/OLI \\ 30 m \\ \end{tabular} } & Two level classification \\  & 2017 & & 30 m & system of level 1 and \\ \cline{1-1} \cline{2-4}  & FROM-GLC10 & LandsatTM/ETM/OLI/ & \
\begin{tabular}{c} level 2 \\ 30 m \\ \end{tabular} & 10 classes \\ \cline{1-1} \cline{2-4}  &
\begin{tabular}{c} 10 classes \\ (2017) \\ \end{tabular} & Sentinel-2 & 10 m & 10 classes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the main characteristics of global land cover datasets.  
The Hansen 2010 Global Forest Cover Dataset was developed in collaboration with the GLAD (Global Land Analysis and Discovery) Laboratory at the University of Maryland, Google, the U.S. Geological Survey, and NASA [27]. It used clear surface observations from more than 600,000 images analyzed by Google Earth Engine (a cloud-based platform for earth observation and data analysis). The tree coverage per pixel was determined using supervised learning algorithms [28].  
The ESA's Land Cover (LC-CCI) project, built on the experience of the GlobCover, delivered four consecutive GLC maps for five-year epochs centered around 2000, 2005, 2010, and 2015, with a spatial resolution of 300 m [19]. The product has a fine-grained classification system, with a legend based on LCCS with 22 classes, which is more suitable for large-scale, long-time series land cover change studies.  
GLC-FCS has two datasets of different years. The first version became available in 2015. It is based on location information from the image spectral library and the time-series Landsat8 surface reflectance product, combined with a multi-temporal classification model and a time-series reflectance dataset [29]. On this basis, GLC-FCS30-2020 has been produced, combining the 2019-2020 time-series Landsat surface reflectance data, Sentinel-1 SAR data, DEM, global thematic auxiliary datasets, and a priori knowledge datasets [30]. The dataset contains a total of 30 types of land cover information for global terrestrial regions. However, the secondary forest classes (dense and open) in 2015 are only reflected in some local areas, and remain indistinguishable in most global regions, including Myanmar. This problem is effectively improved in 2020.  
FROM-GLC is the first 30 m resolution global land cover map by using a support vector machine classifier from Landsat imagery [20; 21]. Later, the world's first 10 m resolution global land cover product, FROM-GLC10, was released in 2019. Compared with FROM-GLC30, which has a 30 m resolution, it provides more spatial details. In terms of the classification system, FROM-GLC2015 produced to the secondary category, its forest was subdivided into 6 subcategories. FROM-GLC2017 and FROM-GLC10 focused on the primary category, and different types of tree species all corresponded to \"forest\".  
GlobeLand30 of the National Geomatics Centre of China (NGCC) was developed using a pixel-object-knowledge (POK) to derive a global land cover map at 30 m resolution by multispectral images [22]. The 2020 version also used GF-1 multispectral images with 16 m resolution. Its classification system included ten land cover types, with forests as one of them.