# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
###### Abstract  
Accurate and up-to-date forest monitoring plays a significant role in the country's society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their specific classification systems, methodologies, and remote sensing data sources, which makes end-users difficult to select an appropriate dataset in different regions. This study aims to explore the accuracy, consistency, and discrepancies of eight widely-used forest datasets in Myanmar, including Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020. Firstly, accuracy assessment is conducted by using 934 forest and non-forest samples with four different years. Then, spatial consistency of these eight datasets is compared in area and spatial distribution. Finally, the factors influencing the spatial consistency are analyzed from the aspects of terrain and climate. The results indicate that in Myanmar the forest area derived from GlobeLand30 has the best accuracy, followed by FROM-GLC10 and FROM-GLC2017. The eight datasets differ in spatial detail, with the mountains of northern Myanmar having the highest inconsistency and the seaward areas of southwestern Myanmar having the highest inconsistency, such as Rakhine and the Ayeyarwady. In addition, it is found that the spatial consistency of the eight datasets is closely related to the terrain and climate. The highest consistency among the eight datasets is found in the range of 1000-3500 m above sea level and 26\({}^{\circ}\)-35\({}^{\circ}\) slope. In the subtropical highland climate (Cwb) zone, the percentage of complete consistency among the eight datasets is as high as 60.62%, which is the highest consistency among the six climatic zones in Myanmar. Therefore, forest mapping in Myanmar should devote more effort to low topography, seaward areas such as border states like Rakhine, Irrawaddy, Yangon, and Mon. This is because these areas have complex and diverse landscape types and are prone to confusion between forest types (e.g., grassland, shrub, and cropland). The approach can also be applied to other countries, which will help scholars to select the most suitable forest datasets in different regions for analysis, thus providing recommendations for relevant forest policies and planning in different countries. land cover; forest datasets; validation; classification accuracy; spatial consistency +
Footnote †: journal:of the most forested tropical countries in mainland Southeast Asia [4], forest cover is particularly important in Myanmar. This is because forests in Myanmar support many important and endemic species of great value to global biodiversity conservation [5], and play a significant social and economic role in the country [6]. Due to its rapid socio-economic development, Myanmar is facing a critical situation with severe deforestation [7]. Therefore, it is necessary to conduct forest mapping regularly to monitor forest cover and its changes in Myanmar. Satellite remote sensing technology is widely used for forest mapping and monitoring [8; 9; 10]. Currently, many land cover datasets have been produced with different spatial and temporal resolutions. Since the earliest 1 km resolution UMD [11] and IGBP [12; 13] were derived, many other land cover maps have been produced, such as the 1 km Global Land Cover 2000 (GLC2000) [14] and 500 m MODIS Land Cover dataset [15; 16]. The advent of medium-resolution satellites has facilitated the emergence of datasets with higher spatial resolution such as GlobCover [17; 18] and European Space Agency Climate Change Initiative Land Cover (CCI-LC) products [19], which have a resolution of 300 m. The Finer Resolution Observation and Monitoring Global LC(FROM-GLC) dataset [20; 21] and GlobeLand30 [22] both reach a spatial resolution of 30 m representing significant advances. Furthermore, now the resolution of the land cover dataset FROM-GLC10 has reached 10 m [23].