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 consistency 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°–35° 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.

A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data

“…ESRI2020 achieved the highest overall accuracy of 91.64%. However, in the reference [26], it was found that FROM-GLC10 achieved higher accuracy in the consistency of forest mapping products in Myanmar, with an overall accuracy of 84.48%. It is apparent that our research attained higher accuracy, primarily due to the utilization of field verification products and the improved referencing of field verification points during the manual densification of Google's high-resolution image verification points.…”

MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework

“…Myanmar is a country with vast stretches of land and abundant natural resources, including diverse forests, rivers, and wildlife [23], [24]. Given its strategic position in Southeast Asia and significant geopolitical and economic value, Myanmar holds immense importance from a global perspective.…”