# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 4 Discussion
### Challenges and Prospect of Forest Mapping at Large Scale  
Through the comparative analysis of forests in Myanmar, we found that a challenge in forest mapping at large scales was the spatial resolution and classification methods. The current land cover dataset is still dominated by low to medium resolution and with automatic classification methods, which impose limitations on the mapping of forests. From the results of the accuracy assessment in this paper, the forest mapping accuracy of FROM-GLC10 with 10 m resolution is the highest, but the best forest classification of GlobeLand30 is obtained by combining various accuracy indicators. Firstly, the higher spatial resolution of GlobeLand30 plays an important role in high precision mapping. Multiple imaging sources from satellites such as Landsat 8, sentinel, and GF, as well as the development of machine learning, also provide sufficient guarantees for high spatial resolution mapping [43; 44]. Secondly, GlobeLand30 adopts the POK classification method, while adding manual checking and knowledge modification based on automatic computer classification. It can achieve better accuracy results, although the mapping efficiency is reduced. Mixed image elements also affect forest mapping. Even the FROM-GLC10 inevitably has mixed pixels, but improving the spatial resolution can alleviate the problem to some extent. In terms of the solution method, a decision tree combined with mixed image element decomposition can be used [45]. A percentage soft classification scheme can also be used instead of the traditional binary classification, the ratio can reflect the distribution of forest within a pixel [25]. They can also effectively solve the problem of mixed image elements.  
NDVI, EVI, and reflectance data are still the three main types of satellite data for forest mapping, making full use of the implied phenological information in NDVI and EVI is important [46]. In the development of remote sensing mapping, it has been shown that adding other data sources such as radar data, hyperspectral data, and so on, can provide new possibilities for surface coverage mapping [47; 48; 49; 50].  
Nowadays, remote sensing mapping and applications are developing rapidly. Data sources are becoming more abundant and progressively more accurate. The WFV (Wide Field of View) image of GF-6 has two new red-edge bands compared with the similar image of GF-1, which is helpful to improve the monitoring capability of the forest [51]. The methods of land cover monitoring have also evolved with the times. Tsinghua University has proposed the conceptual framework of smart remote sensing mapping. They led to the completion of the 30 m resolution global day-by-day satellite remote sensing data cube (SDC), and the seasonal global surface coverage map over 36 years [52]. It helps to significantly advance the development of remote sensing applications, provides decision support for land resource management, and contributes greatly to future forest monitoring and mapping. In addition, the development of cloud computing also supplies an effective means for remote sensing forest monitoring. For example, Google Earth Engine (GEE) provides a global-scale geospatial analysis service [53] that can be used to monitor global forest changes.