## 4 Discussion  
### Advantage of the PALSAR/Landsat Forest Map Compared to Other Data Forest Maps  
Managing the forest resources in the Loess Plateau is a challenging task because the area and spatial distribution of forest are being affected by a variety of factors, including complex terrain, changing forest markets, unstable climate, and the economic situation of land owners [1; 52]. Therefore, forest management and policy formulation [53] urgently require timely and detailed spatial resolution forest maps. Compared with the other forest maps, the PL-based forest map with the higher accuracy  
Figure 14: Reforestation in the pilot county (Wuqi County) of the Grain for Green Project (GGP). The forest map of Wuqi County in 2007 (**a**) and 2017 (**b**) the spatial changes of Wuqi County from 2007 to 2017 (**c**), and the image of Wuqi County and the neighboring counties in 2017 from Google Earth (**d**). and lower uncertainty in this study could discriminate more forests in the Loess Plateau, especially in the fragmented and fragile regions. As cloud and cloud shadows limit the availability and practicability of data, Landsat imagery is usually selected from many years to increase the number of high-quality observations and to generate land cover maps, including large-scale forest maps [6; 15; 48]. Although, these maps cannot detect forest changes and may introduce considerable uncertainty in the estimation of the forest fragmentation, forest biomass and carbon cycle [2; 53]. The extensive forest cover loss and benefits were founded in the Loess Plateau according to the resultant PALSAR/Landsat forest maps from 2007 to 2017, especially in the fragile regions. The integration of PALSAR and Landsat images provided complementary information for other datasets. The PALSAR/Landsat forest maps could provide the forest extension and its temporal and spatial changes for policy makers in this region. Meanwhile, the PL-based forest map integrated the advantage of forest spatial structural information and spectral information which can identify more detailed biomass information and help decision makers to better formulate policies. The JAXA forest map was purely generated based on the ALOS PALSAR fine dual-polarized data which is vulnerable to the terrain and soil water content. Therefore, the product includes commission errors from the buildings in the urban area with high backscatter values. The PALSAR data was collected from June to October, and the seasonal land change from June to October had some influence on the classification accuracy. According to our previous survey [2; 27; 28], PALSAR data is one of the most suitable remote sensing images for forest mapping. However, its own advantage did not show in the fragile areas of the Loess Plateau which indicates the PALSAR remote sensing image is suitable for grasping the dense forest and large area of forest coverage, and not suitable for the fragile areas like the Loess Plateau. The OU-FDL forest product improved the accuracy by involving the PALSAR and the MODIS to capture more information of forest and reduced the commission errors compared to the JAXA forest map. However, MODIS data has a coarse resolution and the fusion is not much improved due to the mixed pixel issue. The other forest maps are generated only from Landsat data, but different factors are taken into account in the selection of data sources. Specifically, GlobalLand30 forest product uses a static scenario with the least amount of clouds, supplemented by HJ-1 satellite data, so the impact of clouds and cloud shadows still exists. FROM-GLC and GLCF-VCF forest products use multiple Landsat images to eliminate the effect of cloud and cloud shadows. The PALSAR/Landsat forest maps were generated by the integration of ALOS PALSAR and Landsat images, which contain more information like structure, biomass, and spectral information of forests to reduce some commission errors in the forest maps compared to other PALSAR-based forest maps and Landsat-based forest maps.