## Data and Methods
### Mapping Algorithms  
The workflow of using PALSAR and Landsat TM/ETM+ images to map the forests is illustrated in Figure 2. We produced the resultant PALSAR/Landsat forest map in two steps. Firstly, we generated the PALSAR-based forest map using the HV, HH, difference, and ratio of the PALSAR data. Secondly, we generated PALSAR/Landsat forest maps with high accuracy and low uncertainties using the integration  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Main Scale of the PALSAR**} \\ \hline
**Mode** & **High Resolution** & **Scanning Synthetic Aperture** & **Polarization** \\ \hline Center frequency & & & 1270 MHz (L-band) & \\ Linear frequency & 28 MHz & 14 MHz & 14 MHz, 28 MHz & 14 MHz \\ modulation width & & & & \\ Polarization mode & HH or VV & HH + HV or VV + VH & HH or VV & HH + HV + VH + VV \\ Incident angle & 8\({}^{\circ}\)–60\({}^{\circ}\) & 8\({}^{\circ}\)–60\({}^{\circ}\) & 18\({}^{\circ}\)–43\({}^{\circ}\) & 8\({}^{\circ}\)–30\({}^{\circ}\) \\ Spatial resolution & 7–44 m & 14–88 m & 100 m & 24–89 m \\ Width & 40–70 km & 40–70 km & 250–350 km & 20–65 km \\ \hline \multicolumn{4}{c}{Satellite parameters} \\  
of PALSAR and Landsat. In summary, forest was first identified using structure-based metrics from PALSAR images, then reduced commission errors using time series of Landsat. According to the United Nations Food and Agriculture Organization (UN FAO), the forest definition includes both forest structure and canopy information using a unit of land (\(>\)0.5 ha) with tree cover \(>\) 10% and the minimum height \(>\) 5 m. The previous studies found that the threshold-based approach for forest mapping is robust and extendable in different regions, such as the monsoon region in Asia, Oklahoma in USA [27; 28], and the North China Plain, in China [29]. A detailed workflow was developed to map the forest and multi-source forest dataset comparison in Figure 2. We used the L-band PALSAR data which can penetrate into forests with substantial volume scattering through the incident energy interaction with trunks and branch components to capture the structure and above ground grass (AGB) of forests. The previous studies show that forest and forest AGB exhibit a certain range of PALSAR backscattering coefficients, respectively [25]. In this study, we tried to use the same decision classification algorithm used in our previous study in the North China Plain, in China [29]. Therefore, we used the HH, HV (Table 1), the difference (HH - HV), and the ratio (HH/HV) to map the initial PALSAR forest baseline map. The threshold values were determined based on the workflow addressed in Figure 2 and the final threshold values (Figure 3) were obtained by signature analyses of training samples. In total, we collected 2489 ROIs which include 660 forest ROIs, 410 cropland ROIs, 253 water ROIs, and 1160 built-up land and other ROIs to determine the thresholds in this study. The histogram of the HH image (Figure 3a) shows that water has much lower backscatter than forest and partly overlapped with cropland, as water bodies tend to be relatively calm and smooth, and can reflect most of the backscatter through specular reflection. This means water is separable from forests, built-up lands, and most cropland. Both forests and built-up lands have higher HH backscatter values and some  
Figure 2: The workflow of forest mapping based on the ALOS PALSAR and Landsat TM/ETM+ images. built-up lands such as large building complexes have even higher backscatter values than forests due to the strong reflectance environment caused by their complex structure and corner reflectors. Forests, cropland, and built-up lands also have some overlap of HH backscatter values. The histogram of the HV image (Figure 3b) shows that forests have higher HV values than water and cropland, and have relatively more overlap with built-up lands due to large patches of forests distributed in urban areas.