**3. Results**

#### *3.1. Canopy Closure and Stand Density*

The five types of statistical regression models were built as illustrated in Figure 4. Among five models, logarithmic, and quadratic power regressions with the largest values of *R*<sup>2</sup> were the best at explaining relationship of canopy closure with LAI and FVC, respectively. Considered the much larger *R*<sup>2</sup> value of a LAI-based model, LAI derived from Sentinel-2 was selected to map canopy closure based on the logarithmic regression model. Likewise, the exponential regression model with FVC was selected to map stand density. For comparison with field measured values, the modeled output of canopy closure and stand density were divided into several levels for displaying (Figure 5). Specifically, each level had an equal number of measured sample sites. The better performance of spatial modeling of canopy closure and stand density can be indicated by the agreemen<sup>t</sup> pattern at each level. Generally, predicted canopy closure and stand density were close to field measured values (Table 2). The large values of canopy closure and stand density were distributed in lower altitude regions. There is no forest in the high elevation alpine tundra and the volcanic summit of the study site. The southern slope of the Changbai Mountain showed less canopy closure and stand density than the north, as affected by historical volcanic damages (Figure 5).

**Figure 4.** Statistical regressions of canopy closure and stand density based on Sentinel-2 leaf area index (LAI) and fraction of vegetation cover (FVC). Regressions of canopy closure by LAI and FVC were illustrated as (**<sup>a</sup>**,**b**), respectively. Models of stand density by LAI and FVC were shown in (**<sup>c</sup>**,**d**).

**Figure 5.** Canopy closure (**a**) and stand density (**b**) in the CMNNR.

#### *3.2. Stand Volume and Forest Age*

The fitting line of HV showed a much flatter range than that of HH (Figure 6), indicating that HV was much more sensitive to stand volume than HH. It was also shown that the L band ALOS-2 backscatters reached saturation at around 400 m<sup>3</sup>/ha, which was greater than 97.4% of measured stand volume (Table 2). Thus, ALOS-2 data were considered suitable for spatial modeling of stand volume in the study area. Based on 1000 decision trees and one feature, the RF model was built to predict stand volume with more contribution from HV than HH. The result was depicted in Figure 7a and different levels were divided against measured values. It was delineated that the northeastern part of the study area was a large valued region.

The RF model with 1000 decision trees and six features was trained to predict forest age. The attribute importance ranking in decreasing order was H, Slope, Global environmental monitoring index (GEMI), Aspect, normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), topographic wetness index (TWI), Sentinel-2 red-edge position index (S2REP), HV, Cv, Ch, VV, HH, and VH. It was indicated that topographic and vegetation indices contributed more than SAR backscatters. According to measured age, the majority of forests were mature or over-mature with a median value of four (Table 2). The predicted forest age, as Figure 7b, was consistent with the measured values. Greater values of forest age were found in the western part of the study area. It was also shown that small values of forest age and stand density in higher altitude regions where forest distribution was limited.

**Figure 6.** Relationships between backscatters and stand volume using the simple water cloud model. (**a**) horizontal transmit-horizontal channel (HH) backscatters from pure forest canopy is −5.82 dB, from bare soil is −6.58 dB, and the extinction coefficient is 0.012. (**b**) horizontal transmit-vertical channel (HV) backscatters from pure forest canopy is −11.03 dB, from bare soil is −12.04, and the extinction coefficient is 0.014.

**Figure 7.** Stand volume (**a**) and forest age (**b**) in the CMNNR.
