# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
###### Abstract  
The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the _NIR_ reflectance. The relationship between forest stands and red edge is the most significant, and \(\tau\) is 0.53, and the relationship between Simple Ratio Index (\(SR\)), near-infrared reflectance of vegetation (\(NIR_{\text{e}}\)), normalized difference vegetation index (\(NDVI\)), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that \(SR\), \(NDVI\) and red edge (\(B_{S}\)) could explain 98% of all spectral information. \(SR\), \(NDVI\) and red edge (\(B_{S}\)) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (\(R^{2}=0.63\)). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (\(R^{2}=0.63\)) was higher than that of the linear regression model (\(R^{2}=0.61\)), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (<20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau. forest stand; remote sensing; Loess Plateau; vegetation index (VI) 20233 Methods for retrieving forest stand age by remote sensing include the vegetation index (VI) method [5], stand growth equations (SGEs) [6] and the vegetation change tracker (VCT) algorithm [7]. As an important remote sensing characteristic parameter of plants, the VI method can be used to establish a remote sensing forest stand age simulation model based on the relationship between differences in forest reflectance and forest stand age. The dynamic changes in leaf chlorophyll content (LCC) and photosynthetic characteristics during the phenological periods of plants will have an important impact on canopy spectral response patterns, in which the leaves of mature forest canopy tend to show higher near-infrared and red reflectance contrast characteristics [8]. However, the red band, the near-infrared band and their component vegetation index are the key parameters of most remote sensing models for estimating forest stand age. In terms of the inversion model, an artificial neural network (ANN) and multiple linear regression model (MLR) are selected [5;8]. [PERSON] et al. [9] used single-band (_red_, _NIR_ and _SWIR_) TM data and VIs (_NDVI_ and _RVI_) to construct a forest stand age inversion model using an MLR and ANN model, and the results confirmed that _NIR_ had a strong correlation with the forest stand age (\(r=0.89\)), but a nonlinear model can better explain the relationship between forest stand age and model parameters. [PERSON] et al. [10] used time-series images of _NDVI_ to estimate the age of rubber, and the average error of age was 1.53a. [PERSON] et al.