# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
###### Abstract
[11] established a remote sensing inversion model of forest stand age using _red_, _NIR_ and _SWIR_, and their model showed that the relationship between the forest stand age and spectral information of a rubber stand was different at different stages; the correlation between the near-infrared band and the forest stand in October (\(R^{2}=-0.40\)) was much higher than that in June (\(R^{2}=0.13\)). Forest structural parameters and photosynthetic parameters are the main information for retrieving forest stand age, so they must be taken into account in the selection of characteristic bands. SGE methods are widely used to describe the relationship between forest stand variables and forest stand age [12]. Forest height is one of the widely used variables in the stand growth equations because forest height is closely related to forest stand age, especially for forest average age [13]. Many SGEs are developed by combining forest height data. Examples include the Mitscherlich equation, Gompertz equation, logistic equation, Korf equation and Richards equation [14]. [PERSON] et al. [15] used SGEs coupled with maps of forest structure and site index to map forest stand age. In addition to stand parameters, topographic variables (such as elevation, slope and aspect) and climatic variables (such as temperature and precipitation) also affect forest growth, and taking these effects into account can greatly improve the accuracy of forest stand age mapping. However, it is very difficult to obtain these parameters, resulting in insufficient accuracy of the SGE model. With the development of the Landsat satellite, a large number of forest disturbance and restoration studies have been carried out using time-series Landsat data, and the results have been used to estimate forest structure parameters [16; 17; 18]. [PERSON] et al. realized the spatial distribution mapping of forest stand age using Landsat data and the VCT algorithm [17]. [PERSON] et al. [18] also used the random forest algorithm for recursive feature elimination (RFE-RF) to estimate the forest stand age in non-disturbed areas using the spectral and texture features of Landsat images. [PERSON] et al. [19] extracted the forest stand age from a change detection analysis of time-series Landsat images and then established a regression model between the forest stand age and the spectrum and texture factor of WorldView-2 to estimate the age of rubber trees. Although much progress has been made in forest stand age mapping based on remote sensing methods, there are many differences in the advantages of different algorithms. Due to the limitation of time series of remote sensing observation data, the VCT algorithm can only invert the forest stand age for the previous 30 years, but it cannot invert forest stand age information from more than 30 years ago. The accuracy of the stand growth model is high, but because of the \"small old trees\" caused by drought in the Loess Plateau, the forest height cannot clearly reflect the forest stand age [1]. VIs are widely used in remote sensing inversion of forest stand age, but single VIs are mainly applied, and the indication of the physiological functions of the forest and the physical basis for selecting a vegetation index are neglected. As a result, the accuracy of inversion needs to be improved. Therefore, for this study, remote sensing inversion and mapping of forest stand age in the Loess Plateau were carried out under the consideration of forest physiological function, forest structural characteristics and the physical process of remote sensing of VIs. The main objectives of this study were as follows: (1) to quantitatively describe the relationship between spectral information and forest stand age; (2) to establish a remote sensing inversion model of forest stand age with strong spatiotemporal suitability; (3) to verify accuracy and evaluate the error of remote sensing data of forest stand age in the Loess Plateau. This study could provide a guarantee for the ecological construction of the Loess Plateau.