Growing Stock Volume Estimation for Daiyun Mountain Reserve Based on Multiple Linear Regression and Machine Learning

Abstract

Remote sensing provides an easy, inexpensive, and rapid method for detecting forest stocks. However, the saturation of data from different satellite sensors leads to low accuracy in estimations of the growing stock volume in natural forests with high densities. Thus, this study added actual data to improve the accuracy. The Daiyun Mountain Reserve was the study area. Landsat 8 operational land imager data were combined with remote sensing data and actual measurements. Multiple linear regression (MLR) and machine learning methods were used to construct a model for estimating the growing stock volume. The decision tree model showed the best fit. By adding the measured data to the model, the saturation could effectively be overcome to a certain extent, and the fitting effect of all the models can be improved. Among the estimation models using only remote sensing data, the normalized difference vegetation index showed the strongest correlation with the model, followed by the annual rainfall and slope. The decision tree model was inverted to produce a map of the accumulation distribution. From the map, the storage volume in the west was lower than that in the east and was primarily confined to the middle-altitude area, consistent with field survey results.