Objectives  Forest canopy density is an important factor in forest resource surveys. It plays an important role in forest quality evaluation and forest resource management. In recent years, artificial intelligence technology and remote sensing technology have developed rapidly and they have been successfully applied to forestry remote sensing quantitative estimation. It is significant to study how to use deep learning methods to effectively integrate the remote sensing data with different spatial coverage capabilities in regional forest canopy closure estimation.

  Methods  This paper proposes a deep learning model (UnetR) which aims to estimate forest canopy closure based on deep learning model with the high-density light detection and ranging and high spatial resolution satellite imagery. We optimized the loss function of Unet for image classification, and added a batch normalization layer after the convolution layer, the model had the ability to quantitatively estimate continuous variables.

  Results  The comparative evaluation results with fully convolutional networks, random forest and support vector regression models show that the root mean square error of the UnetR model was lower, the estimation accuracy was higher determination coefficient is 0.777, root mean square error is 0.137, estimation accuracy is 75.60%.

  Conclusions  This paper provided a low labor cost and high degree of automation estimation model for remote sensing monitoring of regional forest canopy closure.