基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测模型

森林郁闭度是森林资源调查中的一个重要因子，对森林质量评价具有重要作用。随着人工智能技术和遥感技术的不断发展，研究如何利用深度学习有效协同不同空间覆盖能力的遥感数据实现区域森林郁闭度的估测具有重要意义。由此提出了一种协同应用高密度无人机激光雷达和高空间分辨率卫星遥感数据，对区域森林郁闭度进行定量估测的深度学习模型（UnetR）。对用于图像分类的Unet模型的损失函数进行改进，并在卷积层后加入批量归一化层，使其具有对连续变量进行定量估测的能力。与全卷积神经网络、随机森林和支持向量机回归模型进行对比实验。结果表明, UnetR模型的均方根误差较低，估测精度较高，为实现区域森林郁闭度遥感监测提供了一种人力成本低、自动化程度高的估测方法。

An Estimation Model for Regional Forest Canopy Closure Combined with UAV LiDAR and High Spatial Resolution Satellite Remote Sensing Data

  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.