一种基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测方法：UnetR

徐恩恩; 郭颖; 陈尔学; 李增元; 赵磊; 刘清旺

doi:10.13203/j.whugis20210001

一种基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测方法：UnetR

doi: 10.13203/j.whugis20210001

徐恩恩^1,2,
郭颖^2, ,,
陈尔学²,
李增元²,
赵磊²,
刘清旺²

1 西安科技大学, 陕西西安, 710054;
2 中国林业科学研究院资源信息研究所, 北京, 100091

基金项目:

国家重点研发计划（2017YFD0600900）

详细信息

作者简介:
徐恩恩,硕士生,研究方向为林业遥感应用。E-Mail:18404969811@163.com

A deep learning remote sensing estimation method (UnetR) for regional forest canopy closure combined with UAV LiDAR and high spatial resolution satellite remote sensing data

1 Xi' an University of Science and Technology, Xi' an 710054, China;
2 Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China

Funds:

the National Key Research and Development Program of China (2017YFD0600900).

摘要: 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的作用。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应用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。本研究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）。通过对用于图像分类的Unet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（fully convolutional networks，FCN）、随机森林（random forest，RF）和支持向量机回归（supportvector regression，SVR）模型的对比评价结果表明，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。
- 区域森林郁闭度 /
- 深度学习 /
- Unet /
- 光学遥感 /
- 激光雷达
Abstract: 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. There is a great significance 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. Therefore, this research proposed a deep learning model (UnetR) which aimed to estimate forest canopy closure based on deep learning model with the high-density light detection and ranging and high spatial resolution satellite imagery. By optimizing the loss function of Unet for image classification, and adding a batch normalization layer after the convolution layer, the model had the ability to quantitatively estimate continuous variables. The comparative evaluation results with fully convolutional networks (FCN), random forest (RF) and support vector regression (SVR) models showed that the root mean square error of the UnetR model was lower, the estimation accuracy was higher (determination coefficient (R²)=0.777, root mean square error (RMSE)=0.137, estimation accuracy (EA)=75.60%). This research provided a low labor cost and high degree of automation estimation method for remote sensing monitoring of regional forest canopy closure.
- regional forest canopy closure /
- deep learning /
- Unet /
- optical remote sensing /
- LiDAR

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一种基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测方法：UnetR

doi: 10.13203/j.whugis20210001

作者简介:
徐恩恩,硕士生,研究方向为林业遥感应用。E-Mail:18404969811@163.com

A deep learning remote sensing estimation method (UnetR) for regional forest canopy closure combined with UAV LiDAR and high spatial resolution satellite remote sensing data

计量

一种基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测方法：UnetR

doi: 10.13203/j.whugis20210001

1 西安科技大学, 陕西西安, 710054;

2 中国林业科学研究院资源信息研究所, 北京, 100091

作者简介:
徐恩恩,硕士生,研究方向为林业遥感应用。E-Mail:18404969811@163.com

English Abstract

A deep learning remote sensing estimation method (UnetR) for regional forest canopy closure combined with UAV LiDAR and high spatial resolution satellite remote sensing data

1 Xi' an University of Science and Technology, Xi' an 710054, China;

2 Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China

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一种基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测方法：UnetR

doi: 10.13203/j.whugis20210001

作者简介: 徐恩恩,硕士生,研究方向为林业遥感应用。E-Mail:18404969811@163.com

A deep learning remote sensing estimation method (UnetR) for regional forest canopy closure combined with UAV LiDAR and high spatial resolution satellite remote sensing data

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一种基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测方法：UnetR

doi: 10.13203/j.whugis20210001

1 西安科技大学, 陕西 西安, 710054; 2 中国林业科学研究院资源信息研究所, 北京, 100091

作者简介: 徐恩恩,硕士生,研究方向为林业遥感应用。E-Mail:18404969811@163.com

English Abstract

A deep learning remote sensing estimation method (UnetR) for regional forest canopy closure combined with UAV LiDAR and high spatial resolution satellite remote sensing data

1 Xi' an University of Science and Technology, Xi' an 710054, China; 2 Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China

全文HTML

目录

作者简介:
徐恩恩,硕士生,研究方向为林业遥感应用。E-Mail:18404969811@163.com

1 西安科技大学, 陕西西安, 710054;

2 中国林业科学研究院资源信息研究所, 北京, 100091

作者简介:
徐恩恩,硕士生,研究方向为林业遥感应用。E-Mail:18404969811@163.com

1 Xi' an University of Science and Technology, Xi' an 710054, China;

2 Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China