| 李 琦,辛 亮,孟 陈.基于高分辨率航空遥感影像的林分因子智能识别技术研究[J].林业调查规划,2023,48(4):24-27 |
| 基于高分辨率航空遥感影像的林分因子智能识别技术研究 |
| Intelligent Recognition Technology of Forest Stand Factors Based on High-resolution Aerial Remote Sensing Images |
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| DOI： |
| 中文关键词: 智能识别技术 高分辨率航空遥感影像 林分调查因子 自动判读 |
| 英文关键词: intelligent identification technology high-resolution aerial remote sensing images forest stand survey factors automatic interpretation |
| 基金项目:上海市绿化和市容管理局科学技术项目（G201209）. |
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| 摘要点击次数: 579 |
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| 中文摘要: |
| 森林资源监测的数字化和智能化是未来发展的主要趋势。基于高分辨率航空、多光谱遥感数据和数字地表模型（DSM）等数据，利用计算机深度学习方法，研究乔木林小班的郁闭度、平均树高、总株数3项主要林分调查因子的数字化智能提取方法。结果表明，郁闭度判读的平均准确率可达到98.6%；平均树高判读的平均准确率可达到90%；株数判读的平均准确率可达到82.36%。 |
| 英文摘要: |
| The digitization and intelligence of forest resource monitoring is the main trend in future development. Based on high-resolution aerial, multispectral remote sensing data, and digital surface model (DSM) data, this paper studied the digital intelligent extraction method for three main forest stand survey factors, namely canopy density, average tree height, and total plant number, in the subcompartment of arboreal forest by using computer deep learning. The results showed that the average accuracy of canopy density interpretation reached 98.6%; the average accuracy of average tree height interpretation reached 90%; the average accuracy of plant number interpretation reached 82.36%. |
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