| 融合无人机影像与LiDAR点云的山区地表覆被景观特征探测 |
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| 引用本文： | 高莎,袁希平,甘淑,杨明龙,袁新悦,罗为东.融合无人机影像与LiDAR点云的山区地表覆被景观特征探测[J].测绘通报,2022,0(1):110-115. |
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| 作者姓名： | 高莎 袁希平 甘淑 杨明龙 袁新悦 罗为东 |
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| 作者单位： | 1. 昆明理工大学国土资源工程学院, 云南 昆明 650093;2. 滇西应用技术大学云南省高校山地实景点云数据处理及应用重点实验室, 云南 大理 671006;3. 云南省高校高原山地空间信息测绘技术应用工程研究中心, 云南 昆明 650093;4. 昆明理工大学城市学院, 云南 昆明 650093 |
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| 基金项目： | 国家自然科学基金（41861054）；云南省自然科学基金（2015FA016） |
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| 摘 要： | 机载LiDAR数据能够准确提供对象的三维空间位置信息，无人机高分辨影像具备丰富的色彩信息与纹理信息，综合两种数据的优点，可进行数据集成融合。针对山区普遍存在的分布广泛的植被覆盖类型基质景观，本文通过构建可见光植被指数（VDVI）融合光谱信息点云数据，进行典型植被特征提取的研究。为了验证该方法提取信息的准确度，分别构建了3种数据源并依次进行山区地表植被提取试验。对试验结果定性定量分析表明，融合光谱点云数据的植被覆被率为56.8%，较另外两种数据类型的植被覆被率更加接近参考值（58.2%），可信度相对较高，效果更好，植被图斑轮廓更加清晰，更适用于目标对象植被特征提取，使融合影像信息的点云数据分类优势得以体现，证实了该方法面向山区植被特征提取的可行性。
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| 关 键 词： | 无人机影像 LiDAR数据 数据融合 可见光植被指数 植被覆被率 |
| 收稿时间： | 2021-01-06 |
| 修稿时间： | 2021-09-07 |
Fusion of UAV image and LiDAR point cloud to study the detection technology of mountain surface cover landscape characteristics |
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| GAO Sha,YUAN Xiping,GAN Shu,YANG Minglong,YUAN Xinyue,LUO Weidong.Fusion of UAV image and LiDAR point cloud to study the detection technology of mountain surface cover landscape characteristics[J].Bulletin of Surveying and Mapping,2022,0(1):110-115. |
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| Authors: | GAO Sha YUAN Xiping GAN Shu YANG Minglong YUAN Xinyue LUO Weidong |
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| Abstract: | Airborne LiDAR data can accurately provide three-dimensional spatial location information of objects, and UAV high-resolution image has rich color information and texture information. By integrating the advantages of two kinds of data expression, data integration and fusion are carried out. Aiming at the matrix landscape of the most widely distributed vegetation cover type in mountainous areas, this paper proposes to construct visible vegetation index (VDVI) and then carry out the research on the typical vegetation feature extraction of the fusion spectral information point cloud data based on this technology. In order to verify the accuracy of the method, three data sources are constructed and the mountain is carried out in turn. The experiment of vegetation extraction in the area. The qualitative and quantitative analysis of the experimental results shows that the vegetation coverage rate of the fusion spectral point cloud data is 56.8%, which is closer to the reference value of 58.2% than the other two data types. The credibility is relatively high, the effect is better, the vegetation patch contour is clearer, and it is more suitable for the target object vegetation feature extraction, so that the advantages of the fusion image information point cloud data classification can be reflected. The feasibility of this classification method for mountain vegetation feature extraction is discussed. |
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| Keywords: | UAV image LiDAR data data fusion visible light vegetation index vegetation coverage rate |
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