Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves
Reflectance anisotropy in remote sensing images can complicate the interpretation of spectral signature, and extracting precise structural information under these pixels is a promising approach. Low-altitude unmanned aerial vehicle (UAV) systems can capture high-resolution imagery even to centimeter...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/93 |
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| author | Kaiyi Bi Yifang Niu Hao Yang Zheng Niu Yishuo Hao Li Wang |
| author_facet | Kaiyi Bi Yifang Niu Hao Yang Zheng Niu Yishuo Hao Li Wang |
| author_sort | Kaiyi Bi |
| collection | DOAJ |
| description | Reflectance anisotropy in remote sensing images can complicate the interpretation of spectral signature, and extracting precise structural information under these pixels is a promising approach. Low-altitude unmanned aerial vehicle (UAV) systems can capture high-resolution imagery even to centimeter-level detail, potentially simplifying the characterization of leaf anisotropic reflectance. We proposed a novel maize point cloud generation method that combines an advanced UAV cross-circling oblique (CCO) photography route with the Structure from the Motion-Multi-View Stereo (SfM-MVS) algorithm. A multi-spectral point cloud was then generated by fusing multi-spectral imagery with the point cloud using a DSM-based approach. The Rahman–Pinty–Verstraete (RPV) model was finally applied to establish maize leaf-level anisotropic reflectance models. Our results indicated a high degree of similarity between measured and estimated maize structural parameters (R<sup>2</sup> = 0.89 for leaf length and 0.96 for plant height) based on accurate point cloud data obtained from the CCO route. Most data points clustered around the principal plane due to a constant angle between the sun and view vectors, resulting in a limited range of view azimuths. Leaf reflectance anisotropy was characterized by the RPV model with R<sup>2</sup> ranging from 0.38 to 0.75 for five wavelength bands. These findings hold significant promise for promoting the decoupling of plant structural information and leaf optical characteristics within remote sensing data. |
| format | Article |
| id | doaj-art-ec7cc70ac40b4e7bb51d8b9a76dce197 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ec7cc70ac40b4e7bb51d8b9a76dce1972025-01-10T13:20:12ZengMDPI AGRemote Sensing2072-42922024-12-011719310.3390/rs17010093Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize LeavesKaiyi Bi0Yifang Niu1Hao Yang2Zheng Niu3Yishuo Hao4Li Wang5Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaThe Information Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaReflectance anisotropy in remote sensing images can complicate the interpretation of spectral signature, and extracting precise structural information under these pixels is a promising approach. Low-altitude unmanned aerial vehicle (UAV) systems can capture high-resolution imagery even to centimeter-level detail, potentially simplifying the characterization of leaf anisotropic reflectance. We proposed a novel maize point cloud generation method that combines an advanced UAV cross-circling oblique (CCO) photography route with the Structure from the Motion-Multi-View Stereo (SfM-MVS) algorithm. A multi-spectral point cloud was then generated by fusing multi-spectral imagery with the point cloud using a DSM-based approach. The Rahman–Pinty–Verstraete (RPV) model was finally applied to establish maize leaf-level anisotropic reflectance models. Our results indicated a high degree of similarity between measured and estimated maize structural parameters (R<sup>2</sup> = 0.89 for leaf length and 0.96 for plant height) based on accurate point cloud data obtained from the CCO route. Most data points clustered around the principal plane due to a constant angle between the sun and view vectors, resulting in a limited range of view azimuths. Leaf reflectance anisotropy was characterized by the RPV model with R<sup>2</sup> ranging from 0.38 to 0.75 for five wavelength bands. These findings hold significant promise for promoting the decoupling of plant structural information and leaf optical characteristics within remote sensing data.https://www.mdpi.com/2072-4292/17/1/93multi-spectral point cloudUAVoblique photographyreflectance anisotropyMaize |
| spellingShingle | Kaiyi Bi Yifang Niu Hao Yang Zheng Niu Yishuo Hao Li Wang Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves Remote Sensing multi-spectral point cloud UAV oblique photography reflectance anisotropy Maize |
| title | Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves |
| title_full | Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves |
| title_fullStr | Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves |
| title_full_unstemmed | Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves |
| title_short | Multi-Spectral Point Cloud Constructed with Advanced UAV Technique for Anisotropic Reflectance Analysis of Maize Leaves |
| title_sort | multi spectral point cloud constructed with advanced uav technique for anisotropic reflectance analysis of maize leaves |
| topic | multi-spectral point cloud UAV oblique photography reflectance anisotropy Maize |
| url | https://www.mdpi.com/2072-4292/17/1/93 |
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