Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees
3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/7/1106 |
| _version_ | 1827719547104264192 |
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| author | J. M. Jurado L. Ortega J. J. Cubillas F. R. Feito |
| author_facet | J. M. Jurado L. Ortega J. J. Cubillas F. R. Feito |
| author_sort | J. M. Jurado |
| collection | DOAJ |
| description | 3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability. |
| first_indexed | 2024-03-10T20:46:38Z |
| format | Article |
| id | doaj.art-2e21d271e04641fe915481509ee04768 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:46:38Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2e21d271e04641fe915481509ee047682023-11-19T20:13:14ZengMDPI AGRemote Sensing2072-42922020-03-01127110610.3390/rs12071106Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive TreesJ. M. Jurado0L. Ortega1J. J. Cubillas2F. R. Feito3Computer Graphics and Geomatics Group of Jaén, University of Jaén, 23001 Jaén, SpainComputer Graphics and Geomatics Group of Jaén, University of Jaén, 23001 Jaén, SpainComputer Graphics and Geomatics Group of Jaén, University of Jaén, 23001 Jaén, SpainComputer Graphics and Geomatics Group of Jaén, University of Jaén, 23001 Jaén, Spain3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.https://www.mdpi.com/2072-4292/12/7/1106unmanned aerial vehiclesheterogeneous data fusion3D olive tree modelsmultispectral imagingmulti-temporal analysis |
| spellingShingle | J. M. Jurado L. Ortega J. J. Cubillas F. R. Feito Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees Remote Sensing unmanned aerial vehicles heterogeneous data fusion 3D olive tree models multispectral imaging multi-temporal analysis |
| title | Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees |
| title_full | Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees |
| title_fullStr | Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees |
| title_full_unstemmed | Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees |
| title_short | Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees |
| title_sort | multispectral mapping on 3d models and multi temporal monitoring for individual characterization of olive trees |
| topic | unmanned aerial vehicles heterogeneous data fusion 3D olive tree models multispectral imaging multi-temporal analysis |
| url | https://www.mdpi.com/2072-4292/12/7/1106 |
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