Multispectral mapping on 3D models and multi-temporal monitoring for individual characterization of olive trees
Fecha
2020-02
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MDPI
Resumen
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.
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Palabras clave
heterogeneous data fusion, 3D olive tree models, multispectral imaging