scholarly journals Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

2018 ◽  
Vol 9 ◽  
Author(s):  
Hengbiao Zheng ◽  
Tao Cheng ◽  
Dong Li ◽  
Xia Yao ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Nitrogen Concentration ◽  
Hyperspectral Data ◽  
Multispectral Imagery ◽  
Plant Nitrogen ◽  
Concentration Estimation ◽  
Aerial Vehicle

scholarly journals Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 957 ◽  
Author(s):  
Hengbiao Zheng ◽  
Jifeng Ma ◽  
Meng Zhou ◽  
Dong Li ◽  
Xia Yao ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Spectral Information ◽  
Growth Stages ◽  
Nitrogen Accumulation ◽  
Multispectral Imagery ◽  
Plant Nitrogen ◽  
N Nutrition ◽  
Aerial Vehicle

This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

scholarly journals Morphological Band Registration of Multispectral Cameras for Water Quality Analysis with Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (12) ◽  
pp. 2024 ◽  
Author(s):  
Wonkook Kim ◽  
Sunghun Jung ◽  
Yongseon Moon ◽  
Stephen C. Mangum
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Red Tide ◽  
Spatial Scales ◽  
Quality Analysis ◽  
Geometric Transformation ◽  
Concentration Estimation ◽  
Individual Bands ◽  
Aerial Vehicle ◽  
Short Time ◽  
The Impact

Multispectral imagery contains abundant spectral information on terrestrial and oceanic targets, and retrieval of the geophysical variables of the targets is possible when the radiometric integrity of the data is secured. Multispectral cameras typically require the registration of individual band images because their lens locations for individual bands are often displaced from each other, thereby generating images of different viewing angles. Although this type of displacement can be corrected through a geometric transformation of the image coordinates, a mismatch or misregistration between the bands still remains, owing to the image acquisition timing that differs by bands. Even a short time difference is critical for the image quality of fast-moving targets, such as water surfaces, and this type of deformation cannot be compensated for with a geometric transformation between the bands. This study proposes a novel morphological band registration technique, based on the quantile matching method, for which the correspondence between the pixels of different bands is not sought by their geometric relationship, but by the radiometric distribution constructed in the vicinity of the pixel. In this study, a Micasense Rededge-M camera was operated on an unmanned aerial vehicle and multispectral images of coastal areas were acquired at various altitudes to examine the performance of the proposed method for different spatial scales. To assess the impact of the correction on a geophysical variable, the performance of the proposed method was evaluated for the chlorophyll-a concentration estimation. The results showed that the proposed method successfully removed the noisy spatial pattern caused by misregistration while maintaining the original spatial resolution for both homogeneous scenes and an episodic scene with a red tide outbreak.

scholarly journals Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

2017 ◽  
Vol 9 (4) ◽  
pp. 308 ◽  
Author(s):  
Johanna Albetis ◽  
Sylvie Duthoit ◽  
Fabio Guttler ◽  
Anne Jacquin ◽  
Michel Goulard ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Multispectral Imagery ◽  
Flavescence Dorée ◽  
Aerial Vehicle ◽  
Grapevine Disease
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