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Drones in Agriculture
Published in S. S. Nandhini, M. Karthiga, S. B. Goyal, Computational Intelligence in Robotics and Automation, 2023
Abhishek Choubey, Bharath Chandan Reddy
The Red-Edge camera sensor is used to identify disease in the crops before it was visible in NDVI. The data of plant indices designed from Red-Green-Blue (RGB), normalized difference vegetation index, and CIR Composite layers where there is no RED edge in the formula, the farm field appeared healthy and unvaried. But it helps to finds in establishing the reason for causing crop stress in the field of the particular region. When we use red edge sensor data, it shows the stress in patches in the field and going through the area showing the disease symptoms and can be explained due to the decrease in the chlorophyll content in the crop, and it can also be seen in the Chlorophyll map where the red-edge sensor is used. From the analysis, we can see the area where it got infected and taking the action within the field. These applications facilitate farmers in reducing the chances of infections that impact the farm produce.
Overview of Applications of Hyperspectral Satellites in Earth Observations
Published in Shen-En Qian, Hyperspectral Satellites and System Design, 2020
The spectral reflectance signature illustrates a dramatic increase in the reflection for healthy vegetation at around 0.70 µm, which is referred to as red-edge. In near-infrared (NIR) between 0.70 µm and 1.30 µm, a plant leaf typically reflects between 40% and 50% of incident radiation, the rest of the radiation is transmitted, with only about 5% being adsorbed. Structural variability in leaves in this range allows human to differentiate between species, even though they might look the same in the visible region (Lillesand and Kiefer 1999). Beyond 1.30 µm the incident energy on the vegetation is largely absorbed or reflected with very little transmittance of energy. Two strong water absorption bands are noted at 1.40 µm and 1.90 µm within this spectral range.
The Campus as a High Spatial Resolution Mapping Laboratory – Small Unmanned Aerial Systems (sUAS) Data Acquisition, Analytics, and Educational Issues
Published in J.B. Sharma, Applications of Small Unmanned Aircraft Systems, 2019
J.B. Sharma, J. Zachary Miller, Brian Duran, Lance Hundt
The Micasense RedEdge multispectral camera captures a 5-band image that includes the blue, green red, red-edge, and the Near Infra-Red (NIR) bands (Micasense 2015). It can be mounted on an Inspire 1 or Inspire 2 quadcopter, among other sUAS platforms, with a custom mounting kit. The red-edge band is useful for detecting plant stress and is very useful for vegetation and agricultural mapping. It has a global shutter that minimizes image distortion. An NDVI layer is easily generated and high overlap imagery results in very good 2D orthophoto mosaics that have a spatial resolution of about 8 cm/GSD from a flying height of 120 m. The 3D models, DSM, and DEM created are in excellent registration with the multispectral orthophoto making this data suitable for automated classification using GEOBIA techniques. The RedEdge MX imagery is calibrated using a reflectance panel before and after the flight. It also has an irradiance sensor (downwelling light sensor) mounted on top of the sUAS that is used to normalize the radiance collected by the camera such that the illumination conditions in the imagery collected remain the same throughout the flight operations on a particular day. This type of calibration and normalization yields orthophoto mosaics that are at sub-decimeter GSD and are at a 16-bit radiometric resolution. They are highly suited for comparison and change detection over time. The experiments with GEOBIA rulesets and classification using data from this sensor will be elaborated upon in section 11.5.3.
A novel-optimal monitoring index of rocky desertification based on feature space model and red edge indices that derived from sentinel-2 MSI image
Published in Geomatics, Natural Hazards and Risk, 2022
Bing Guo, Fei Yang, Jialin Li, Yuefeng Lu
In this paper, the objective of the study is to introduce the red edge indices that derived from Sentinel-2 MSI image to establish a novel-optimal monitoring index of rocky desertification. Sentinel-2 MSI image had the unique red edge bands, which provide more spectral information for surface ecosystem detecting related to the vegetarian (Han et al. 2016). The red edge bands describe the edge of the absorption valley formed by the strong absorption of chlorophyll by the vegetation in the red bands, and the sharp increase in the reflectivity between the strong reflection peaks formed by the multiple scattering of light within the leaves in the near infrared band (Xu et al. 2022). The red edge index retrieved by the red edge bands can effectively reflect the health status of crops, chlorophyll content and leaf structure information, which is also an important information source for vegetation physiologicization (Arthur et al. 2019). Moreover, the feature space model, compared with single index method and comprehensive index evaluation method, is able to analyze the complex influences of multi-factors and nonlinear actions among different surface parameters (Sun et al. 2022). In this paper, a novel-optimal feature space monitoring index of rocky desertification has been proposed based on feature space model and red edge indices that derived from Sentinel-2 MSI image. This model with higher spatial and temporal resolution has better applicability in monitoring the quick evolution process of rocky desertification in karst mountainous area.