Remote sensing (RS) is the science of obtaining and interpreting information from a distance, using sensors that are not in physical contact with the object being observed. The science of remote sensing includes satellites, light aircraft, drones, and ground-based monitoring systems. Remote Sensing is usually restricted to methods that detect and measure electromagnetic energy including visible and non-visible radiation that interact with surface materials and the atmosphere.
Remote Sensing Platforms
Remote sensing applications in precision viticulture are typically classified according to the type of platform for the sensor, including satellite, aerial, unmanned aerial vehicles (UAVs), and ground-based monitoring systems. These platforms and their associated imaging systems can be differentiated based on the altitude of the platform, the spatial resolution of the image, and the minimum return frequency for sequential imaging.
Satellite Remote Sensing
Satellite remote sensing is becoming increasingly used, although it generally has a lower spatial and spectral resolution than airborne remote sensing. In addition, satellites have a limited operational flexibility compared with aircraft.
Airborne Remote Sensing
Airborne remote sensing refers to the acquisition of digital imagery from light aircraft. This is not to be confused with satellite remote sensing whereby digital imagery is acquired from satellites operating at hundreds of miles above the earth. Aerial imagery can be used for highly detailed images down to centimeter resolution.
Unmanned Aerial Vehicles (UAVs)
Unmanned aerial vehicles (UAVs) , commonly known as drones, are emerging as a cost effective way to collect data with many advantages over the traditional forms (See Figure 33.2). UAVs are as the name suggests an unmanned vehicle, which flies over the vineyard to collect data. The drones are good at monitoring crops for a very low relative price. When compared with satellites or manned planes/ helicopters, low cost drones are cheaper and can be deployed every day, even in a cloudy day. These machines are generally compact, can be cheap, mechanically simple, and are on their way to being easy to operate with advanced autopilot systems.
Regulation by Federal Aviation Administration: Federal Aviation Administration (FAA) regulates use of all UAVs. The FAA announced in February of 2015 the long-awaited Notice of Proposed Rulemaking for UAVs. Under the new FAA proposal, drones that weigh less than 55 pounds would be able to fly up to 500 feet above the ground at speeds up to 100 mph.
Ground-based Monitoring Systems
In order to cope with the issues of remote sensing problems due vertical trellising, ground-based monitoring systems as shown in Figure 33.3 have been developed to assess and map canopy properties (plant biomass, vine size and photosynthetic activity). Satellite-based remote sensing can be influenced by a number of non-vegetation factors: atmospheric conditions (e.g. clouds and atmospheric path-specific variables, aerosols, water vapor), satellite geometry and calibration (view and solar angles), as well as soil backgrounds and crop canopy. The angle of incidence of solar radiation also has a strong effect on vegetation indexes.
The Electromagnetic Spectrum
The electromagnetic spectrum (EM) consists of all wavelengths of electromagnetic energy. The continuous spectrum as shown in Figure 33.4, is usually subdivided into some familiar types of electromagnetic energy like x-rays, ultraviolet rays, visible, infrared, microwaves, and radio waves. These different types of electromagnetic energy are categorized by their positions, or wavelengths, in the electromagnetic spectrum.
Four things happen when the visible and invisible light waves hit an object. They are reflected (wave is reflected off the surface), absorbed (wave is absorbed into the material of the object), scattered (wave is bounced in random directions), or transmitted (wavelength goes through the material). All materials will reflect, absorb, scatter, and transmit wavelengths to different degrees.
Reflectance Properties of Soil, Water, and Plants
When the sun’s energy strikes a surface, the amount and type of reflectance depends on the composition of the surface it strikes and the angle of incidence. For example, light-colored soil reflects more sunlight than dark soil. Conversely, dark soil absorbs more of the sunlight energy and warms up more quickly. Bodies of water have different reflectance characteristics than bare soil, and the quality of the reflectance varies with the depth and turbidity of the water.
Remote sensing images are generally processed to produce vegetative indices, such as Normalized Difference Vegetative Index (NDVI) or Plant Cell Density (PCD) on a per pixel basis. These indices are often used as an estimate of vine vigor. In viticulture, vigor generally refers to the vine (shoot) growth rate whereas in remote sensing, vigor is viewed as a combination of plant biomass (vine size) and photosynthetic activity termed the “photosynthetically active biomass” (PAB). The indices computed from remote sensing are related to vigor.
Normalized Difference Vegetation Index (NDVI)
The Normalized Difference Vegetation Index (NDVI) is created by transforming each multi-waveband image pixel according to the following formula:
Plant Cell Density (PCD) or Ratio Vegetation Index (RVI)
The Plant Cell Density (PCD) or Ratio Vegetation Index RVI are calculated from the formula: = NIR/ RED where “near infrared” (NIR) and “Red” are respectively the reflectances in each of the two wavebands The PCD index is similar to NDVI in that the difference between the high relative reflectance in the NIR waveband versus low relative reflectance in the red waveband is highlighted. Values are high for high photosynthetically active biomass (PAB) and low for low PAB.
Characteristics of Remote Sensors
Remote sensors differ in their sensitivity to various wavelengths, the size of the objects that can be “seen,” the frequency at which data is collected, and the ability to distinguish differences in EM energy. These differences are described as spatial, spectral, radiometric, and temporal resolution and are what characterize the various remote-sensing systems.
Spatial resolution refers to the size of the smallest object that can be detected in an image. The basic unit in an image is called a pixel. One-meter spatial resolution means each pixel image represents an area of one square meter.
The spectral resolution of a remote sensing system can be described as its ability to distinguish different parts of the range of measured wavelengths. In essence, this amounts to the number of wavelength intervals (“bands”) that are measured.
Radiometric resolution refers to the sensitivity of a remote sensor to variations in the reflectance levels.
In addition to spatial, spectral, and radiometric resolution, the concept of temporal resolution (sometimes called frequency of coverage) is also important to consider in a remote sensing system.
Use of Remote Sensing Data
It is not possible to measure soil moisture content or nutrient levels in plant leaves directly using remote sensing. We can, however, infer this information from remote sensing measurements. The inferences from measurements require the use of data analysis tools to find relationships between sensor data and soil or plant data actually measured.
Remote Sensing Data and Image Errors
Remote sensing image distortions can be systematic (predictable) or random (unpredictable) in nature. In either case, they can be accounted for by various techniques. There are two broad categories of image correction techniques: radiometric correction and geometric correction.
Satellite Imagery Providers
Remote sensors are classified as passive or active depending on the light source. Passive sensors measure the amount of sun energy reflected from the objects. Because these sensors rely on sunlight, data can only be recorded when the sun is illuminating the target area and cloud cover is minimal.
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