Precision Viticulture
Remote Sensing
Remote sensing is science of acquiring, processing, and interpreting images and related data that are obtained from satellites, air planes, unmanned aerial vehicles (UAVs), and ground-based platforms that detect and measure electromagnetic radiation including visible and nonvisible radiation interaction with soil or plant material. The most useful wavelengths in remote sensing cover visible light (VIS), and extends through the near (NIR) and shortwave (SWIR) infrared, to thermal infrared (TIR) and microwave bands. Given its characteristics, remote sensing offers the opportunity of mapping and monitoring crop and soil variability and an efficient way of mapping and monitoring the effects of any condition that affects plant health, yield, or quality of a crop. There are two primary types of remote sensing, passive and active (See Table 34.1). Passive sensors record radiation reflected from the earth’s surface.
Remote Sensing Platforms
Remote sensing applications in precision viticulture are typically classified according to the type of platform for the sensor, including satellite, aircraft, unmanned aerial vehicles (UAVs), and ground-based monitoring systems. The mounting platform affects the detail of the data collected (size of the objects seen by the sensor), the coverage area observed by the sensor, and the data delivery time.
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.
Aircraft Remote Sensing
Aircraft often have a definite advantage because of their mobilization flexibility. They can be deployed wherever and whenever weather conditions are favorable. Aircraft on site can respond with a moment’s notice to take advantage of clear conditions, while satellites are locked into a schedule dictated by orbital parameters.
Unmanned Aerial Vehicle Sensing
Unmanned aerial vehicles (UAVs), commonly known as drones, are defined as powered aerial vehicles that can fly autonomously or piloted remotely over the vineyard to collect data (See Figure 34.1). Unmanned aerial vehicles have become almost of ubiquitous use in the last five years with affordability and camera payloads ranging from visible, near and thermal infrared, and 3D LIDAR. Many people refer to UAVs as Unmanned Aerial Systems (UAS), which reflects the complexity of systems onboard these vehicles.
Ground-Based or Proximal Sensing
Given the limitations of the other remote sensing platforms (e.g., satellite, plane, and UAV) used for precision viticulture, there has been significant interest in groundbased remote sensing techniques, commonly referred to as proximal sensing, to assess crop growth and crop stress.
The Electromagnetic Spectrum
In the electromagnetic spectrum (EM) there are many different types of waves with varying frequencies and wavelengths as shown in Figure 34.2. The continuous spectrum is 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.
Vegetation Indices
The ability of a remote sensor to detect subtle differences in vegetation makes it a useful tool for quantifying withinvineyard variability, evaluating crop growth, and managing fields based on current conditions that may be overlooked using typical ground-based visual scouting methods. An effective method used to easily interpret remote sensing data is calculating vegetation indices (VIs) rather than using reflectance from individual spectral bands.
Normalized Difference Vegetation Index
Plants capture visible light to drive photosynthesis. However, near infrared (NIR) photons don’t carry enough energy for photosynthesis but they do bring lots of heat, so plants have evolved to reflect NIR light. This reflection mechanism breaks down as the leaf dies. Near Infrared sensors take advantage of this property by monitoring the difference between the NIR reflectance and the visible reflectance, a calculation known as normalized difference vegetation index or NDVI. A strong NDVI signal means a high density of plants and weak NDVI indicates problem areas in the vineyard. The NDVI is created by transforming each multi-waveband image pixel according to the following formula:
Plant Cell Density or Ratio Vegetation Index
The Plant Cell Density (PCD) or Ratio Vegetation Index RVI are calculated from the formula:
Image Resolution
There are four types of resolutions in remote sensing that need to be considered while analyzing images. These are spatial, spectral, temporal, and radiometric. Among these resolution types, spatial and spectral are particularly significant as they influence the ability to extract detailed information from an image.
Spatial Resolution
Spatial resolution describes the size of the individual measurements taken by the remote sensor system. This concept is closely related to scale. With an analog sensor, such as film, the spatial resolution is commonly expressed in the same terms as the scale (e.g., 1:500)
Spectral Resolution
Spectral resolution refers to an ability of a sensor to differentiate between wavelength intervals in the electromagnetic spectrum. This is the main factor that distinguishes multispectral images from hyperspectral. Compared to multispectral images which deal with fewer bands, hyperspectral images deal with thousands of fine wavelength intervals to provide detailed information.
Temporal Resolution
Temporal resolution signifies the frequency at which images are collected over the same area (e.g., field). Images from manned aircraft and UAVs can have higher temporal resolution than satellite images due to flexibility in scheduling flight plans (versus fixed re-visit cycles of satellites).
Radiometric Resolution
Radiometric resolution describes the number of unique values that can be recorded by a sensor system when measuring reflected or emitted electromagnetic radiation. In a digital system this is easily quantified as a number.
Converting Remote Sensing Data into Geospatial Data
Remote sensing applications are rarely successful without at least some direct measurements / ground truth being taken within the area. However, “truth” is really a misnomer since there is always at least some error in measurements, even if they are taken directly. “Ground reference” would be a better descriptor. A correct term for measurements taken directly as opposed to remote measurements is in situ data collection.
Geometric Correction
When remote sensor data is initially collected it is not geospatial data. In order to make the transition to geospatial data, geometric correction must be applied to make the data into a real-world coordinate system. Beyond having no real-world coordinates assigned, the raw data also contains geometric distortion.
Photogrammetry Correction
In order to create an image that is free from all major distortions, the terrain and sensor-induced distortions must be accounted for explicitly. This is done by using a combination of GPS control points, a digital elevation model (DEM), and a detailed report of the distortion present in the sensor system.
Radiometric Correction
Radiometric corrections are necessary to reduce the imagedistorting effects of variations in sensed radiation resulting from sources such as atmospheric variations (including those caused by fog or haze), variations in sensor viewing angles that arise during scanning, variations in illumination of the areas viewed, poor sensor performance, and variations in image processing procedures.
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