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The normalized difference red edge index (NDRE) is a metric that can be used to analyse whether images obtained from multi-spectral image sensors contain healthy vegetation or not. [1] It is similar to Normalized Difference Vegetation Index (NDVI) but uses the ratio of Near-Infrared and the edge of Red as follows:
The red edge is the part of the spectrum centred around 715 nm.
Precision agriculture (PA) is a farming management strategy based on observing, measuring and responding to temporal and spatial variability to improve agricultural production sustainability. It is used in both crop and livestock production. Precision agriculture often employs technologies to automate agricultural operations, improving their diagnosis, decision-making or performing. The goal of precision agriculture research is to define a decision support system for whole farm management with the goal of optimizing returns on inputs while preserving resources.
Multispectral imaging captures image data within specific wavelength ranges across the electromagnetic spectrum. The wavelengths may be separated by filters or detected with the use of instruments that are sensitive to particular wavelengths, including light from frequencies beyond the visible light range, i.e. infrared and ultra-violet. It can allow extraction of additional information the human eye fails to capture with its visible receptors for red, green and blue. It was originally developed for military target identification and reconnaissance. Early space-based imaging platforms incorporated multispectral imaging technology to map details of the Earth related to coastal boundaries, vegetation, and landforms. Multispectral imaging has also found use in document and painting analysis.
Red edge refers to the region of rapid change in reflectance of vegetation in the near infrared range of the electromagnetic spectrum. Chlorophyll contained in vegetation absorbs most of the light in the visible part of the spectrum but becomes almost transparent at wavelengths greater than 700 nm. The cellular structure of the vegetation then causes this infrared light to be reflected because each cell acts something like an elementary corner reflector. The change can be from 5% to 50% reflectance going from 680 nm to 730 nm. This is an advantage to plants in avoiding overheating during photosynthesis. For a more detailed explanation and a graph of the photosynthetically active radiation (PAR) spectral region, see Normalized difference vegetation index § Rationale.
The normalized difference vegetation index (NDVI) is a widely-used metric for quantifying the health and density of vegetation using sensor data. It is calculated from spectrometric data at two specific bands: red and near-infrared. The spectrometric data is usually sourced from remote sensors, such as satellites.
Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. There are three general types of spectral imagers. There are push broom scanners and the related whisk broom scanners, which read images over time, band sequential scanners, which acquire images of an area at different wavelengths, and snapshot hyperspectral imagers, which uses a staring array to generate an image in an instant.
Chemical imaging is the analytical capability to create a visual image of components distribution from simultaneous measurement of spectra and spatial, time information. Hyperspectral imaging measures contiguous spectral bands, as opposed to multispectral imaging which measures spaced spectral bands.
The enhanced vegetation index (EVI) is an 'optimized' vegetation index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences. EVI is computed following this equation:
Chlorophyll fluorescence is light re-emitted by chlorophyll molecules during return from excited to non-excited states. It is used as an indicator of photosynthetic energy conversion in plants, algae and bacteria. Excited chlorophyll dissipates the absorbed light energy by driving photosynthesis, as heat in non-photochemical quenching or by emission as fluorescence radiation. As these processes are complementary processes, the analysis of chlorophyll fluorescence is an important tool in plant research with a wide spectrum of applications.
Plant stress measurement is the quantification of environmental effects on plant health. When plants are subjected to less than ideal growing conditions, they are considered to be under stress. Stress factors can affect growth, survival and crop yields. Plant stress research looks at the response of plants to limitations and excesses of the main abiotic factors, and of other stress factors that are important in particular situations. Plant stress measurement usually focuses on taking measurements from living plants. It can involve visual assessments of plant vitality, however, more recently the focus has moved to the use of instruments and protocols that reveal the response of particular processes within the plant
Satellite crop monitoring is the technology which facilitates real-time crop vegetation index monitoring via spectral analysis of high resolution satellite images for different fields and crops which enables to track positive and negative dynamics of crop development. The difference in vegetation index informs about single-crop development disproportions that speaks for the necessity of additional agriculture works on particular field zones—that is because satellite crop monitoring belongs to precision agriculture methods.
The Dualex is an optical sensor developed by Force-A for the assessment of flavonol, anthocyanin, and chlorophyll contents in leaves. The sensor is a result of technology transfer from the CNRS and University of Paris-Sud Orsay. It allows to perform real-time and non-destructive measurements. The main applications are plant science and agriculture research.
Multiplex sensor is a hand-held multiparametric optical sensor developed by Force-A. The sensor is a result of 15 years of research on plant autofluorescence conducted by the CNRS and University of Paris-Sud Orsay. It provides accurate and complete information on the physiological state of the crop, allowing real-time and non-destructive measurements of chlorophyll and polyphenols contents in leaves and fruits.
Empirically derived NDVI products have been shown to be unstable, varying with soil colour, soil moisture, and saturation effects from high density vegetation. In an attempt to improve NDVI, Huete developed a vegetation index that accounted for the differential red and near-infrared extinction through the vegetation canopy. The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths.
Normalized Difference Water Index (NDWI) may refer to one of at least two remote sensing-derived indexes related to liquid water:
A vegetation index (VI) is a spectral imaging transformation of two or more image bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations.
Resurs-P is a series of Russian commercial Earth observation satellites capable of acquiring high-resolution hyperspectral (HSI), wide-field multispectral (MSI), and panchromatic imagery. These spacecraft cost over 5 billion rubles and are operated by Roscosmos replacing the Resurs-DK No.1 satellite.
Sentinel-4 is a European Earth observation mission developed to support the European Union Copernicus Programme for monitoring the Earth. It focuses on monitoring of trace gas concentrations and aerosols in the atmosphere to support operational services covering air-quality near-real time applications, air-quality protocol monitoring and climate protocol monitoring. The specific objective of Sentinel-4 is to support this with a high revisit time over Europe.
Remote sensing is used in the geological sciences as a data acquisition method complementary to field observation, because it allows mapping of geological characteristics of regions without physical contact with the areas being explored. About one-fourth of the Earth's total surface area is exposed land where information is ready to be extracted from detailed earth observation via remote sensing. Remote sensing is conducted via detection of electromagnetic radiation by sensors. The radiation can be naturally sourced, or produced by machines and reflected off of the Earth surface. The electromagnetic radiation acts as an information carrier for two main variables. First, the intensities of reflectance at different wavelengths are detected, and plotted on a spectral reflectance curve. This spectral fingerprint is governed by the physio-chemical properties of the surface of the target object and therefore helps mineral identification and hence geological mapping, for example by hyperspectral imaging. Second, the two-way travel time of radiation from and back to the sensor can calculate the distance in active remote sensing systems, for example, Interferometric synthetic-aperture radar. This helps geomorphological studies of ground motion, and thus can illuminate deformations associated with landslides, earthquakes, etc.
The moment distance index (MDI) is a shape-based metric or shape index that can be used to analyze spectral reflectance curves and waveform LiDAR, proposed by Salas and Henebry in 2014. In the case of spectral data, the shape of the reflectance curve should unmask fine points of the spectra usually not considered by existing band-specific indices. It has been used to identify spectral regions for chlorophyll and carotenoids, detect greenhouses using WorldView-2, Landsat, and Sentinel-2 satellite data, identify greenhouse crops, compute canopy heights, estimate green vegetation fraction, and optimize Fourier-transform infrared (FTIR) scans for soil spectroscopy.
Land cover maps are tools that provide vital information about the Earth's land use and cover patterns. They aid policy development, urban planning, and forest and agricultural monitoring.
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