Hyperspectral narrow-band spectral data are used as practical solutions in modeling and mapping vegetation. The hyperspectral data is being tested several range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species.
However, in general hyperspectral remote sensing can be applied in much wider fields such as in:
a- Commercial: mineral exploration, agriculture and forest production
b- Ecology: chlorophyll, leaf water, cellulose, pigments, lignin
c- Coastal Waters: chlorophyll, phytoplankton, dissolved organic materials, suspended sediments
d- Geology: mineral and soil types
e- Atmosphere: water vapor, cloud properties, aerosols.
Today, hyperspectral remote sensing is more popular in precision farming application. The data is used for crop monitoring for nutrients, water-stress, disease, insect attack and overall plant health is a vital aspect of successful agricultural operations. It replaced traditional visual examination of crops on the ground or sometimes from the air. Where these methods are limited by the ability of the human eye to discriminate between healthy foliage and foliage suffering various kinds of stress. Modern precision agriculture relies on site-specific management tactics to maximize yield and resources while reducing environmental impacts such as over-fertilization and the broad applications of pesticides.
Forest species classification
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Hyperspectral Data of Forestry
Geoprecision tech hyperspectral imaging, collects and processes information from across the electromagnetic spectrum. The hyperspectral imaging obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. It is like other multispectral and other spectral imaging.
Hyperspectral remote sensing use is increasing for monitoring the development and health of crops such as develop an early warning system for disease outbreaks. Some work is underway to use hyperspectral data to detect the chemical composition of plants which can be used to detect the nutrient and water status of in crops.
With the advent of hyperspectral technology, some people use thermal infrared hyperspectral camera for outdoor surveillance and UAV applications without an external light source such as the sun or the moon.
Hyperspectral imagining has many advantages due to the entire spectrum is acquired at each point, the operator needs no prior knowledge of the sample, and post processing allows all available information from the dataset to be mined. Hyperspectral imaging can also take advantage of the spatial relationships among the different spectra in a neighbourhood, allowing more elaborate spectral-spatial models for a more accurate segmentation and classification of the image
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