Remote sensing is a term commonly used to describe a setting where information is acquired from a distance, without physical contact to the object being detected. Traditionally remote sensing has been conducted from manned aircrafts and satellites, but during the last few years a tremendous increase has been seen in the use of unmanned airborne vehicles (UAVs) for data acquisition. These vehicles can be deployed to conduct periodical tasks such as the monitoring of health status of crops, or e.g. to acquire data from areas dangerous to human health.

Difference between multispectral and hyperspectral

Irrespective of the data acquisition platform, optical remote sensing data are acquired by spectrometers that are either passive or active. Here the focus is on the passive spectrometers that can be grouped into multispectral and hyperspectral based on the number of bands that are used to record electromagnetic radiation. The large number of bands (typically hundreds) of hyperspectral sensors enable the detection of phenomena that are difficult or impossible to detect by means of multispectral sensors or other remote sensing techniques.

Spatial and spectral resolutions

Spatial and spectral resolutions are important factors when evaluating the ability to use hyperspectral imagers to different applications. The higher the spectral resolution, the better the ability to detect the physico-chemical properties of a target. The spatial resolution of an image is determined by the distance between the spectrometer and the target, and the intrinsic properties of the spectrometer. In practice, spatial resolution is expressed as the pixel size of an image such that the smaller the pixel size, the higher the spatial resolution of an image. High spatial resolutions are particularly important when detecting small targets.

Remote sensing has a nearly limitless array of existing and potential applications, partially driven by the technological advances such as new data acquisition platforms  and increasingly high spatial and spectral resolutions of the imaging spectrometers. In this paper, the possibilities of hyperspectral imaging are explored in the context of agriculture, forestry and mineral exploration. Special emphasis is placed on applications that are typically conducted using the visible-near infrared wavelength region data (VNIR; 400-1300 nm).

Precision agriculture and forestry

Precision agriculture is a farming management practice that aims to improve yields while minimizing the negative impacts of farming on the environment. This is achieved by placing spectrometers to obtain information from the conditions in the field (e.g. the moisture content of the soil) in, or close to real time, and which can be used to apply the right amount of seeds, water and chemical substances to the right locations at the right time. Traditionally, satellite imagery has been used for this end, but the typically low spatial resolutions of such datasets restricts their use for the detection of large scale phenomena1. Contrary to this, the high spatial and spectral resolutions of hyperspectral airborne imagery acquired and analyzed during the growing season can be used to identify and address problems immediately before they become critical.

In addition to crops, insects and other pests can attack forests, leading to significant socioeconomic and ecological losses. These infestations can lead to discoloration of foliage and defoliation, eventually weakening and killing vegetation. However, visible damage to the vegetation is often irreversible by the time it can be detected by the human eye2. One of the key benefits of hyperspectral imaging is the ability to detect problems at an early stage, allowing for preventive measures to be taken to limit and reduce damage. Current research3 has shown that infested trees can accurately be detected using high resolution hyperspectral data, helping to employ informed management strategies and mitigation measures to restrict the extent of damage.

Insect infestations can be induced either by native of invasive species. Invasive species are organisms that are introduced by humans outside their original habitat. According to the European Environment Agency, invasive species pose a great risk to biodiversity, human health and economy4. Invasive species can have a competitive advantage over native species which allows them to spread uncontrollably, potentially resulting in a loss of biodiversity5. Therefore it is crucial to be able to detect the extent and intensity of such infestations. Recent research6 suggests that hyperspectral imagery can be applied to this end.

Mineral exploration

In addition to forestry and agriculture, mineral exploration plays a key role in modern societies. The objective of mineral exploration is to find economically viable ore deposits. Short-wave infrared (SWIR; 1300-2500 nm) wavelength region data are commonly used for this end, but the VNIR wavelength region also offers two important geological applications:

  • the detection of iron oxide minerals
  • the detection of rare earth elements

Iron oxide minerals are crucial for mineral exploration because they are often the only visible clues to the underlying mineral deposits. Iron oxide minerals have characteristic spectral features in the 800-1000 nm wavelength region7, which can be used to identify them by remote sensing. Iron oxide concentrations are associated with specific rock types (the “host rocks”), which are sometimes linked to potential ore deposits. Recent research8 has shown that hyperspectral imaging can be applied to classify iron oxide concentrations based on their host rock substrates, increasing the potential to detect rock outcrops that are most likely to be associated with ore deposits.

Rare earth elements (REEs) are a group of elements that are indispensable for the manufacturing of high-technology products such as consumer electronics. These elements occur in certain minerals and can be identified due to their key spectral features located in the VNIR wavelength region9. Furthermore, these spectral characteristics have the potential to be used for the remote sensing detection of the REEs. This is important, because despite being relatively abundant, REEs are not easily exploitable economically. Hence, remote sensing provides a potentially cost-saving tool for the exploration of economic concentrations of REE-bearing minerals.


Remote sensing techniques are crucial for agriculture, forestry and mineral exploration. In the future, more needs to be produced with fewer resources to provide for the increasing population. Also, more intensive forestry management practices are needed to ensure sustainable production of raw materials. Moreover, new techniques and tools are needed to focus mineral exploration efforts on the most promising rock outcrops. This is critical to provide a sustainable and cost-efficient response to the global demand of raw materials. To achieve these goals, hyperspectral imaging offers a non-destructive and non-invasive technique that can be implemented to cover wide areas. By applying UAVs to this end, increasingly high spatial resolutions can be attained, further enabling new and existing remote sensing applications.


1Yang, C., Everitt, J.H., Du, Q., Luo, B., Chanussot, J., 2013. Using high-resolution airborne and satellite imagery to assess crop growth and yield variability for precision agriculture. Proceedings of the IEEE 101, 582-592.

2Kim, Y., Glenn, D.M., Park, J., Ngugi, H.K. and Lehman, B.L., 2010. Hyperspectral image analysis for plant stress detection. ASABE Annual International Meeting. Pennsylvania, USA, June 2010.

3Abdel-Rahman, E.M., Mutanga, O., Adam, E., Ismail, R., 2014. Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machine classifiers. ISPRS Journal of Photogrammetry and Remote Sensing 88, 48-59.

European Environment Agency, 2013. Invasive alien species: A growing problem for environment and health. Available at:

5Butchart, S.H.M. et al., 2010. Global biodiversity: Indicators of recent declines. Science 328, 1164-1168.

6Mirik, M., Ansley, R.J., Steddom, K., Jones, D.C., Rush, C.M., Michels, Jr. G.J., Elliott, N.C., 2013. Remote distinction of a noxious weed (Musk Thistle: Carduus Nutans) using airborne hyperspectral imagery and the support vector machine classifier. Remote Sensing 5, 612-630.

7Cornell, R.M. and Schwertmann, U., 2003. The Iron Oxides: Structure, Properties, Reactions, Occurrences and Uses. John Wiley & Sons, 703 pages.

8Laakso, K., Rivard, B. and Rogge, D., 2016. Enhanced detection of gossans using hyperspectral data: Example from the Cape Smith Belt of northern Quebec, Canada. ISPRS Journal of Photogrammetry and Remote Sensing 114, 137-150.

9Adams, J.W., 1965. The visible region absorption spectra of rare-earth minerals. The American Mineralogist 50, 356-366.

Kati Laakso, a geologist by profession, Kati joined Specim in October 2014 after finishing her PhD in mineral spectroscopy at the University of Alberta, Department of Earth and Atmospheric Sciences. An eager advocate of hyperspectral imaging, Kati uses her experience in image analysis to conduct feasibility studies across a broad range of laboratory and remote sensing applications.