Airborne / Hyperspectral (e.g. HySpex)

Owned by Maiwenn Herpe
Last updated: Jun 21, 2021 by Frederika Mourot
2 min read

Examples of potential applications

  • Water transparency with assessment of Dissolved Organic Carbon (coloured dissolved organic matter), turbidity (suspended particulate matter, turbidity, diffuse attenuation coefficient), and Secchi disc depth (Secchi disc depth, euphotic depth).

  • Water biota with assessment of for algal blooms: Chlorophyll-a (phytoplankton), Phycocyanin (cyanobacteria), phenology: timeseries analyses of Chlorophyll-a, and species composition: submerged aquatic vegetation, emerged vegetation, lake bottom sediment.

  • Water surface temperature.

  • Tree species characterisation / detection plant stress or decline.

  • Detection of water and aquatic zones.

Range of flight height and captured zone width (m)

Flight altitude: c. 1000 m.

Spectral Range (nm)

c. 500-2500 nm,
with narrow band passes (4-5 nm).

Spatial Resolution (m)

Depends on flight altitude but typically < 1 m.

Benefits

  • Often high spatial resolution, with many narrow contiguous spectral band that allow good ability to identify materials.

  • Can adjust the time of imagery capture to the needs.

  • Ability to capture data in remote, unsafe or difficult to access locations, lowering safety risks.

  • Data acquisition can be done without disrupting operations on the ground.

Limitations

  • Depending on capture mode can be expensive, for small one off capture, but if larger areas or several scattered targets then can be cost effective.

  • Often difficult to calibrate data and produce repeatable results which limits scaling.

  • Specialist software required to process the data.

  • Generally considered as an area of ongoing research.

  • Limited by visibility constraints and poor weather conditions.

Selection of references

Giardino C, Bresciani M, Valentini E, Gasperini L, Bolpagni R, Brando VE. 2015. Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake. Remote Sensing of Environment. 157:48-57. doi:10.1016/j.rse.2014.04.034.

https://www.sciencedirect.com/science/article/pii/S0034425714002338


Beck, R.; Zhan, S.; Liu, H.; Tong, S.; Yang, B.; Xu, M.; Ye, Z.; Huang, Y.; Shu, S.; Wu, Q.; et al. 2016. Comparison of Satellite Reflectance Algorithms for Estimating Chlorophyll-a in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. Environ. 178, 15–30.

https://www.mdpi.com/2072-4292/9/6/538