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📊 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 ChlChlorophyll-a, and species composition: submerged aquatic vegetation, emerged vegetation, lake bottom sediment.

  • Water surface temperature.

  • Tree species charcaterisation 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.

Tip

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.

Note

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:https://doi.org/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