The spectral calibration of LiDAR sensors that collect reflectance data from the
returning laser pulse must also be validated. This is most easily achieved through
comparison with optical field measurements acquired using a hand-held spectroradiometer. These measurements must be made coincident in space and time to the
airborne LiDAR survey. For remote sensing coral reefs, such bottom reflectance
measurements are also part of the typical work-flow for inversion of hyperspectral
radiative transfer modeling and hence there is considerable overlap between the
two applications (Tuell and Park 2004). Other LiDAR products, such as habitat
characteristics and water quality, must be calibrated utilizing in situ measurements
acquired coincident with the LiDAR data acquisition. This too is comparable to
efforts undertaken during multispectral and hyperspectral investigations.
As the capabilities of LiDAR sensors and associated analysis tools continue to
evolve, so too does the sophistication of the map products that can be derived
using this technology. This development trajectory holds great promise for LiDAR
to play an important role in future coral reef remote sensing applications.
Acknowledgments Sam Purkis was supported by the National Coral Reef Institute, Nova
Southeastern University.
Suggested Reading
Brock JC, Purkis SJ (eds) (2009) Coastal applications of airborne LiDAR remote sensing. J Coast
Res 25(6):59–65 (Special issue)
Guenther GC (2007) Digital elevation model technologies and applications: the DEM users
manual. In: Maune D (ed) Airborne LiDAR bathymetry, 2nd edn. American Society for
Photogrammetry and Remote Sensing, USA, pp 253–320 (Chapter 8)
Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th
edn. Wiley, New York
Purkis SJ, Klemas V (2011) Global environmental change and remote sensing. Wiley, New York
References
Ackermann F (1999) Airborne laser scanning: present status and future expectations. ISPRS J
Phogrammetry Remote Sens 54:64–67
Adams MD (2000) LiDAR design, use, and calibration concepts for correct environmental
detection. IEEE Trans Robot Autom 16:753–761
Alpers W, Hennings I (1984) A theory for the imaging mechanism of underwater bottom
topography by real and synthetic aperture radar. J Geophys Res 89:10529–10546
Arefi H, Hahn M (2005) A hierarchical procedure for segmentation and classification of airborne
LiDAR images. In: Geoscience and remote sensing symposium, IGARSS ‘05, Vol 7,
pp 4950–4953
Arens JC, Wright CW, Sallenger AH, Krabill WB, Swift RN (2002) Basis and methods of NASA
airborne topo-graphic mapper LiDAR surveys for coastal studies. J Coast Res 18:1–13
Babichenko S, Poryvkina L (1992) Laser remote sensing of phytoplankton pigments. LiDAR
Remote Sens SPIE 1714:127–131
138
S. J. Purkis and J. C. Brock
returning laser pulse must also be validated. This is most easily achieved through
comparison with optical field measurements acquired using a hand-held spectroradiometer. These measurements must be made coincident in space and time to the
airborne LiDAR survey. For remote sensing coral reefs, such bottom reflectance
measurements are also part of the typical work-flow for inversion of hyperspectral
radiative transfer modeling and hence there is considerable overlap between the
two applications (Tuell and Park 2004). Other LiDAR products, such as habitat
characteristics and water quality, must be calibrated utilizing in situ measurements
acquired coincident with the LiDAR data acquisition. This too is comparable to
efforts undertaken during multispectral and hyperspectral investigations.
As the capabilities of LiDAR sensors and associated analysis tools continue to
evolve, so too does the sophistication of the map products that can be derived
using this technology. This development trajectory holds great promise for LiDAR
to play an important role in future coral reef remote sensing applications.
Acknowledgments Sam Purkis was supported by the National Coral Reef Institute, Nova
Southeastern University.
Suggested Reading
Brock JC, Purkis SJ (eds) (2009) Coastal applications of airborne LiDAR remote sensing. J Coast
Res 25(6):59–65 (Special issue)
Guenther GC (2007) Digital elevation model technologies and applications: the DEM users
manual. In: Maune D (ed) Airborne LiDAR bathymetry, 2nd edn. American Society for
Photogrammetry and Remote Sensing, USA, pp 253–320 (Chapter 8)
Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th
edn. Wiley, New York
Purkis SJ, Klemas V (2011) Global environmental change and remote sensing. Wiley, New York
References
Ackermann F (1999) Airborne laser scanning: present status and future expectations. ISPRS J
Phogrammetry Remote Sens 54:64–67
Adams MD (2000) LiDAR design, use, and calibration concepts for correct environmental
detection. IEEE Trans Robot Autom 16:753–761
Alpers W, Hennings I (1984) A theory for the imaging mechanism of underwater bottom
topography by real and synthetic aperture radar. J Geophys Res 89:10529–10546
Arefi H, Hahn M (2005) A hierarchical procedure for segmentation and classification of airborne
LiDAR images. In: Geoscience and remote sensing symposium, IGARSS ‘05, Vol 7,
pp 4950–4953
Arens JC, Wright CW, Sallenger AH, Krabill WB, Swift RN (2002) Basis and methods of NASA
airborne topo-graphic mapper LiDAR surveys for coastal studies. J Coast Res 18:1–13
Babichenko S, Poryvkina L (1992) Laser remote sensing of phytoplankton pigments. LiDAR
Remote Sens SPIE 1714:127–131
138
S. J. Purkis and J. C. Brock