Difference between revisions of "Talk:Data processing and output of Lidar"
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| − | + | ==Review by Andrea Taramelli (January 2013)== | |
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| − | Missing topics | + | |
| − | There exists an extensive literature on Lidar acquisition and accuracy, which should provide context for this work. Also missing | + | ==General remarks== |
| + | |||
| + | The article provides an introduction to some important technical issues related to Lidar. But it has a major red flag: the introduction is out of date and some important topics are missing, especially in the hydrographic Lidar. In particular, there is a lack of precision in the formulation of technical details, suggesting that the paper demonstrates things that it clearly does not. This is unfortunate, because what the paper does show is interesting in its own right. There are some serious flaws in the descriptions, which could affect the conclusions (especially for the hydrographic part). In addition, the discussion of the characteristics of the used data sets is incomplete. | ||
| + | I suggest the following improvements. | ||
| + | |||
| + | ==Missing topics== | ||
| + | |||
| + | There exists an extensive literature on Lidar data acquisition and accuracy, which should provide context for this work. Also missing are more references to the integration between Lidar and Hyperspectral datasets, which is really on the front end of airborne coastal data acquisition: | ||
- Chust, G., et al. (2008), Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery, Estuarine, Coastal & Shelf Science, 78(4), 633–643. | - Chust, G., et al. (2008), Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery, Estuarine, Coastal & Shelf Science, 78(4), 633–643. | ||
| Line 36: | Line 41: | ||
- Guenther GC, Thomas RWL. 1984. Prediction and Correction of Propagation-induced Depth Measurement Biases Plus Signal Attenuation and Beam Spreading for Airborne Laser Hydrography. NOAA Technical Report NOS. NOAA: Rockville, Md. [online] Available from: http://www.ngs.noaa.gov/PUBS_LIB/PredictionAndCorrectionOfPropagationInducedDepthMeasurementBiasesForAirborneLaserHydrography_TR_NOS106_CGS2.pdf (Accessed 14 May 2012) | - Guenther GC, Thomas RWL. 1984. Prediction and Correction of Propagation-induced Depth Measurement Biases Plus Signal Attenuation and Beam Spreading for Airborne Laser Hydrography. NOAA Technical Report NOS. NOAA: Rockville, Md. [online] Available from: http://www.ngs.noaa.gov/PUBS_LIB/PredictionAndCorrectionOfPropagationInducedDepthMeasurementBiasesForAirborneLaserHydrography_TR_NOS106_CGS2.pdf (Accessed 14 May 2012) | ||
| − | Suggested Improvement | + | ==Suggested Improvement== |
| − | |||
| − | One of the key aspects recently highlighted in literature and under publication is the central role of the | + | The paragraph “Typical topographic Lidar products” is completely ignoring the LAS format, which is one of the major formats relevant for the end user. |
| + | |||
| + | One of the key aspects recently highlighted in the literature and under publication is the central role of the signal amplitude of the hydrographic Lidar: | ||
- A. Taramelli; E. Valentini; C. Innocenti; F. Filipponi; R. Proietti; L. Nicoletti; M. Gabellini, 2012, Linking morphology to ecosystem structure using air-borne lidar and Hyperspectral sensors for monitoring the Coastal Landascape, AGU 2012 Fall meeting, EP31B-0810. | - A. Taramelli; E. Valentini; C. Innocenti; F. Filipponi; R. Proietti; L. Nicoletti; M. Gabellini, 2012, Linking morphology to ecosystem structure using air-borne lidar and Hyperspectral sensors for monitoring the Coastal Landascape, AGU 2012 Fall meeting, EP31B-0810. | ||
| Line 48: | Line 54: | ||
| − | Suggested replacement | + | ==Suggested replacement== |
| − | The bathymetric | + | |
| + | The bathymetric Lidar uses two distinct frequencies: a band in the infrared (wavelength of 1064 nm) for the measurement of the free surface and one in the green band (wavelength of 532 nm) for the measurement of seabed (Schnurr, 2009; Taramelli et al., 2009). The depths identified by bathymetric Lidar are determined for each pulse from the time difference between the return signal received from the water surface and the return signal received from the seabed. In fact, the two different channels that work as a receiver are used separately: one for the estimation of the return of a surface and the other to estimate the return from the bottom (Guenther et al., 1994). In this way the effects of surface waves are eliminated (Thomas and Guenther, 1990), as well as a series of errors of measurement of the surface (Guenther, 1986) that influence the bottom detection (Guenther and Thomas, 1984). Although in the literature a number of technical difficulties related to turbidity has been solved in different ways for different acquisition systems, many of the limitations of bathymetric Lidar involving the physics of light propagation in water still subsist in the different acquisition systems. The final Lidar point clouds, due to inhomogeneities in the points’ density of the surveys (Guenther, 2007; Irish and White, 1998; Quadros et al., 2008; Schnurr, 2009), are more focused on the edges of the strips. It has been shown (Guenther and Thomas, 1984) that the Lidar beam does not consider the full waveform and returns from different targets. The beam propagates along different paths in the water, producing different prebound/backscatter. The effective beam diameter in deep water is half the depth (number of scattering lengths); in shallow water this causes an overestimation of the bathymetry (Goodman et al., 2008). Bearing in mind the effects of the density of points available within a single cell, it is understandable how an overestimation can result from the density of data acquisition. The ratio of the beam area and the target cell area provides, in fact, a value that determines the accuracy of return. According to the literature (Guenther et al., 1996), the ratio is 1.78 for 3x3 m, 0.64 for 5x5 m and 0.16 for 10x10 m. This applies only when the accuracy is less than unity. Considering the values of literature, the biggest advantage of the actual factor of 1.78 occurs in the 4x4 m acquisition with an accuracy of around 0.5 (Guenther et al., 1996). | ||
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| − | |||
| − | ISPRA. 2009-2010-2011-2012. Rilievo di dettaglio della batimetria costiera laziale con tecnologie LiDAR e valutazione delle caratteristiche fisiche e biologiche in aree marine della costa laziale di specifico interesse ambientale | + | The article states that “Very few reports are available to date on validating bathymetric Lidar with acoustic data”. There is a huge project that has produced several technical reports on the bathymetric and topographic lidar validation: Lidlaz Project ISPRA. 2009-2010-2011-2012. Rilievo di dettaglio della batimetria costiera laziale con tecnologie LiDAR e valutazione delle caratteristiche fisiche e biologiche in aree marine della costa laziale di specifico interesse ambientale |
Latest revision as of 10:29, 8 September 2020
Contents
Review by Andrea Taramelli (January 2013)
General remarks
The article provides an introduction to some important technical issues related to Lidar. But it has a major red flag: the introduction is out of date and some important topics are missing, especially in the hydrographic Lidar. In particular, there is a lack of precision in the formulation of technical details, suggesting that the paper demonstrates things that it clearly does not. This is unfortunate, because what the paper does show is interesting in its own right. There are some serious flaws in the descriptions, which could affect the conclusions (especially for the hydrographic part). In addition, the discussion of the characteristics of the used data sets is incomplete. I suggest the following improvements.
Missing topics
There exists an extensive literature on Lidar data acquisition and accuracy, which should provide context for this work. Also missing are more references to the integration between Lidar and Hyperspectral datasets, which is really on the front end of airborne coastal data acquisition:
- Chust, G., et al. (2008), Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery, Estuarine, Coastal & Shelf Science, 78(4), 633–643.
- Deronde, B., et al. (2006), Use of airborne hyperspectral data and laserscan data to study beach morphodynamics along the Belgian Coast, Journal of Coastal Research, 22(5), 1108–1117.
- Elaksher, A. F. (2008), Fusion of hyperspectral images and lidar-based DEMs for coastal mapping, Optics & Lasers in Engineering, 46(7), 493–498.
- Gilvear, D., A. Tyler, and C. Davids (2004), Detection of estuarine and tidal river hydromorphology using hyper-spectral and LiDAR data: Forth estuary, Scotland, Estuarine, Coastal & Shelf Science, 61(3), 379–392.
- Jones, T. G., et al. (2010), Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada, Remote Sensing of Environment, 114(12), 2841–2852.
- Lee, D. S. (2003), Combining lidar elevation data and IKONOS multispectral imagery for coastal classification mapping, Marine Geodesy, 26, 117–127.
- Pallottini, E., Cappucci, S., Taramelli, A., Innocenti, C., Screpanti, A., (2010), Variazioni morfologiche stagionali del sistema spiaggia-duna del Parco Nazionale del Circeo, Studi Costieri, 17, 107-126.
- Taramelli A, Giardino C, Valentini E, Bresciani M, Gasperini L, (2010), Linking morphology to ecosystem structure using air-borne sensors for monitoring the Earth System, AGU Fall Meeting, 13-17 December 2010, San Francisco, California, abstract n. 960716
- Taramelli A., Giardino C., Gasperini L., del Bianco F., Bresciani M., Valentini E., Pizzimenti L., Zucca F., Disperati L., (2010), Biophysical and morphological study of coastal habitats from imaging spectrometry, lidar and in situ data acquisition, Extended abstract, The hyperspectral workshop 2010, ESRIN 17-19 Marzo 2010 Frascati.
- Innocenti C, Taramelli A, Valentini E, Cappucci S. 2011. Use of airborne LiDAR and hyperspectral data to study the sandy beach morphology along the Lazio region coast (Italy), Geophysical Research Abstracts 13: EGU2011-8725, 2011 EGU General Assembly 2011
- Guenther GC. 1986. Wind and nadir angle effects on airborne lidar water’surface’ returns. Presented at the Ocean optics VIII. Orlando, FL. 277–286 pp.
- Guenther GC. 2007. Airborne lidar bathymetry. In Digital elevation model technologies and applications: the DEM users manual. American Society of Photogrammetry and Remote Sensing Bethesda, Maryland; 253–320.
- Guenther GC, Eisler TJ, Riley JL, Perez SW. 1996. Obstruction detection and data decimation for airborne laser hydrography . DTIC Document [online] Available from: http://www.dtic.mil/cgibin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA487820 (Accessed 16 May 2012)
- Guenther GC, LaRocque PE, Lillycrop WJ. 1994. Multiple surface channels in Scanning Hydrographic Operational Airborne Lidar Survey (SHOALS) airborne lidar. presented at the Ocean Optics XII. 422 pp.
- Guenther GC, Thomas RWL. 1984. Prediction and Correction of Propagation-induced Depth Measurement Biases Plus Signal Attenuation and Beam Spreading for Airborne Laser Hydrography. NOAA Technical Report NOS. NOAA: Rockville, Md. [online] Available from: http://www.ngs.noaa.gov/PUBS_LIB/PredictionAndCorrectionOfPropagationInducedDepthMeasurementBiasesForAirborneLaserHydrography_TR_NOS106_CGS2.pdf (Accessed 14 May 2012)
Suggested Improvement
The paragraph “Typical topographic Lidar products” is completely ignoring the LAS format, which is one of the major formats relevant for the end user.
One of the key aspects recently highlighted in the literature and under publication is the central role of the signal amplitude of the hydrographic Lidar:
- A. Taramelli; E. Valentini; C. Innocenti; F. Filipponi; R. Proietti; L. Nicoletti; M. Gabellini, 2012, Linking morphology to ecosystem structure using air-borne lidar and Hyperspectral sensors for monitoring the Coastal Landascape, AGU 2012 Fall meeting, EP31B-0810.
- Valentini E, Taramelli A, Innocenti C, (2012), Coastal complex seabed mapping: the FHyL Approach, Remote Sensing of Environment, in review
- Taramelli A, Valentini E, Innocenti C, Cappucci S, Proietti R., (2012) Use of Airborne Lidar and Hyperspectral data to study the sandy beach morphology: the FhyL approach, in review Geomorphology.
Suggested replacement
The bathymetric Lidar uses two distinct frequencies: a band in the infrared (wavelength of 1064 nm) for the measurement of the free surface and one in the green band (wavelength of 532 nm) for the measurement of seabed (Schnurr, 2009; Taramelli et al., 2009). The depths identified by bathymetric Lidar are determined for each pulse from the time difference between the return signal received from the water surface and the return signal received from the seabed. In fact, the two different channels that work as a receiver are used separately: one for the estimation of the return of a surface and the other to estimate the return from the bottom (Guenther et al., 1994). In this way the effects of surface waves are eliminated (Thomas and Guenther, 1990), as well as a series of errors of measurement of the surface (Guenther, 1986) that influence the bottom detection (Guenther and Thomas, 1984). Although in the literature a number of technical difficulties related to turbidity has been solved in different ways for different acquisition systems, many of the limitations of bathymetric Lidar involving the physics of light propagation in water still subsist in the different acquisition systems. The final Lidar point clouds, due to inhomogeneities in the points’ density of the surveys (Guenther, 2007; Irish and White, 1998; Quadros et al., 2008; Schnurr, 2009), are more focused on the edges of the strips. It has been shown (Guenther and Thomas, 1984) that the Lidar beam does not consider the full waveform and returns from different targets. The beam propagates along different paths in the water, producing different prebound/backscatter. The effective beam diameter in deep water is half the depth (number of scattering lengths); in shallow water this causes an overestimation of the bathymetry (Goodman et al., 2008). Bearing in mind the effects of the density of points available within a single cell, it is understandable how an overestimation can result from the density of data acquisition. The ratio of the beam area and the target cell area provides, in fact, a value that determines the accuracy of return. According to the literature (Guenther et al., 1996), the ratio is 1.78 for 3x3 m, 0.64 for 5x5 m and 0.16 for 10x10 m. This applies only when the accuracy is less than unity. Considering the values of literature, the biggest advantage of the actual factor of 1.78 occurs in the 4x4 m acquisition with an accuracy of around 0.5 (Guenther et al., 1996).
The article states that “Very few reports are available to date on validating bathymetric Lidar with acoustic data”. There is a huge project that has produced several technical reports on the bathymetric and topographic lidar validation: Lidlaz Project ISPRA. 2009-2010-2011-2012. Rilievo di dettaglio della batimetria costiera laziale con tecnologie LiDAR e valutazione delle caratteristiche fisiche e biologiche in aree marine della costa laziale di specifico interesse ambientale
