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Remote Sens. 2017, 9(2), 120; doi:10.3390/rs9020120

The Highest Gradient Model: A New Method for Analytical Assessment of the Efficiency of LiDAR-Derived Visualization Techniques for Landform Detection and Mapping

1
Université Clermont Auvergne, Université Blaise Pascal, GEOLAB, BP 10448, F-63000 CLERMONT-FERRAND, France
2
CNRS, UMR 6042, GEOLAB, F-63057 CLERMONT-FERRAND, France
3
Université Clermont Auvergne, Université Blaise Pascal, Maison des Sciences de l’Homme, BP 10448, F-63000 CLERMONT-FERRAND, France
4
CNRS, USR 3550, MSH, F-63057 Clermont-Ferrand, France
*
Author to whom correspondence should be addressed.
Academic Editors: Rosa Lasaponara, Richard Gloaguen and Prasad S. Thenkabail
Received: 16 November 2016 / Revised: 20 January 2017 / Accepted: 26 January 2017 / Published: 10 February 2017
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
View Full-Text   |   Download PDF [6246 KB, uploaded 10 February 2017]   |  

Abstract

ALS-derived raster visualization techniques have become common in recent years, opening up new possibilities for subtle landform detection in earth sciences and archaeology, but they have also introduced confusion for users. As a consequence, the choice between these visualization techniques is still mostly supported by empirical knowledge. Some attempts have been made to compare these techniques, but there is still a lack of analytical data. This work proposes a new method, based on gradient modelling and spatial statistics, to analytically assess the efficacy of these visualization techniques. A selected panel of outstanding visualization techniques was assessed first by a classic non-analytical approach, and secondly by the proposed new analytical approach. The comparison of results showed that the latter provided more detailed and objective data, not always consistent with previous empirical knowledge. These data allowed us to characterize with precision the terrain for which each visualization technique performs best. A combination of visualization techniques based on DEM manipulation (Slope and Local Relief Model) appeared to be the best choice for normal terrain morphometry, occasionally supported by illumination techniques such as Sky-View Factor or Negative Openness as a function of terrain characteristics. View Full-Text
Keywords: LiDAR; visualization techniques; highest gradient model; spatial statistics; landforms detection; microtopography LiDAR; visualization techniques; highest gradient model; spatial statistics; landforms detection; microtopography
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mayoral, A.; Toumazet, J.-P.; Simon, F.-X.; Vautier, F.; Peiry, J.-L. The Highest Gradient Model: A New Method for Analytical Assessment of the Efficiency of LiDAR-Derived Visualization Techniques for Landform Detection and Mapping. Remote Sens. 2017, 9, 120.

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