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

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.

Medieval Archaeology Under the Canopy with LiDAR. The (Re)Discovery of a Medieval Fortified Settlement in Southern Italy

“…Keith Challis et al [44] discussed the strengths and weaknesses of each technique and set up a generic toolkit, ad hoc suited for the visualization of airborne LiDAR data for archaeological purposes. Finally, Mayoral et al [45] proposed an analytical method for the assessment of the efficacy of visualization techniques using gradient modelling and spatial statistics.…”

“…(i) The noise reduction has been performed because some LDMs with particular reference to SVF, ASVF, and Openness are generally noisy (with this respect see Mayoral et al [45]). For this reason, it is necessary to use filtering.…”

Aplicación de la fotogrametría automatizada y de técnicas de iluminación con herramientas SIG para la visualización y el análisis de una piedra con relieves antropomorfos

“…Estudios recientes aplican estás técnicas comparando los resultados a partir de datos LiDAR de distinta resolución (1 m o 5 m), con datos fotogramétricos, concluyendo que la fotogrametría de alta resolución mejora los resultados de los datos LiDAR (Fernández-Lozano & Gutiérrez-Alonso, 2016). Además, las técnicas de visualización sobre MDE como slopeshade y LRM, unidas a técnicas de iluminación como sky-view factor o negative openness, se presentan como las herramientas más precisas para la caracterización morfológica de terrenos (Mayoral, Toumazet, Simon, Vautier, & Peiry, 2017;Moyes & Montgomery, 2016). En cuanto al análisis de grabados sobre pequeñas piezas, se han obtenido resultados prometedores con la combinación de determinadas herramientas 3D, dando lugar a la metodología AsTrend, que resalta las microtopografías de la pieza, destacando las concavidades de la superficie y mejorando la visualización de los detalles (CarreroPazos et al, 2016).…”

“…El uso de shaders combinado con el hillshading, sobre todo el high pass, mejora la visualización en las zonas en sombra. Recientes estudios han analizado las diferentes técnicas de visualización en función de las características del terreno con el objetivo de proponer directrices para la elección de una técnica en función del terreno a estudiar y con la finalidad añadida del análisis y extracción de datos automatizado (Mayoral et al, 2017). Por el contrario, como indica en otros estudios, no hay un método automático que represente de forma completa las tallas o relieves de una superficie por lo que es y será necesaria la interpretación de los resultados, la experiencia previa y la investigación específica en cada caso (Carrero-Pazos et al, 2017).…”

Multi‐scale relief model (MSRM): a new algorithm for the visualization of subtle topographic change of variable size in digital elevation models

“…Some techniques, like Sky View Factor (Zakšek et al ., ), Openness (Yokoyama et al ., ), I‐Factor (Lin et al ., ) and principal component analysis (PCA) of multi‐azimuth shaded relief maps (Devereux et al ., ), are based on digital illumination of the surface, while others, such as Slope Gradient (Doneus and Briese, ; Challis et al ., ) and Local Relief Model (Hesse, ), are based on topographic filtering and analysis. Combinations of these methods have also been attempted (Mayoral et al ., ). In general, these techniques were designed to highlight small‐scale micro‐topographies in high‐resolution datasets, particularly LiDAR‐derived DTMs.…”