Deconstructing a Polygenetic Landscape Using LiDAR and Multi-Resolution Analysis

Abstract:

In many earth surface systems characteristic morphologies are associated with various regimes both past and present. Aeolian systems contain a variety of features differentiated largely by morphometric differences, which in turn reflect age and divergent process regimes. Using quantitative analysis of high-resolution elevation data to generate detailed information regarding these characteristic morphometries enables geomorphologists to effectively map process regimes from a distance. Combined with satellite imagery and other types of remotely sensed data, the outputs can even help to delineate phases of activity within aeolian systems. The differentiation of regimes and identification of relict features together enables a greater level of rigor to analyses leading to field-based investigations, which are highly dependent on site-specific historical contexts that often obscure distinctions between separate process-form regimes. We present results from a Principal Components Analysis (PCA) performed on a LiDAR-derived elevation model of a largely stabilized aeolian system in South Texas. The resulting components are layered and classified to generate a map of aeolian morphometric signatures for a portion of the landscape. Several of these areas do not immediately appear to be aeolian in nature in satellite imagery or LiDAR-derived models, yet field observations and historical imagery reveal the PCA did in fact identify stabilized and relict dune features. This methodology enables researchers to generate a morphometric classification of the land surface. We believe this method is a valuable and innovative tool for researchers identifying process regimes within a study area, particularly in field-based investigations that rely heavily on site-specific context.