Metrics for quantifying anthropogenic impacts on geomorphology: road networks

Abstract

This work tests the capability of a recently published topographic index, the Slope Local Length of Auto‐correlation (SLLAC), to portrait and delineate anthropogenic geomorphologies. The patterns of the anthropogenic pressure are defined considering the road network density and the Urban Complexity Index (UCI). First, the research investigates the changes in the SLLAC in two derived parameters (average SLLAC and the SLLAC surface peak curvature – Spc – per km2) connected to the increasing of the anthropogenic structures. Next, natural and anthropogenic landscapes are clustered and classified. The results show that there is a direct correlation between the road network density and the UCI, and the mean SLLAC per km2. However, the Spc is inversely correlated with the anthropogenic pressure (network density and urban complexity). This shows that the surface morphology (slope) of regions presenting anthropogenic structures tends to be well organized (low Spc) and, in general, self‐similar at a long distance (higher average SLLAC). The results of the clustering approach show that the procedure can correctly depict anthropogenic landscapes having a road network density greater than about 3 km/km2, also in areas covered by vegetation. This latter result is promising for the use of such a procedure in regions that cannot be seen directly from orthophotos or satellite images. The proposed method can actively capture the alteration produced by road networks on surface morphology identifying different signatures of urban development: exploration and densification networks that are responsible for increasing the local density of the network and expanding the network into new areas, respectively. The effects of this alteration on surface processes could be significant for future research, creating new questions about differences due to human or landscape forcing on Earth surface processes. Copyright © 2015 John Wiley & Sons, Ltd.

Humans and the Earth's surface

“…The authors show the importance of monitoring critical areas of the Dead Sea, involving also the participation of stakeholders. Sofia et al (2016) present a methodology based on the slope local length of auto-correlation (SLLAC, Sofia et al 2014a) method, to portray and to delineate the morphological alterations produced by road networks. The work allowed the identification of different signatures of urban development responsible for increasing the local density of the network and expanding the network into new areas.…”

Comparison of Pleiades and LiDAR Digital Elevation Models for Terraces Detection in Farmlands

“…In addition, recent literature has underlined how statistical analyses along with LiDAR derived topographic parameters facilitate the objective recognition of different types of landscape features and processes (e.g. [42], [43]). For this study, we applied the feature extraction technique proposed by [44] and effectively tested by [4] for identifying terrace walls.…”

Geology and geomorphology control suspended sediment yield and modulate increases following timber harvest in temperate headwater streams

“…Mountainous headwater streams may disproportionally contribute to global sediment discharge (Kao and Milliman, 2008;Milliman et al, 1999;Milliman and Syvitski, 1992), particularly if impacted by land-use practices that often increase fine sediment transport and deposition (Binkley and Brown, 1993;Croke and Hairsine, 2006;Gomi et al, 2005;Montgomery, 2007;Sofia et al, 2016;Tarolli and Sofia, 2016). Fine sediment can negatively impact fishes and other aquatic ecosystem elements (Kemp et al, 2011;Suttle et al, 2004) and degrade water quality (Brown and Binkley, 1994;Wood and Armitage, 1997).…”