Open-pit mining geomorphic feature characterisation

Among the anthropogenic topographic signatures on Earth, open-pit mines are of great importance. Mining is of interest to geomorphologists and environmental researchers because of its implication in geomorphic hazards and processes. In addition, open-pit mines and quarries are considered the most dangerous industrial sector, with injuries and accidents occurring in numerous countries. Their fast, accurate and low-cost investigation, therefore, represents a challenge for the Earth science community. The purpose of this work is to characterise the open-pit mining features using high-resolution topography and a recently published landscape metric, the Slope Local Length of Auto-Correlation (SLLAC) (Sofia et al., 2014). As novel steps, aside from the correlation length, the terrace’s orientation is also calculated, and a simple empirical model to derive the percentage of artificial surfaces is tested. The research focuses on two main case studies of iron mines, both located in the Beijing district (P.R. China). The main topographic information (Digital Surface Models, DSMs) was derived using an Unmanned Aerial Vehicle (UAV) and the Structure from Motion (SfM) photogrammetric technique. The results underline the effectiveness of the adopted methodologies and survey techniques in the characterisation of the main mine’s geomorphic features. Thanks to the SLLAC, the terraced area given by open-cast/open-pit mining for iron extraction is automatically depicted, thus, allowing researchers to quickly estimate the surface covered by the open-pit. This information could be used as a starting point for future research (i) given the availability of multi-temporal surveys to track the changes in the extent of the mine; (ii) to relate the extent of the mines to the amount of processes in the area (e.g. pollution, erosion, etc.), and to (iii) combine the two points, and analyse the effects of the change related to changes in erosion. The analysis of the correlation length orientation also allows researchers to identify the terrace’s orientation and to understand the shape of the open-pit area. The tectonic environment and history, or inheritance, of a given slope can determine if and how it fails, and the orientation of the topographic surface or excavation face, with respect to geologic features, is of major significance. Therefore, the proposed approach can provide a basis for a large-scale and low-cost topographic survey for sustainable environmental planning and, for example, for the mitigation of environmental anthropogenic impacts due to mining.     

Metrics for quantifying anthropogenic impacts on geomorphology: road networks

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.