Measuring rill erosion by laser scanning
Introduction
The following sections summarize the most recent advances in the soil erosion and in the applications of high-resolution topography. The goal is to present the state of the art, providing useful information to better understand the purposes of the paper.
Soil erosion and runoff are the result of a complex interaction between soil and rainfall. These two processes are characterized by a considerable spatial–temporal variability (Nachtergaele et al., 2001, Nachtergaele et al., 2002). It was also clearly stressed recently by González-Hidalgo et al. (2007) that the most intense erosive events play a very important role in the annual soil loss. These are typically characterized by the presence of both interrill and rill erosion. Rill erosion is caused by the concentrated flow of water (Bryan, 2000, Govers et al., 2007, Knapen et al., 2007) and is considered to be the most important process of sediment production (and thus, soil loss) (Cerdan et al., 2002, Di Stefano et al., 2013, Poesen, 1987). Generally, rill erosion is understood as the effect of flowing water exceeding a certain threshold of soil resistance (Knapen et al., 2007). During the last decades, several approaches have been developed to describe and predict soil detachment and sediment transport in rills, and great efforts have been made to evaluate their suitability for that purpose (Giménez and Govers, 2002, Govers et al., 2007, Hessel and Jetten, 2007). Unfortunately, the different approaches to describe this phenomenon have turned out to be weak, if not contradictory (Giménez and Govers, 2002, Govers et al., 2007, Merz and Bryan, 1993). This is attributed mainly to methodological differences in all the monitoring and experimental setups to achieve the rills (Knapen et al., 2007, Merz and Bryan, 1993). It also appears that particle detachment and sediment transport may be controlled by different characteristics of the flowing water and, therefore, a comprehensive description may not be possible (Govers et al., 2007). However, soil erosion measurements are still lacking (Stroosnijder, 2005) and there is a recognized need to perform field experiments to ascertain the role of rills in soil erosion (Govers et al., 2007). As the observation of erosion in the field is subordinated to the stochastic character of the erosion events (Auerswald et al., 2009) and to a high dependency of the measurement technique (Casali et al., 2006), standardized and reproducible field experiments are needed, in which it is possible to produce data to characterize the behavior of rills in their environment (Wirtz et al., 2012, Wirtz et al., 2013). However, there are few field studies for the quantification of the associated soil loss (Di Stefano et al., 2012, Vinci et al., 2014) and for the direct observation of the rill formations (Bruno et al., 2008, Di Stefano and Ferro, 2011, Di Stefano et al., 2013, Mancilla et al., 2005).
In the last decade, new remote-sensing techniques have led to an important increase in terrain information, providing a basis for developing new methods for analyzing Earth surfaces (Tarolli et al., 2009). Among the available remote-sensing technologies there are the Unmanned Aerial Vehicle (UAV), the Time-of-Flight (ToF) cameras (or range cameras) and airborne and terrestrial Light Detection and Ranging (LiDAR). The UAV, commonly known as a drone, is an aircraft without a human pilot on board. It has integrated autopilot technology, which gives it semi- or fully-autonomous navigation, flight control and image acquisition capabilities (Hugenholtz et al., 2013). This recent remote sensing technology is growing fast and the scientific community is witnessing a significantly increasing use of UAVs for Earth surface analysis (e.g. Jaakkola et al., 2010). Hugenholtz et al. (2013) used a small unmanned aircraft system for the feature detection and accuracy assessment of a photogrammetrically-derived Digital Terrain Model (DTM) and the results gave evidence of the effectiveness of this technology in Earth surface analysis (Tarolli, 2014). The Time-of-Flight cameras are a new generation of active sensors, which allows the acquisition of 3D point clouds without any scanning mechanism and from just one point of view at video frame rates. The working principle is the measurement of the Time-of-Flight of a signal emitted by the device towards the object to be observed, with the advantage of simultaneously measuring the distance for each pixel of the camera sensor (Piatti and Rinaudo, 2012).
LiDAR is a technology for measuring positions of physical objects, rapidly. Furthermore it is useful because it can collect tens of thousands to over a million positions per second. LiDAR data can be collected from airborne or terrestrial vehicles, from fixed positions, usually on a tripod, and offshore platforms. In recent years the use of LiDAR has grown rapidly, both in terms of the number of application domains and in the prevalence of the method in real-world practice (Fekete et al., 2010, Lato et al., 2009, Mason et al., 2007, Zhou et al., 2004). In the case of airborne acquisition, the LiDAR is placed in an unobstructed location in a fixed wing aircraft or helicopter. The density of the measurements is determined by the LiDAR data collection rate, the elevation and ground speed of the aircraft. Data from airborne scans is corrected for position and then processed for various output products. One typical product is the generation of a bare-Earth elevation model. In terrestrial acquisition, the LiDAR unit is mounted either on a tripod or on a vehicle. The point density will be much higher than the airborne LiDAR, as great as a few points per square cm.
LiDAR (aerial and terrestrial) provides high resolution topographic data with notable advantages over traditional surveying techniques (Slatton et al., 2007); in particular, the capability to produce sub-meter resolution Digital Terrain Models (DTMs), Digital Surface Model (DSM) and to better characterize and differentiate landslide morphology (Cavalli et al., 2008). LiDAR relies on two sets of measurements to generate a cloud of point locations for features around the known location of the scanner. First, the position and pointing direction of the laser must be known for each measurement. Depending on the physical mechanism of the scanner the points may be evenly or unevenly distributed on the target, and because systems normally operate on an angular offset between successive measurements, targets closer to the device will have a higher point density than those farther away. The second piece of information needed is the distance. The distance of each scanned point could be measured by different approaches: Time-of-Flight and phase-based. Time-of-Flight LiDAR sends a laser pulse, waits, and measures the time of arrival of the return pulse (s). Given the travel time (the speed of light) and very precise time measurement, a distance can be derived. Phase-based LiDAR employs an amplitude modulated continuous waveform (AMCW) laser. When the beam interacts with a target, the phase is reset, and the returned shifted signal is processed to derive distance in combination with the duration of the flight.
Valuable characteristics of the TLS, compared to more traditional photogrammetric techniques, are: 1) the capability to derive topographic data related to the bare ground surface by automatically filtering vegetation or other objects on the surface; and 2) the capability to produce sub-meter resolution Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) over large areas.
Furthermore, thanks to the availability of high‐resolution topography, the procedures for channel network extraction (Passalacqua et al., 2010, Pirotti and Tarolli, 2010, Pirotti et al., 2012, Sofia et al., 2011) have been reconsidered, introducing new methods and achieving more detailed results than those obtained in the past (Tarolli, 2014).
The identification of a channel network is of fundamental importance in landscape-scale geomorphic and hydrologic analyses (Montgomery and Foufoula-Georgiou, 1993), and it is a key step when studying catchment hydrological responses to rainfall events (Tucker et al., 2001).
The new approaches for channel network extraction (e.g. Lashermes et al., 2007, Pirotti and Tarolli, 2010, Tarolli and Dalla Fontana, 2009) proposed the direct detection of channel head morphology from the DTM.
In the last few years, some researchers have started to use high-resolution topography for such analyses. Lashermes et al. (2007) introduced the use of wavelet analysis to locally filter LiDAR elevation data and to detect thresholds in topographic curvature and slope-direction changes for defining valleys and probable channelized portions of the valley. They used the quantile–quantile plot of local curvature to define the threshold curvature for objective channel extraction. Tarolli and Dalla Fontana (2009) used the curvature to assess the capability of high-resolution topography to recognize the convergent hollow morphology of surveyed channel heads. They suggested an objective method to extract the channel network based on the threshold range identified as being n-times the standard deviation of landform curvature. These works indicate the effectiveness of high-resolution elevation data and the related landform curvature maps in the analysis of land surface morphology.
The aim of this work is to assess the capability of the Terrestrial Laser Scanner (TLS) for detecting and mapping rill formations at the plot scale, i.e. at a resolution of centimeters. Hence, first the cloud derived by the TLS was interpolated to estimate the 0.02 m DEM with the natural neighbor method (Pirotti and Tarolli, 2010, Sibson, 1981). Then three methods for rill recognition were applied: slope analysis (Horn, 1981, Wood, 1996), the method of Broscoe (1959) and the method of landform curvature (Evans, 1979, Tarolli et al., 2012).
Finally, a comparison between the geometric characteristics and the eroded volumes was made using data measured both by the manual technique (profilometer) and by the TLS survey.
Section snippets
Experimental site and measurement techniques
The Masse experimental station for soil erosion measurements at the plot spatial scale has recently been set up by the Agricultural and Forest Hydraulics Research Division of the University of Perugia's Department of Agricultural, Food and Environmental Sciences. The station is located 20 km south of Perugia, in Umbria Region (Central Italy). The area is characterized by a hilly topography where a specific analysis and precise quantification of erosion processes are particularly important for
Comparison between the rill recognition techniques
In Fig. 3 the comparison between the method of Horn (1981) and the method of Wood (1996) for the slope calculation to a 3 × 3 kernel is shown. The two methods were found to be equivalent for the rill detection. Furthermore, the slope analysis conducted on increasing window sizes (Fig. 4) has shown the effects of different window sizes on slope maps. In particular, the 5 × 5 kernel seems to be suitable for a good recognition of the rill formations along the plot. At the bottom of the plot, the 3 × 3
Conclusions
In this paper the capability of Terrestrial Laser Scanning (TLS) to detect and to map the rill erosion at the plot scale was assessed.
The comparison between the method of the slope (Horn, 1981, Wood, 1996), the method of Broscoe (1959) and the method of landform curvature (Pirotti and Tarolli, 2010, Tarolli et al., 2012), applied to an increasing window size (3 × 3, 5 × 5,7 × 7,11 × 11,23 × 23, 27 × 27, 31 × 31, 51 × 51), for the automatic recognition of rills has shown a good agreement. Therefore, the DEMs
Acknowledgments
This research was financially supported by PRIN 2010-11 Project and C.R.P. cod. 2012.0110.021.
The authors thank the Guest Editors and two anonymous reviewers for their insightful comments, which improved this work.
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