Soil degradation resulting from accelerated water and wind-induced erosion is a serious problem in drylands, and will remain so throughout this century. The detachment and transport of soil particles degrade the fertility of agricultural land and consequently reduce its productivity (Lyles and Tartako 1986 ).Many of the particles involved in soil erosion processes, such as raindrops, wind velocity, soil aggregates, sediment, and siltation have characteristic dimensions on the millimeter scale (Huang 1998). The addition of organic matter increases the connection between aggregate by physical and chemical bounding. The strongly bonding aggregation induces the increase of soil porosity and permeability, which result the decrease of water erosion. The bigger aggregate also decrease the wind erosion due to their heaviness.
The modeling and quantification of such processes require detailed measurements of the physical, chemical, and biological properties of soils (Soil Conservation Service 1976). However, these measurements are too slow, tedious, and expensive for routine or regular monitoring.
Several researchers have already used aerial photography to assess soil erosion. A precise form of this photography, photogrammetry, has the advantage of very efficiently and cost effectively providing detailed information about a large area. Together with aerial photography, the use of remotely sensed data forms the basis for land use mapping and change detection (Pellikk et al. 2004). In particular, for inaccessible areas, photogrammetry is far superior to traditional ground surveys. The subsequent convergence in recent years of photogrammetry and digital imaging technology has led to an increase in the use of digital elevation models (DEMs) in modern studies involving the monitoring of landscape changes (Prosser and Aberneathy 1996; De Rose et al. 1998).The areas measured experimentally in microtopographical studies of soil erosion range from 1 to approximately 20 m2. In general, the DEMs used for analysis have grid resolutions of 1 to 15 mm (Elliot et al. 1997; Darboux and Huang 2003). A variety of instruments and methods are used by soil scientists to acquire measurement coordinates, including mechanical point gauges (Elliot et al. 1997) that make contact with the soil surface, optoelectronic measurement devices such as laser scanners (Huang et al. 1988; Darboux and Huang 2003), and image processing techniques (Abd-Elbasit et al. 2008). Point gauges have been widely replaced by laser scanners, because the former make contact with the soil and can thus disturb it or sink into it (Römkens et al. 1988). While laser scanners have proven their usefulness in many experiments, a photogrammetric system is more advanced, comparatively cheaper, and provides images and morphological properties simultaneously (Hodge et al. 2009; Chandler et al. 2005).
Automated digital photogrammetry allows DEMs to be generated with sufficient resolution for microtopographical analysis. Jeschke (1990) applied correlation matching to soft-copy images taken by a Zeiss SMK 40 camera to analyze soil microtopography. Recent advances in digital image processing and camera calibration techniques make it possible to use the digitized images taken by consumer-grade analog cameras to automate the generation of DEMs (Brasington and Smart 2003; Abd-Elbasit et al. 2009) Some researchers, e.g., Chandler et al. (2002) and Lascelles et al. (2002), have calibrated consumer-grade cameras and employed the images taken with them to generate DEMs automatically on digital photogrammetric workstations, which are becoming increasingly accessible to non-photogrammetrists.
Analytical photogrammetry has often been used in geomorphological studies of gully and rill formation (Elliot et al. 1997; Pyle and Richards 1997; Helming et al. 1998; De Rose et al. 1998; Pellikk et al. 2004; Rieke-Zapp and Nearing 2005). In these previous studies, the DEM resolutions were generally produced from photographs taken under a no-rainfall condition, i.e., the photographs were taken just before and after the rainfall and wind events (Rieke-Zapp and Nearing 2005). Moreover, study reporting this method to monitor sheet and wind erosions, which predominate in drylands, is relatively few. It is necessary to evaluate the reliability of the DEM produced using either a camera and rainfall simulator or camera and wind tunnel at the laboratory scale before field scale application. The purpose of this study was to generate DEMs with high spatial and temporal resolutions from soil surfaces that developed sheet and wind erosions. Digital photogrammetry was used to measure the erosion rates and to monitor the evolution of the soil surface network under laboratory simulated conditions.
2. Overview of the photogrammetry system
In order to study in three dimensions the soil surface evolution that results from water erosion, a new automated photogrammetry system was developed by Tottori University’s Arid Land Research Center (ALRC), in collaboration with Asia Air Survey Co., Ltd. Fig. 1 shows a flow chart of this photogrammetry system (Moritani et al. 2006).
Two Nikon D2H digital cameras were focused on the center of the target object, as shown in Fig. 2. A focus length of 50 mm was used. The CCD sensor had a matrix of 2464 × 1632 picture elements (pixels). The distance between two pixel centers,
It is well known that even two cameras of the same type do not have exactly equal characteristics such as the shape of the lens and the spatial arrangement of the CCD and lens (Weng et al. 1992). This made inner orientation calibration necessary for each camera to obtain more accurate DEM data. As shown in Fig. 5, the camera calibration was performed using a three-dimensional calibration field (CF) with 32 well-distributed control points, known with an accuracy of 0.2 mm. This CF was equipped with 20 square poles with three different lengths, and 12 points on the planar table (which was placed between the square poles by 3 horizontal lines). Pictures of the CF were taken by each camera from a fixed distance,
The gradient of the pair of pictures was adjusted to minimize the parallax, and then the relative orientation was determined with the resulting points fixed on the x-axis. The result of this process is called a rectified photograph. The relative orientation yielding the rectified images was determined by a complicated equation based on a geometric consideration of the coplanar condition (Fig. 3), in which the image points
The three-dimensional calculation consisted of two methods: (1) point measurement and (2) surface measurement. Point measurement was used for visual matching to acquire a limited number of DEMs, while surface measurement was used to automatically calculate an enormous number of dense DEMs such as for the contour line of a surface. In the point measurement, as illustrated in Fig. 4, the cursor (shown as a ☆) was first moved onto a reference pixel point selected in the left rectified picture. The corresponding cursor in the right picture automatically followed along the y-axis to a position that matched that on the left. Then, the cursor in the right picture was moved along the x-axis (epipolar line) to the same corresponding point. Three-dimensional data were calculated based on the absolute orientation from the
3. Materials and methods
3.1. Accuracy of the DEM
The accuracy of this inner orientation was examined using the known DEM values of the CF board. The positions of the two D2H cameras were oriented to the CF board to include the 32 ground control points (GCPs). The CF was photographed at five different positions, ranging from a distance
The accuracy of the photogrammetry system was also evaluated under a no-rainfall condition using a soil box with a width, length, and height of 30 cm, 50 cm, and 10 cm, respectively. Sandy soil was packed into the soil box and twenty nails were inserted into the soil surface to be used as “unknown” measurement points, with a × mark made on the top of each nail. The measured values,
Point measurement was used to determine the photogrammetric value
3.2. Accuracy of the DEM under the rainfall application
Water erosion experiment was conducted using the rainfall simulator at ALRC. Three soil bulk densities, 0.91, 0.98, and 1.09 g/cm3 were prepared, which was taken from a paddy field in Tottori prefecture, Japan. The soil was saturated from the bottom of the soil box with tap water, and then gravimetrically drained for one day to obtain a condition similar to the soil’s field capacity. Simulated rain was delivered from a tower 12 m high. The rainfall distribution uniformity, which was calculated from the equation of uniformity coefficient developed by Christiansen, was set at 80%, and about 85% of the drops had a diameter of less than 2 mm (Andry et al. 2007). A rainfall intensity of 60 mm/h, developing an energy of 27.1 J m–2 mm–1 (Van Dijk et al. 2002; Andry et al. 2008), was applied for an hour to the soil box under a 10º slope. The runoff and splashed soil samples were collected every 5 or 10 minutes, just after taking pictures at
3.3. Generating DEM of soil surface induced by wind erosion
The sandy soil is high tolerance against water erosion because of its high permeability. However sandy soil is easily transported by wind erosion, which lead to damage for agriculture. In the field, sand particles start to move as saltation when wind velocity is reaching the critical friction velocity. The particle of saltation crashes another particle in upward, which result in the acceleration of wind erosion. Wind tunnel is usually designed with the long length of working section such as 7 to 16 m enough to studying saltation and crash (Shao et al, 1993; Liu et al, 2007). However, this wind tunnel size costs much and also occupies large space. In this research the wind tunnel experiment was conducted inside an existence climate chamber whose width, height and length are 2, 2, and 3 m, respectively. This chamber can be used to control wind velocity at maximum of 3 m/s which is not enough for producing wind erosion. As a result, the wind tunnel was redesigning in order to produce a wind velocity of 12 m/s by narrowing the cross-section of wind path by wood boards.
The friction speed is usually determined from the altitudinal profile of wind velocity. The wind speed closed to zero at the soil surface due to increase in soil roughness which in turn increases the resistance against wind. The logarithm of height increases the wind velocity proportionally. The friction speed is defined as this gradient of proportional line (Leys and Raupach, 1991). The wind tunnel used was low height of 20 cm which was not enough to measure the profile of wind by altitude. Therefore the wind velocity at 20 cm height was representative as measured velocity. The wind velocity when sand particle started taking off was considered as critical wind velocity.
The dune sand was put inside the soil box whose width, length and height were 23, 35, and 3 cm, respectively. The soil box with sand was fit in the opening on basal plane. In front of the soil box, the inclined plane was put to prevent the eddy flow at edge of soil box. Additionally, the effect of organic matter incorporated in the dune sand was assessed on the wind erosion. The organic matter made from bark of acicular tree which consists of bark of cedar and cypress at the weight ratio of 80 to 20%. The mixing weight ratios used in this study were 0% as control, and 1.0, 5.0, 10 and 100%. Wind was applied after packing the soil in soil box. At first, wind velocity of 4 m/s was applied for 3 min on the soil box known weight. Then the soil box was weighted after wind, and the soil was again refilled with soil. And next, the wind velocity was incremented by 1 m/s and repeated same procedure until the velocity reaching 12 m/s. The eroded soil (cm/hour) was calculated from the following Eqn 1.
The photogrammetry experiment to capture the wind ripple was carried out in the straight-line puff wind tunnel at ALRC (Fig.6). There were difficulties encountering when taking photograph during the wind application. The cameras were not easily to put inside the wind tunnel due to the small space. Even if cameras were put, the picture was not clear due to the camera shaking by wind energy and the interfering of the saltation as well. As a result, the experiments of photogrammetry and soil sampling were separately conducted, which was not the same with rainfall experiment that could be performed simultaneously. Moreover, to overcome these difficulties, the experiment of photogrammetry was performed only on the working section of this wind tunnel with open superior wall of 2.0 m long, 0.45 m wide, and 0.55 m high. A 2 cm depth of air-dried sandy soil was placed in the wind tunnel and subjected to wind velocity of 4.5 m/s for 45 min. The soil surface was photographed using a selected target object at a distance of 0.6 m, using a base length of 0.3 m, before and after the wind events. The DEM soil surface data were obtained using the surface measurement process. The accuracy of the photogrammetry system was evaluated using the DEM values for 20 nails on the z-axis, i.e., comprising the Mp (mm) values measured from the soil surface with a point gauge against the Ap values from the point measuring method.
4. Results and discussions
4.1. The influence of inner orientation on the DEM accuracy
The accuracy of the DEM along the z-axis, which is the soil depth
It was found that accuracy was also influenced by (5) the declinations in the principal coordinate points of the lenses in the longitudinal and horizontal directions and (6) the distortion of the lenses. However, factors (5) and (6) can be compensated for by calculating the inner orientation of each camera.
Thus, the precision is proportional to the factor of
In a case where the inner orientation was not accounted for, the value of
4.2. Accuracy of the DEM without the application of rainfall or wind
The accuracy of the DEM was determined using point and surface measurements with inner orientation. In the point measurement, the value of
In the surface measurement, the value of
4.3. Accuracy of the DEM under the application of rainfall
In the field water-capacity procedure, soil samples with three different bulk densities (0.91, 0.98, and 1.09 g/cm3) were subjected to a rainfall intensity of 60 mm/h. Fig. 8 shows the amount of soil erosion, as determined by the sampling and photogrammetry methods. As the soil bulk density increases to 1.09 g/cm3, the relationship between the sampling and photogrammetry values approaches a slope of 1.0 with a high correlation coefficient. The
80% of the soil surface was divided into two regions, upstream and downstream, along the slope. The results show that the quantity of soil eroded in the upstream region was 0.49 mm greater than in the downstream region (Fig. 9). This observation implies that it is possible to evaluate the amount of soil eroded in a specific area, and monitor the erosion mechanisms. When
Bar is the value of
4.4. Accuracy of the DEM under the application of wind
The relationship between wind velocity and the amount of eroded soil is shown in Fig. 10. The curve in the figure was calculated from Eqn. 6 which has been modified based on the equation of Bagnold (1936).
RMSE shows the low value of 0.2-4.1 cm/hour, which was low error between measured and estimated value. The control soil started to be eroded with 0.01 mm/hour with low value from 5.0 m/s of wind velocity, which was assumed as critical wind velocity. The eroded soil increased sharply from 5.0 m/s with increasing wind velocity. Maximum eroded soil of 56.4 cm/hour was found at the maximum wind velocity of 12 m/s. The mixture of organic matter reduced the eroded soil. The critical wind velocity in 1.0, 5.0 and 10 % of organic matter were 7.7, 8.4 and 8.9 cm/hour. The treatment of 1, 5 and 10% mixture with organic matter decreased the eroded soil by 14.2, 68.3 and 92.2%, respectively. This result showed the organic material reduced the wind erosion significantly. The eroded soil in 100% of organic matter was 0.32 cm/hour at 12 m/s of wind velocity with over than 99% of reduction than that of control since the cohesion between particles was strongly increased as a result of organic matter application. This result shows that incorporation of the organic material with fiber shape can be use as wind erosion conservation that leads to the sustainable agriculture in the sandy soil.
Fig. 11 shows the shapes of the wind ripples. Here the
The experiments of wind and water erosion were performed using wind tunnel and rainfall simulator, respectively. The amount of eroded soil was measured by applying photogrammetry and soil sampling. The amount of soil erosion was estimated by a digital photogrammetry system that used two cameras from difference of soil surface changing. It was found that the accuracy was influenced by declinations in the principal coordinate points of the lenses and the distortion of the lenses. However, these factors could be compensated for by calculating the inner orientation of each camera. The value of the cumulative eroded depth determined by photogrammetry under a no-rainfall or wind condition was significantly proportional to the measured value at the 1% level, although the accuracy under rainfall was influenced by the soil compaction as a result of the raindrop impact. Therefore, because of its high accuracy, this system could be applied when monitoring the changes in soil surface as result of water and wind erosion, or when measuring the amount of eroded soil in the case of soil with a high bulk density.
The sandy soil is high tolerance against water erosion because of its high permeability. However sandy soil moves easily by wind erosion, which lead to damage for agriculture. It was found that monitoring the sandy soil surface using photogrammetry method under wind application involves some difficulties such as unclear soil displacement-settlement and dust interference. Further study on appropriate use of photogrammetry system method during a wind erosion application is needed to consider. This research focused on the accuracy of photogrammetry when used to measure soil erosion. However, a more specific investigation of soil surfaces in relation to the spatial coherence of erosion/deposition patterns should be performed.