沙漠研究 19-1, 283- 286 (2009 )Journal of Arid Land Studies“Desert Technology IX ”
Refereed Paper

Using of Close Range Photogrammetry for Interrill Soil Erosion Quantification
Mohamed A.M. ABD ELBASIT*1, 2, Tadaomi SAITO2, Hiroshi YASUDA2, Hisao ANYOJI2
Abstract: The needs for evaluation of interrill soil erosion spatially and validation of spatial
explicit erosion model were behind the initiation of this study. The interrill soil erosion
quantification using multi-temporal digital elevation model (DEM) generated by close range
photogrammetry system was assessed. The DEM accuracy was evaluated using the root meansquare error (RMSE) on the check points (CP). The average of the RMSE for the four trays
was 7.52, 9.69, and 7.61 mm for the x, y, and z directions, respectively. The soil erosion was
calculated from the soil surface DEM generated before and after the rainfall simulation andthen compared with the sediment amount collected at the outlet.
Keywords: Digital elevation model, Interrill soil erosion, Rainfall simulator
1. Introduction
Soil erosion by water is a complex phenomenon, which involves surface flow detachment andtransport of the top fertile soil materials and nutrients as a result of rainfall. Interrill soil erosion can bedefined as an erosion process that results from mechanical forces such as rainfall impact and water filmtransport, and that occurs in region between two rills (Meyer, 1981). The notion of spatial demarcation ofrill and interrill erosion started with the necessity to model the process of erosion by mass balance equation(Meyer, 1981). Such processes that work in an interrill area are soil detachment by raindrop impact andsurface flow, transport by raindrop impact and surface flow, and deposition of soil particles (Meyer, 1981).
Generally, the upland and farmland interrill erosion is the main source of sediment, and related nutrientsand pollutants discharged to water bodies and streams. The most common models used to evaluate theinterrill soil erosion are empirical equation, even under the physically based erosion models .
The new generation of erosion models are capable of simulating the event and annual sedimentbudget in a whole watershed of farmland although, these models fail to describe the spatial distribution ofsoil erosion (Favis-Mortlock, 1998). Recently, some models have used spatial data in order to simulate
the erosion distribution rather than lumped quantitative sediment production at the outlet of the watershed.
In order, to validate the capability of these models to simulate the spatial distribution of soil erosion, thequantification methodology of these models and the achievement of actual spatial distributed data formodel results must be validated.
Soil topography represents the main input for spatial explicit models. There are different methods
to monitor the soil topography in a macro- and micro-scale, which can be divided to contact andnon-contact methods.
The widely used non-contact methods are laser scanner and digital
photogrammetry. The digital photogrammetry is a modified version of analog and analyticalphotogrammetry, which is an efficient, rapid and inexpensive tool compared with laser scanning and othermethods (Chandler, 1999; Rieke-Zapp and Nearing, 2005). This study was attempted to assess the use of
an inexpensive close range photogrammetry system for interrill soil erosion quantification using temporal
DEM differences.
2. Methodology
2.1 Rainfall Simulator Experiments
A laboratory dripper-type rainfall simulator (Fig. 1) at the Arid Land Research Center, Tottori
University, Japan was used to generate artificial rainfall on a 0.5m × 1.0m × 0.16m steel tray (Fig. 2).
Two rainfall intensities (35 and 50 mm h-1) were used under two slope angles (15 and 20 degrees). The soil
was packed in the steel tray over a 0.04 m gravel filter up to 0.09 m depth with approximately 1.1 g cm-3
bulk density for all experiments. The soil trays were saturated from the bottom for 24 h and left for 12 h
1 The United Graduate School of Agricultural Sciences, Tottori University, Japan.

2Arid Land Research Center, Tottori University, Tottori, Japan.

* E-mail: mbasit@alrc.tottori-u.ac.jp; m_abdelbasit@hotmail.com.

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to remove the excess water. The saturation was preformed for two reasons, first to decrease the soil airtransport (splash loss); second, to reduce the impact on soil surface elevations due to raindrop impact
energy (soil compaction or consolidation). The runoff was collected at the soil tray’s outlet (Fig. 2) using
plastic bottle, and then the water was evaporated to determine the sediment mass.

Fig. 1. Rainfall simulator.

Fig. 2. View of soil tray with runoff and sediment collector
before rainfall simulation.

2.2 Photogrammetry System
Eleven adhesive tape marks were fixed on theedges of the soil trays as ground control points (GCP) (Fig.
3) and check points (CP). Two images for each experimentwere acquired from left and right for the soil tray before andafter the rainfall simulation (Fig. 3). A Canon® Power-Shot50
digital camera (Consumer-grade) was used for imaging thesoil trays. The images were imported into a photogrammetry
system developed by Asia Air Survey Co. The
photogrammetry system follows the standard methods togenerate the three dimension data of the soil surface from apair of images. There are two steps of the photogrammetric
processingfor
eachstereo-pair:photogrammetrictriangulation involved matching the image by fixing theGCPs for the left and right images, and automated DEM
generation. The DEM was extracted as a distributed x, y, andz coordinates for surface before and after rainfall simulation.

Fig. 3. Soil tray and photogrammetry
system.

2.3 The DEM Analysis
The DEMs were first manipulated to detect and remove the error points before and after the interpolation.
The error points were detected and removed before interpolation using simple method. In this method,
depending on the differences between the raw DEM and simulated smooth surface (reference surface) has asame slope angle of the soil tray and must not exceed certain value (threshold) (Abd Elbasit et al., 2008).
The DEMs were then interpolated to different grid size using inverse difference weight method (IDW). Theinterpolated DEMs were manipulated for error rectification using parametric statistical method (Flicísmo,1994) using simple FORTRAN code. The center area (200 × 700 mm) of the soil tray’s DEM was selected

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for the erosion quantification analysis to avoid the effect of the soil tray borders.
The interrill soil water erosion was estimated using the elevation difference between the DEM
generated before the rainfall event and the DEM after the rainfall event using equation (1) as follow:
Δ Z i, j = Z i, jfinal− Z i, jcase :Δ Z i , j < 0 ⇒ Erosion

initial(1 )grid

Δ Z i , j > 0 ⇒ Deposition
Δ Z i , j = 0 ⇒ No − change

grid
grid

where ∆Zi,j is the elevation difference at the i,j point, Zi,jinitial is the elevation at point i,j before the rainfall,and Zi,jfinal is the elevation difference after the rainfall simulation. This code is simply tried to quantify
and classify the erosion and deposition from the elevation difference and draw a general figure about thespatial distribution of the interrill soil erosion.
3. Results and Discussion
3.1 The DEM Accuracy
The DEM accuracy was assessed by comparing the measured and photogrammetrically estimated CP
coordinates for each soil tray. The root mean square error (RMSE) was used to calculate the accuracy of
the DEM at x, y, and z direction (Table 1). The average RMSE for the four experiments was 7.52, 9.69
and 7.61 mm for the x, y and z directions, respectively. There is no standard procedure available toevaluate the accuracy of the DEM at the micro-scale. The optimum accuracy depends, mainly, on thedesired accuracy in which the DEM will be applied (Chandler, 1999). Generally, the accuracy has many
integrated factors, such as the size of the object, camera proficiency, number of images, method of image
acquisition, and software flexibility.
Table 1. Root mean square error for the DEM of soil trays.
Root Mean Square Error (mm)Surfacesource
xyz
6.18
11.76
10.07
35/15*
6.23
9.61
8.74
35/20
10.27
8.55
4.88
50/15
7.38
8.84
6.73
50/20
*The first two digits stand for rainfall intensity (mm h-1) and last two digits stand for slope angle (degree)

3.2 Quantification of Soil Erosion
Based on equation (1), the amount of eroded soil was calculated and compared to the observed
sediment at the outlet (Fig. 4). There were noticeable differences between observed and calculated
erosion which could be attributed to the DEM accuracy, optimum grid size, quantification method, errors
related to soil splashing, and soil erosion and deposition occurring outside the selected area. The erosion
calculated from the DEM usually over-estimated the soil erosion compared to the observed outlet sediment.
On the other hand, the sediment quantification was an underestimate for the 50 mm h-1 rainfall intensityand 20 degrees slope angle. Previous research attempted to quantify the soil erosion from DEM, althoughnumerical data were rarely given on the status of soil erosion quantification compared with the measured
collected sediment at the outlet.
3. 3 Spatial Distribution of Soil Erosion
The spatial distribution indicating whether the soil was eroded or deposited was generated in thisstudy by overlaying the erosion grids and deposition grids (Fig. 5). This method is widely used in theanalysis of large-scale and long-term land formation. This technique can also be useful in a micro-scaleand short-term soil erosion evaluation in order to define the hot erosion source areas (Valette et al., 2005).

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Soil erosion (kg m-2 )4.5
Soil erosion at the outlet
Q uantified soil erosion

3

1.5

035/15

35/20

50/15

50/20

ExperimentFig. 4. Soil erosion quantification.

4. Conclusions
The main objective of this study was to assess thepotential use of close range photogrammetry for soil
erosion evaluation. The average RMSE was 7.52 mm,9.69 mm and 7.61 mm in the x, y, and z direction,
Fig. 5. Spatial distribution of interrill soil
respectively. Although the difference between observed
erosion.
outlet sediment and the calculated amount of soil fromthe DEM was distinct, this study presented comparable
figures to previous studies. Also, this study suggested that the use of soil surface features development(surface roughness and rill formation) as indicators for interrill soil erosion has a potential rather than thedirect elevation comparison.
Acknowledgements
This research was financially supported by the Japanese Ministry of Education, Sports, Science andTechnology MEXT.
References
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Chandler J. (1999): Effective application of automated digital photogrammetry for geomorphological research. Earth Surface
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