Maximum Depression Storage and Surface Drainage Network in Uneven Agricultural Landforms

Fernando Carvajal Ramírez; Manuel A. Aguilar; Francisco Agüera Vega; Fernando J. Aguilar; Juan Vicente Giráldez Cervera


Biosystems Engineering 95: 281–293


The volume and spatial distribution of the water available for cultivated plant roots largely depends on the soil surface relief. In traditional irrigation systems, soil micro-relief plays an important part in distributing the surface drainage produced by rainfall or irrigation. In this paper, an algorithm is proposed for estimating the water distribution network on a soil surface. It has been applied to plots of around 3 m2, and the digital elevation model (DEM) has been obtained in three surface morphologies.

With each of these DEMs, a flow directions map was calculated, a hydrodynamic classification was made, vortices were detected and eliminated, a drainage network was traced, and surface flow accumulation, maximum depression storage (MDS) and the soil surface roughness index were calculated. All of these processes were carried out using a programme specifically designed for this study, called REDES©. The method designed makes it possible to establish the influence of soil morphology on surface drainage by means of a complete characterisation of the soil and its visual comparison with soil morphologies. With respect to the drainage network, the maximum convergence level appeared in gully morphology, with 306 flow paths in the rill pixel located in the lower position. Convergence levels in the others two morphologies, tilled soil and non-tilled soil, are similar and close to half of the convergence of gully morphology. The biggest contributing basin was in gully morphology, about 1440 cm2, followed by non-tilled soil, with 470 cm2, and by tilled soil, with 260 cm2.

A study of the relationship between the DEM resolution and precision to estimate the MDS is included. When the pixel size became 24 times larger than the ‘true model’, the standard deviation of unitary vectors (SDUV) increased between 2 and 9%. Introducing this relationship, a simple predictive model to find out the estimation error of the MDS has been adjusted. The input variables used were soil roughness and DEM resolution.

Effects of Terrain Morphology, Sampling Density, and Interpolation Methods on Grid DEM Accuracy

Fernando J. Aguilar, Francisco Agüera Vega, Manuel A. Aguilar, Fernando Carvajal Ramírez


Photogrammetric Engineering & Remote Sensing 71: 805–816


This paper explores the effects of terrain morphology, sampling density, and interpolation methods for scattered sample data on the accuracy of interpolated heights in grid Digital Elevation Models (DEM). Sampled data were collected with a 2 by 2 meters sampling interval from seven different
morphologies, applying digital photogrammetric methods to large scale aerial stereo imagery (1:5000). The experimental design was outlined using a factorial scheme, and an analysis of variance was carried out. This analysis yielded the following main conclusions: DEM accuracy (RMSE) is affected significantly by the variables studied in this paper according to “morphology > sampling density > interpolation” method. Multiquadric Radial Basis Function (RBF) was rated as the best interpolation method, although Multilog RBF performed similarly for most morphologies. The rest of RBF interpolants tested (Natural Cubic Splines, Inverse Multiquadric, and Thin Plate Splines) showed numerical instability working with low smoothing factors. Inverse Distance Weighted interpolant performed worse than RBF Multiquadric or RBF Multilog. In addition, it is found that the relationship between the RMSE and the sampling density N is adjusted to a decreasing potential function that may be expressed as RMSE/Sdz = 0.1906(N/M)exp(-0.5684) (R2 = 0.8578), being Sdz the standard deviation of the heights of the M check points used for accuracy estimation, and N the number of sampling points used for creating the DEM.
The results obtained in this study allow us to observe the possibility of establishing empirical relationships between the RMSE expected in the interpolation of a Grid DEM and
such variables as terrain ruggedness, sampling density, and the interpolation method, among others that could be added. Therefore, it would be possible to establish a priori the optimum grid size required to generate or storage a DEM of a particular accuracy, with the economy in computing time and file size that this would signify for the digital flow of the mapping information.

The Evaluation of Close-range Photogrammetry for the Modelling of Mouldboard Plough Surfaces

Manuel A. Aguilar, Fernando J. Aguilar, Francisco Agüera Vega, Fernando Carvajal Ramírez


Biosystems Engineering 90: 397–407


The geometric heterogeneity existing in mouldboard ploughs hinders its inclusion in models in which the redistribution of soil particles is predicted, or the force exerted on the working component is measured. In this study, both the mathematical surface (second-, third- and fourth-order polynomials, both splines and Bezier) and the number of control points required for its adjustment (100, 49, 25, 20, 16), are examined to produce optimal modelling of the mouldboard plough geometry. The three-dimensional coordinates of 193 points on the surface of a plough are measured with great accuracy (root mean square 0·66 mm), using low-cost close-range photogrammetry. The results obtained indicate that the surface of a mouldboard plough can be shaped with an accuracy of approximately 1·30 mm, with the adjustment of a cubic polynomial surface to just 25 control points randomly distributed over the surface of the plough.