Terrestrial laser scanning soil surfaces: a field methodology to examine soil surface roughness and overland flow hydraulics

2010 ◽  
Vol 25 (6) ◽  
pp. 842-860 ◽  
Author(s):  
Mark W. Smith ◽  
Nicholas J. Cox ◽  
Louise J. Bracken
Keyword(s):  
Surface Roughness ◽  
Laser Scanning ◽  
Overland Flow ◽  
Terrestrial Laser Scanning ◽  
Soil Surface ◽  
Flow Hydraulics ◽  
Soil Surface Roughness ◽  
Field Methodology

scholarly journals Applying Terrestrial Laser Scanning for Soil Surface Roughness Assessment

2015 ◽  
Vol 7 (2) ◽  
pp. 2007-2045 ◽  
Author(s):  
Milutin Milenković ◽  
Norbert Pfeifer ◽  
Philipp Glira
Keyword(s):  
Surface Roughness ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Soil Surface ◽  
Soil Surface Roughness ◽  
Roughness Assessment

scholarly journals Assessing soil surface roughness decay during simulated rainfall by multifractal analysis

2008 ◽  
Vol 15 (3) ◽  
pp. 457-468 ◽  
Author(s):  
E. Vidal Vázquez ◽  
R. García Moreno ◽  
J. G. V. Miranda ◽  
M. C. Díaz ◽  
A. Saá Requejo ◽  
...  
Keyword(s):  
Multifractal Analysis ◽  
Laser Scanning ◽  
Overland Flow ◽  
Soil Surface ◽  
Data Sets ◽  
Simulated Rainfall ◽  
Initial State ◽  
Surface Microrelief ◽  
Elevation Data ◽  
Soil Surface Roughness

Abstract. Understanding and describing the spatial characteristics of soil surface microrelief are required for modelling overland flow and erosion. We employed the multifractal approach to characterize topographical point elevation data sets acquired by high resolution laser scanning for assessing the effect of simulated rainfall on microrelief decay. Three soil surfaces with different initial states or composition and rather smooth were prepared on microplots and subjected to successive events of simulated rainfall. Soil roughness was measured on a 2×2 mm2 grid, initially, i.e. before rain, and after each simulated storm, yielding a total of thirteen data sets for three rainfall sequences. The vertical microrelief component as described by the statistical index random roughness (RR) exhibited minor changes under rainfall in two out of three study cases, which was due to the imposed wet initial state constraining aggregate breakdown. The effect of cumulative rainfall on microrelief decay was also assessed by multifractal analysis performed with the box-count algorithm. Generalized dimension, Dq, spectra allowed characterization of the spatial variation of soil surface microrelief measured at the microplot scale. These Dq spectra were also sensitive to temporal changes in soil surface microrelief, so that in all the three study rain sequences, the initial soil surface and the surfaces disturbed by successive storms displayed great differences in their degree of multifractality. Therefore, Multifractal parameters best discriminate between successive soil stages under a given rain sequence. Decline of RR and multifractal parameters showed little or no association.

Spatiotemporal variations of soil surface roughness from in-situ laser scanning

CATENA ◽  
2009 ◽  
Vol 79 (2) ◽  
pp. 128-139 ◽  
Author(s):  
Sören-Nils Haubrock ◽  
Matthias Kuhnert ◽  
Sabine Chabrillat ◽  
Andreas Güntner ◽  
Hermann Kaufmann
Keyword(s):  
Surface Roughness ◽  
Laser Scanning ◽  
Soil Surface ◽  
Spatiotemporal Variations ◽  
Soil Surface Roughness

Evolution of soil surface roughness and flowpath connectivity in overland flow experiments

CATENA ◽  
2002 ◽  
Vol 46 (2-3) ◽  
pp. 125-139 ◽  
Author(s):  
F Darboux ◽  
Ph. Davy ◽  
C Gascuel-Odoux ◽  
C Huang
Keyword(s):  
Surface Roughness ◽  
Overland Flow ◽  
Soil Surface ◽  
Soil Surface Roughness ◽  
Flow Experiments

scholarly journals Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion

2020 ◽  
Vol 12 (1) ◽  
pp. 232-241
Author(s):  
Na Ta ◽  
Chutian Zhang ◽  
Hongru Ding ◽  
Qingfeng Zhang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Soil Surface ◽  
Rainfall Events ◽  
Tillage Practices ◽  
Surface Microtopography ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
The Difference ◽  
Sheet Erosion

AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.

scholarly journals Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4386
Author(s):  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
Tarahom Mesri-Gundoshmian ◽  
Aitazaz A. Farooque ◽  
Hassan Afzaal
Keyword(s):  
Surface Roughness ◽  
Stereo Vision ◽  
Agricultural Soils ◽  
Depth Map ◽  
Soil Surface ◽  
Regression Equations ◽  
Height Profile ◽  
Peak Fitting ◽  
Soil Surface Roughness ◽  
Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

scholarly journals Equipments to manage soil and irrigated rice straw for the sequential sowing of soybean in tropical floodplains1

2019 ◽  
Vol 49 ◽  
Author(s):  
José Geraldo da Silva ◽  
Adriano Stephan Nascente ◽  
Pedro Marques da Silveira
Keyword(s):  
Surface Roughness ◽  
Fuel Consumption ◽  
Rice Straw ◽  
Soil Surface ◽  
Plant Residues ◽  
Operating Speed ◽  
Randomized Design ◽  
Consequent Reduction ◽  
Soil Surface Roughness ◽  
Energetic Performance

ABSTRACT The presence of straw hinders the sowing of soybean cultivated in succession to rice, in areas irrigated by flooding. This study aimed to evaluate the combination of different configurations of a rice harvester and subsequent activities in the operational and energetic demand of rice straw management and in the soil surface roughness, in order to cultivate soybean in succession. Three independent experiments were conducted in a completely randomized design, as well as evaluated the fuel consumption, effective operating speed, working capacity and final surface roughness of the ground. The energy costs of harvesting rice do not increase when the automated harvester operates with a spreader to distribute the straw on the ground and to avoid the formation of furrows. The presence of rice plant residues in the field increases the skidding of the tractor when pulling the knife-roller, with a consequent reduction of the operating speed, but this does not affect the operational capacity and the fuel consumption. The increase in the number of light harrowings, from one to two operations, in areas worked with knife-roller or intermediate harrow, requires more time and fuel in the management of the soil and rice straw, but leaves the ground with less surface roughness. The management system with knife-roller operation and two light harrowings is the most appropriate method to prepare the soil for soybean cultivation after rice, because it provides the best combination of technical and energetic performance.

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