Soil surface roughness measurement: A new fully automatic photogrammetric approach applied to agricultural bare fields

2017 ◽  
Vol 134 ◽  
pp. 63-78 ◽  
Author(s):  
J.M. Gilliot ◽  
E. Vaudour ◽  
J. Michelin
Keyword(s):  
Surface Roughness ◽  
Soil Surface ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Soil Surface Roughness ◽  
Fully Automatic

Ground-based imaging system for soil surface roughness measurement

2004 ◽  
Author(s):  
Zhiling Long ◽  
Ping-Rey Jang ◽  
Jiann-Cherng Su ◽  
Yun Sun ◽  
J. A. Thomasson ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Imaging System ◽  
Soil Surface ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Soil Surface Roughness

scholarly journals Automatic snow surface roughness estimation using digital photos

2012 ◽  
Vol 58 (211) ◽  
pp. 993-1007 ◽  
Author(s):  
Terhikki Manninen ◽  
Kati Anttila ◽  
Tuure Karjalainen ◽  
Panu Lahtinen
Keyword(s):  
Surface Roughness ◽  
Control Point ◽  
Digital Camera ◽  
Snow Surface ◽  
Analysis Software ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Manual Intervention ◽  
Fully Automatic ◽  
Surface Roughness Estimation

AbstractA surface roughness measurement system for snow is presented. It is based on a background board with scales on the edges and a digital camera. Analysis software is developed for automatic processing of images to produce calibrated profiles. The image analysis and calibration was fully automatic in >99% of the studied cases. In the others, the intensity adjustment or board detection needed manual intervention. Profile detection, control point picking and calibration always worked autonomously. The accuracy of the system depends on the photographing configuration, and is typically of the order of 0.1 mm vertically and 0.04 mm horizontally. The method tolerates relatively well cases of snowfall, traces of wiping the black background dry, uneven shading, reflected sunlight, reflected flash light, litter on the snow surface and a tilted plate. The repeatability of the system is at least 1%.

Surface Roughness Measurement of Thermal Barrier Coating using Terahertz Waves

2017 ◽  
Vol 137 (3) ◽  
pp. 147-152 ◽  
Author(s):  
Tetsuo Fukuchi ◽  
Norikazu Fuse ◽  
Mitsutoshi Okada ◽  
Tomoharu Fujii ◽  
Maya Mizuno ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Thermal Barrier Coating ◽  
Thermal Barrier ◽  
Roughness Measurement ◽  
Terahertz Waves ◽  
Surface Roughness Measurement ◽  
Barrier Coating

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.

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