Impact of root water content on root biomass estimation using ground penetrating radar: evidence from forward simulations and field controlled experiments

Guo, Li; Lin, Henry; Fan, Botao; Cui, Xihong; Chen, Jin

doi:10.1007/s11104-013-1710-4

Cited by 60 publications

(34 citation statements)

References 22 publications

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“…Previous studies reported that when root diameter is less than 5 mm, the root could not be detected using GPR with frequencies ranging from 400 MHz to 1500 MHz [13,14]. The contrast of water content between roots and soil affects the contrast of permittivity, which also determines the amplitude of reflection signals [12].…”

Section: Root Property Factorsmentioning

confidence: 99%

“…Several studies have pointed out that in practice, field soil conditions have dramatic impact on root detection using GPR [10,13]. Dry sand soil with low water content is an ideal medium for GPR detection [10].…”

Section: Soil Background Factorsmentioning

confidence: 99%

“…Since 1999, GPR has provided an important method for non-invasive study of plant root system, including coarse root mapping [4][5][6][7], root system architecture reconstruction [8,9], and root diameter or biomass estimation [10][11][12][13][14][15]. Unlike optical images, raw GPR radargrams provide insufficient geometrical information of buried targets and are always disturbed by soil clutter noise.…”

Section: Introductionmentioning

confidence: 99%

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Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Cui

Guo

et al. 2016

Remote Sensing

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Abstract:As a nondestructive geophysical tool, Ground penetrating radar (GPR) has been applied in tree root study in recent years. With increasing amounts of GPR data collected for roots, it is imperative to develop an efficient automatic recognition of roots in GPR images. However, few works have been completed on this topic because of the complexity in root recognition problem. Based on GPR datasets from both controlled and in situ experiments, the randomized Hough transform (RHT) algorithm was evaluated in root object recognition for different center frequencies (400 MHz, 900 MHz, and 2000 MHz) in this paper. Reasonable accuracy was obtained (both a high recognition rate and a low false alarm rate) in these datasets, which shows it is feasible to apply the RHT algorithm for root recognition. Furthermore, we evaluated the influence of root and soil factors on the recognition. We found that the performance of RHT algorithm is mainly affected by root interval length, root orientation, and clutter noise of soil. The recognition results by RHT could be applied for large scale root system distribution study in belowground ecology. Further studies should be conducted to reduce clutter noise and improve the recognition of the complex root reflections.

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Section: Root Property Factorsmentioning

confidence: 99%

Section: Soil Background Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Cui

Guo

et al. 2016

Remote Sensing

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“…Groundpenetrating radar (GPR), a non-destructive geophysical tool for subsurface detection, displays many advantages over the traditional invasive methods for field root investigation, such as its non-destructive nature, fast data collection rate, and repeatability of measurements (Hruška et al 1999;Butnor et al 2011). So far, GPR has provided an important alternative for coarse root study in the field (Nadezhdina and Čermák 2003;Guo et al 2013a), and its applications include root system mapping (e.g., Hruška et al 1999;Čermák et al 2000;Bassuk et al 2011;Isaac and Anglaaere 2013), threedimensional root system architecture reconstruction (e.g., Stokes et al 2002;Wu et al 2014a;Zhu et al 2014), and root diameter and biomass estimation (e.g., Butnor et al 2001;Stover et al 2007;Hirano et al 2009;Cui et al 2011Cui et al , 2013Guo et al 2013b).…”

Section: Introductionmentioning

confidence: 99%

“…The great advantage of time interval index is its independence of root depth, root water content, and the relative position between a root and a GPR survey line (Barton and Montagu 2004;Cui et al 2011;Guo et al 2013b;Tanikawa et al 2013). However, because the wavelets used to define the time intervals are identified arbitrarily, extraction of time intervals from a reflection waveform is sometimes questionable (Guo et al 2013a).…”

Section: Introductionmentioning

confidence: 99%

Calibrating the impact of root orientation on root quantification using ground-penetrating radar

et al. 2015

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Background and aims Ground-penetrating radar (GPR) has provided a non-invasive means for field root investigation. However, the horizontal cross angle (x) of root orientation intersecting a survey line considerably impacts the amplitude area (A) reflected from a root and impairs the accuracy of GPR-based root quantification. Prediction of A(90°) (the value of A scanning at x=90°) from multiple A(x) measurements could correct such impact. Previous method of A(90°) prediction focused on target roots at field point scale. The aim of this study is to develop a method to predict A(90°) at field plot scale. Methods A(90°) was predicted by a pair of A(x) measured at two arbitrary scanning lines together with an estimated soil background amplitude area. Three independent datasets were employed to test the proposed method. The field experiment included radar data collected for six roots of Caragana microphylla in a sandy-clay soil at four cross angles (30°, 45°, 60°, and 90°). The sand box experiment included radar data for 12 dowels at 13 cross angles (0°to 180°, in 15°steps). The simulation experiment included A(x) of 46 simulated roots at 13 cross angles (0°to 180°, in 15°s teps).Results For all experiments, A(90°) was accurately estimated. Root orientation could also be determined.After correcting the impact of cross angle, the accuracy of root diameter estimation improved. Correlation coefficient between actual and estimated root diameters increased from 0.77 to 0.81, with RMSE declining from 9.53 to 7.05 mm.Conclusions A method of correcting the influence of root orientation on root GPR signal at the field plot scale has been established. This method enhances root quantification using GPR.

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Quantitative, nondestructive estimates of coarse root biomass in a temperate pine forest using 3‐D ground‐penetrating radar (GPR)

2017

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Coarse root biomass was estimated in a temperate pine forest using high‐resolution (1 GHz) 3‐D ground‐penetrating radar (GPR). GPR survey grids were acquired across a 400 m2 area with varying line spacing (12.5 and 25 cm). Root volume and biomass were estimated directly from the 3‐D radar volume by using isometric surfaces calculated with the marching cubes algorithm. Empirical relations between GPR reflection amplitude and root diameter were determined for 14 root segments (0.1–10 cm diameter) reburied in a 6 m2 experimental test plot and surveyed at 5–25 cm line spacing under dry and wet soil conditions. Reburied roots >1.4 cm diameter were detectable as continuous root structures with 5 cm line separation. Reflection amplitudes were strongly controlled by soil moisture and decreased by ~40% with a twofold increase in soil moisture. GPR line intervals of 12.5 and 25 cm produced discontinuous mapping of roots, and GPR coarse root biomass estimates (0.92 kgC m−2) were lower than those obtained previously with a site‐specific allometric equation due to nondetection of vertical roots and roots <1.5 cm diameter. The results show that coarse root volume and biomass can be estimated directly from interpolated 3‐D GPR volumes by using a marching cubes approach, but mapping of roots as continuous structures requires high inline sampling and line density (<5 cm). The results demonstrate that 3‐D GPR is viable approach for estimating belowground carbon and for mapping tree root architecture. This methodology can be applied more broadly in other disciplines (e.g., archaeology and civil engineering) for imaging buried structures.

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Impact of root water content on root biomass estimation using ground penetrating radar: evidence from forward simulations and field controlled experiments

Cited by 60 publications

References 22 publications

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Calibrating the impact of root orientation on root quantification using ground-penetrating radar

Quantitative, nondestructive estimates of coarse root biomass in a temperate pine forest using 3‐D ground‐penetrating radar (GPR)

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