Understanding Soil and Plant Interaction by Combining Ground‐Based Quantitative Electromagnetic Induction and Airborne Hyperspectral Data

Hebel, Christian von; Matveeva, Maria; Verweij, Elizabeth; Rademske, Patrick; Kaufmann, Manuela Sarah; Brogi, Cosimo; Vereecken, Harry; Rascher, Uwe; Kruk, Jan van der

doi:10.1029/2018gl078658

Cited by 32 publications

(37 citation statements)

References 51 publications

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“…In mountainous regions, where soil salinity and soil temperature do not vary significantly at the local scale, soil electrical conductivity (soil EC) is often an indicator of the spatial variability of soil thickness, clay content, water content, or some combination of these characteristics (Binley et al, 2015;Miller et al, 2008;Rubin & Hubbard, 2005). Because of the strong influence of these characteristics on plant physiology, studies have found significant correlations between soil EC patterns and leaf area index (LAI; Rudolph et al, 2015), plant photosynthetic activity and growth (von Hebel et al, 2018), and vegetation vigor (Dafflon et al, 2017). The spatially extensive nature of geophysical data allows exploration of how soil properties vary spatially with characteristics such as plant species and dynamics.…”

Section: Introductionmentioning

confidence: 99%

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High‐Resolution Remote Sensing and Surface Geophysical Data

et al. 2019

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This study aims to investigate the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain‐hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain‐hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain‐hillslope transect at 0.50‐m resolution and extracted LiDAR‐derived metrics to model the rapid change in microtopography. We then investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long‐term monitoring to capture community‐specific physiological responses to perturbations, offering the possibility of bridging local plot‐scale observations with large landscape monitoring.

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Section: Introductionmentioning

confidence: 99%

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High‐Resolution Remote Sensing and Surface Geophysical Data

et al. 2019

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“…Geophysical soil sensing to assess soil spatial variability has been documented over the last four decades [17,27,37,42,65,68]. Determining soil variability as affected by human-induced land activities using electromagnetic induction (EMI)-based apparent electrical conductivity (ECa), can effectively discriminate the magnitude of soil variability.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic-induction and spatial analysis for assessing variability in soil properties as a function of land use in tropical savanna ecosystems

Atwell

Wuddivira

2019

SN Appl. Sci.

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Identifying spatial patterns in the variability of key soil properties to delineate the extent of land degradation could ensure efficient management of natural resources in terrestrial ecosystems. However, little is still known in tropical savannas that are subjected to indiscriminate land use. We evaluated the soil variability at the plot-scale in a terrestrial tropical ecosystem subjected to varying land use/land cover management to assess the impact of uncontrolled land uses on the soil natural capital. The non-invasive, time/cost efficient electromagnetic induction (EMI) technique was assessed for its potential, to determine the effect of land uses on soil spatial variability in a changing land use gradient from pristine land use/land cover conditions. The investigation was carried out in a natural tropical ecosystem in Aripo, Trinidad with soils of predominantly ultisols order and influenced by anthropogenic disturbances. EMI-based apparent electrical conductivity (ECa) measurements were obtained at two depth ranges (shallow = 0-0.5 m and deep = 0-1.5 m). Soil properties showed that the residential anthropogenic land use had a higher mean apparent electrical conductivity shallow (ECa s ) value (ECa s = 305.9 mS/m) than all other land uses. Higher ECa s values in the residential site suggest that human influences can increase the magnitude of electrical conductivity, which can alter the biogeochemical cycles of the soil affecting services provided by the ecosystem. Also anthropogenic land use/land covers exhibited lower coefficient of variation for soil texture (silt and clay) than natural land uses, indicating lower sensitivity of soil texture to land use due to the mixing of soils, which encourages uniformity in anthropogenic sites. Soil texture dominated the ECa s signal in the natural land use/land covers with the relationship between ECa s and silt in the Forest (r = 0.486) and Grass (r = − 0.495) significant at P < 0.05. Soil texture showed greater sensitivity to land use in natural sites than in anthropogenic sites. The dominance of soil texture in the natural sites indicates that in tropical soils that are predominantly light textured (clay content < 21%), silt content controls the EMI signal, which can become of low influence following disturbance. The magnitude of electrical conductivity can increase due to human influences. This can alter the biogeochemical cycles of the soil, affecting services provided by the ecosystem.

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“…Such systems provide apparent electrical conductivity (σ a ) values that can be used to infer the soil water content distribution within a catchment [1,2], and/or upscale other soil properties [3,4] up to the sub-continental scale [5]. In precision agriculture, σ a patterns help to optimize fertilizer and irrigation application [6,7], to investigate long-term treatment effects [8], to investigate plant and soil interactions [9,10,11,12], and to test the ability of plants to grow in saline soil conditions [13]. The spatial distribution of σ a can also be used to identify buried cables [14], flood embankments [15], and features within morphological, geological, and geoarchaelogical units [16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction Data

Hebel

Kruk

Huisman

et al. 2019

Sensors

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Multi-coil electromagnetic induction (EMI) systems induce magnetic fields below and above the subsurface. The resulting magnetic field is measured at multiple coils increasingly separated from the transmitter in a rigid boom. This field relates to the subsurface apparent electrical conductivity (σa), and σa represents an average value for the depth range investigated with a specific coil separation and orientation. Multi-coil EMI data can be inverted to obtain layered bulk electrical conductivity models. However, above-ground stationary influences alter the signal and the inversion results can be unreliable. This study proposes an improved data processing chain, including EMI data calibration, conversion, and inversion. For the calibration of σa, three direct current resistivity techniques are compared: Electrical resistivity tomography with Dipole-Dipole and Schlumberger electrode arrays and vertical electrical soundings. All three methods obtained robust calibration results. The Dipole-Dipole-based calibration proved stable upon testing on different soil types. To further improve accuracy, we propose a non-linear exact EMI conversion to convert the magnetic field to σa. The complete processing workflow provides accurate and quantitative EMI data and the inversions reliable estimates of the intrinsic electrical conductivities. This improves the ability to combine EMI with, e.g., remote sensing, and the use of EMI for monitoring purposes.

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Understanding Soil and Plant Interaction by Combining Ground‐Based Quantitative Electromagnetic Induction and Airborne Hyperspectral Data

Cited by 32 publications

References 51 publications

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High‐Resolution Remote Sensing and Surface Geophysical Data

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High‐Resolution Remote Sensing and Surface Geophysical Data

Electromagnetic-induction and spatial analysis for assessing variability in soil properties as a function of land use in tropical savanna ecosystems

Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction Data

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