Combination of electromagnetic induction and gamma spectrometry using K‐means clustering: A study for evaluation of site partitioning

Altdorff, Daniel; Dietrich, Peter

doi:10.1002/jpln.201100262

Cited by 15 publications

(5 citation statements)

References 58 publications

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“…Therefore, we use selected variables from both methods for a joint analysis based on a cluster algorithm in order to identify zones of similar soil conditions. The so-called zonal approach (based on e.g., k means or fuzzy c means clustering) has become a common tool in geophysical data analysis for delineating subsurface structures and estimating petrophysical parameters (e.g., Tronicke et al, 2004;Dietrich and Tronicke, 2009;Paasche et al, 2010;Altdorff and Dietrich, 2012). We chose k means clustering because of its simple performance and robust results, using the software Systat.…”

Section: Hill-slope Characterisation and Partitioningmentioning

confidence: 99%

Assessment of shallow subsurface characterisation with non-invasive geophysical methods at the intermediate hill-slope scale

Popp

Altdorff

Dietrich

2013

Hydrol. Earth Syst. Sci.

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Abstract. Hill-slopes of several hectares in size represent a difficult scale for subsurface characterisation, as these landscape units are well beyond the scope of traditional pointscale techniques. By means of electromagnetic induction (EMI) and gamma-ray spectroscopy, spatially distributed soil proxy data were collected from a heterogeneous hill-slope site. Results of EMI mapping using the EM38DD showed that soil electrical conductivity (ECa) is highly variable at both temporal and spatial scales. Calibration of the integral ECa signal to a specific target like soil moisture is hampered by the ambiguous response of EMI to the clay-rich hill-slope underground. Gamma-ray results were obtained during a single survey, along with EMI measurements and selected soil sampling. In contrast to ECa, a noticeable correlation between Total Count and K emission data and soil-water content seemed to be present. Relevant proxy variables from both methods were used for k means clustering in order to distinguish between hill-slope areas with different soil conditions. As a result, we obtained a suitable partition of hillslope that was comparable with a previously obtained zonation model based on ecological factors.

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Section: Hill-slope Characterisation and Partitioningmentioning

confidence: 99%

Assessment of shallow subsurface characterisation with non-invasive geophysical methods at the intermediate hill-slope scale

Popp

Altdorff

Dietrich

2013

Hydrol. Earth Syst. Sci.

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“…More recently, gamma‐ray ( γ ‐ray) data (e.g. potassium [K] – % and total count [TC] – cps) have been used in combination with EC a to identify zones (Altdorff & Dietrich, ). In this regard, Huang et al .…”

Section: Introductionmentioning

confidence: 99%

Identifying soil management zones in a sugarcane field using proximal sensed electromagnetic induction and gamma‐ray spectrometry data

Dennerley

Huang

Nielson

et al. 2018

Soil Use and Management

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Spatial variation in soils is required to supply ameliorants and fertilisers in the Australian sugarcane industry. However, traditional approaches are cost‐prohibitive. We investigated how a digital soil mapping (DSM) approach could be used to identify management zones. First, ancillary data including electromagnetic induction and gamma‐ray spectrometry data were collected. Using fuzzy k‐means (FKM) clustering, two to six management zones were identified. A similar approach was used to cluster percentage yield variations (2014, 2015 and 2016). Using restricted maximum likelihood analysis of topsoil (0–0.3 m) and subsoil (0.6–0.9 m) physical (e.g. clay) and chemical (e.g. exchangeable sodium percentage [ESP], and exchangeable calcium and magnesium) properties, three zones were found to minimise the mean squared prediction error (σnormalp,normalC2). By comparison, the three zones obtained using the percentage yield variation only minimised σnormalp,normalC2 for subsoil ESP, which suggested it had some influence on sugarcane yield and productivity. Different rates of gypsum were required to manage the moderately sodic topsoil ESP for each zone. This was similarly the case with lime to overcome deficiencies in exchangeable calcium and magnesium. The results were consistent with yield variance, suggesting the smaller yield in some zones was due to topsoil sodicity and strongly sodic subsoil with a greater clay content. We concluded that the DSM approach was successful in identifying soil management zones and can be used to improve soil structural stability and fertility. The zones also had ramifications for strip trials to determine yield increases by comparing variable rate applications of gypsum and lime.

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“…The popularity of ancillary data in soil mapping is evident, mostly in EMI (e.g. Islam et al ., ) but increasingly in γ ‐ ray data (Wilford & Minty, ), and where these data are being used to identify soil management zones (Altdorff & Dietrich, ; Van Meirvenne et al ., ). Recently, Huang et al .…”

Section: Introductionmentioning

confidence: 99%

“…The popularity of ancillary data in soil mapping is evident, mostly in EMI (e.g. Islam et al, 2011) but increasingly in c-ray data (Wilford & Minty, 2006), and where these data are being used to identify soil management zones (Altdorff & Dietrich, 2012;Van Meirvenne et al, 2013). Recently, Huang et al (2014) showed how fuzzy kmeans (FKM) clustering of EMI and c-ray could be used to identify soil types that were related to geology for two fields in Nottinghamshire, UK.…”

Section: Introductionmentioning

confidence: 99%

Scope to map soil management units at the district level from remotely sensed γ‐ray spectrometry and proximal sensed EM induction data

Jing

Huang

2017

Soil Use and Management

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The traditional method of soil mapping involves classifying soil into pre‐existing classes using morphological observations and then air‐photograph interpretation to extrapolate the information. To accelerate the process, less costly ancillary data can be used to assist mapping. However, digital soil mapping (DSM) is still affected by the classifications used to identify soil types. One reason is because the morphological characteristics are not mutually exclusive, which causes misclassification. In this study, we used a DSM approach, where ancillary data were surrogate for morphological data, with soil types identified by numerical clustering of remotely and proximally sensed data collected across a farming district near Gunnedah, Australia. Remotely sensed data were obtained from an air‐borne gamma‐ray (γ‐ray) spectrometer survey, including potassium (K), thorium (Th), uranium (U) and total counts. Proximally sensed data were measured using EM38 (i.e. EM38h and EM38v). Using fuzzy k‐means and a linear mixed model with measured physical (e.g. clay) and chemical (e.g. CEC) properties from the topsoil (0–0.30 m) and subsoil (0.9–1.2 m), we found that k = 5 was also optimal given that mean‐squared prediction error (i.e. σnormalp,normalC2) was minimised. The approach highlighted subtle differences in physical and chemical properties in productive areas. The DSM was unsuccessful in identifying small units; however, inclusion of elevation data might overcome this limitation. This research has implications for providing fast, accurate and meaningful DSM at a district scale, where traditional methods are too expensive.

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Combination of electromagnetic induction and gamma spectrometry using K‐means clustering: A study for evaluation of site partitioning

Cited by 15 publications

References 58 publications

Assessment of shallow subsurface characterisation with non-invasive geophysical methods at the intermediate hill-slope scale

Assessment of shallow subsurface characterisation with non-invasive geophysical methods at the intermediate hill-slope scale

Identifying soil management zones in a sugarcane field using proximal sensed electromagnetic induction and gamma‐ray spectrometry data

Scope to map soil management units at the district level from remotely sensed γ‐ray spectrometry and proximal sensed EM induction data

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