2013
DOI: 10.1002/esp.3476
|View full text |Cite
|
Sign up to set email alerts
|

Mapping gamma radiation and its uncertainty from weathering products in a Tasmanian landscape with a proximal sensor and random forest kriging

Abstract: The radionuclides of potassium (40 K), uranium (238U) and thorium (232Th) emit from the land surface gamma radiation that is characteristic of the underlying rocks and the distribution of their weathering products in the landscape. We measured the radiation along widely separated transects using a mobile proximal sensor over a 10 000‐ha region of Tasmania. We supplemented the transect data with information from soil and geological maps and dense data from LandSat and SPOT imagery, a digital elevation model and… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
28
0

Year Published

2015
2015
2024
2024

Publication Types

Select...
7
1

Relationship

1
7

Authors

Journals

citations
Cited by 45 publications
(28 citation statements)
references
References 41 publications
0
28
0
Order By: Relevance
“…The algorithm as summarized by Viscarra-Rossel et al (2013) functions by drawing Tv, v=1, 2,…, V new bootstrap samples from the data that become 'trees in the forest'. This consists of both 'in-the-bag' (training) and 'out-of-bag' (validation) samples.…”
Section: Random Forestsmentioning
confidence: 99%
“…The algorithm as summarized by Viscarra-Rossel et al (2013) functions by drawing Tv, v=1, 2,…, V new bootstrap samples from the data that become 'trees in the forest'. This consists of both 'in-the-bag' (training) and 'out-of-bag' (validation) samples.…”
Section: Random Forestsmentioning
confidence: 99%
“…Random forest kriging (RFK) was the extension of RF, which integrated RF prediction values and estimation of the residuals by ordinary kriging (OK) using Equation (3) [101]. It considered spatial parametric non-stationarity with the effects of environmental variables derived from the benefits of RF [102,103]. RFK also added the spatial dependence of the residuals interpolated through OK to the estimated trend, as part of the spatial autocorrelation.…”
Section: Spatial Modeling Of Soil Fertility By Random Forest Krigingmentioning
confidence: 99%
“…It considered spatial parametric non-stationarity with the effects of environmental variables derived from the benefits of Remote Sens. 2019, 11, 3004 9 of 25 RF [102,103]. RFK also added the spatial dependence of the residuals interpolated through OK to the estimated trend, as part of the spatial autocorrelation.…”
Section: Spatial Modeling Of Soil Fertility By Random Forest Krigingmentioning
confidence: 99%
“…After the re-design to predict quantitative outputs and solve regression problems, this algorithm came to be the support vector machine for regression (SVR) and acquired wide successes in stand volume modeling [19,20]. Hybrid approaches involve either the statistical regression or machine learning model between the target variable and remote sensing predictors, interpolating residuals of predictions by kriging, and combining them [21][22][23]. Those two-step approaches both consider the spatial heterogeneity conveyed by remote sensing predictors and autocorrelation of neighboring observed data [24,25].…”
Section: Introductionmentioning
confidence: 99%