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Alternative sampling and analysis me...

Brungard, Colby W.

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Alternative sampling and analysis methods for digital soil mapping in southwestern Utah.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Alternative sampling and analysis methods for digital soil mapping in southwestern Utah./
Author:	Brungard, Colby W.
Description:	283 p.
Notes:	Source: Masters Abstracts International, Volume: 48-02, page: 0867.
Contained By:	Masters Abstracts International48-02.
Subject:	Agriculture, Soil Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1470183
ISBN:	9781109475913

Alternative sampling and analysis methods for digital soil mapping in southwestern Utah.
Brungard, Colby W.

Alternative sampling and analysis methods for digital soil mapping in southwestern Utah. - 283 p.

Source: Masters Abstracts International, Volume: 48-02, page: 0867.

Thesis (M.S.)--Utah State University, 2009.

Digital soil mapping (DSM) relies on quantitative relationships between easily measured environmental covariates and field and laboratory data. We applied innovative sampling and inference techniques to predict the distribution of soil properties, soil attributes, taxonomic classes, and dominant vegetation across a 30,000-ha complex Great Basin landscape in southwestern Utah. This arid rangeland was characterized by rugged topography, diverse vegetation, and intricate geology. Environmental covariates calculated from digital elevation models (DEM) and spectral satellite data were used to represent factors controlling soil development and distribution. We investigated optimal sample size and sampled the environmental covariates using conditioned Latin Hypercube Sampling (cLHS). We demonstrated that cLHS, a type of stratified random sampling, closely approximated the full range of variability of environmental covariates in feature and geographic space with small sample sizes. Site and soil data were collected at 300 locations identified by cLHS. Random forests was used to generate spatial predictions and associated probabilities of site and soil characteristics. Balanced random forests and balanced and weighted random forests were investigated for their use in producing an overall soil map. Overall and class errors (referred to as out-of-bag [OOB] error) were within acceptable levels. Quantitative covariate importance was useful in determining what factors were important for soil distribution. Random forest spatial predictions were evaluated based on the conceptual framework developed during field sampling.

ISBN: 9781109475913Subjects--Topical Terms:

1017824
Agriculture, Soil Science.

Alternative sampling and analysis methods for digital soil mapping in southwestern Utah.
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020 $a 9781109475913
035 $a (UMI)AAI1470183
035 $a AAI1470183
040 $a UMI $c UMI
100 1 $a Brungard, Colby W. $3 1678678
245 1 0 $a Alternative sampling and analysis methods for digital soil mapping in southwestern Utah.
300 $a 283 p.
500 $a Source: Masters Abstracts International, Volume: 48-02, page: 0867.
500 $a Adviser: Janis L. Boettinger.
502 $a Thesis (M.S.)--Utah State University, 2009.
520 $a Digital soil mapping (DSM) relies on quantitative relationships between easily measured environmental covariates and field and laboratory data. We applied innovative sampling and inference techniques to predict the distribution of soil properties, soil attributes, taxonomic classes, and dominant vegetation across a 30,000-ha complex Great Basin landscape in southwestern Utah. This arid rangeland was characterized by rugged topography, diverse vegetation, and intricate geology. Environmental covariates calculated from digital elevation models (DEM) and spectral satellite data were used to represent factors controlling soil development and distribution. We investigated optimal sample size and sampled the environmental covariates using conditioned Latin Hypercube Sampling (cLHS). We demonstrated that cLHS, a type of stratified random sampling, closely approximated the full range of variability of environmental covariates in feature and geographic space with small sample sizes. Site and soil data were collected at 300 locations identified by cLHS. Random forests was used to generate spatial predictions and associated probabilities of site and soil characteristics. Balanced random forests and balanced and weighted random forests were investigated for their use in producing an overall soil map. Overall and class errors (referred to as out-of-bag [OOB] error) were within acceptable levels. Quantitative covariate importance was useful in determining what factors were important for soil distribution. Random forest spatial predictions were evaluated based on the conceptual framework developed during field sampling.
590 $a School code: 0241.
650 4 $a Agriculture, Soil Science. $3 1017824
650 4 $a Remote Sensing. $3 1018559
690 $a 0481
690 $a 0799
710 2 $a Utah State University. $b Agriculture. $3 1031180
773 0 $t Masters Abstracts International $g 48-02.
790 1 0 $a Boettinger, Janis L., $e advisor
790 1 0 $a White, Michael A. $e committee member
790 1 0 $a Tarboton, David G. $e committee member
790 $a 0241
791 $a M.S.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1470183