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references.bib
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@Book{xie2015,
title = {Dynamic Documents with {R} and knitr},
author = {Yihui Xie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {2nd},
note = {ISBN 978-1498716963},
url = {http://yihui.org/knitr/},
}
@article{Malone,
abstract = {The conditioned Latin hypercube sampling (cLHS) algorithm is popularly used for planning field sampling surveys in order to understand the spatial behavior of natural phenomena such as soils. This technical note collates, summarizes, and extends existing solutions to problems that field scientists face when using cLHS. These problems include optimizing the sample size, re-locating sites when an original site is deemed inaccessible, and how to account for existing sample data, so that under-sampled areas can be prioritized for sampling. These solutions, which we also share as individual R scripts, will facilitate much wider application of what has been a very useful sampling algorithm for scientific investigation of soil spatial variation.},
address = {CSIRO, Agriculture and Food, Canberra, ACT, Australia.; The Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW, Australia.; The Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW, Australia.; Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USA.},
auid = {ORCID: 0000-0002-0473-8518},
author = {Malone, Brendan P and Minansy, Budiman and Brungard, Colby},
cois = {Budiman Minansy is an Academic Editor for PeerJ.},
crdt = {2019/03/05 06:00},
date = {2019},
date-added = {2023-11-28 08:37:45 +0100},
date-modified = {2023-11-28 08:37:45 +0100},
dep = {20190225},
doi = {10.7717/peerj.6451},
edat = {2019/03/05 06:00},
issn = {2167-8359 (Print); 2167-8359 (Electronic); 2167-8359 (Linking)},
jid = {101603425},
journal = {PeerJ},
jt = {PeerJ},
keywords = {Conditioned Latin Hypercube; Digital soil mapping; Fieldwork; Legacy soil data; Optimization; Pedometrics; Sample optimization; Sampling; Soil sampling; Soil survey},
language = {eng},
lid = {10.7717/peerj.6451 {$[$}doi{$]$}; e6451},
lr = {20201001},
mhda = {2019/03/05 06:01},
oto = {NOTNLM},
own = {NLM},
pages = {e6451},
phst = {2018/10/02 00:00 {$[$}received{$]$}; 2019/01/15 00:00 {$[$}accepted{$]$}; 2019/03/05 06:00 {$[$}entrez{$]$}; 2019/03/05 06:00 {$[$}pubmed{$]$}; 2019/03/05 06:01 {$[$}medline{$]$}},
pii = {6451},
pl = {United States},
pmc = {PMC6394343},
pmid = {30828486},
pst = {epublish},
pt = {Journal Article},
status = {PubMed-not-MEDLINE},
title = {Some methods to improve the utility of conditioned Latin hypercube sampling.},
volume = {7},
year = {2019},
bdsk-url-1 = {https://doi.org/10.7717/peerj.6451}
}
@article{minasny2006,
author = {Minasny, Budiman and McBratney, Alex},
year = {2006},
month = {11},
pages = {1378-1388},
title = {A Conditioned Latin Hypercube Method for Sampling in the Presence of Ancillary Information},
volume = {32},
journal = {Computers & Geosciences},
doi = {10.1016/j.cageo.2005.12.009}
}
@Manual{Roudier2011,
title = {clhs: a R package for conditioned Latin hypercube sampling},
author = {Roudier, Pierre and Brugnard, Colby and Beaudette, Dylan and Louis, Benjamin and Daust, Kiri and Clifford, David},
year = {2011},
note = {R package version 0.9.0},
url = {https://cran.r-project.org/web/packages/clhs/},
}
@article{SENA2021e00354,
title = {Soil sampling strategy in areas of difficult acess using the cLHS method},
journal = {Geoderma Regional},
volume = {24},
pages = {e00354},
year = {2021},
issn = {2352-0094},
doi = {10.1016/j.geodrs.2020.e00354},
url = {https://www.sciencedirect.com/science/article/pii/S2352009420301036},
author = {Nathalie Cruz Sena and Gustavo Vieira Veloso and Alisson Oliveira Lopes and Marcio Rocha Francelino and Elpídio Inácio Fernandes-Filho and Eduardo Osório Senra and Luiz Aníbal da {Silva Filho} and Viviane Flaviana Condé and David Lukas de Arruda Silva and Raphael Wakin de Araújo},
keywords = {Digital soil mapping, Sampling design, Field accessibility, cLHS},
abstract = {The cLHS is considered as a robust sampling strategy for the selection of representative samples of the landscape, which uses environmental variables and their multivariate distributions. In this study, we propose a method based on cLHS for selecting soil sampling points in areas of difficult access, with the objective of minimizing inaccessibility problems in field campaigns, considering obtaining alternative samples with a lower cost and time demand. The study aims to analyze, above all, the practical operational performance of the method based on the potential and restrictions for application in digital soil mapping. For this, five predictive models (GBM, RF, SVM, kNN and C5.0) were initially used to select the most important variables to be inserted in the cLHS. The k-means method was applied to select the alternative points closest to the original points of the cLHS. Restrictions such as the euclidean distance from roads and the exclusion of urban and mining areas were incorporated. Approximately 30% of the original sample points of the cLHS could not be accessed in the field, the main operational restriction was due to the lack of access/routes to the selected points. However, the use of alternative sampling points allowed greater flexibility and accessibility in the field, where it was possible to collect 17% of the points and reduce the demand for time and cost. With this, the sampling strategy adopted made it possible to obtain an ideal minimum size of sampling points to be used in predictive models in digital soil mapping studies.}
}
@Manual{sgsR,
title = {sgsR: a structurally guided sampling toolbox for
LiDAR-based forest inventories.},
author = {Tristan R.H. Goodbody and Nicholas C. Coops and Martin
Queinnec and Joanne C. White and Piotr Tompalski and Andrew T.
Hudak and David Auty and Ruben Valbuena and Antoine LeBoeuf and
Ian Sinclair and Grant McCartney and Jean-Francois Prieur and
Murray E. Woods},
journal = {Forestry: An International Journal of Forest Research},
year = {2023},
doi = {10.1093/forestry/cpac055},
}
@incollection{BRUS2006183,
title = {Chapter 14 Designing Spatial Coverage Samples Using the k-means Clustering Algorithm},
editor = {P. Lagacherie and A.B. McBratney and M. Voltz},
series = {Developments in Soil Science},
publisher = {Elsevier},
volume = {31},
pages = {183-192},
year = {2006},
booktitle = {Digital Soil Mapping},
issn = {0166-2481},
doi = {10.1016/S0166-2481(06)31014-8},
url = {https://www.sciencedirect.com/science/article/pii/S0166248106310148},
author = {D.J. Brus and J.J. {de Gruijter} and J.W. {van Groenigen}},
abstract = {In situations where we do not want to use available environmental data at the sampling stage of a soil survey because we are uncertain about the correlation with the soil attribute, the best thing we can do is to disperse the points in geographical space. This chapter describes a simple method for selecting such spatial coverage samples. The study area is partitioned into geographically compact subregions from which one point is selected purposively. The partitioning is done by clustering the cells of a fine raster with the k-means clustering algorithm, using the x- and y-coordinates of the midpoints of the cells as classification variables. The centroids of the clusters are used as sample points. The method was tested in two case studies. In the first study, the locations of 23 points were optimised within a square area, and in the second study, 32 points were added to 6 prior points in an irregular shaped area. In both studies, the average kriging variance (AKV) and maximum kriging variance (MaxKV) for the spatial coverage samples were compared with the AKV and MaxKV for the geostatistical samples obtained by directly minimising these criteria with the spatial simulated annealing (SSA) algorithm. The AKV values for the spatial coverage samples and the geostatistical samples obtained with AKV as a minimisation criterion were comparable. If we want to minimise MaxKV, then the SSA procedure is preferable.}
}
@Book{Brus2022,
title = {Spatial Sampling with {R}},
author = {Brus, D.J.},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2022},
edition = {1st},
note = {ISBN 9781032193854},
doi = {10.1201/9781003258940},
url = {https://dickbrus.github.io/SpatialSamplingwithR/},
}
@article{CLIFFORD201462,
title = {Pragmatic soil survey design using flexible Latin hypercube sampling},
journal = {Computers & Geosciences},
volume = {67},
pages = {62-68},
year = {2014},
issn = {0098-3004},
doi = {10.1016/j.cageo.2014.03.005},
url = {https://www.sciencedirect.com/science/article/pii/S0098300414000557},
author = {David Clifford and James E. Payne and M.J. Pringle and Ross Searle and Nathan Butler},
keywords = {Soil erosion, Soil survey, Sampling, Digital soil mapping},
abstract = {We review and give a practical example of Latin hypercube sampling in soil science using an approach we call flexible Latin hypercube sampling. Recent studies of soil properties in large and remote regions have highlighted problems with the conventional Latin hypercube sampling approach. It is often impractical to travel far from tracks and roads to collect samples, and survey planning should recognise this fact. Another problem is how to handle target sites that, for whatever reason, are impractical to sample â should one just move on to the next target or choose something in the locality that is accessible? Working within a Latin hypercube that spans the covariate space, selecting an alternative site is hard to do optimally. We propose flexible Latin hypercube sampling as a means of avoiding these problems. Flexible Latin hypercube sampling involves simulated annealing for optimally selecting accessible sites from a region. The sampling protocol also produces an ordered list of alternative sites close to the primary target site, should the primary target site prove inaccessible. We highlight the use of this design through a broad-scale sampling exercise in the Burdekin catchment of north Queensland, Australia. We highlight the robustness of our design through a simulation study where up to 50% of target sites may be inaccessible.}
}
@Manual{sgsr,
title = {Structurally Guided Sampling},
author = {Tristan RH Goodbody and Nicholas C Coops and Martin Queinnec},
year = {2023},
note = {R package version 1.4.0},
url = {https://cran.r-project.org/package=sgsR},
}