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An application of a spatial simulated annealing sampling optimization algorithm to support digital soil mapping

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Tartalom: https://ojs3.mtak.hu/index.php/hungeobull/article/view/2868
Archívum: Hungarian Geographical Bulletin
Gyűjtemény: Articles
Cím:
An application of a spatial simulated annealing sampling optimization algorithm to support digital soil mapping
Létrehozó:
Szatmári, Gábor
Barta, Károly
Pásztor, László
Kiadó:
Research Centre for Astronomy and Earth Sciences
Dátum:
2015-04-20
Téma:
spatial simulated annealing
sampling optimization
geostatistics
regression kriging prediction error variance
digital soil mapping
Tartalmi leírás:
Spatial simulated annealing (SSA) was applied to optimize the sampling configuration for soil organic matter mapping through various sampling scenarios in a Hungarian study site. Prediction-error variance of regression kriging was applied as quality measure in the optimization procedures. Requisites of SSA come from a legacy soil dataset and from spatial auxiliary information. Four scenarios were set to represent the major capabilities of SSA. Scenario 1 and 2 represented completely new sampling designs to optimize with predefined constraints. In scenario 1, number of new observations was the constraint, whilst in scenario 2, it was the value of the quality measure. In both scenarios, areas inaccessible for sampling (roads, farms etc.) were also taken into account. Scenario 3 and 4 represented complementary sampling configurations to optimize taking the previously collected samples into consideration. In scenario 3, the constraint was the number of new observations, whilst in scenario 4, it was the value of the quality measure. In both cases, two types of previously collected sampling design were simulated, a regular and a clustered configuration. The resulted designs were evaluated by Kolmogorov-Smirnov test, nearest neighbour distribution function and empty space function. In cases of scenario 1 and 3, the results showed that, all of the optimized sampling configurations cover properly both geographic and feature space, respectively. In cases of scenario 2 and 4, the resulted calibration curves can be used to determine the sample size for a given quality measure value. Furthermore, we could determine the minimal sample size for a given scenario, which has to be collected to represent properly both geographic and feature space. In conclusion, SSA is a valuable tool to optimize the sampling design considering a lot of constraints.
Nyelv:
angol
Típus:
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Formátum:
application/pdf
Azonosító:
10.15201/hungeobull.64.1.4
Forrás:
Hungarian Geographical Bulletin; Vol. 64 No. 1 (2015); 35-48
Hungarian Geographical Bulletin; Évf. 64 szám 1 (2015); 35-48
2064-5147
2064-5031
Kapcsolat:
Létrehozó:
Copyright (c) 2015 Gábor Szatmári, Károly Barta, László Pásztor