Predicting the Influence of Multi-Scale Spatial Autocorrelation on Soil-Landform Modeling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027073%3A_____%2F16%3AN0000044" target="_blank" >RIV/00027073:_____/16:N0000044 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.sciencesocieties.org/publications/sssaj/abstracts/80/2/409?access=0&view=pdf" target="_blank" >https://dl.sciencesocieties.org/publications/sssaj/abstracts/80/2/409?access=0&view=pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2136/sssaj2015.10.0370" target="_blank" >10.2136/sssaj2015.10.0370</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting the Influence of Multi-Scale Spatial Autocorrelation on Soil-Landform Modeling

Popis výsledku v původním jazyce

Although numerous soil–landform modeling investigations have documented the effects and importance of spatial autocorrelation (SAC), little is known about how to predict the magnitude of those effects from the degree of SAC in the model variables. In this study, we quantified the SAC inherent in soil and landform variables of four widely divergent pedogeomorphological systems around the world to examine general relationships between SAC and spatial regression model results. Spatial regressions were performed by incorporating spatial filters, extracted by spatial eigenvector mapping, into non-spatial models as additional predictor variables. Results indicated that incorporation of spatial filters improved the performance of the non-spatial regressions—increases in R2 and decreases in both Akaike Information Criterion (AIC) and residual SAC were observed. More remarkable was that the degree of improvement was strongly and linearly related (i.e., proportional) to the level of SAC inherently possessed by each soil variable. Our findings show that spatial modeling outcomes are sensitive to the degree of SAC possessed by a soil property when treated as a response variable. Thus, the level of SAC present in a soil variable can serve as a direct indicator for how much improvement a non-spatial model will undergo if that SAC is appropriately taken into account.

Název v anglickém jazyce

Predicting the Influence of Multi-Scale Spatial Autocorrelation on Soil-Landform Modeling

Popis výsledku anglicky

Although numerous soil–landform modeling investigations have documented the effects and importance of spatial autocorrelation (SAC), little is known about how to predict the magnitude of those effects from the degree of SAC in the model variables. In this study, we quantified the SAC inherent in soil and landform variables of four widely divergent pedogeomorphological systems around the world to examine general relationships between SAC and spatial regression model results. Spatial regressions were performed by incorporating spatial filters, extracted by spatial eigenvector mapping, into non-spatial models as additional predictor variables. Results indicated that incorporation of spatial filters improved the performance of the non-spatial regressions—increases in R2 and decreases in both Akaike Information Criterion (AIC) and residual SAC were observed. More remarkable was that the degree of improvement was strongly and linearly related (i.e., proportional) to the level of SAC inherently possessed by each soil variable. Our findings show that spatial modeling outcomes are sensitive to the degree of SAC possessed by a soil property when treated as a response variable. Thus, the level of SAC present in a soil variable can serve as a direct indicator for how much improvement a non-spatial model will undergo if that SAC is appropriately taken into account.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

DF - Pedologie

OECD FORD obor

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Projekt

<a href="/cs/project/LH12039" target="_blank" >LH12039: Význam disturbancí pro pedogenezi a variabilitu půd temperátních lesů: syntéza napříč půdotvornými procesy, prostorovými a časovými škálami</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Soil Science Society of America Journal

ISSN

0361-5995

e-ISSN

—

Svazek periodika

80

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

409-419

Kód UT WoS článku

000376399200014

EID výsledku v databázi Scopus

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