Oblique geographic coordinates as covariates for digital soil mapping
<p>Decision tree algorithms, such as random forest, have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit spatial trends into these algorithms has proven problematic. Using <span class="inline-formula"><i>x<...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-07-01
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| Series: | SOIL |
| Online Access: | https://soil.copernicus.org/articles/6/269/2020/soil-6-269-2020.pdf |
| Summary: | <p>Decision tree algorithms, such as random forest, have
become a widely adapted method for mapping soil properties in geographic
space. However, implementing explicit spatial trends into these algorithms
has proven problematic. Using <span class="inline-formula"><i>x</i></span> and <span class="inline-formula"><i>y</i></span> coordinates as covariates gives
orthogonal artifacts in the maps, and alternative methods using distances as
covariates can be inflexible and difficult to interpret. We propose instead
the use of coordinates along several axes tilted at oblique angles to
provide an easily interpretable method for obtaining a realistic prediction
surface. We test the method on four spatial datasets and compare it to
similar methods. The results show that the method provides accuracies better
than or on par with the most reliable alternative methods, namely kriging
and distance-based covariates. Furthermore, the proposed method is highly
flexible, scalable and easily interpretable. This makes it a promising tool
for mapping soil properties with complex spatial variation.</p> |
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| ISSN: | 2199-3971 2199-398X |