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Terrain attributes were used as covariates.
Terrain attributes were derived from the resulting DEM.
These terrain attributes are computed over a neighborhood (spatial extent).
Regression tree models were calibrated to predict principal components (PC) scores based on terrain attributes.
For the first step, we used the random forest algorithm with 10 terrain attributes.
Stepwise multiple-linear regression was performed on the normalised terrain attributes and on the principal components constructed from the normalised terrain attributes to avoid multi-collinearity.
Each cell has intrinsic terrain attributes: altitude, soil type and land use.
The resulting spatial patterns correctly showed a significant relationship with the terrain attributes.
Terrain attributes were computed to characterize surface structure and to examine correlations with mass change.
Terrain attributes of four elevation models reflect the diversification of surface structures.
Terrain attributes associated with the initiation of disturbances were similar regardless of the location.
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