Open AccessBook
Error Propagation in Environmental Modelling with GIS
TLDR
An error model for quantitative spatial attributes identification of the error model - a case study error propagation with local GIS operations - the use of multidimensional simulation implementation of error propagation techniques in GIS.Abstract:
Definition and identification of an error model for quantitative spatial attributes identification of the error model - a case study error propagation with local GIS operations - theory error propagation with local GIS operations - applications error propagation with global GIS operations - the use of multidimensional simulation implementation of error propagation techniques in GIS.read more
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Journal ArticleDOI
On digital soil mapping
TL;DR: The generic framework, which the authors call the scorpanSSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited.
Book
Spatial Data Analysis: Theory and Practice
TL;DR: This work focuses on the development of models for statistical modeling of spatial variation in the context of scientific and policy context, as well as the nature of spatial data.
Journal ArticleDOI
Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics
TL;DR: In this article, the authors present different equations describing hydraulic characteristics and of pedotransfer functions used to predict parameters in these equations, as well as the use of different soil properties as predictors.
Journal ArticleDOI
SoilGrids1km--global soil information based on automated mapping.
Tomislav Hengl,Jorge Mendes de Jesus,R. A. MacMillan,Niels H. Batjes,Gerard B. M. Heuvelink,Eloi Ribeiro,Alessandro Samuel-Rosa,Bas Kempen,Johan G. B. Leenaars,Markus G. Walsh,Maria Ruiperez Gonzalez +10 more
TL;DR: SoilGrids1km provides an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available and results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices, lithology, and taxonomic mapping units derived from conventional soil survey.
Journal ArticleDOI
Recommendations for the quantitative analysis of landslide risk
Jordi Corominas,C.J. van Westen,Paolo Frattini,Leonardo Cascini,Jean-Philippe Malet,Stavroula Fotopoulou,Filippo Catani,M. van den Eeckhaut,Olga Mavrouli,Federico Agliardi,Kyriazis Pitilakis,Mike G. Winter,Manuel Pastor,Settimio Ferlisi,Veronica Tofani,Javier Hervás,J.T. Smith +16 more
TL;DR: In this article, the authors present recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results.