Spatial disaggregation of soil map polygons to estimate continuous soil property values at a resolution of 90 m for a pilot study area in Manitoba, Canada

GlobalSoilMap ◽  
2014 ◽  
pp. 201-206 ◽  
Author(s):  
G Lelyk ◽  
Robert MacMillan ◽  
Scott Smith ◽  
Bahram Daneshfar
Keyword(s):  
Pilot Study ◽  
Soil Property ◽  
Property Values ◽  
Soil Map ◽  
Spatial Disaggregation

scholarly journals Comparing three approaches of spatial disaggregation of legacy soil maps based on DSMART algorithm

2019 ◽  
Author(s):  
Yosra Ellili ◽  
Brendan Philip Malone ◽  
Didier Michot ◽  
Budiman Minasny ◽  
Sébastien Vincent ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Soil Type ◽  
Expert Knowledge ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Soil Profiles ◽  
Soil Maps ◽  
Soil Map ◽  
Soil Landscape ◽  
Spatial Disaggregation

Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of Digital Soil Mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially available at coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining Soil Map Units (SMU), where each SMU can include one or several Soil Type Units (STU) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at finer spatial resolution by machine learning algorithms using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithm (DSMART with extra soil profiles, DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on independent 135 soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1 : 25 000 soil maps. Pairwise comparisons were also performed, using Shannon entropy measure, to spatially locate differences between disaggregated maps. The main results show that adding soil landscape relationships in the disaggregation process enhances the performance of prediction of soil type distribution. Considering the three most probable STU and using 135 independent soil profiles, the overall accuracy measures are: 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost twofold higher when validated using 3 × 3 windows. They achieved 28.5 % for DSMART with soil landscape relationships, 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships as well as extra soil observations constraints the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial to obtain consistent soil maps with clear internal disaggregation of SMU across the landscape.

scholarly journals Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm

SOIL ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 371-388 ◽  
Author(s):  
Yosra Ellili-Bargaoui ◽  
Brendan Philip Malone ◽  
Didier Michot ◽  
Budiman Minasny ◽  
Sébastien Vincent ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Soil Type ◽  
Expert Knowledge ◽  
Classification Trees ◽  
Machine Learning Algorithms ◽  
Soil Profiles ◽  
Soil Maps ◽  
Soil Map ◽  
Soil Landscape ◽  
Spatial Disaggregation

Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25 000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape.

Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils

Geoderma ◽  
2012 ◽  
Vol 185-186 ◽  
pp. 37-47 ◽  
Author(s):  
Tim Häring ◽  
Elke Dietz ◽  
Sebastian Osenstetter ◽  
Thomas Koschitzki ◽  
Boris Schröder
Keyword(s):  
Decision Tree ◽  
Forest Soils ◽  
Soil Map ◽  
Spatial Disaggregation ◽  
Bavarian Forest

scholarly journals Reducing Statistical Uncertainty in Elastic Settlement Analysis of Shallow Foundations Relying on Targeted Field Investigation: A Random Field Approach

Geosciences ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 20 ◽  
Author(s):  
Panagiotis Christodoulou ◽  
Lysandros Pantelidis
Keyword(s):  
Finite Element ◽  
Random Fields ◽  
Soil Property ◽  
Property Values ◽  
Field Investigation ◽  
Shallow Foundations ◽  
Statistical Uncertainty ◽  
Sampling Points ◽  
Elastic Settlement ◽  
Settlement Analysis

The present paper deals with the practical problem of reducing statistical uncertainty in elastic settlement analysis of shallow foundations by relying on targeted field investigation with the aim of an optimal design. In a targeted field investigation, the optimal number and location of sampling points are known a priori. As samples are taken from the material field (i.e., the ground), which simultaneously is a stress field (stresses caused by the footing), the coexistence of these two fields allows for some points in the ground to better characterize the serviceability state of structure. These points are identified herein through an extensive parametric analysis of the factors controlling the magnitude of settlement; the number of different cases considered was 3318. This is done in an advanced probabilistic framework using the Random Finite Element Method (RFEM) properly considering sampling of soil property values. In this respect, the open source RSETL2D program, which combines elastic finite element analysis with the theory of random fields, has been modified as to include the function of sampling of soil property values from the generated random fields and return the failure probability of footing against excessive settlement. Two sampling strategies are examined: (a) sampling from a single point and (b) sampling a domain (the latter refers to e.g., continuous cone penetration test data). As is shown in this work, by adopting the proper sampling strategy (defined by the number and location of sampling points), the statistical error can be significantly reduced. The error is quantified by the difference in the probability of failure comparing different sampling scenarios. Finally, from the present analysis, it is inferred that the benefit from a targeted field investigation is much greater as compared to the benefit from the use of characteristic values in a limit state design framework.

scholarly journals Spatial Disaggregation of Multi-Component Soil Map Units Using Legacy Data and a Tree-Based Algorithm in Southern Brazil

2018 ◽  
Vol 42 (0) ◽  
Author(s):  
Israel Rosa Machado ◽  
Elvio Giasson ◽  
Alcinei Ribeiro Campos ◽  
José Janderson Ferreira Costa ◽  
Elisângela Benedet da Silva ◽  
...  
Keyword(s):  
Southern Brazil ◽  
Soil Map ◽  
Spatial Disaggregation ◽  
Legacy Data

Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships

Geoderma ◽  
2018 ◽  
Vol 311 ◽  
pp. 130-142 ◽  
Author(s):  
Sébastien Vincent ◽  
Blandine Lemercier ◽  
Lionel Berthier ◽  
Christian Walter
Keyword(s):  
Regional Scale ◽  
Soil Map ◽  
Soil Landscape ◽  
Spatial Disaggregation

Pilot study on criteria in cavity preparation-facts or artifacts?

1973 ◽  
Vol 37 (11) ◽  
pp. 27-31 ◽  
Author(s):  
G Salvendy ◽  
WM Hinton ◽  
GW Ferguson ◽  
PR Cunningham
Keyword(s):  
Pilot Study ◽  
Cavity Preparation
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