The Relationship Between Soil Taxonomy and Soil Mapping

Soil Horizons ◽  
1982 ◽  
Vol 23 (3) ◽  
pp. 5 ◽  
Author(s):  
R. L. Guthrie
Keyword(s):  
Soil Mapping ◽  
Soil Taxonomy ◽  
The Relationship

Measurement error-filtered machine learning in digital soil mapping

2021 ◽  
Author(s):  
Stephan van der Westhuizen ◽  
Gerard Heuvelink ◽  
David Hofmeyr
Keyword(s):  
Machine Learning ◽  
Measurement Error ◽  
Soil Property ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Simulation Studies ◽  
True Value ◽  
Linear Relationships ◽  
The Difference ◽  
The Relationship

<p>Digital soil mapping (DSM) may be defined as the use of a statistical model to quantify the relationship between a certain observed soil property at various geographic locations, and a collection of environmental covariates, and then using this relationship to predict the soil property at locations where the property was not measured. It is also important to quantify the uncertainty with regards to prediction of these soil maps. An important source of uncertainty in DSM is measurement error which is considered as the difference between a measured and true value of a soil property.</p><p>The use of machine learning (ML) models such as random forests (RF) has become a popular trend in DSM. This is because ML models tend to be capable of accommodating highly non-linear relationships between the soil property and covariates. However, it is not clear how to incorporate measurement error into ML models. In this presentation we will discuss how to incorporate measurement error into some popular ML models, starting with incorporating weights into the objective function of ML models that implicitly assume a Gaussian error. We will discuss the effect that these modifications have on prediction accuracy, with reference to simulation studies.</p>

scholarly journals A Comparative Analysis of a Detailed and Semi-Detailed Soil Mapping for Sustainable Land Management Using Conventional and Currently Applied Methodologies in Greece

Land ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 154 ◽  
Author(s):  
Orestis Kairis ◽  
Vassiliki Dimitriou ◽  
Chrysoula Aratzioglou ◽  
Dionisios Gasparatos ◽  
Nicholas Yassoglou ◽  
...  
Keyword(s):  
Soil Classification ◽  
Botanical Garden ◽  
Soil Mapping ◽  
Classification Systems ◽  
Soil Survey ◽  
Sustainable Land Use ◽  
Soil Taxonomy ◽  
Reference Base ◽  
World Reference Base ◽  
Soil Surveys

Two soil mapping methodologies at different scales applied in the same area were compared in order to investigate the potential of their combined use to achieve an integrated and more accurate soil description for sustainable land use management. The two methodologies represent the main types of soil mapping systems used and still applied in soil surveys in Greece. Diomedes Botanical Garden (DBG) (Athens, Greece) was used as a study area because past cartographic data of soil survey were available. The older soil survey data were obtained via the conventional methodology extensively used over time since the beginnings of soil mapping in Greece (1977). The second mapping methodology constitutes the current soil mapping system in Greece recently used for compilation of the national soil map. The obtained cartographic and soil data resulting from the application of the two methodologies were analyzed and compared using appropriate geospatial techniques. Even though the two mapping methodologies have been performed at different mapping scales, using partially different mapping symbols and different soil classification systems, the description of the soils based on the cartographic symbols of the two methodologies presented an agreement of 63.7% while the soil classification by the two taxonomic systems namely Soil Taxonomy and World Reference Base for Soil Resources had an average coincidence of 69.5%.

Predictive soil mapping: a review

2003 ◽  
Vol 27 (2) ◽  
pp. 171-197 ◽  
Author(s):  
P. Scull ◽  
J. Franklin ◽  
O. A. Chadwick ◽  
D. McArthur
Keyword(s):  
Information Science ◽  
Soil Mapping ◽  
Western World ◽  
Digital Terrain ◽  
Predictive Map ◽  
The Subject ◽  
Digital Terrain Modeling ◽  
The Relationship ◽  
State Factor ◽  
Complex Methods

Predictive soil mapping (PSM) can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic data base to create a predictive map. PSM is made possible by geocomputational technologies developed over the past few decades. For example, advances in geographic information science, digital terrain modeling, remote sensing, fuzzy logic has created a tremendous potential for improvement in the way that soil maps are produced. The State Factor soil-forming model, which was introduced to the western world by one of the early Presidents of the American Association of Geographers (C.F. Marbut), forms the theoretical basis of PSM. PSM research is being driven by a need to understand the role soil plays in the biophysical and biogeochemical functioning of the planet. Much research has been published on the subject in the last 20 years (mostly outside of geographic journals) and methods have varied widely from statistical approaches (including geostatistics) to more complex methods, such as decision tree analysis, and expert systems. A geographic perspective is needed because of the inherently geographic nature of PSM.

scholarly journals DIGITAL SOIL MAPPING WITH REGRESSION TREE CLASSIFICATION APPROACHES BY RS AND GEOMORPHOMETRY COVARIATE IN THE QAZVIN PLAIN, IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 773-777
Author(s):  
S. R. Mousavi ◽  
F. Sarmadian ◽  
A. Rahmani ◽  
S. E. Khamoshi
Keyword(s):  
Data Mining ◽  
Regression Tree ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Slope Position ◽  
Features Selection ◽  
Kappa Index ◽  
Soil Taxonomy ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Abstract. Digital soil mapping applies soil attributes, Remote sensing and Geomorphometrics indices to estimate soil types and properties at unobserved locations. This study carried out in order to comparison two data mining algorithms such as Random Forest (RF) and Boosting Regression tree (BRT) and two features selection principal component analysis (PCA) and variance inflation factor (VIF) for predicting soil taxonomy class at great group and subgroup levels. A total of 61 soil profile observation based on stratified random determined and digged in area with approximately 16660 hectares.19 RS indices and geomorphometrics covariates derivated from Landsate-8 imagery and DEM with 30 meters’ resolution in ERDAS IMAGINE 2014 and SAGA GIS version 7.0 software’s. Also to run four Data mining algorithms scenarios (PCA-RF, VIF-RF, PCA-BRT, VIF-BRT) from “Randomforest” and “C.5” packages were used in R studio software. 80% and 20% from soil profiles were applied for calibrating and validating. The results showed that in PCA and VIF approaches, eight covariates such as (Relative slope position, diffuse insolation, modified catchment, normalized height, RVI, Standard height, TWI, Valley depth) and six covariates (NDVI, DVI, Catchment area, DEM, Salinity index, Standard height) were selected. The validation results based on overall accuracy and kappa index for scenarios at great group level indicated that 88,93,62, 54 and 75,83,51,45 percentages and for subgroup level had 70, 77, 54, 47 and 60, 71, 43, 37 percentages, respectively. Generally, VIF-RF had accuracy rather than from other scenarios at two categorical level in this study area.

Interstellar gas in the central region of the Galaxy

1967 ◽  
Vol 31 ◽  
pp. 239-251 ◽  
Author(s):  
F. J. Kerr
Keyword(s):  
Central Region ◽  
Galactic Plane ◽  
Neutral Hydrogen ◽  
Continuum Emission ◽  
Galactic Centre ◽  
Interstellar Gas ◽  
Hydrogen Recombination ◽  
The Galaxy ◽  
Centre Region ◽  
The Relationship

A review is given of information on the galactic-centre region obtained from recent observations of the 21-cm line from neutral hydrogen, the 18-cm group of OH lines, a hydrogen recombination line at 6 cm wavelength, and the continuum emission from ionized hydrogen.Both inward and outward motions are important in this region, in addition to rotation. Several types of observation indicate the presence of material in features inclined to the galactic plane. The relationship between the H and OH concentrations is not yet clear, but a rough picture of the central region can be proposed.

The relationship of the scleractinian corals to the rugose corals

Paleobiology ◽  
1980 ◽  
Vol 6 (02) ◽  
pp. 146-160 ◽  
Author(s):  
William A. Oliver
Keyword(s):  
Critical Points ◽  
Scleractinian Corals ◽  
Convincing Evidence ◽  
Early Triassic ◽  
Rugose Corals ◽  
History Of ◽  
The Subject ◽  
Relationship Of ◽  
The Relationship ◽  
Biologic Factors

The Mesozoic-Cenozoic coral Order Scleractinia has been suggested to have originated or evolved (1) by direct descent from the Paleozoic Order Rugosa or (2) by the development of a skeleton in members of one of the anemone groups that probably have existed throughout Phanerozoic time. In spite of much work on the subject, advocates of the direct descent hypothesis have failed to find convincing evidence of this relationship. Critical points are:(1) Rugosan septal insertion is serial; Scleractinian insertion is cyclic; no intermediate stages have been demonstrated. Apparent intermediates are Scleractinia having bilateral cyclic insertion or teratological Rugosa.(2) There is convincing evidence that the skeletons of many Rugosa were calcitic and none are known to be or to have been aragonitic. In contrast, the skeletons of all living Scleractinia are aragonitic and there is evidence that fossil Scleractinia were aragonitic also. The mineralogic difference is almost certainly due to intrinsic biologic factors.(3) No early Triassic corals of either group are known. This fact is not compelling (by itself) but is important in connection with points 1 and 2, because, given direct descent, both changes took place during this only stage in the history of the two groups in which there are no known corals.

Choosing a Markov blanket

2020 ◽  
Vol 43 ◽  
Author(s):  
Thomas Parr
Keyword(s):  
Markov Blanket ◽  
Target Article ◽  
The Relationship

Abstract This commentary focuses upon the relationship between two themes in the target article: the ways in which a Markov blanket may be defined and the role of precision and salience in mediating the interactions between what is internal and external to a system. These each rest upon the different perspectives we might take while “choosing” a Markov blanket.

Tinkering with cognitive gadgets: Cultural evolutionary psychology meets active inference

2019 ◽  
Vol 42 ◽  
Author(s):  
Paul Benjamin Badcock ◽  
Axel Constant ◽  
Maxwell James Désormeau Ramstead
Keyword(s):  
Evolutionary Psychology ◽  
Research Program ◽  
Active Inference ◽  
Innovative Research ◽  
Cognitive Faculties ◽  
Cultural Evolutionary ◽  
The Relationship

Abstract Cognitive Gadgets offers a new, convincing perspective on the origins of our distinctive cognitive faculties, coupled with a clear, innovative research program. Although we broadly endorse Heyes’ ideas, we raise some concerns about her characterisation of evolutionary psychology and the relationship between biology and culture, before discussing the potential fruits of examining cognitive gadgets through the lens of active inference.

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