scholarly journals Oblique geographic coordinates as covariates for digital soil mapping

2019 ◽  
Author(s):  
Anders Bjørn Møller ◽  
Amélie Marie Beucher ◽  
Nastaran Pouladi ◽  
Mogens Humlekrog Greve
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Alternative Methods ◽  
Agricultural Field ◽  
Geographic Space ◽  
Promising Tool ◽  
Tree Algorithms ◽  
Realistic Prediction

Abstract. Decision tree algorithms such as Random Forest have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit geographic relationships into these methods has proven problematic. Using x- and y-coordinates as covariates gives orthogonal artefacts 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 for mapping topsoil organic matter contents in an agricultural field in Denmark. The results show that the method provides accuracies on par with the most reliable alternative methods, namely kriging and the use of buffer distances to the training points. 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. We believe that the method will be highly useful for mapping soil properties in larger areas, and testing it for this purpose is a logical next step.

scholarly journals Oblique geographic coordinates as covariates for digital soil mapping

SOIL ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 269-289 ◽  
Author(s):  
Anders Bjørn Møller ◽  
Amélie Marie Beucher ◽  
Nastaran Pouladi ◽  
Mogens Humlekrog Greve
Keyword(s):  
Soil Properties ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Alternative Methods ◽  
Geographic Space ◽  
Promising Tool ◽  
Spatial Trends ◽  
Tree Algorithms ◽  
Realistic Prediction ◽  
Better Than

Abstract. 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 x and y 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.

Digital Soil Mapping of Soil Properties in Honduras Using Readily Available Biophysical Datasets and Gaussian Processes

2008 ◽  
pp. 367-380 ◽  
Author(s):  
Juan Pablo Gonzalez ◽  
Andy Jarvis ◽  
Simon E. Cook ◽  
Thomas Oberthür ◽  
Mauricio Rincon-Romero ◽  
...  
Keyword(s):  
Soil Properties ◽  
Gaussian Processes ◽  
Soil Mapping ◽  
Digital Soil Mapping

Predicting spatial variability of selected soil properties using digital soil mapping in a rainfed vineyard of central Chile

2020 ◽  
Vol 22 ◽  
pp. e00289
Author(s):  
Lwando Mashalaba ◽  
Mauricio Galleguillos ◽  
Oscar Seguel ◽  
Javiera Poblete-Olivares
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Central Chile

Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging

Geoderma ◽  
2020 ◽  
Vol 366 ◽  
pp. 114253 ◽  
Author(s):  
Yakun Zhang ◽  
Wenjun Ji ◽  
Daniel D. Saurette ◽  
Tahmid Huq Easher ◽  
Hongyi Li ◽  
...  
Keyword(s):  
Soil Properties ◽  
Three Dimensional ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Regression Kriging ◽  
Field Scale

scholarly journals QUANTITATIVE COMPARISON OF TWO CLOSELY RELATED SOIL MAPPING UNITS

1974 ◽  
Vol 54 (1) ◽  
pp. 7-14 ◽  
Author(s):  
L. S. CROSSON ◽  
R. PROTZ
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Organic Matter Content ◽  
Matter Content ◽  
Soil Mapping ◽  
Probability Level ◽  
Ap Horizon ◽  
Modal Values ◽  
True Measure ◽  
Mapping Units

Many soil mapping units (MU) have not been adequately sampled to provide a true measure of their variability; therefore, their descriptions must be regarded as incomplete, and valid statistical comparisons cannot be made with other closely related MU. The number of samples required to detect the differences in means of 18 soil properties between Brantford and Beverly Silt Loam MU were calculated and they ranged from 4 at the 80% probability level (10 at the 95% probability level) for organic matter content of the Ap horizon to several thousand for pH of the Ap horizon. Calculation of required sample numbers indicated that sufficient samples had been collected to make valid statistical comparisons between seven of the soil properties. All seven properties were found to be significantly different between the two MU at the 95% probability level. However, only two of the properties, hue and organic matter content of the Ap horizon, had distinctly different modal values between the two MU and neither of these properties is easily measured in the field. Therefore, it was concluded that the 18 soil properties examined were impractical and unreliable criteria for separating the MU in the field. But, the MU separations can be readily and validly made on the basis of landscape position.

Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

2020 ◽  
Author(s):  
Nada Mzid ◽  
Stefano Pignatti ◽  
Irina Veretelnikova ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Central Italy ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Field Scale ◽  
Umbria Region ◽  
Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

The effectiveness of digital soil mapping to predict soil properties over low-relief areas

2016 ◽  
Vol 188 (3) ◽  
Author(s):  
Zohreh Mosleh ◽  
Mohammad Hassan Salehi ◽  
Azam Jafari ◽  
Isa Esfandiarpoor Borujeni ◽  
Abdolmohammad Mehnatkesh
Keyword(s):  
Soil Properties ◽  
Soil Mapping ◽  
Digital Soil Mapping

Digital soil mapping: the challenge to obtain the best soil dataset and create a precise environmental model to support land use management at a national level (Ecuador).

2020 ◽  
Author(s):  
Daphne Armas ◽  
Mário Guevara ◽  
Fernando Bezares ◽  
Rodrigo Vargas ◽  
Pilar Durante ◽  
...  
Keyword(s):  
Land Use ◽  
Information System ◽  
Soil Properties ◽  
Soil Profile ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Land Use Management ◽  
National Scale ◽  
Global Soil ◽  
Soil Information

<p>One of the biggest challenges for digital soil mapping is the limited of field soil information (e.g., soil profile descriptions, soil sample analysis) for representing soil variability across scales. Global initiatives such as the Global Soil Partnership (GSP) and the development of a <strong>Global Soil Information System</strong> (GloSIS), World Soil Information Service (WoSis) or SoilGrids250m for global pedometric mapping highlight new opportunities but the crescent need of new and better soil datasets across the world. Soil datasets are increasingly required for the development of soil monitoring baselines, soil protection and sustainable land use strategies, and to better understand the response of soils to global environmental change.  However, soil surveys are a very challenging task due to their high acquisition costs such data and operational complexity. The use of legacy soil data can reduce these sampling efforts.</p><p>The main objective of this research was the rescue, synthesis and harmonization of legacy soil profile information collected between 2009 and 2015 for different purposes (e.g., soil or natural resources inventory) across Ecuador. This project will support the creation of a soil information system at the national scale following international standards for archiving and sharing soil information (e.g., GPS or the GlobalSoilMap.net project). This new information could be useful to increase the accuracy of current digital soil information across the country and the future development of digital soil properties maps.</p><p>We provided an integrated framework combining multiple data analytic tools (e.g., python libraries, pandas, openpyxl or pdftools) for the automatic conversion of text in paper format (e.g., pdf, jpg) legacy soil information, as much the qualitative soil description as analytical data,  to usable digital soil mapping inputs (e.g., spatial datasets) across Ecuador. For the conversion, we used text data mining techniques to automatically extract the information. We based on regular expressions using consecutive sequences algorithms of common patterns not only to search for terms, but also relationships between terms. Following this approach, we rescued information of 13.696 profiles in .pdf, .jpg format and compiled a database consisting of 10 soil-related variables.</p><p>The new database includes historical soil information that automatically converted a generic tabular database form (e.g., .csv) information.</p><p>As a result, we substantially improved the representation of soil information in Ecuador that can be used to support current soil information initiatives such as the WoSis, Batjes et al. 2019, with only 94 pedons available for Ecuador, the Latin American Soil Information System (SISLAC, http://54.229.242.119/sislac/es),  and the United Nations goals  towards increasing soil carbon sequestration areas or decreasing land desertification trends.  In our database there are almost 13.696 soil profiles at the national scale, with soil-related (e.g., depth, organic carbon, salinity, texture) with positive implications for digital soil properties mapping. </p><p>With this work we increased opportunities for digital soil mapping across Ecuador. This contribution could be used to generate spatial indicators of land degradation at a national scale (e.g., salinity, erosion).</p><p>This dataset could support new knowledge for more accurate environmental modelling and to support land use management decisions at the national scale.</p><p> </p>

scholarly journals Decision trees for digital soil mapping on subtropical basaltic steeplands

2011 ◽  
Vol 68 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Elvio Giasson ◽  
Eliana Casco Sarmento ◽  
Eliseu Weber ◽  
Carlos Alberto Flores ◽  
Heinrich Hasenack
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Land Use Planning ◽  
Spatial Information ◽  
Soil Mapping ◽  
Soil Survey ◽  
Digital Soil Mapping ◽  
Tree Algorithms ◽  
Mapping Techniques ◽  
Soil Surveys

When soil surveys are not available for land use planning activities, digital soil mapping techniques can be of assistance. Soil surveyors can process spatial information faster, to assist in the execution of traditional soil survey or predict the occurrence of soil classes across landscapes. Decision tree techniques were evaluated as tools for predicting the ocurrence of soil classes in basaltic steeplands in South Brazil. Several combinations of types of decicion tree algorithms and number of elements on terminal nodes of trees were compared using soil maps with both original and simplified legends. In general, decision tree analysis was useful for predicting occurrence of soil mapping units. Decision trees with fewer elements on terminal nodes yield higher accuracies, and legend simplification (aggregation) reduced the precision of predictions. Algorithm J48 had better performance than BF Tree, RepTree, Random Tree, and Simple Chart.

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