Digital soil mapping of a coastal acid sulfate soil landscape

Soil Research ◽  
2014 ◽  
Vol 52 (4) ◽  
pp. 327 ◽  
Author(s):  
Jingyi Huang ◽  
Terence Nhan ◽  
Vanessa N. L. Wong ◽  
Scott G. Johnston ◽  
R. Murray Lark ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Traditional Approach ◽  
Management Strategies ◽  
Error Variance ◽  
Optimal Number ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Acid Sulfate ◽  
Soil Landscape ◽  
Number Of Classes

Coastal floodplains are commonly underlain by sulfidic sediments and coastal acid sulfate soils (CASS). Oxidation of sulfidic sediments leads to increases in acidity and mobilisation of trace metals, resulting in an increase in the concentrations of conducting ions in sediment and pore water. The distribution of these sediments on floodplains is highly heterogeneous. Accurately identifying the distribution of CASS is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) using ancillary information. Proximal sensing instruments such as an EM38 can provide data on the spatial distribution of soil salinity, which is associated with CASS, and can be complemented by digital elevation models (DEM). We used EM38 measurements of the apparent soil electrical conductivity (ECa) in the horizontal and vertical modes in combination with a high resolution DEM to delineate the spatial distribution of CASS. We used a fuzzy k-means algorithm to cluster the data. The fuzziness exponent, number of classes (k) and distance metric (i.e. Euclidean, Mahalanobis and diagonal) were varied to determine a set of parameters to identify CASS. The mean-squared prediction error variance of the class mean of various soil properties (e.g. EC1:5 and pH) was used to identify which of these metrics was suitable for further analysis (i.e. Mahalanobis) and also determine the optimal number of classes (i.e. k = 4). The final map is consistent with previously defined soil–landscape units generated using traditional soil profile description, classification and mapping. The DSM approach is amenable for evaluation on a larger scale and in order to refine CASS boundaries previously mapped using the traditional approach or to identify CASS areas that remain unmapped.

Long-term financial sustainability: An evaluation methodology with threats considerations

2020 ◽  
pp. 47-69
Author(s):  
Lidiya Guryanova ◽  
Olena Bolotova ◽  
Vitalii Gvozdytskyi ◽  
Sergienko Olena
Keyword(s):  
Management Strategies ◽  
Optimal Number ◽  
Evaluation Methodology ◽  
Security Level ◽  
Preventive Strategy ◽  
Optimal Dimension ◽  
Long Term Stability ◽  
Proactive Management ◽  
Number Of Classes

It is shown that one of the directions for increasing the efficiency of managing corporate systems (CS) under the influence of a large number of destabilizing fa-tors ("shocks", threats) is the development of a set of models of estimation and analysis of the long-term stability of CS in proactive contour of management, which allow timely diagnosing a decrease in the company's security level and adopting effective preventive management decisions. A review of existing approa-ches to the formation of such a set of models showed a number of limitations, the result of which is a low forecasting accuracy. The proposed approach, unlike the existing ones, allows to: 1) determine the optimal dimension of the information space of diagnostic factors; 2) find the optimal number of classes of situations for which differentiated management strategies can be developed; 3) determine the period of pre-emption, which does not require updating the models of retrospective diagnostics. This makes it possible to identify the class of not only current, but also forecast situations for a given horizon of proactive management and to choose an adequate preventive strategy.

Global soil mapping with Neural Networks 

2021 ◽  
Author(s):  
Giulio Genova ◽  
Luis de Sousa ◽  
Tanja Mimmo ◽  
Luigi Borruso ◽  
Laura Poggio
Keyword(s):  
Neural Networks ◽  
Prediction Models ◽  
Traditional Approach ◽  
Contextual Information ◽  
Water Security ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Environmental Covariates ◽  
Complex Architectures ◽  
Global Soil

<p>High quality global soil maps are crucial to face several challenges such as reducing soil erosion, climate change adaptation and mitigation, ensuring food and water security, and biodiversity conservation planning. To obtain accurate and robust soil properties maps, research and development are necessary to identify the most appropriate prediction models and to develop efficient and robust workflows. A few recent studies used Artificial Neural Networks (ANN) in Digital Soil Mapping, in some cases improving the accuracy of the predicted maps compared to other methods like Random Forest (RF). In this study we tested different ANN architectures on a global top-soil dataset of ca. 110 000 samples, comparing the results for the different architectures with the more traditional approach of RF. The target variables considered are pH, Soil Organic Carbon, Sand, Silt, and Clay. We selected 40 environmental covariates from a pool of over 400 to represent the most important soil forming factors. We tried simpler architectures (single input – single target) using point observations for one target variable with corresponding raster cell values for spatially explicit environmental covariates. We also used more complex architectures (multi input - multi target) incorporating contextual information surrounding an observation (convolutional) and with multiple target variables. Preliminary results show that increasing the number of hidden layers in the neural network does not significantly influence the results, while changing the type of architecture can play a bigger role in the overall accuracy of the model. The overall prediction accuracy of the ANN was comparable with the RF model. We conclude that ANN are a promising, relatively new, approach for Global Digital Soil Mapping and that further research is needed to improve performance.</p>

scholarly journals Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

2017 ◽  
Vol 52 (8) ◽  
pp. 633-642
Author(s):  
Mario Sergio Wolski ◽  
Ricardo Simão Diniz Dalmolin ◽  
Carlos Alberto Flores ◽  
Jean Michel Moura-Bueno ◽  
Alexandre ten Caten ◽  
...  
Keyword(s):  
Soil Mapping ◽  
Soil Survey ◽  
Digital Soil Mapping ◽  
Limiting Factor ◽  
Topographic Map ◽  
Kappa Index ◽  
Terrain Attributes ◽  
Soil Landscape ◽  
Mapping Techniques ◽  
Map Scale

Abstract: The objective of this work was to test the extrapolation of soil-landscape relationships in a reference area (RA) to a topographic map (scale 1:50,000), using digital soil mapping (DSM), and to compare these results to those obtained in similar studies previously conducted in Brazil. A soil survey in a 10 km2 RA, using conventional mapping techniques (scale 1:10,000), was made in order to map a 678 km2 physiographically similar area (scale 1:50,000) using DSM. The decision tree technique was employed to build a predictive extrapolation model based on soil classes and eight terrain attributes in the RA. The validation of DSM by application of field observation points resulted in a 66.1% global accuracy and in 0.36 kappa index. The most representative soils in the area were correctly predicted, whereas the less representative and less frequent soils in the landscape (and consequently with reduced sampling) had their prediction compromised. The RA proportion, which equals 1.5% of the total area, is a limiting factor in the formulation of soil-landscape relationships to precisely represent the mapped area by DSM.

scholarly journals Spatial distribution of selected soil features in Hajdú-Bihar county represented by digital soil maps

2016 ◽  
Vol 10 (3-4) ◽  
pp. 203-213 ◽  
Author(s):  
László Pásztor ◽  
Annamária Laborczi ◽  
Katalin Takács ◽  
Gábor Szatmári ◽  
Gábor Illés ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Information Requirements ◽  
Soil Features ◽  
Soil Information ◽  
Mapping Techniques ◽  
Digital Soil Maps

With the ongoing DOSoReMI.hu project we aimed to significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil optimally general as well as specific national and international demands from the aspect of thematic, spatial and temporal accuracy. In addition to relevant and available auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened. In our paper we present some results in the form of brand new soil maps focusing on the territory of Hajdú-Bihar county.

Digital soil mapping using machine learning techniques in a varied tropical environment

2021 ◽  
Author(s):  
Rodrigo Miranda ◽  
Rodolfo Nobrega ◽  
Estevão Silva ◽  
Jadson Freire ◽  
José Filho ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Information ◽  
The State ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Environmental Drivers ◽  
Gradient Boosting ◽  
Soil Attributes ◽  
Environmental Models

<p>Environmental models often require soil maps to represent the spatial variability of soil attributes. However, mapping soils using conventional in-situ survey protocols is time-consuming and costly. As an alternative, digital soil mapping offers a fast-mapping approach that might be used to monitor soil attributes and their interrelationships over large areas. In Brazil, conventional survey methods are still widely used, and thus maps still in development are considered as the state-of-the-art products for decades. In this study, we address this lack of updated spatial information on many soil attributes by producing regional statistical soil models using an innovative framework. This new framework attempts to reduce prediction redundancies due to high multicollinearity, by implementing a Feature Selector algorithm. This is expected to improve a model’s strength by decreasing its unexplained variance. The framework’s core is composed of the Soil-Landscape Estimation and Evaluation Program (SLEEP) and a calibrated Gradient Boosting Model capable of modelling the spatial distribution of soil attributes at multiple soil depths. These models allowed us to explain the spatial distribution of some basic soil attributes (physical and chemical), and its environmental drivers. The model training and testing approach used 30 environmental attributes, and data from 223 soil profiles for the state of Pernambuco, Brazil. Our models demonstrated a consistent potential to perform spatial extrapolations with r<sup>2</sup> ranging from 0.8 to 0.97, and PBIAS from -0.51 to 2.03. The properties related to topographic and climatic conditions were dominating when estimating the number of horizons, percentage of silt and the sum of bases (a measure of soil fertility). We believe that our framework features high flexibility, while reducing capital investments when compared to <em>in situ</em> surveys and traditional mapping protocols. These findings also have implications for the improvement and testing of pedotransfer functions. We thank FACEPE for funding this through APQ 0646-9.25/16.</p>

Comparing traditional and digital soil mapping at a district scale using residual maximum likelihood analysis

Soil Research ◽  
2018 ◽  
Vol 56 (5) ◽  
pp. 535 ◽  
Author(s):  
E. Zare ◽  
M. F. Ahmed ◽  
R. S. Malik ◽  
R. Subasinghe ◽  
J. Huang ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Soil Profile ◽  
Traditional Approach ◽  
Clay Content ◽  
Soil Mapping ◽  
Classification Systems ◽  
Digital Soil Mapping ◽  
Likelihood Method ◽  
Ancillary Data ◽  
Residual Maximum Likelihood

Conventional soil mapping uses field morphological observations to classify soil profiles into predefined classification systems and extrapolates the classified soils to make a map based on aerial photographs and the experience of the surveyor. A criticism of this approach is that the subjectivity of the surveyor leads to non-reproducible maps. Advances in computing and statistical analysis, and an increased availability of ancillary data have cumulatively led to an alternative, referred to as digital soil mapping (DSM). In this research, two agriculturally productive areas (i.e. Warren and Trangie) located in central New South Wales, Australia, were considered to evaluate whether pedoderms and soil profile classes defined according to the traditional approach can also be recognised and mapped using a DSM approach. First, we performed a fuzzy k-means analysis to look for clusters in the ancillary data, which include data from remote-sensed gamma-ray (γ-ray) spectrometry and proximal-sensed electromagnetic (EM) induction. We used the residual maximum likelihood method to evaluate the maps for various numbers of classes (k = 2–10) to minimise the mean square prediction error (σ2p,C) of soil physical (i.e. clay content, field capacity (FC), permanent wilting point (PWP) and available water content (AWC)) and chemical (pH, EC of 1 : 5 soil water extract (EC1:5) and cation exchange capacity (CEC)) properties of topsoil (0–0.3 m) and subsoil (0.6–0.9 m). In terms of prediction, the calculated σ2p,C was locally minimised for k = 8 when accounting for topsoil clay, FC, PWP, pH and CEC, and subsoil FC, EC1:5 and CEC. A comparison of σ2p,C of the traditional (seven pedoderm components) and DSM approach (k = 8) indicated that only topsoil EC1:5 and subsoil pH was better accounted for by the traditional approach, whereas topsoil clay content, and CEC and subsoil clay, EC1:5 and CEC were better resolved using the DSM approach. The produced DSM maps (e.g. k = 3, 6 and 8) also reflected the pedoderm components identified using the traditional approach. We concluded that the DSM maps with k = 8 classes reflected the soil profile classes identified within the pedoderms and that soil maps of similar accuracy could be developed from the EM data independently.

scholarly journals Spatial Mapping of Soil Depth, Coarse Fragments and Textural Classes of Tamil Nadu Using Digital Soil Mapping Approach

2021 ◽  
Author(s):  
B Kalaiselvi ◽  
S. Dharumarajan ◽  
M. Lalitha ◽  
R. Sriniv ◽  
R. Vasundhara ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Random Forest ◽  
Soil Properties ◽  
Tamil Nadu ◽  
Soil Depth ◽  
Soil Mapping ◽  
Random Forest Model ◽  
Digital Soil Mapping ◽  
Environmental Covariates ◽  
Forest Model

Abstract Knowledge on spatial distribution of soil depth, coarse fragments and texture are crucial for land resource management and environmental soil modeling. Digital soil mapping approach helps in prediction of spatial soil information by establishing the relationship between soil and environmental covariates. In the present study, we assessed spatial distribution of soil depth, coarse fragments (CF) and soil textural classes over 0.13 M sq.km area of Tamil Nadu state. About 2100 samples were used for the prediction of soil properties using random forest model (RF). Out of which, 80 per cent samples were used for training and 20 percent samples were used for testing. Different environmental covariates such as digital elevation model outputs, landsat data and bioclimatic variables were related to predict the soil properties. The predicted soil depth and CF ranged from 46-200 cm and 1-42 per cent respectively. The RF model performed well by explaining the variability (R 2 ) of 43% for soil depth and 21% for coarse fragments with RMSE of 38 cm and 13%, respectively. The RF classifier classified the soil textural classes with 64% overall accuracy and 43% kappa index. Variable importance ranking of Random forest model showed that elevation, MrVBF are the important predictors used for prediction of soil depth and CF, whereas remote sensing vegetation indices such as NDVI, EVI were acted as primary variable for prediction of soil textural classes. In this study, 250 m resolution detailed soil depth, CF and textural class maps were prepared which will be useful for different environmental modeling and proper agricultural management purposes.

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