Supplemental sampling for digital soil mapping based on prediction uncertainty from both the feature domain and the spatial domain

Li, Yan; Zhu, A‐Xing; Shi, Zhou; Liu, Jing; Du, Fei

doi:10.1016/j.geoderma.2016.08.013

Cited by 17 publications

(6 citation statements)

References 50 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the spatial structure of the parameters is being observed. The geostatistical method of kriging is widely applied in the mapping of soil attributes in unsampled areas [50]. At the same time, this enables quantification of the main spatial characteristic of soil attributes and spatial interpolation methods considering only the neighbouring points of estimation data [51,52].…”

Section: Krigingmentioning

confidence: 99%

Predicting the Spatial Distributions of Elements in Former Military Operation Area Using Linear and Nonlinear Methods Across the Stavnja Valley, Bosnia and Herzegovina

Alijagić

Šajn

2020

Minerals

View full text Add to dashboard Cite

This study has the purpose of developing a realistic soil prediction maps of the spatial distribution of elements by evaluating and comparing different modelling techniques: Kriging, artificial neural network-multilayer perceptron (ANN-MLP) and multiple polynomial regressions (MPR). The Stavnja Valley was selected as a test area due to the following reasons: (1) intensive metal ore mining and metallurgical processing; (2) peculiar geomorphological natural features; (3) regular geological setting, and (4) the remaining minefields. Geospatial parameters from digital elevation models (DEM) are used as an input to advanced prediction modelling techniques: ANN-MLP and MPR. Soil measurements, land use data, and remote sensing are applied, developed, and finally incorporated into the models of spatial distribution in the form of 2D or 3D maps. In order to reconstruct the different processes that influenced the entire study area simultaneously, we used novel approaches to modelling. This comprehensive approach not only represents an innovation in contamination mapping, but developed prediction models also help in the reconstruction of main distribution pathways, assess the real size of the affected area, and improve the data interpretation.

show abstract

Section: Krigingmentioning

confidence: 99%

Predicting the Spatial Distributions of Elements in Former Military Operation Area Using Linear and Nonlinear Methods Across the Stavnja Valley, Bosnia and Herzegovina

Alijagić

Šajn

2020

Minerals

View full text Add to dashboard Cite

show abstract

“…The same results were observed by Kidd et al (2015), suggesting that maps should be created with continuous improvements, from the input of newly collected data. Prediction uncertainty can help to choose supplemental sampling to improve the DSM (Li et al, 2016). Figure 6.…”

Section: -D Approachmentioning

confidence: 99%

Mapping soil properties in a poorly-accessible area

Costa¹,

Pinheiro²,

Anjos³

et al. 2020

Revista Brasileira De Ciência Do Solo

View full text Add to dashboard Cite

Soil maps are important to evaluate soil functions and support decision-making process, particularly for soil properties such as pH, carbon content (C), and cation exchange capacity (CEC), but the spatial resolution and soil depth should meet the needs of users. On another hand, the efficiency of statistical models to create soil maps, with an acceptable level of accuracy, often require a large number of samples with an appropriate distribution across the area of interest. However, accessibility for sampling can be a trouble in remote areas, such as the Itatiaia National Park (INP). The hypothesis of this work is that it is possible to obtain a viable result in soil mapping of areas with limited access by using DSM tools. The general objective of this paper was to create 2-and 3-D maps of the soil properties pH, carbon content, and CEC, with the correspondent spatial uncertainty, in the INP plateau. The sampling strategy was designed using conditioned Latin Hypercube Sample (cLHS), and different methods were tested to produce the soil properties maps. For calibration of the models: linear (MLR, multiple linear regression) and nonlinear (GAM, Generalised Additive Models). The results showed differences in predictive performance for all statistical methods and covariate selection approaches. The GAM, with covariates selection based on soil formation factors, was the best method for the limited number of soil samples. The greatest uncertainty was associated with areas with the lowest accessibility and, consequently, with low sampling density and/or noises in covariates. Even though the 2-and 3-D maps of soil properties, each associated with explicit uncertainty, can contribute to the INP decision makers/ managers by providing information not available before.

show abstract

“…The uncertainty computed based on the representativeness of a sample point to a prediction point as expressed in similarity between the geographic configuration of the sample and that of the prediction point is extremely useful not only in assessing the quality of the results from spatial prediction but also in allocating error reduction efforts (Zhang et al 2016;Li et al 2016). This uncertainty is different from the error variance of prediction from the Kriging family of models in that the Kriging family of methods report the error variance of prediction due to the spatial distribution of samples while the uncertainty computed based on the Third Law measures how well the various geographic configurations in the study area have been represented by the configurations already captured by the sample points.…”

Section: Differences From the First Law And The Second Law Of Geographymentioning

confidence: 99%

Spatial prediction based on Third Law of Geography

et al. 2018

Self Cite

View full text Add to dashboard Cite

Current methods of spatial prediction are based on either the First Law of Geography or the statistical principle or the combination of these two. The Second Law of Geography contributes to the revision of these methods so they are adaptive to local conditions but at the cost of increasing demand for samples. This paper presents a new thinking about spatial prediction based on the Third Law of Geography which focuses on the similarity of geographic configuration of locations. Under the Third Law of Geography, spatial prediction can be made on the basis of the similarity of geographic configurations between a sample and a prediction point. This allows the representativeness of a single sample to be used in prediction. A case study in predicting spatial variation of soil organic matter content was used to compare the spatial prediction based the Third Law of Geography with those based on the First Law and the statistical principle. It is concluded that spatial prediction based on the Third Law of Geography does not require samples to be over certain size nor to be of a particular spatial distribution to achieve a high quality prediction. The prediction uncertainty associated with spatial prediction based on the Third Law of Geography is more indicative to quality of the prediction, thus more effective in allocating error reduction efforts. These properties make spatial prediction based on the Third Law of Geography more suitable for prediction over large and complex geographic areas.

show abstract

Supplemental sampling for digital soil mapping based on prediction uncertainty from both the feature domain and the spatial domain

Cited by 17 publications

References 50 publications

Predicting the Spatial Distributions of Elements in Former Military Operation Area Using Linear and Nonlinear Methods Across the Stavnja Valley, Bosnia and Herzegovina

Predicting the Spatial Distributions of Elements in Former Military Operation Area Using Linear and Nonlinear Methods Across the Stavnja Valley, Bosnia and Herzegovina

Mapping soil properties in a poorly-accessible area

Spatial prediction based on Third Law of Geography

Contact Info

Product

Resources

About