Toward delineating hydro-functional soil mapping units using the pedostructure concept: A case study

Digital soil mapping (DSM) is currently the primary framework for predicting the spatial variation of soil information (soil type or soil properties). Random forests and similarity-based methods have been used widely in DSM. However, the accuracy of the similarity-based approach is limited, and the performance of random forests is affected by the quality of the feature set. The objective of this study was to present a method for soil mapping by integrating the similarity-based approach and the random forests method. The Heshan area (Heilongjiang province, China) was selected as the case study for mapping soil subgroups. The results of the regular validation samples showed that the overall accuracy of the integrated method (71.79%) is higher than that of a similarity-based approach (58.97%) and random forests (66.67%). The results of the 5-fold cross-validation showed that the overall accuracy of the integrated method, similarity-based approach, and random forests range from 55% to 72.73%, 43.48% to 69.57%, and 54.17% to 70.83%, with an average accuracy of 66.61%, 57.39%, and 59.62%, respectively. These results suggest that the proposed method can produce a high-quality covariate set and achieve a better performance than either the random forests or similarity-based approach alone.

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Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Land ◽

10.3390/land9090319 ◽

2020 ◽

Vol 9 (9) ◽

pp. 319 ◽

Cited By ~ 1

Author(s):

Mohamed Ali Mohamed

Keyword(s):

Urban Areas ◽

Human Impacts ◽

Soil Mapping ◽

Fuzzy Classification ◽

Small Scale ◽

Ground Truth Data ◽

Terrain Attributes ◽

Lidar Dem

In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.

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QUANTITATIVE COMPARISON OF TWO CLOSELY RELATED SOIL MAPPING UNITS

Canadian Journal of Soil Science ◽

10.4141/cjss74-002 ◽

1974 ◽

Vol 54 (1) ◽

pp. 7-14 ◽

Cited By ~ 6

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.

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