Soil Genesis and Classification
Mansooreh Khaleghi; Azam Jafari; Mohammad Hadi Farpour
Abstract
Introduction Soil digital mapping represents a set of mathematical computations to predict the distribution of soil classes in the landscape. This approach relies on statistical relationships between measured soil observations and environmental covariates at the sampling locations. The need for digital ...
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Introduction Soil digital mapping represents a set of mathematical computations to predict the distribution of soil classes in the landscape. This approach relies on statistical relationships between measured soil observations and environmental covariates at the sampling locations. The need for digital soil mapping as an addition to conventional soil surveys results from a worldwide growing demand for high- resolution digital soil maps for environmental protection and management as well as projects of the public authorities. Digital soil data is increasing based on new processing tools and various digital data. The digital identification of soils as a tool for creating soil spatial data provides ways to address the growing need for high-resolution soil maps. The main objective of this study is to generate the digital soil map based on the legacy soil data. Materials and methods The study area is located in southeastern Iran, 330 km from Kerman city, in Faryab distinct. In this study, a Latin hypercube sampling design was applied and the sampling was done according to the difference in landforms (geomorphology map), topography (including digital elevation map) and geology. The geographic locations of 70 profiles were identified. Soil profiles were described according to U.S. Soil Taxonomy (Soil Survey Staff, 2014) and finally, the soil samples were taken from their diagnostic horizons. The collected soil samples were transferred to the laboratory, and some physical and chemical analyzes were performed based on routine standard methods. Environmental data include the parameters derived from the digital elevation model, Landsat satellite images (remote sensing indexes), geology map, geomorphic units (geomorphology map) and legacy soil map of the study area. All environmental variables were derived using ENVI and SAGA software. In this research, a multinomial logistic regression model was used to predict soil classes and the modeling was done in two scenarios: 1- modeling without the legacy soil map and 2- modeling with the legacy soil map. Estimation of predictive accuracy of soil classes was also done using the overall accuracy index and Kappa coefficient. Results and discussion The result of the modeling with the multinomial logistic regression method in two sets of input variables showed that the topographic position index is the most effective variable in predicting soil classes. This confirms topographic importance on soil genesis in the studied area. After topographic variables, the legacy soil data is an effective parameter in modeling. The legacy data of soil is a strong and valuable database for predicting soil characteristics. The old soil map consists of the salt surfaces and Inceptisols order. Unlike the hot and arid climate of the study area, Inceptisols order was identified in the old soil map. Soil survey with very small scale was probably led to generalization of the studied soils and hiding the main soils of the study area. However, the small-scale mapping and the presentation of different soils in the region do not prevent the presence of the old soil map as an important predictor. It seems that there is a high concordance between the borders of old soil map and the described soils diversity in the study area. The matching and concordance between the boundaries of the old map and the described soil profiles help the model to differentiate different soils, although the correspondence between the soils type of the old soil map and the observed soils can play a more effective role in predicting by the model. Soil legacy information is a powerful and valuable database for predicting any feature of the soil. In both predicted maps, four major groups of Haplosalids, Haplocambids, Haplocalcids and Torriorthents were identified. The great group of Torriorthents is located in the north of the region and in the alluvial fan landform. Haplosalids great groups were most commonly found in clayey surfaces. Haplocambids and Haplocalcids great groups are located more in the geomorphic surface of the cultivated fan and the piedmont plain, respectively. The results of the predictive quality of the logistic regression model showed that the number of well-estimated soils in the presence of the old soil map is more than when there is no old soil map in the modeling. In addition, the results of the validation of the models showed that the map accuracy and kappa index increased in presence of the legacy soil map. As a result, the model's validation indices including the map purity and Kappa index increased from 0.47 and 0.16 to 0.63 and 0.43, respectively. In both models, the highest accuracy of the estimation was obtained for Haplocambids great group. Conclusions The results showed that topographic position index was the most important and powerful variable for forecasting in both models, and confirms that topography or relief is the most important soil forming factor in the study area. Using the legacy soil map as one of the environmental variables in modeling, efficiency and accuracy are more accurate than modeling without the legacy soil map. If the old soil maps as legacy information are used in digital soil mapping, the similarity and matching of the soils of the studied area shoud be cheched even with the very small scale because the high concordance leads to rational prediction, and random and chance predictions do not occur.
