Maryam Izadi Bidani; A Jafari; Mohammad Hadi Farpoor; Mojtaba Zeraatpisheh
Abstract
Introduction: Soil digital mapping represents a set of mathematical computations to predict the distribution of soil classes in the landscape. . 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 use ...
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Introduction: Soil digital mapping represents a set of mathematical computations to predict the distribution of soil classes in the landscape. . 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 use of digital soil mapping technique has been expanded considerably; therefore, new methods of mapping and preparing digital maps have been developed by researchers to eliminate the limitations of traditional methods. This approach relies on statistical relationships between measured soil observations and environmental covariates at the sampling locations. Digital soil data is increasing based on new processing tools and various digital data. The present study was conducted with the purpose of digital soil mapping in Kouhbanan region of Kerman based on a Multinomial logistic regression model. Materials and methods: The study area is located in southeastern Iran, northwest of Kerman city, in Kouhbanan distinct. This study covers a 2000 ha area. 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. Finally, 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 R software using nnet package. It is worth noting that leave-one-out cross validation was used for validation. Estimation of predictive accuracy of soil classes was also done using the overall accuracy index and Kappa coefficient.Results and discussion: The results showed that the soils of the study area were mainly classified in the Aridisols and Entisols orders. The modeling results showed that the terrain attributes were recognized as the effective auxiliary variables in the prediction process of soil classes. This confirms topographic importance on soil genesis in the studied area. After that, geomorphology map was an important tool in soil mapping that helps to increase predictive accuracy. Among the soil classes, the prediction of Haplocambids was accompanied with low accuracy, while Haplosalids great groups were predicted with high accuracy. The low estimation accuracy of the great group of Haplocambids is probably due to the low sample size of this class of soil in the study area. A good identification of the relationships between the predictor variables and the target variable depends primarily on the size and distribution of the sample in the layers. There were only two examples of Haplocambids in the area. Therefore, low accuracy is expected because the model has failed to establish a relationship between this class with environmental variables and makes it difficult to identify threshold values for classifying soil classes and, consequently, a poorly trained model. It is also possible that low prediction accuracy is the result of the conceptual model being incomplete, since there is no characteristic feature that can help model training and ultimately prediction. Among the soil great groups, the best predictions were obtained for the great group of Haplosalids, which demonstrates high values of user accuracy and reliability. Accurate prediction of the class of Haplosalids is highly correlated with the spatial distribution of indices such as wetness index and NDVI. Kappa index and purity map were calculated 0.45 and 0.65 for digital soil map derived from multinomial logistic regression. In the predicted map, six major groups of Haplosalids, Haplocambids, Haplocalcids, Haplogypsids, Calcigypsids and Torrifluvents were identified. The great groups of Haplocalcids, Haplosalids, and Calcigypsids cover most of the area and the great groups Haplocambids and Haplogypsids occupy lowest of the area. The great group of Haplosalids is located in the north of the region and in the piedmont plain landform. Haplocalcids great groups were most commonly found in alluvial fan landform, while Calcigypsids are located in pediments, alluvial fans, and piedmont plain landforms. Haplocambids and Haplogypsids great groups are located more in the geomorphic surface of the alluvial fan and the piedmont plain, respectively. The parts of the region with the most variations or diversity of soil classes are exactly where the geomorphological map has the most segmentation. Therefore, the presence of different soil classes in the least-differentiated and most similar regions is resulted to an inefficient conceptual model and poor prediction results. Conclusions: The results showed that topographic parameters were the most important and powerful variable in modeling, and confirms that topography or relief is the most important soil forming factor in the study area. Predictive results of soil classes in Kuhbanan area of Kerman province showed that geomorphological map in the study area is very useful and necessary and also is effective in understanding and communicating between soil and landscape. Using this map as a qualitative auxiliary variable can explain much of the variability of soils in the study area. Careful field observation, satellite imagery consideration, study and interpretation of data obtained from soil profiles indicate that the study area has been evolved by geological, geomorphological, and hydrological processes that lead to the formation of various landforms including rock outcrops, hills, pediment , alluvial fan and plain. For the multinomial logistic regression model in the study area, terrain attributes have the most influence on the prediction of soil classes and soil properties than the remote sensing indices. The strong relationship between soil data and environmental parameters is one of the factors influencing model accuracy. Logistic regression models will have great potential in predicting soil classes if a complete understanding of the study area and proper selection of auxiliary variables are carried out.
Zahra Masoudi; A. Jafari; Mohammad Hady Farpoor
Abstract
Introduction: Soil maps are a common source of information for land suitability studies. Land suitability studies are to compare land characteristics with the needs of land-use types and to select the best land-use productivity types for cultivation. Land evaluation analysis is considered as an interface ...
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Introduction: Soil maps are a common source of information for land suitability studies. Land suitability studies are to compare land characteristics with the needs of land-use types and to select the best land-use productivity types for cultivation. Land evaluation analysis is considered as an interface between land resources and land use planning and management. However, the conventional soil surveys are usually not useful for providing quantitative information about the spatial distribution of soil properties that are used in many environmental studies. Development of the computers and technology lead to develop the digital and quantitative approaches in soil studies. These new techniques rely on the relationships between soil and the environmental variables that explain the soil forming factors or processes and finally predict soil patterns on the landscape. Different types of the machine learning approaches have been applied for digital soil mapping of soil classes. To our knowledge, most of the previous studies applied land suitability evaluation based on the conventional approach. Therefore, the main objective of this study was to assess the performance of digital mapping approaches for the qualitative land suitability evaluation in the Jiroft plain of Kerman province. Materials and Methods: An area in the Jiroft plain of Kerman Province, Iran, across 28º14′ and 28º 26′N, and 57º 30′ and 57º 46′E was chosen. The study area is placed on alluvial plain, gravelly alluvial fans, eroded hills. Based on Google Earth image, geomorphology and topography maps and also field survey, 62 pedons were selected and excavated, and soil samples were taken from different soil horizons. Then, soil physicochemical properties were determined. To assess the climate, the climate information obtained from the Jiroft Synoptic Station. The average of soil properties was determined by considering the depth weighted coefficient up to 100 centimeters for potato. Qualitative land suitability evaluation for potato was determined by matching the site conditions (climatic, hydrology, vegetation and soil properties) with studied crop requirement tables presented by Givi (5). Land suitability classes were determined using parametric method. Land suitability classes reflect degree of suitability as S1 (suitable), S2 (moderately suitable), S3 (marginally suitable) and N (unsuitable). For digital approach, multinomial logistic regression (MLR) was used to test the predictive power for mapping the land suitability evaluation. Terrain attributes (elevation, slope, aspect, wetness index and multiresolution valley bottom flatness (MrVBF)), remote sensing indices (normalized difference vegetation index (NDVI), perpendicular vegetation index (PVI), and ratio vegetation index (RVI)), geology map, and geomorphology map were used as auxiliary information. Finally, all of the environmental covariates were projected onto the same reference system (WGS 84 UTM 40 N). Training and validating the model was done by leave-one-out cross validation. The accuracy of the predicted soil classes was determined using error matrices and overall accuracy. Results and Discussion: The results showed that climatic conditions are suitable (S1) for potato. The most important limiting factors were the gravel content, soil acidity and soil salinity for potato growing in the study area. Land suitability classes S2 to N were determined based on land index in the study area. The modelling results demonstrated overall accuracy 0.47 and 0.25 for class and subclass of land suitability, respectively. It seems that low number of soil samples for training and validating of the model were probably caused to low accuracy as compared to the other researches. In addition, the overall accuracy decreased from class to subclass. The terrain attributes (slope and aspect), remote sensing indices (normalized difference vegetation index) and geomorphology map were the most important auxiliary information to predict the land suitability classes and subclasses. This indicates the importance of geomorphological processes for determining the land suitability class in the study area. Conclusion: Results suggest that the land form, land position and geomorphology processes affect soil properties and then, land suitability classes. Therefore, variability of land suitability classes is function of variability of soil properties. Digital approaches could help to obtain the information with high resolution, provided that the criteria of suitability are associated with variability of soil properties. Although digital mapping approaches increase our knowledge about the variation of soil properties, integrating the management of the sparse lands with different owners should be considered as the first step for optimum soil and land use management.
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.
