Soil Genesis and Classification
Vahideh Sadeghizadeh; seyed ali abtahi; Majid Baghernejad; Azam Jafari; Seyed Ali Akbar Moosavi
Abstract
Introduction: The number of environmental variables used in digital soil mapping has increased rapidly, which has made it a challenge to select and focus on the most important covariates. No environmental covariates have the same predictability in modeling, and some covariates may introduce noise that ...
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Introduction: The number of environmental variables used in digital soil mapping has increased rapidly, which has made it a challenge to select and focus on the most important covariates. No environmental covariates have the same predictability in modeling, and some covariates may introduce noise that reduces the predictive power of the models used. On the other hand, it is beneficial to identify all environmental variables to obtain spatial information that can improve predictions. In this regard, the feature selection algorithms help reduce the dimensions of the predictive model by identifying the associated covariates. Therefore, this study aims to investigate different feature selection algorithms in the selection of auxiliary variables and evaluation their effect on the predictive model. Materials and Methods: The area under study is a part of Darab city in the southeast of Fars province with an area of about 31000 hectares. In the study area 140 profiles were determined and excavated according to the diversity of geomorphological units and thus the type of soils. After excavating the profiles and checking the morphological characteristics of each soil profile, a sufficient amount of soil samples were collected from the genetic horizons and transported to the laboratory for further analysis. Some of the physical and chemical parameters of soils were tested using accepted techniques after air drying and passing through a 2 mm sieve. Finally, all profiles up to the great group level were classified using the U.S. Soil Taxonomy based on the data collected from field observations and the outcomes of laboratory analysis. Environmental variables include the parameters derived from the Digital Elevation Model, Landsat 8 images, geology and geomorphology maps of the study area. All parameters were derived using ArcGIS, SAGAGIS and ENVI softwares. In the present study, four different feature selection techniques including Variance Inflation Factor (VIF), Principal Component Analysis (PCA), Boruta and Recursive Feature Elimination (RFE), were used to identify an optimal set of covariates for predicting spatial classification of soil classes at the great group level. In addition, a Random Forest model (RF) with 10-fold cross-validation and the 5-repeat method, was used to compare different feature selection strategies in soil class mapping. The comparison of different feature selection techniques in estimating soil classes, was based on the evaluation criteria of accuracy and Kappa coefficient between observed and predicted values.Results and Discussion: The results showed that the prediction accuracy increased by using variables selected with different feature selection methods compared to using all variables in the model. In addition, the improvement in predictive performance is different between the four types of feature selection. The VIF and PCA methods had the highest and lowest accuracy index and Kappa coefficient, respectively. The Boruta method, with the lowest number of variables, improved the model's performance after the VIF method. However, the Kappa coefficient showed poor agreement between predicted and observed values for all approaches. The imbalance of soil classes could be a reason for decreasing the accuracy index and Kappa coefficient. However, the random forest model, with and without feature selection methods, identified all soil great groups in the study area. Therefore, it can be concluded that the Random Forest algorithm is a very powerful technique for spatial prediction of soil classes in the study area. Although the performance of the model varied using different feature selection algorithms, the predicted soil maps had similar spatial patterns. Based on the prediction of model with the variables selected by the VIF, the resulting map indicates that Ustorthents soils are mainly located in high altitude regions with steep slopes. Haplustepts, Calciustepts, and Calciusterts great groups have developed in places with low to medium slopes. Haplosalids have developed downstream of the salt dome. Great groups of Ustifluvents were discovered in fluvial sedimentary plains. Endoaquepts were found in the floodplains, which had the smallest area on the predicted map. Conclusion: Overall, the findings indicate that the feature selection methods can utilize significant dependencies among relevant covariates to predict soil classes and to improve modeling accuracy. In the current study, the environmental factors, obtained from the Digital Elevation Model, were selected as key variables, showing the importance of topography and morphology in the classification of soil types in the area. Although the selected variables improved the performance of the model, the prediction of soil classes was random. This could be attributed to the imbalance of soil classes.
Soil Physics, Erosion and Conservation
Heidar Ghafari; Hadi Ameri khah
Abstract
Introduction: The processes of soil erosion and sediment transport along rivers are the main causes of some socio-economic and environmental problems, such as a reduction in water quality, storage capacity of dams, destruction of aquatic habitats, failure of hydroelectric power plants, and soil degradation. ...
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Introduction: The processes of soil erosion and sediment transport along rivers are the main causes of some socio-economic and environmental problems, such as a reduction in water quality, storage capacity of dams, destruction of aquatic habitats, failure of hydroelectric power plants, and soil degradation. Therefore, understanding the sedimentation status of watersheds is crucial for the effective management of soil and water resources. However, due to the lack of technical and human resources, continuous recording of sediment data is not possible in most sediment measuring stations, and sediment data are recorded only for a few days. In such a situation, a model that can estimate the amount of sediment load using auxiliary variables such as stream discharge and rainfall becomes crucial. Today, it is believed that techniques based on artificial intelligence have a much greater ability to uncover hidden relationships between variables than classical methods and are thus very useful and effective in modeling natural processes.Materials and Methods: In this study, various machine learning techniques, including Artificial Neural Network (ANN), Adaptive Fuzzy-Neural Inference System (ANFIS), and Random Forest (RF), were used for sediment load modeling and sediment forecast for days without measurements. To achieve the research objectives, long-term meteorological and hydrometric data ranging from 2000 to 2020 were collected from related organizations and pre-processed before entering the model. The input variables for the models included 24-hour rainfall, flow rate, normalized difference vegetation index, maximum and minimum temperature, and daily suspended sediment as the dependent variable. Prior to modeling, the entire dataset was divided into two parts, training and testing, in a 70:30 ratio. Relationship modeling was performed using the training data, and model validation was conducted using the test dataset. The efficiency of the models was evaluated using two indicators, the coefficient of explanation (R2) and the root mean square error (RMSE). Additionally, morphometric parameters such as form factor (FF), drainage density (DF) coefficient, and relief ratio (RR) were utilized in modeling.Results and Dscussion: The hydrological analysis of the basin revealed that the highest annual amount of rainfall and erosivity index were recorded at the Sheyvand station in the east of the basin, while the lowest values were observed at the Ramhormoz station. The highest average monthly flow rate of 5.8 cubic meters per second was obtained at the Manjeniq station in April, and at the Mashin station, the highest average monthly flow rate of 8.8 cubic meters per second was recorded in December and January. Morphometrically, the studied basin belonged to the class of elongated basins, sloping basins in terms of relief, and the medium class in terms of drainage density. Analysis of the time series of NDVI index showed that the highest vegetation cover occurred in March, while the lowest values were recorded in September and October. The annual trend of the vegetation index indicated an overall improvement in vegetation cover in the region from 2000 to 2020, with the NDVI value increasing from 0.15 to 0.22.Among the different machine learning techniques studied, the Artificial Neural Network (ANN) model had the highest coefficient of explanation (R2=0.87) and the lowest RMSE for both sediment measuring stations in the region, making it the best model. The optimal inputs for the neural network model at Mashin station were daily average flow adjusted by the basin shape factor, daily rainfall, last day's rainfall, daily minimum temperature and daily maximum temperature. For the Manjeniq station, the optimal inputs were daily average flow, daily rainfall, last day's rainfall, cumulative rainfall for the past two days, and cumulative rainfall for the past three days. The NDVI index was removed from the model due to its low significance. The Random Forest (RF) model ranked second, and the Adaptive Fuzzy-Neural Inference System (ANFIS) model ranked third, with weak performance, especially for the Mashin station, where out-of-range errors occurred.Temporal analysis of sediment values showed that the highest sediment production occurred in December and January for Mashin station and in April for Manjeniq station. The highest production of sediment occurred in 2006 and 2002, and the trend of changes from 2011 to 2018 showed a decline, attributed to consecutive droughts and lack of rainfall. The annual average sediment production calculated using the values estimated with the neural network model was 88017 tons, equivalent to 1 ton per hectare per year. Conclusion: Overall, this research demonstrated that machine learning methods, especially the neural network model, are highly effective for modeling and predicting sediment on a daily scale. These methods can compensate for the lack of sediment measuring facilities and equipment in most existing hydrometric stations in the country and eliminate the need for continuous sediment data and other water quality parameters.
F. Torkamani; H. Piri Sahragard; M.R. Pahlavan Rad; M. Nohtani
Abstract
Introduction Spatial variations of soil properties is a natural event, which recognizing these changes is inevitable in order to planning and right management of both agricultural and natural resources. Soil organic carbon (SOC) is the most important factor in soil fertility and quality, climate change ...
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Introduction Spatial variations of soil properties is a natural event, which recognizing these changes is inevitable in order to planning and right management of both agricultural and natural resources. Soil organic carbon (SOC) is the most important factor in soil fertility and quality, climate change and reduction of greenhouse gas emissions. Furthermore, evaluating the rates and spatial distribution of the soil properties, land improvement and restoration can be traced from the carbon sequestration index. According to the above, providing quantitative and qualitative conservation of soil properties such as SOC can be considered an effective way to achieve sustainable development of natural and environmental resources. Digital soil mapping (DSM) can determine the spatial variations of soil organic carbon by exploring the relationship between soil properties and effective environmental variables. Different statistical models such as regression trees and random forest are used in order to communicate between soil characteristics and its spatial distribution. The present study was carried out to investigate the spatial distribution of SOC, as well as, to determine the most important variables affecting their prediction in Ravang watershed in Minab County. Materials and MethodsRavang watershed with an area of 13821.6 hectares is located in Hormozgan province, Minab city. The maximum and minimum elevations are 357 and 33 meters, respectively. Digital Elevation Model of Ravang watershed was used to extract 17 environmental covariates (such as elevation, aspect, slope, valley depth,…) by SAGA software (http://www.gdem.aster.ersdac.or). Moreover, two environmental covariates related to remote sensing including Normalized Difference Salinity Index (NDSI) and Normalized Difference Vegetation Index (NDVI) were determined in the study area. In addition, the maps of land use, sand, silt, clay and pH were used as covariates in modeling. In order to determining the location of sampling points, the conditioned hyper-cube technique was used. After determining of soil sample location, field sampling was carried out at a depth of 0-30 cm. then, 100 soil samples were taken and the amount of SOC was measured. Random forest model was applied to the relationship between SOC and covariates. The model includes two user-defined parameters, including the number of variables used in the construction of each tree, which expresses the power of each independent tree and the number of trees in each forest. Considering the strength of independent trees, the predictive accuracy of the model increases, conversely, the correlation between them will decrease. The accuracy of the soil organic carbon distribution was also evaluated using root mean square error (RMSE), mean error (ME) and correlation coefficient (R2), which were determined. Results and Discussion Based on the present study results, elevation, soil silt and sand maps, channel network base level, slope and NDVI are the most important factors on predicting the of SOC variations. The results indicated that RMSE, ME and R2 were 0.36, 0.26 and 0.38, respectively .Results also showed that affecting erosion and sediment, as well as, human effect, have the most impact on the SOC soil spatial distribution in the Ravang watershed. Moreover, result show SOC deficiency in the soil of Ravang watershed due to high salinity, low percent of vegetation cover and land use changes. In addition, drought intensifying and decrease in precipitation have reduced SOC content, which itself causes changes in the texture and chemical properties of the soil and, as a consequence, makes them more susceptible to erosion. Conclusion The variability of SOC is very high in the study area because of intensive water erosion and land use change. Overall, the results of the present study indicated that the critical condition of soil organic carbon in the Ravang watershed, which requires a comprehensive management of the region's water and soil resources to improve soil conditions and increase the reserves of this important and influential variable in the soil structure. On the other hand, despite of the acceptable performance of the random forest model in estimating of soil properties, due to high variability of some soil properties, model prediction performance may be decreased.
Soil Genesis and Classification
samaneh Tajik; shamsollah ayoubi; mohmmad mehdi darvisihi; hossein khademi
Abstract
Introduction Soil snails constitute an important part of the forest ecosystem and play an essential role in litter decomposition and soil calcium concentration. Snails are known as bioindicators because of narrow distribution, short lifetime, and high sensitivity (22, 24). The abundance and distribution ...
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Introduction Soil snails constitute an important part of the forest ecosystem and play an essential role in litter decomposition and soil calcium concentration. Snails are known as bioindicators because of narrow distribution, short lifetime, and high sensitivity (22, 24). The abundance and distribution of soil snails are dependent on different environmental conditions, such as precipitation, pH, soil calcium, and plant cover. Also, soil properties are mainly related to topographic parameters. Because ecosystem components have complex relationships, we need powerful models to find effective factors and spatial variations of the soil fauna (23). Linear Regression and random forest are popular and applicable models in soil science. Up to the present, no study has investigated the effect of soil parameters on snail abundance using linear regression and random forest. This study was performed to investigate the effect of soil properties and topographic parameters on the abundance of soil snails and their distribution in a part of forest area located in Bahramnia forest, an experimental site in Golestan Province, in the north of Iran. Materials and Methods This study was conducted in Shast Kalate (Bahramnia) forest, an experimental forest of Gorgan University of Agricultural Sciences and Natural Resources, located at the eastern Caspian region, north of Iran (36° 43′ 27″ N latitudes, 54°24′ 57″ E longitudes). 153 soil samples were collected from 0-10 cm; then soil snails were gathered and classified into the Gastropoda taxonomic class group. Soil properties, such as Soil particle size distribution (clay, silt, and sand), soil pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), soil organic carbon (OC), total nitrogen (TN), and Soil microbial respiration (Resp), were measured via laboratory analysis. Also, digital elevation model and satellite images were used to determine the topographic parameters, such as Elevation, slope, slope aspect (Aspect), land surface temperature (land temp) wetness index (WI) and normalized difference vegetation index (NDVI). We used linear regression and nonlinear random forest models for investigating linear and nonlinear relationships between soil properties, topographic parameters, and the abundance of soil snails. Likewise, sensitive analysis was done to find the importance of the input parameters. Results and Discussion The PCA analysis showed that first and second components explain 38 and 21 percent of the variation. In the first component, EC, OC, TN, pH, and silt were the most variable, and in the second component CCE, Clay, OC, sand, and EC were the most important parameters. In both components, topographic parameters had no effect. The PCA graph showed that CCE, sand, and pH had the most correlation with snail abundance and EC, Resp, OC, and TN affected their abundance. The validation results of regression and random forest models showed that random forests have more accuracy (0.49) and low error (1.82). In addition, the sensitive analysis showed that CCE, pH, EC, OC, aspects, elevation, and land temp are the most important parameters on snail abundance. Different studies reported that pH and CCE are effective parameters on snail abundance (20, 17). Also, Ondina., et al. (27) reported that EC has an important effect on soil snail abundance. We hypothesize that topographic parameters affect soil snail nonlinearly and by affecting soil properties. Aspect is one of the topographic parameters that, via an effect on land temperature, land cover, and pH (8), has an important role in soil snails. In this way, elevation, by affecting pH, wetness, land temperature, OC, and TN, affects soil snail abundance (13). Land temperature is the other topographic parameter that is affected by aspect and elevation and had a significant effect on snail abundance by affecting OC and wetness (17). Conclusion Based on the results, nonlinear random forest model had more accuracy than linear regression in predicting snail abundance. Results showed that calcium carbonate equivalent, pH, EC, and organic carbon were the most effective soil priorities on snail abundance. There was no linear relation between soil properties and soil snails, but in the nonlinear model, we found their role. Aspect, elevation, and land temperature were the most effective parameters on snail abundance that probably affected soil properties, such as calcium carbonate and soil moisture.
