Showing 7 results for Soil Mapping
] Esfandiarpor, M.h Salehi, N Tomanian, J Mohamadi,
Volume 13, Issue 49 (10-2009)
Abstract
Geopedology is a systematic approach of geomorphic analysis for soil mapping that construct field operation mainly upon the work in sample area. The main goal of this research is to determine the effect of location of sample area and expert knowledge on credibility of generalization of the results obtained from geopedological approach for similar landforms in south-east of Borujen area. Upon preparation of primary interpretation map of the study area on air photos (1:20,000 scale), and considering different locations of Pi111 unit that encompasses the maximum surface of the study area, the sample area was planned in three different locations. Then, a second-order soil survey was conducted and final soil map was prepared. Also, two different experts were considered to determine the amount of credibility of generalization of the results obtained through geopedological approach for the mentioned unit. Results showed that changing the location of sample area has taxonomic levels (order, subgroup and/or family) and map unit type (complex and consociation) differences in Pi111 unit. In spite of similarity of the profiles selected by the two experts, soil taxonomies of these profiles were different in comparison with representative pedons (at family level). Therefore, the use of landform phases is recommended to increase the accuracy of geopedological results.
M. Bagheri Bodaghabadi, M. H. Saleh, I. Esfandiarpoor Borujeni, J. Mohammadi, A. Karimi Karouyeh, N. Toomanian,
Volume 16, Issue 61 (10-2012)
Abstract
Discrete Models of Spatial Variability (DMSV) have limitations for soil identification in traditional soil maps. New approaches, generally called digital soil mapping (DSM), using continuous methods (CMSV), try to predict soil classes or soil properties based on easily-available environmental variables. The objective of this study was to map the soil classes of the Borujen area, Chaharmahal-va-Bakhtiari province, using digital elevation model (DEM) and its attributes and Soil-Land Inference Model (SoLIM). To do this, eighteen terrain attributes were derived from the DEM of the area. The primary analysis showed seven attributes are the most important derivatives. These derivatives as well as three dominant soil subgroups and seven soil families of the region (41 profiles from 125 profiles) were used to construct the input data matrix of the model. Then, output fuzzy soil maps of SoLIM were converted to polygonal soil map, using ArcGIS. Results showed that different combinations of DEM attributes have different accuracy rates for soil prediction. The accuracy of the interpolation was twice that of the extrapolation. Although SoLIM had an acceptable accuracy for soil nomination, and identification of soil map units’ types, it did not have enough accuracy for the location of soil classes. It seems that using other data like parent material and geomorphic surface maps will increase the accuracy of the model prediction.
Y. Safari, I. Esfandiarpour Boroujeni,
Volume 17, Issue 65 (12-2013)
Abstract
In order to study the precision of qualitative land suitability classification method for main irrigated crops (i.e. potato, sugar beet, wheat and alfalfa) in the Shahrekord plain, qualitative land suitability maps were obtained for all the studied crops according to representative pedon analysis using simple limitation method. In the next step, a regular grid sampling consisting of 100 sample points with a distance of 375 m was designed. Then all required analyses were done to recognize the suitability class of these sites for each land use. Finally, land suitability results for all the observation points in each map unit were compared with the results of its representative pedon. The results showed the average of measured compatibility between representative pedon and other observation points in each map unit in class and subclass levels was about 60 % and 38 %, respectively. Due to the generalization of representative pedon analyses to all unit area, the use of soil map units as land suitability units may lead to unsatisfactory results. Therefore, the use of representative pedon is not recommended in sustainable land management and precision agriculture. However, new techniques like geostatistics can be used to improve the conventional soil mapping methods.
S. Ayoubi, R. Taghizadeh, Z. Namazi, A. Zolfaghari, F. Roustaee Sadrabadi,
Volume 20, Issue 76 (8-2016)
Abstract
Digital soil mapping techniques which incorporate the digital auxiliary environmental data to field observation data using software are more reliable and efficient compared to conventional surveys. Therefore, this study has been conducted to use k- Nearest Neighbors (k-NN) and artificial neural network (ANN) to predict spatial variability of soil salinity in Ardekan district in an area of 700 km2, in Yazd province. In this study, 180 soil samples were collected in a grid sampling manner and then soil chemical and physical properties were measured in laboratory. Environmental auxiliary variables were included topographic attributes, remote sensing data (ETM+) and apparent electrical conductivity (ECa). The result of the study showed that the K-mean nearest neighborhood had higher accuracy than ANN models for predicting soil electrical conductivity (ECe). Overall, k-NN models could provide significant relationships between soil salinity data and environmental auxiliary variables. The k-NN model had the root mean square and coefficient of determination of 12.10 and 0.92, respectively, between predicted and observed ECe data. Also, apparent EC, and remotely sensed indices and wetness index were identified as the most important factors for predicating the soil salinity in the studied area.
E. Mehrabi Gohari, H. R. Matinfar, R. Taghizadeh,
Volume 21, Issue 3 (11-2017)
Abstract
Typical routine surveys of soils are relatively expensive in terms of time and cost and due to the fact that maps have been traditionally developed and considering their dependence on experts' opinions, updating maps is time consuming and sometimes not economical as well. While soil digital mapping, using soil various models - the Landscape, leads to simplification of the complexity found in natural soil systems and provides users with quick and inexpensive updates. In fact, the model represents a simplified form of the complex relationships between the soil and the land. This study aims to consider inferential model Soil-Land (SOLIM) in mapping and estimating soil classes in Aran area, Isfahan province. For this purpose, the SOLIM model inputs are digital geological and environmental layers of digital elevation model (DEM) including elevation, slope in percent, slop direction, curvature of the earth's surface, wetness indicator, flow direction, flow accumulation, and satellite images of Landsat 8. The seven subcategory of soil in the study area are input data of SOLIM model. Then fuzzy maps were prepared for seven types of soil and final maps of soil prediction were created by non-fuzzy action. Results showed that the SOLIM using environment variables has very high ability to separate soil types in greater detail and soils with different parent materials, geology, climate and vegetation can be separated from each other by this model with a high degree of accuracy. Comparing error matrix shows that the overall accuracy of the map derived from the model SOLIM is 92.36%.
A. Fariabi, H. Matinfar,
Volume 22, Issue 3 (11-2018)
Abstract
One of the problems with the traditional mapping of soils is the expert’s opinion, it time-consuming and timely preparation, and the updating of the maps. While digital soil mapping, using different soil-earth models leads to the simplification of the complexity of the soil system. The purpose of this study was to investigate Soil-Environment Inference (SIE) in soil mapping with an emphasis on using the expert knowledge and fuzzy logic. For this purpose, the digital layer of geology and peripheral layers were derived from a digital elevation model including elevation, slope, and curvature of the ground surface, and auxiliary index, which comprised the input data of the SIE model. Then, the fuzzy maps prepared for the five soil types and the final map of soil prediction were created by hardening. The results showed that the SIE model, which used environmental variables, had a high ability to isolate soil types with more detailed compositions of soils with different maternal materials. The comparison of the error matrix showed that the overall accuracy of the derived map of the SIE model was equal to 75%, and the matching of the digital mapping results with conventional mapping accounted for 74.71% of the results. The difference in the compliance rate could be attributed to the difference in the nature of the two methods.
H. R. Matinfar, Z. Mghsodi, S. R. Mossavi, M. Jalali,
Volume 24, Issue 4 (2-2021)
Abstract
Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC.
