Sheler Eskandari; kamal nabiollahi; Ruhollah Taghizade-Mehrjardi
Abstract
Introduction: Soil organic carbon is one of the most important soil properties which its spatial variability is essential to crop management, land degradation and environmental studies. Investigation of variability of soil organic carbon using traditional methods is expensive and time consuming. Therefore, ...
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Introduction: Soil organic carbon is one of the most important soil properties which its spatial variability is essential to crop management, land degradation and environmental studies. Investigation of variability of soil organic carbon using traditional methods is expensive and time consuming. Therefore, one of the ways to overcomethis challenge is using digital soil mapping whichcan predict soil characteristics using auxiliary data and data mining methods. Previous studies have shown that digital elevation model (DEM) and remotely sensed data are the most commonly useful ancillary data for soil organic carbon prediction. Artificial neural network (ANN) is a common technique of digital mapping. The region of Marivan in Kurdistan province is one of the forested areas inIran. In recent decades, due to population growth and the increased need for food, thisforested area has been threatened and some parts are now cultivated. Therefore, accurate mapping of soil organic carbon so as to improve land management and prevent land degradation is necessary. The purpose of this research wasusing ANN model and auxiliary data to mapsoil organic carbon.
Materials and Methods: The study area is located in Kurdistan Province, Marivan(cover 20000 ha). Soil moisture and temperature regimes are Xeric and Mesic, respectively. Elevation also varies between 1280 and 1980 m. The main land use typesarecropland, forestland and wetland. The major physiographic units are piedmont plain, mountain and hills with flat to steep slopes. Using stratified random soil sampling method, 137 soil samples (for the depth of 0-30 cm) were collectedand soil organic carbon were measured. In the current study,auxiliary data were terrain attributes and ETM+ data of Landsat 7. Terrain parameters (including 15 factors), bands 1, 2, 3, 4, 5, 6, 7, brightness index (BI) and normalized difference vegetative index (NDVI) were computed and extracted using SAGA and ArcGIS software, respectively. ANN model was applied to establish a relationship between soil organic carbon and auxiliary data. Finally, soil organic carbon weremappedusing ANN and validated based oncross validation method. Three different statistics were used for evaluating the performance of model in predicting soil organic carbon, namely the coefficient of determination (R2), mean error (ME) and root mean square error (RMSE).
Results and Discussion: Based on sensitive analysis of ANN model, auxiliary variables includingwetness index, index of valley bottom flatness (MrVBF), LS factor, NDVI index, and B3were the most important factors for prediction of soil organic carbon. The quantities of R2, ME and RMSE calculated for ANN model were0.80, 0.01 and 0.67, respectively.Soil organic carbon content ranged from0.26 to 8.45 % and the highest contentwasobserved in forestland with hill and mountain physiography and wetland around the lake. It is noteworthy that the differences fordifferent land uses were not statistically significant. Auxiliary data including wetness index, index of valley bottom flatness, LS factor, and B3 in different land uses had statistically significant difference (p
kamal nabiollahi; ahmad haidari; rohollah taghizade mehrjardi
Abstract
Soil texture is an important soil physical property that governs most physical, chemical, biological, and hydrological processes in soils. Detailed information on soil texture variability is crucial for proper crop and land management and environmental studies. Therefore, at present research, 103 soil ...
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Soil texture is an important soil physical property that governs most physical, chemical, biological, and hydrological processes in soils. Detailed information on soil texture variability is crucial for proper crop and land management and environmental studies. Therefore, at present research, 103 soil profiles were dogged and then sampled in order to prepare digital map of soil texture in Bijar, Kurdistan. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes, Landsat 7 ETM+ data and a geomorphologic surfaces map. To make a relationship between the soil data set (i.e. Clay, sand and silt) and auxiliary data, regression tree (RT) and artificial neural network (ANN) were applied. Results showed that the RT had the higher accuracy than ANN for spatial prediction of three parameters. For the clay fraction, determination of coefficient (R2) and root mean square root (RMSE) calculated for two models were 0.46, 0.81 and 17.10, 12.50, based on validation data set (20%). Our results showed some auxiliary variables had more influence on predictive soil class model which included: geomorphology map, wetness index, multi-resolution index of valley bottom flatness, elevation, slope length, and B3. In general, results showed that decision tree models had higher accuracy than ANN models and also their results are more convenient for interpretation. Therefore, it is suggested using of decision tree models for spatial prediction of soil properties in future studies.
M. Zarinibahador; - K. Nabiollahi; M. Norouzi
Abstract
Introduction: Spatial variation of soil properties is significantly influenced by numerous environmental factors such as landscape features, including position, topography, slope gradient and aspect, parent material, climate and vegetation. Soil properties vary spatially in south- and north-facing hill ...
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Introduction: Spatial variation of soil properties is significantly influenced by numerous environmental factors such as landscape features, including position, topography, slope gradient and aspect, parent material, climate and vegetation. Soil properties vary spatially in south- and north-facing hill slopes. This factor (different slope aspects) can affect the distribution of soil organic matter, the presence or absence of a layer, pH, nutrient levels, soil mineralogical and micromorphological properties. Topographic factors such as the orientation of the hill slope and the steepness of the slope affect microclimate, vegetation establishment, water movement and erosion. Aspect and slope control the movement of water and materials in a hill slope and contribute to differences in soil properties. Temperature, precipitation and climate vary with elevation and influence pedogenic processes. Accelerated rates of weathering and soil development were found to occur in soils on south-facing slopes. Slopes with a south aspect are dominated by stone and bare soil patches, while slopes with a north aspect are dominated by biotic components. Northern slopes have higher productivity and species diversity compared to Southern slopes. Slope aspect has a significant effect on the composition, species richness, structure and density of plant communities, differed significantly between North- and South- facing slopes.
Materials and Methods: In the present study, the effects of two slope aspects on some soil properties and soil evolution was investigated in Northern Rostam Abad region in the Guilan Province. Five profiles in Southern hill slope(South-facing hill slopes) and five profiles in Northern hill slopes(North-facing hill slopes) with 40% slope and same parent material (basaltic andesite) and same plant cover were dug. The elevation of two slope aspects was 240 meters from the sea level. Average annual temperatures and precipitation are16 degrees centigrade and 1359 mm, respectively. Thus, the soil moisture and temperature regimes are udic and thermic, respectively. The physical and chemical analysis were carried out on soil samples including particle size distribution, bulk density, pH, organic carbon, total nitrogen, available phosphor and cation exchange capacity. This study was done in a completely randomized design several observational with five replications. The total of 34 soil samples were collected in the two slope aspect of the profile and all samples were tested and statistical analyzed. For the micromorphological study, thin sections were prepared from undisturbed samples. The samples were impregnated with polyester resin and later sectioned. The thin sections were prepared and analyzed in petrographic microscope equipped with polarized light.
Results and Discussion: The results of multivariable analysis of variance (MANOVA) and Hotteling's T2 showed that there is significant different in soil properties between two hill slopes(p≤0.01). Also, the results of t-test showed the values of pH, content of sand, sand to clay ratio and available phosphorous significantly was higher in Southern hill slope in comparison with Northern hill slope(p≤0.01). Whereas, clay content and cation exchange capacity significantly were higher in Northern hill slope in comparison with Southern hill slope(p≤0.05). Also observed micromorphological studies showed biological activity was stronger in Northern hill slope in comparison with Southern hill slope. Properties observed in thin sections of Northern slope aspect include fungal hyphae, spherical and ellipsoid excrement of microorganisms in root residual (related to oribatid mites) which indicated stronger biology in Northern slope aspect soils as compare to Southern slope aspect soils. Also, more accumulates* of clay inside voids, nodules, fragmented of coating of well-oriented, micro laminated, reddish-brown clay, chamber voids in Northern slope soils toward Southern slope soils were observed. B-fabricobserved in Northern hill slope soils is stipple speckled in surface horizons and in subsurface horizons is grano-striated and stipple speckled and b-fabric observed in Southern hill slopes soils in surface horizons and subsurface horizons is stipple speckled.
Conclusion: Higher content of clay, Cation exchange capacity, Accumulation of clay in pores, Fragments of clay coating (papule), chamber pores, Fe/Mn oxide nodule and micro-laminations in Northern hill slope and higher values of pH, higher content of sand, sand to clay ratio and available phosphorous, lithorelict in Southern hill slope showed that weathering was higher in Northern hill slope in comparison with Southern hill slope. Generally, Southern hill slope had less developed soils (Entisols and Udorthents great group) and Northern hill slope had high developed soils (Alfisols and Hapludalfs great group).