| چکیده انگلیسی مقاله |
Introduction and Goal
Soil organic carbon affects all physical, chemical, and biological properties of the soil, causes soil fertility and is one of the most important soils qualities. Soil organic carbon is important in terrestrial ecosystems from various aspects such as reducing greenhouse gas effects, increasing soil yield and fertility, reducing soil erodibility, increasing water and nutrient retention capacity, etc. Therefore, today it is classified as one of the determinants of soil health and is referred to as the heart of sustainable agriculture and a vital part of production systems. Organic carbon management is an inevitable necessity, especially in soils with organic carbon less than one percent (which includes most of the soils in Iran). The aim of this study was to evaluate the power of spectral and non-spectral soil factors to predict the spatial distribution of topsoil organic carbon in the semi-arid Chehelgazi rangelands of Sanandaj using factor analysis and multiple linear regression.
Materials and Methods
The satellite data used in this study was obtained from the United States Geological Survey (USGS) database. This data is Level 1 data from the Landsat 8 satellite dated 18/06/2019, with path and row numbers 167 and 35, respectively. To prepare the elevation model map of the region, 1: 25,000 topographic maps of the study area, numbers 5360-1 NE, 5360-1NW, and 5361-2 SW, respectively named Satile, Todarmala, and Haneh-Gallan, were used. By conducting field visits and applying the physiognomic-floristic method, the boundaries of homogeneous units were modified and refined, and the final map of the plant species was prepared. In order to estimate the parameters of the percentage of canopy cover, litter, stones and pebbles, and bare soil in each plant type, the step-point method was used. Soil samples from different types of rangeland in the Chehelgazi watershed were taken using a stratified random sampling method. In this regard, a number of 135 training sites were selected in the homogeneous units. Of these sites, 105 sites were used to calibrate the model and 30 sites were used to validate it. In each of the training sites, a mixed soil sample includes of 9 observations was taken from a depth of 0 to 20 cm of the surface soil, randomly. In each sample, the SOC content was determined by Walkley-Black titration method. In order to determine an appropriate model for predicting the topsoil SOC, the 21 variables were used. These variables include 14 spectral variables and the seven morphometric variables. Using Factor Analysis (EFA), technique based on exploratory method, the considered variables were classified into three factors (soil color, vegetation, lithology and physical characteristics of the soil). These three factors explained a total of 80.356% of the data changes. Using multiple linear regression, a suitable regression equation (R2=0.66) was calculated to predict topsoil organic carbon.
Results and Discussion
The map of homogeneous units was prepared by overlaying three information layers of elevation classes, slope and slope directions and eliminating micro-units, rocky and inaccessible lands and agricultural lands and consists of 32 homogeneous units. A vegetation map of the region including five major vegetation types. In this study, reflectance spectral variables included 14 variables extracted from satellite images. The non-spectral variables (criterion form) also include seven variables of relative elevation, slope, transformed slope direction, general slope curvature, vertical and horizontal slope curvature, and topographic moisture index, which were obtained from the digital elevation model of the region. Then Measure of sampling adequancye (KMO) was examined by performing a factor analysis test, and variables with a KMO less than 0.5 were eliminated. This test was repeated until a suitable result was achieved, and finally 8 variables (surface albedo, normalized differential vegetation index, vegetation indices, brightness and moisture of the Tesselcap transformation, clay index, slope, and height above sea level) were selected as suitable variables to enter the factor analysis model. The root mean square error and relative mean absolute error of the proposed model were calculated as 0.191 and 0.117, respectively. The comparison of the average topsoil organic carbon capacity in plant species in the studied area using regression analysis was significant (p<0.01). Based on the results of this study, by analyzing the one-way normalized mean plot, the studied plant species were classified into two groups: those with more than the average total organic carbon of the habitat (species 1, 2, and 5) and those with less than the average total organic carbon of the habitat (species 3 and 4).
Conclusion and Suggestions
The results of the regression analysis of the average organic carbon storage in the topsoil of the 5 plant species in the study area showed that the average soil organic carbon in the plant species in the study area differed significantly (p < 0.01). The average weighted of organic carbon in the topsoil of the rangelands of the study area is 1.26%. According to the results of this study, the area and elevation of the region, and the number of samples collected, it is suggested that in order to provide conditions for changing the average weight of organic carbon in the topsoil of the region's rangelands, the number of samples should be increased and sampling points should be determined with a more appropriate distribution. |