saeedeh Nateghi; Rostam Khalifehzadeh; Mahshid Souri; Morteza Khodagholi
Abstract
Soil organic carbon is one of the most important indicators of soil quality. The purpose of this study is to study the spectral and non-spectral behaviors of soil in order to estimate the organic carbon of topsoil using factor analysis and multiple regression methods in the semi-steppe rangelands of ...
Read More
Soil organic carbon is one of the most important indicators of soil quality. The purpose of this study is to study the spectral and non-spectral behaviors of soil in order to estimate the organic carbon of topsoil using factor analysis and multiple regression methods in the semi-steppe rangelands of Asuran, Semnan province. Soil sampling was performed using stratified random sampling method. After creating a map of homogeneous units in the area, in each homogeneous unit according to its area, several sampling points were selected completely randomly. A total of 145 sampling points were collected. At each sampling point, a composite soil sample (a mixture of 9 observations) was taken. Soil organic carbon was measured using Valkyli-Block titration method. Data of 114 samples were used to calibrate the model and data of 31 samples were used to validate it. The results showed that the correlation of spectral variables obtained from Landsat OLI sensor with surface soil organic carbon is higher than non-spectral variables obtained from 1: 25000 topographic maps. Also, the results of factor analysis by principal component analysis with eigenvalues greater than one showed that the total cumulative variance explained by 14 variables was equal to 90.2%, which was explained by three factors. The regression equation generated by the three extracted factors had suitable potential for predicting surface soil organic carbon (R2 = 0.59). The root mean square error (RMSE) of the proposed model was calculated to be 0.3.
sahar sabaghzade; Mohammad Zare; Mohamad Hosein Mokhtari
Abstract
Vegetation is an important component of each global ecosystem. Determining of the biomass of plant is important to assess its impact upon climate, soil erosion, and as well for management of natural resources. The aim of this study was to estimate biomass using vegetation indices based on remote sensing. ...
Read More
Vegetation is an important component of each global ecosystem. Determining of the biomass of plant is important to assess its impact upon climate, soil erosion, and as well for management of natural resources. The aim of this study was to estimate biomass using vegetation indices based on remote sensing. The Landsat 8 data of May 2013 and field studies coinciding with field imaging in Marac (South Khorasan province) were used. Tamarix plant biomass measured in 30 random plots of 11 vegetation indices including DVI, IPVI, NDVI, PVI, RVI, SAVI, TSAVI, WDVI, and Tasselcap were used to estimate biomass of Tamarix.Then, using cluster analysis, vegetation indices were divided into three groups among which SAVI, RVI , and IPVI were chosen. The results showed that indexes which consider soil factors are more accurate than other measures. In this study, biomass map was prepared using the SAVI index.
