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 ...
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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.
Amin Zarratipour; marjan firoozinejad; Khalil Delfan Hasanzadeh
Abstract
Abstract The phenomenon of climate change and global warming and its impact on different ecosystems Needed extensive studies. For this purpose, was done Study of the efficiency of thermal bands and land use changes in determining the surface temperature index using a separate window algorithm in the ...
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Abstract The phenomenon of climate change and global warming and its impact on different ecosystems Needed extensive studies. For this purpose, was done Study of the efficiency of thermal bands and land use changes in determining the surface temperature index using a separate window algorithm in the Bandar-e Emam Khomeyni of Khuzestan province by Landsat 8 satellite imagery. Based on this, was used vegetation index (NDVI) for band ratio between the red and near infrared bands to prepare the LST map and were used four seasonal images in 2016-2017 years. The results of the comparison of the two thermal bands showed that the thermal bond 11 is higher accuracy than the thermal bond 10 (RMSE = 3.6 for the band 11 and RMSE = 4.4 for the band 10) because the wavelength of it is higher. Also, the estimation of the comparison of satellite imagery data with ground truth showed a high accuracy (R2 =0.9). Comparing the temperature of the users was determined that the industrial and urban areas more effective on increasing LST than the vegetation and water areas. Base on bands ratio between near and red infrared bands showed that the vegetation index decreased with increasing temperature. So, the lowest amount of vegetation was estimated in August (-0.42) and the highest was in October (0.35).
Hamidreza Koohbanani; Mohammadreza Yazdani
Abstract
Approximating the evapotranspiration is one of the most difficult parts of the hydrologic cycle. However it plays a very important role in the water balance equation. In the present study, the SEBAL model has been revalidated as an approved and successful approach for calculating the evapotranspiration. ...
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Approximating the evapotranspiration is one of the most difficult parts of the hydrologic cycle. However it plays a very important role in the water balance equation. In the present study, the SEBAL model has been revalidated as an approved and successful approach for calculating the evapotranspiration. In SEBAL model, the evapotranspiration is the unknown value in the equation of surface energy balance for the land surface. Considering the differences in the calibration of LDCM compared to the previous generations of the Landsat satellite, the SEBAL model was reexamined in some especial parts. In order to test of the usefulness of SEBAL model over LDCM, the measured values was compared with the values obtained via Penman-Monteith method in 20 different days. ET estimated by SEBAL compared with PM ET was found to correlate significantly as R2 (0.7) with the correlation coefficient of 0.83. Hence the results of this analysis outline that actual evapotranspiration data can be calculated and mapped in large areas and with high reliability without the need for meteorological information periodically and regularly.
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. ...
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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.
