Sh. Mahmoudi, M. Naderi, J. Mohammadi,
Volume 17, Issue 63 (6-2013)
Abstract
This research was carried out to determine spatial distribution of heavy metals concentration in soil particle size classes using Landsat ETM+ reflectance in Southern Isfahan city in the vicinity of Bama mine. To fulfill this goal, 100 compound soil surface samples were collected randomly from the area. The samples were air dried and soil particle size classes 250-500, 125-250, 75-125, 50-75 and <50 μm were determined using appropriate sieves after dispersion of the bulk samples of soil using ultrasonic apparatus. Total Zn, Pb and Cd concentrations were measured using Atomic Absorption Spectrophotometer after wet digestion of samples in acid nitric. The results indicated significant negative correlation coefficients between heavy metals concentrations of soil particle size classes and soil spectral reflectance in the visible, near infrared and panchromatic bands of Landsat ETM+ satellite. Stepwise multiple regression models were used for estimating heavy metals concentration in soil particle classes through satellite data. Furthermore, spatial distributions of heavy metals were mapped using stepwise multiple regression equations. Results also showed heavy metals concentrations in all soil particle size classes were maximum close to the mines and decreased by increasing the distance from these sources.
R. Samiei Fard, H. Matinfar,
Volume 21, Issue 4 (2-2018)
Abstract
Reflectance spectroscopy is a fast and safe method to predict soil physicochemical and biological properties in low cost ways. Traditional methods to determine soil properties require spending a lot of time and money so that farmers are generally reluctant to use the results of laboratory measurements in soil and water management. Reflectance spectroscopy in the spectral range of 400-2500 nm (VNIR) is an alternative method for estimating the soil properties. The aim of this study was to evaluate the results of laboratory spectrometer to estimate the concentration of Lead (Pb) and Nickel (Ni) in soils irrigated with water from treatment of urban sewage sludge of Rey city and finally to compare these results with the results of measurements of atomic absorption spectrometry. In this study, the Partial Linear Square Regression (PLSR) model was used to estimate the concentration of heavy metals and Residual Mean Square Error (RMSE) was used to evaluate the performance of this model. In this research, after spectral corrections related to elimination of the water absorption bands as well as elimination of the inefficient spectrum from heavy metals estimations, the methods of estimating these elements were studied through mathematical derivation of spectral values and also the acquisition of the continuum removal spectra. The results show that the estimated values from first derivate spectra are more consistent with the results of atomic absorption spectrometers.
V. Rahdari, A. R. Soffianian, S. Pourmanafi, H. Ghaiumi Mohammadi,
Volume 22, Issue 3 (11-2018)
Abstract
Determining the cultivation crops area is important for properly supplying crops. The aim of this study was mapping the cultivation area crops in Chadian city for spring and summer during 2015 by using the time series data of the Landsat 8 satellite of OLI imagery. At first, the under cultivation area was determined by setting a low threshold in the marginal pixels of the agricultural rain fed in the spring image NDVI index. The area cultivated with wheat and alfalfa was prepared by subtracting spring and summer NDVI values. Cultivation maps, which were cultivated with potatoes, corn and orchards, were prepared using the supervised classification with the FISHER method in a step by step manner. Spring and summer cultivation maps were combined; finally, the major cultivation crops maps were produced by the hybrid classification method. Map accuracy assessment was done by producing error matrix and calculating kappa coefficient, total accuracy, commission and omission error, producer, and use accuracy; in all indices, they had an acceptable value, showing the capability of OLI and the used methods in separating each cultivation.
