Showing 5 results for Pourmanafi
S. J. Khajeddin, S. Pourmanafi,
Volume 11, Issue 1 (spring 2007)
Abstract
To detect the rice paddis areas in Isfahan region, the IRS-1D data from PAN, LISS III and WiFS time series were used. Geometric, atmospheric, radiometric and topographic corrections were applied to various images from 2003 to 2004. Necessary preprocessing and various analyses as well as time series composite image analyses were applied and field sampling was done for appropriate times in 2003 and 2004. Image classification was applied using suitable training sites in various images. The SWIR band capabilities were useful for NDWI (Normalized Difference Water Index) to detect the rice paddies. On PAN and LISS III images, urban areas, roads, agricultural lands, non cultivated farms, rocks and brackish soils are detectable. The error matrix was calculated to assess the produced map accuracy using the ground truth data. The total classification accuracy was %91 and the Kappa index value was %89. The rice paddy areas was about 19500 ha in 2003, detected through LISS III data, and 20450 ha through WiFS data. The paddies were 21670 in 2004 through WiFS data. The results of this study confirmed that one can use the LISS III data to detect and determine the rice paddys areas with high accuracy, and WiFS data to estimate the paddies areas with acceptable accuracy.
S. Azadi, S. Soltani Kopaei, M. Faramarzi, A. Soltani Tudeshki, S. Pourmanafi,
Volume 19, Issue 72 (summer 2015)
Abstract
The Palmer Drought Severity Index (PDSI), which uses hydrometeorological variables to solve a simple water balance equation in the soil and considers the drought or wet conditions as dynamic phenomena, is used for the assessment of drought conditions in many parts of the world. The main goal of this study was to assess the PDSI based on its original assumptions, its regionalized status, using the outputs of already calibrated and validated SWAT model in central regions of Iran. The PDSI was assessed through five methods: 1) original Palmer Index without calibration in which the climate coefficients and the severity equation were derived for Kansas and central Iowa 2) original Palmer Index in which the coefficients of severity equations were adjusted 3) the Palmer Index with the calibration of equations in central areas of Iran 4) the Palmer Index using the soil moisture and potential evapotranspiration from SWAT model and 5) the Palmer Index using the soil moisture, potential evapotranspiration and runoff from SWAT model. The evaluation was conducted for 17 major basins covering the entire country with a monthly time step for the period 1990-2002. Then, using all five methods, the severity of the drought for 160 sub basins located in central Iran was calculated and evaluated. The results of this study indicated that method 4 provides more acceptable results. Also, the results of this research showed these methods clearly demonstrated (1992) as the wettest year and (2001) as the driest year. The approach used in this study is applicable to regional calibration of Palmer Index and the outputs of other hydrological models.
V. Rahdari, A. R. Soffianian, S. Pourmanafi, H. Ghaiumi Mohammadi,
Volume 22, Issue 3 (Fall 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.
M. Madanian, A. R. Soffianian, S. Soltani Koupai, S. Pourmanafi, M. Momeni,
Volume 23, Issue 4 (winter 2020)
Abstract
Land surface temperature (LST) is used as one of the key sources to study land surface processes such as evapotranspiration, development of indexes, air temperature modeling and climate change. Remote sensing data offer the possibility of estimating LST all over the world with high temporal and spatial resolution. Landsat-8, which has two thermal infrared channels, provides an opportunity for the retrieval of LST using the split- window method. The main objective of this research was to analyze the LST of land use/land cover types of the central part of Isfahan Province using the split- window algorithm. The obtained results demonstrated that the "other" class which had been mainly covered with bare lands exhibited the highest LST (50.9°C). Impervious surfaces including residential areas, roads and industries had the LST of 45°C. The lowest temperature was observed in the "water" class, which was followed by vegetation. Vegetation recorded a mean LST of 42.3°C. R2 was 0.63 when regression was carried out on LST and air temperature.
P. Mohit Esfahani, S. Soltani, R. Modarres, S. Pourmanafi,
Volume 24, Issue 3 (Fall 2020)
Abstract
Drought, as one of the most complicated natural events, causes many direct and indirect damages each year. Hence, single variable identification and monitoring of drought may not be appropriate enough for decision-making and management. In this study, in order to monitor the meteorological-agricultural drought in Chaharmahal and Bakhtiari province, Multivariate Standardized Drought Index (MSDI) was calculated using precipitation and soil moisture variables. In addition, to evaluate the performance of MSDI in drought identification and monitoring, Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) were used for meteorological and agricultural drought monitoring, respectively. MSDI was calculated based on the soil moisture and precipitation joint probabilities. We used the Gringorten probability as an empirical method and Archimedean copulas as the parametric method to calculate the joint probability between soil moisture and precipitation time series. The results indicated that MSDI was twice more capable of detecting drought as SSI and SPI. Furthermore, the MSDI-based drought monitoring results showed Charmahal and Bakhtiari province had experienced severe meteorological-agricultural drought in 2000, 2008, 2011 and 2014.
