Showing 6 results for Zeaiean
Dr Mitra Saberi, Dr Amir Karam, Parviz Zeaiean, Ali Ahmadabadi,
Volume 0, Issue 0 (3-1921)
Abstract
many geomorphic landforms have fractal structures and their formation and transformation can be explained by mathematical relations. The purpose of this study is to identify and analyze the fractal behavior of landforms of macro geomorphologic regions of Iran,as well as studying and analyzing topographic and landform characteristics based on fractal relationships, and also, analyzing the characteristics of dominant geomorphologic processes based on the theory of fractals. In this study, the contour lines of different landforms of Iran (according to the territorial types) including mountains,hills, plateaus, Plain Domains, Fan Breakout, fan alluvial, for pixel sizes of 30,90,200 m, were drawn and their fractal dimension was estimated by using the box-counting method. The morphometric characteristics of the landforms and their fractal dimensions with indexes (max, mean and standard deviation) related to the five variables (height, gradient, profile curvature and planar curvature of the metric) were analyzed by Arc GIS software at each layer.After investigating their correlation with the fractal dimension, the regression analysis was performed binary and the relationship between the fractal dimension,topography, landforms and processes was analyzed. The fractal dimension has the highest correlation coefficient with the gradient and the standard deviation indices, and the lowest coefficient with the profile curvature and the mean index Moreover, for larger pixel sizes, the correlation coefficient decreases between the indices and the fractal dimension.This research can provide a ground for further research on fractal geometry in geography, geomorphology, geology, environment and other Earth sciences.
Tahereh Karimi, Amir Karam, Parviz Zeaiean Firuzabadi, Seyyed Mohammad Tavakkoli Sabour,
Volume 0, Issue 0 (3-1921)
Abstract
Abstract
The catchment area of Alamut River in Qazvin province is witnessing numerous landslide hazards and landslides every year, which cause significant economic and sometimes life-threatening losses. Diagnosing the unstable areas of slopes through soil texture characteristics is a difficult task due to the difficulties of obtaining soil samples in mountainous areas. For this reason, in the present study, by using Sentinel A1 radar data, by determining the percentage of clay and sand in the soil, the soil texture map at the depths of 10, 60, 100 and 200 cm with two random forest (RF) and support vector machine (SVM) algorithms was produced in the eastern Alamut region, which was verified with soil profile samples. The results indicated that the Kappa index was more accurate in the RF model at three depths of 10, 60 and 100 cm. Then, by extracting the soil moisture map from Sentinel 2 data, at the same time as examining the internal friction angle of the types of soils in the region, comparing the slope and profile of the slopes and the shape of the convex (divergent) and concave (convergent) slopes, the unstable areas of slope movements, RF and SVM models were specified and validated with GPS data, field visits and Google Earth. Research findings show that the instability map resulting from the RF model has a higher accuracy (AUC=0.93) than the instability map resulting from the SVM model (AUC=0.90) and there is more instability in areas with medium to high slope and with soil texture of sandy clay loam and sandy loam. . This method has many advantages in preparing the soil texture map, determining the unstable areas of the slopes against mass movements and landslides, determining the vulnerable areas in mountainous areas and reducing financial and human losses.
Adel Nabi Zadeh Balkhanloo, Zahra Hejazizadeh, Parviz Zeaiean Firoozabadi,
Volume 18, Issue 50 (6-2018)
Abstract
Continuous decline in Lake Urmia water levels In recent years, the decline of rainfall and river flows and constant droughts has become the main concern of the people and the people. To study climate change and increase of temperature in the catchment area of Lake Urmia, two factors for measuring the temperature and properties of satellite images were used which indicate the importance of land surface temperature changes (LST) and normalized vegetation differences (NDVI). This study was carried out using the satellite data of the periodic watershed (2008-2008) to investigate the spatial relationship between NDVI-Ts and NDVI-ΔT to investigate the actual agricultural drought occurrence. The goal is to extract the VTCI (vegetation temperature index) index, which is capable of identifying drought stress at regional scale. The results showed that the slope is negative for the warm edge, where it is positive for the cold edge. The gradient gradient shows that the maximum temperature is reduced when the NDVI value increases for any interval. The slope on the cold edge indicates that the minimum temperature rises when the NDVI value rises. Overall, at the warm and cold edges, it has been observed that the drought trend over 2009-2008 is higher than in 2010. In the days of Julius Day 257, the slope of the cold edge from 2008 to 2010 is decreasing. But at the hot edge, intercept pixels for 2008 is more than 323 degrees Kelvin, where in 2009-2010 it is less than 323 degrees Kelvin. In general, the correlation coefficient (R2) is different in the TS-NDVI spacing between (0.90-0.99). The present study showed that with the integration of satellite satellite data with meteorological data, the VTCI threshold for drought stress varies from year to year depending on the data conditions.
Dr Vahid Riahi, Dr Parviz Zeaiean Firouzabadi, Dr Farhad Azizpour, Ms Parastoo Darouei,
Volume 19, Issue 52 (3-2019)
Abstract
The cognition of cropping pattern is important for planning and resource management .Remote sensing as a science and technology of spatial information and geographic information system due to having the analytical facilities can play a key role in determining the distribution of crops and their lands under cultivation. In this research, in order to identify and separate the lands under cultivation of the dominant crops in Lenjanat of Isfahan province, the multi-temporal images of Landsat 8 satellite, OLI sensor were used in the dates of April 17, July 6, and August 23 in 2016. Using maximum likelihood classification and normalized difference vegetation index (NDVI) of the agriculture crops in different periods of growth and according to their cropping calendar, the map of the cropping pattern of the area was determined. To evaluate the accuracy of the results, the produced maps were examined with reference data. Kappa coefficient and overall accuracy were 0.88 and 90%, respectively, in maximum likelihood classification, and 0.90 and 93%, respectively, in NDVI. Furthermore, statistics presented by Agricultural Jihad Organization of Isfahan province in the 2015-2016 crop year was used for evaluation. The results showed that there were differences equal to 10.2%, 18.6% and 1.8%, in the area under cultivation of wheat and barley, rice, and potato and forage, respectively, in maximum likelihood classification, comparing with the statistics of Agriculture Jihad while the results of NDVI comparing with Jihad statistics showed the errors equal to 6.6 %, 6.5 % and 3.2%, respectively, that indicated the better performance of temporal vegetation indices in estimation of area under cultivation according to its phenology. Investigation of land use and cropping pattern of this area indicate a high centralization of agricultural lands with high water requirements and industries on the proximity of Zayanderud River which necessitates the spatial analysis of land use in this area.
Dr Parviz Zeaiean Firoozabadi,
Volume 20, Issue 59 (1-2021)
Abstract
Various satellite remote sensing data, images and products have proven their place in drought, drought and agriculture studies since the production of this type of information resource. Visible, near-infrared and thermal bands are among the most widely used in the production of products such as vegetation and surface temperature. In this study, from MODIS sensor data to investigate and find the coefficients of spatial relationship between vegetation-surface temperature index (NDVI-TS) and NDVI-ΔTS to extract the time of agricultural drought from June to October 2007 to 2010 in the catchment Siminehrood has been extracted from the Temperature-Vegetation Condition Index (VTCI) and the Water Lack Index (WDI), which are able to detect drought stress on a regional scale. The results of this study showed that in both indicators, the drought stress situation was higher in 2007 and 2008. Also based on the NDVI-TS space relationship in all the years 2007 to 2010 the high slope of the triangular space for the hot edge is negative. This means that with increasing NDVI, the LST level decreases while for the cold edge the slope is positive. In addition, the slope obtained from the NDVI-ΔTS space relationship is negative for the dry line, ie the dry line or the minimum transpiration-sweat line (ETR) shows a negative correlation with NDVI. While for the wet line, especially in 2010, the slope is positive and in other years, no significant change is seen. The present study showed that the VTCI threshold for drought stress was severe in 2007 and 2008.
Mr Mohammad Safaei, Dr Hani Rezayan, Dr Parviz Zeaiean Firouzabadi, Dr Ali Asghar Torahi,
Volume 22, Issue 65 (6-2022)
Abstract
Examining the effects of climate change on the oak spatial distribution, as the main species of Zagros forests and its ecological and economic values is of significant importance. Here, we used species distribution models for simulating current climatic suitability of oak and its potential changes in 2050 and 2070. For this purpose, five regression-based and machine learning approaches, four climatic variables related to temperature and precipitation and two optimistic (RCP 2.6) and pessimistic (RCP 8.5) greenhouse-gas scenarios were used. The results of measuring the accuracy of models by AUC indicated the good performance of all algorithms and Random Forest achieved the highest accuracy (AUC = 0.95) among other methods. The results showed that in both time periods and under both scenarios, changes will occur in oak spatial distribution and the most severe one would be a 42.9 percent loss in the oak climatic suitability in 2070 under pessimistic scenario (RCP 8.5).
