Showing 7 results for Nasserzadeh
Dr Hassan Kharajpour, Dr Zahra Hejazizadeh, Dr Bohloul Alijani, Dr Mohammad Hossein Nasserzadeh,
Volume 0, Issue 0 (3-1921)
Abstract
Considering the undeniable impact of agricultural plants on climatic and regional changes, it seems necessary to conduct regional research to understand the reaction of each agricultural plant in different stages of growth in relation to weather elements. If the temperature of the air along with the warm cloud is lower or higher than a certain threshold, its development will stop. Between the two limits, there is an optimal temperature where the plant grows the fastest. Temperature and clouds are both the most important climatic elements in agriculture. Both climatic parameters together cause stress in wheat and lower the productivity of the product. Considering the strategic nature of wheat, in order to increase the level of production, in the present research, while taking advantage of the experiences and methods and models used in foreign and domestic researches, it was practical in Kermanshah province due to the extent of the land under wheat cultivation and The significant amount of production, which has a special place in this field at the level of the country, the determination of the statistical threshold and the synoptic analysis of warem cloud temperatures on the performance of the wheat crop are investigated. According to the investigations and consultations with agricultural engineers, the maximum temperature along with cloudy days causes the phenomenon of greenhouse and excessive heat, which causes the fall of flowers, rot, sterility of pollen grains, fruit reduction, Premature aging and poverty become seeds, and this phenomenon occurs mostly in the months of May and June.
Dr Mohammad Hossein Nasserzadeh, Dr Alireza Karbalai Derai, Mr Mohammad Siah Kamari,
Volume 0, Issue 0 (3-1921)
Abstract
Drought is one of the natural disasters whose long-term effects affect the economy and society. This phenomenon is considered a challenge in arid and semi-arid regions, including Iran. Nowadays, the use of remote sensing methods can help us in understanding the drought behavior of vegetation. In order to monitor and analyze the behavior of drought in Kermanshah province, the data of Sanjande Weathers products (VIIRS) and AVHRR data indexed by NOAA STAR were used. In this study, the Vegetation Health Index was used in the period of 1982-2021 in a seven-day format with a spatial resolution of 4 x 4 km. After extracting the data in the Kermanshah area, the vegetation drought trend was investigated on 65,387 cells using the Mann-Kendall test. The results showed that in the winter season, the trend of vegetation cover in the western areas of the province was decreasing and significant at the level of 0.05. While in the northern, central and eastern regions of the province, the trend is increasing and significant. In the spring and summer seasons, especially the months of June, July, August and September, which correspond to the dry months of the year, the size of the areas with a significant decreasing trend of vegetation cover has increased, while in the autumn season, with the beginning of the water year, the size of the areas with a decreasing trend has decreased.
, Mohammad Hossein Nasserzadeh,
Volume 6, Issue 8 (- 2007)
Abstract
, , Mohammad Hossein Nasserzadeh,
Volume 11, Issue 20 (6-2011)
Abstract
Yadollah Balyani, Mohammad Saligheh, Hossein Asakereh, Mohammad Hossein Nasserzadeh,
Volume 15, Issue 37 (9-2015)
Abstract
Precipitation is one of the most intractable elements. The oscillating behavior of the crucial environmental planning (explicit and tacit knowledge of the behavior), is the key variable. Spectrum analysis techniques to understand the behavior of overt or covert methods suitable for the extraction and analysis of climate oscillations with different wave lengths. The size range of the distribution variance across all wave lengths may provide time series. In this study, data from 37 stations Heleh and Mond watershed (both rain and synoptic) from its inception until 2011, who had over 30 years of data, to analyze the cycle of annual rainfall, interest has been taken. So that the space is 3-2 year cycles in every area of study, the highest annual rainfall events are returned. On this basis, the Story of annual precipitation 95 percent for each of the stations under study and cycle meaningful estimate of the time series of basin data were extracted.
Mohammad Saligheh, Mohammad Hossein Nasserzadeh, Thmineh Chehreara Ziabari,
Volume 16, Issue 43 (16 2016)
Abstract
In this research, the relationship between NCPI and CACO indices with autumn precipitation of Southern Coast of Caspian Sea (SCCS) was investigated. In this regard, two sets of data were used (Aphrodite and Station). And the days with more rainfall than long-term average rainfall station and on condition that the rainfall is more than 70% of the region rainfall, were chosen as a day of widespread rainfall. The sea level pressure data was extracted and by cluster analysis and coalition method was clustered. Then, a representative of the widespread precipitation days from station dataset was selected, investigated and analyzed accordingly. The results state that within all patterns there exists a high pressure on the upper side of the Caspian Sea, or a margin of high pressure is extended on to the sea itself. These high pressure regions have relatively cold nature that can cause currents in the northern direction while intersecting with the relatively warm water during the summer. These currents can absorb moisture during their motion towards south which can lead to their instability. In addition, one should not forget the fact that in each three investigated patterns, dynamic factors at high levels have intensified the abovementioned phenomenon and enhanced the instability, which as a result brought about widespread precipitation. Continuously, the abovementioned Remote bond indices were extracted on a daily basis and their relation to north coast widespread rainfalls was studied, which came to a meaningful relationship between these index sets and fall index sets. The relationship is direct with NCPI or surveyed stations, and it’s an inverse relationship with CACO. On the other hand, the study of indexes anomalies on the days without rainfall and with rainfall was done by One Way ANOVA and Tukey test. The result was a meaningful index anomaly on the days with and without rainfall.
Mohammad Hossein Nasserzadeh, Zahra Hejazizadeh, Zahra Gholampour, Bohloul Alijani,
Volume 20, Issue 57 (6-2020)
Abstract
The plant community in an area is the most sensitive indicator of climate. A visual comparison of climate and vegetation on a global scale immediately reveals a strong correlation between climatic and vegetation zones and this relationship, of course, are not co-incidental. The main object of this study is to reveal the spatiotemporal association between climatic factors andvegetation Cover (NDVI) incorporate MODIS and TRMM product in Kohkiloyeh O Boirahmad province of Iran. So that the in this paer we use MOD13Q1 of MODIS product as NDVI layer for study area. MOD11A2 as landsurface temperature and 3B43 TRMM as meanmonthly accumulative rainfall for study area during 2002 to 2012 in 0.25° spatial resolution also were used as climatic factors. We use the correlation and cross-correlation analysis in 0.95 confident level(P_value =0.05) to detection the spatial and temporal association between the NDVI and 2 climatic Factor(LST and rainfall). The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months the pixels which have the high value of NDVI are spatiallyassociated with the pixels which have highest value of LST (6 to 14C°).As can be seen in table 1. Season the spatial correlation among NDVI and LST is so high which is statistical significant in 0.99 confident level in winter. In transient months such as May, October and November,(temperate months in study region ) the spatial correlation among NDVI and LST is falling to 0.30 to 0.35 which is not statistical significant in 0.95 confident level. Finally in summer season or warm months including Jun to September, we found the minimum spatial association among the NDVI and LST.. In temporal aspect we found that the maximum correlation between NDVI and LST simultaneously appears and not whit lag time. The spatial correlation of NDVI and TRMM monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time in remain months we don’t find any significant correlation between NDVI and rainfall.
