Extraction, processing, production and display of geographic data
Hossein Asakereh; Somayeh Taheri Alam; Nosrat Farhadi
Abstract
Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological ...
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Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological phenomena, primarily vegetation, which reflects an intricate pattern of changes in climatic elements, particularly temperature, and precipitation. Although the substantial role of climatic elements on the density and geographical distribution of vegetation has been confirmed, it is arduous to estimate the relationship between climate changes and vegetation due to the complexity of the mechanism of different characteristics of climatic elements (such as the amount, type, intensity, season, continuity, etc.), feedback processes, and also the response time of the vegetation to climatic changes.
Materials and Methods
In the current research, the gridded data of the Normalized Difference Vegetation Index (NDVI), a product of the MODIS terra, was used from 2001 through 2016. The data were extracted from a GIOVANNI website. In the present study, Iran's vegetation density classes were determined based on quantitative methods, and the geographical distribution of two-half parts of the understudy periods was compared.
Results and Discussion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. The finding revealed that the NDVI has a direct relationship with the precipitation. Accordingly, the northern, northwestern, and western regions, as wet regions in Iran and comprise proper soil, included high NDVI.
Dividing NDVI data into two 8-year periods revealed that in the first 8 - year, despite the high amount of precipitation, the NDVI was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Conclusion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. Our finding showed that the spatial distribution of NDVI has a direct relationship with the precipitation. Comparing two - half of understudy data showed despite the high amount of precipitation, the NDVI in the first half was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Remote Sensing (RS)
Seyedeh Kosar Hamidi; Asghar Fallah; Nastaran Nazaryani
Abstract
Extended AbstractIntroductionVarious Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro ...
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Extended AbstractIntroductionVarious Climate factors considerably affect the environment and different vegetation covers show different levels of sensitivity to climate factors in the spatial-temporal scale. Data specifically collected from vegetation cover plays an important role in micro and macro planning and information generation. Methods using air temperature recorded in weather stations to estimate the relative heat in urban areas are considered to be both time-consuming and costly. On the other hands, data with relatively high spatial resolution are capable of measuring ground surface parameters more efficiently and accurately. Thus, remote sensing technology is now considered to be a solution used to improve previously mentioned methods. Remotely sensed data are now widely used to find the quantitative relationship between patterns of vegetation cover and the elements of climate. Predicting the conditions of vegetation cover is considered to be essential for planners seeking an efficient plan for its exploitation and protection.Materials & MethodsThe present study seeks to investigate the effects of climatic factors on the vegetation trend observed in Frame forest in Mazandaran province using Sentinel 2 images and to determine the most suitable index for this area. Climatic Data collected from the nearest weather station in Farim City have been used to model climate factors (temperature and precipitation). Changes in the height above mean sea level were also considered. Following the pre-processing and processing of the Sentinel 2 images, the corresponding digital values were extracted from the spectral bands and applied as independent variables. ENVI software was used for image processing and STATISTICA and R software were used for modeling. 70% of the resulting data were used for training and the rest were used for testing or evaluating the model. Mean square error, correlation, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the presented models. Models with the highest correlation and the lowest standard error, the mean square error, the Akaike information evaluation criterion and the Bayesian evaluation criterion were selected as the best models for the studied variables.Results & Discussion A correlation coefficient of 0.43 and 0.56 was observed between temperature and precipitation and vegetation indices. AIC and BIC values equaled (565 and 3209) and (739 and 3383) respectively. Differential Vegetation Index (DVI) has proved to be the most effective parameter in relation to both temperature and precipitation factors in the region. Results indicated that differential vegetation index, green normalized difference vegetation index (GNDVI) and green difference vegetation index (GDVI) have a positive correlation with temperature, while there is a negative correlation between temperature and normalized vegetation index. Precipitation is considered to be one of the most important factors affecting vegetation. Results indicate that differential vegetation index, green difference vegetation index, green normalized difference vegetation index, non-linear vegetation index and normalized difference vegetation index have the highest impact on precipitation. In forest ecosystems, changes in climatic factors may affect trees differently. ConclusionCollecting information about the state of vegetation cover in forests is considered to be very important. Thus, the present study has endeavored to investigate the relationship between indices of vegetation cover and climatic variables. To reach this aim, satellite data are used as a suitable and efficient tool for investigating forest ecosystems with a relatively low cost. This provides the possibility of continuously monitoring land surface. Results indicated that climatic factors affect vegetation indices in the study area. Vegetation cover protects and stabilizes the environment and thus, many researchers have tried to investigate the growth and spatial patterns of vegetation cover in different regions. It is also suggested to study the effects of climatic factors on the vegetation cover of the study areas in different geographical directions. In addition, using other climatic factors such as relative humidity, wind speed, evaporation, transpiration, and higher resolution images can increase the accuracy of the study.
Saeed Farzaneh; Reza Shahhoseini; Iman Kordpour
Abstract
Introduction Drought is considered to be one of the most widespread natural disasters, ranking second in terms of damages. Due to the complex relationship between hydrological cycle parameters and atmospheric observations, predicting or modeling drought lacks the necessary precision. One of the most ...
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Introduction Drought is considered to be one of the most widespread natural disasters, ranking second in terms of damages. Due to the complex relationship between hydrological cycle parameters and atmospheric observations, predicting or modeling drought lacks the necessary precision. One of the most significant problems in drought monitoring is lack of proper spatial coverage for the collected data (due to unavailibility of field data in some regions) and also lack of a suitable time scale (observations and thus drought estimation is not always possible). Since satellite observations do not face challenges like lack of spatial scale which is quite common in field observations, remote sensing satellites can provide a better estimate of droughts. However, satellite observations alone are not capable of accurately estimating the occurrence of droughts. Therefore, a combination of field and satellite observations has been used recentely to reach a better estimate of hydrological problems. Materials & Methods Temporal and spatial complexity of droughts have made a new global index combining ground-based and satellite-based observations quite necessary. Given the kind of data used in MDI index, we cannot expect it to be global. However, its performance is still acceptable in similar environments and climates, and thus it has been used in the United States (Texas). Datasets selected for the present study have different temporal and spatial scales and thus, a common scale must be found before calculating the index. Data received from GRACE satellite and MODIS sensor were downloaded monthly, but precipitation data were collected on a daily basis. Thus, aritmatic mean of precipitation data was calculated to reach a monthly avarage. Regarding the spatial scale, one-degree precipitation data were received from GRACE and MODIS while precipitation data extracted from synoptic stations had a point-based nature. Therefore, Inverse Distance Weighting (IDW) method was used to produce a one-degree network. Three types of observations were used in the present study including data received from synoptic stations of Iran meteorological organization, GRACE mission satellite-based gravity data and MODIS remote sensing satellite-based data. These were selected to identify droughts over a 14-year time series. Results & Discussion The present study has calculated MDI drought index on a one-degree spatial scale and monthly temporal scale for 168 months using Precipitation, NDVI, and TWS data. Severe droughts in northwestern and central areas of Iran from 2004 to 2014 have led to a shortage of water in reservoirs. In addition to drought, too much water harvesting in northwestern Iran has resulted in a decrease in groundwater level and thus, increased water harvesting from rivers and canals leading to the Urmia Lake and reduced water level in this lake. The results of MDI drought index calculated for Iran over the period of 2000 to 2014 show a high correlation with the results of standardized precipitation-evapotranspiration drought index. According to the type of data used to calculate MDI index, it is expected to have a strong correlation with PDSI index due to its sensitivity to precipitation, area temperature and soil moisture content. Since GRACE and MODIS satellite-based data, and data received from synoptic stations were used, a strong correlation with MDI is also expected. It should be noted that PDSI index is higher than MDI index in Iran, although both show the drought trends accurately. For example according to PDSI index, the worst drought of the last two decades in Iran has occurred in 2008, and MDI index shows the same year. Conclusion The present study has introduced a new drought index using a combination of precipitation data, GRACE_TWS and NDVI. These data were selected because of their high sensitivity to drought. GRACE_TWS observations monitor hydrological drought and include surface and subsurface water sources. NDVI observations are mostly used to identify photosynthetic activities of vegetation cover and are therefore very useful for detecting agricultural drought. Precipitation value shows the amount of surface water in the study area. Precipitation can have relatively rapid effects and is therefore useful for monitoring meteorological drought. MDI index has identified several droughts in each region of the country in the period of 2003 to 2016. These identified droughts have generally covered the country over time. However, each drought has had a different impact on ecosystem. In Iran, the most severe droughts have occurred during 2008 to 2009 and 2011 to 2012. Since MDI correlates well with PDSI, both show a drought in these years. In order to develop the proposed algorithm, the effect of different zoning of the study area on MDI index can be studied.
Hadi Ghafourian; Seyed Hossein Sanaei Nejad; Mahdi Jabbari Nowghabi
Abstract
Extended Abstract Introduction Due to the importance of precipitation in various aspects of human life, precipitation data are largely applicable in different fields of study. Therefore, accurate measurement of precipitation is considered to be crucialin various fields such as agriculture, water resources, ...
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Extended Abstract Introduction Due to the importance of precipitation in various aspects of human life, precipitation data are largely applicable in different fields of study. Therefore, accurate measurement of precipitation is considered to be crucialin various fields such as agriculture, water resources, and industrymanagement. Due to the problems related to generalization of point precipitation to regional precipitation, alternative methods have been proposed forthe measurement of this variable. In many cases, short reference period, inadequate density of stations and poor quality of data collected from precipitation measurement networks have challenged the analysis of this climate variable. In order to overcome these problems, it is necessary to identify alternative sources, evaluate and use them to estimate the amount of precipitation. The present study primarily seeks to evaluate precipitation data from the TMPA and provide calibration data for arid, semi-arid, Mediterranean, humid, and very humid regions of Iran on a monthly scale. Materials and Methods In the present study, monthly precipitation data of 15 synoptic stations in 5 regions of Iran (arid, semi-arid, Mediterranean, humid and very humid) were selected as reference data and monthly precipitation data from the TMPA (3B43-v7) were corrected based on them. To ensure reliability of results and reduce errors,stations were selectedrandomly from 15 separate provinces with different topographic conditions. A 20-year reference period (1998-2017) was selected for the study. Collected satellite data have a monthly temporal resolution and a spatial resolution of 0.25 degrees covering 50th parallel south to 50th parallel north. Table 1 shows features of the selected stations and their corresponding pixels. Pre-processing included quality control, homogeneity test, and data accuracy test. Usinga long-term reference period of 20 years, different statistical criteria to evaluate satellite data and a correction relationindependent from ground data are among the advantages of this research. In this study, a more efficient method is used to determine errors and one of the most modern methods of calibration is also used. Followingthe application of log transformation and multiplicative model, monthly C parameter was calculated to rectify satellite data collected from different climates. Results were evaluated using R2 (Coefficient of Determination), MBE, MAE and RMSE. Results and Discussion Findings indicated that the distribution of initial data obtained from TMPA satellite in a monthly scale is similar to the distribution of pattern obtained from ground data (due to a correlation of above 75% (R2>0.6)). Satellite data collected from arid areas are usually overestimated, while data collected from humid areas are generally underestimated. However, determination coefficients (R2) of different climates show a strong correlation between these two sources of data. The initial TMPA data have estimated the monthly precipitation of Bam, Piranshahr and Abali stations with the least amount of error. The highest level of errors were obtained from Marivan, Bandar Anzali, and Koohrang stations. In other words, the highest level of errors have occurred in the very humid region. Calibration of TMPA data collected from the 5 different climates indicated that correction of TMPA monthly data would improve valuesestimated from satellite images. Mean bias error (MBE) was reduced by 88.7, 95.3, 68.4, 38.4 and 63.9 percentin arid, semi-arid, Mediterranean, humid and very humid climates, respectively. Values of the correction parameter (C) in the arid climate indicate that a reduction factor has been applied to rectify satellite data collected in each month of the year. In the semi-arid climate, reduction factorswere obtained for each months of the year. A reduction factor is also required to rectify data collected in the warmest months of the year (June, July, and August) in the Mediterranean climate. Due to the low precipitation of these months, overestimation seems reasonable in these areas. A reduction factor should also be applied in the humid climate for 6 months of spring and summer. Considering the precipitation rate in these areas, decreasing precipitation rate in these seasonsresults in overestimation and error. Due to the significant precipitationrate in the cold months of the year (autumn and winter), decreasing factorand underestimation are expected to occur. In the very humid climate, a reduction factor should be appliedin the warmest months of the year (June, July, and August). Due to the low precipitation rate of these months and higherfrequency of cloudy days, overestimation will be reasonablein these areas. Due to underestimationin the coldest months of the year (autumn and winter), coefficients higher than one must be corrected. Conclusion Based on the results, the model used to correct precipitation in all 5 climates have reduced errors in precipitation measurement. However, this improvement was more obvious in arid and semi-arid climates. Sincea large part of Iran havean arid and semiarid climate, this calibration model is highly recommended. In addition, the final correction model does not depend on ground data and thus, applying the calibration modelto areas other than the specified stations will also be useful.
Farshad Pazhooh; Farzaneh Jafari
Abstract
Extended Abstract
Introduction
Due to its specific geographical situation,Iranhas an especial precipitation pattern. In other words,despitehaving a precipitation equal to one-third of global average,Iran experiences a strong fluctuation in its rainfall regime. According to global classifications, floods ...
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Extended Abstract
Introduction
Due to its specific geographical situation,Iranhas an especial precipitation pattern. In other words,despitehaving a precipitation equal to one-third of global average,Iran experiences a strong fluctuation in its rainfall regime. According to global classifications, floods are considered to be among the most important natural disasters. In recent decades, humaninterferencesin the environment and improper management of land usehave resulted in increasing severity and higher frequency of these natural disasters (Abbas ZadehTehrani et al., 2010: 78). Extreme floodingcaused by climate changeshave resulted in severe damages in different parts of the world during recent decades and the effects of these changes are more significant in dry environments (Negaresh et. al., 2013: 15). Increasing urbanization and constructions has naturally reduced permeable areasin different basins. The resulting impenetrable surfacesare incapable of absorbing the rainfall, and consequently, the total volume of runoff in the city has increased (TaheriBehbahani and Big Zadeh, 1996).
Materials and methods
Two typesofground level data and data collected from higher levels of the atmosphere were used in the present study:
A) Precipitation data collected during the first ten daysof April 2019 by stations in Western and South Western Iran obtained from the Iranian meteorological organization.
B) Data collected from higher levels of the atmosphere including revised geopotential heights, sea level pressure, meridian and orbital winds, omega and especial humidityobtainedfrom the National centre for environmental surveys at Colorado, USA.
For synoptic analysis, environment to circulation approach was used to detect heavy rainfall peak periods and then their synoptic dimensions were reanalysed in the spatial range of 10 to 70 degrees north latitude and 10 to 80 degrees east longitude. Based on the analysis ofprecipitation data, April5th and11th,2019 were selected as having the highest rainfall resulting in the highest level of flooding and damage in the western and southwest regions of Iran.
Results and Discussion
On April 5th,2019 most regions of Iran have receiveda rainfall of more than 20 mm. The maximum levels of rainfall wererecorded in Koohrangstation(187 mm), Izehstationin Khuzestan (155 mm) and Yasoujstation(151 mm). OnlySistan and Baluchestan, Kerman and South Khorasan Province have experienced a stable situation without any precipitation on this day. However, on April 11th,2019, the highest level of rainfall has occurred inwestern stations of the country. The maximumlevels of rainfallon this day were recorded inNahavand and Tuyserkan stations (Hamedan Province) and Noorabad(LorestanProvince) with 126 and 122 mm, respectively. Central and northwesternregions of the country have experienced the next highest level of rainfallfollowing western regions. Figures 1 to 3 show a part of precipitation values in the western and southwestern regions of Iran during rainfall peak periods. Precipitations in more than 16 provinces in the western, southwestern, and central regions of the country have damagedagricultural, economic and social sectors. More than 45 people were killed in thesedays.The highest number of deaths and injurieshas occurred in Shiraz. In the western parts of the country, Poldokhtar and Mamoualn were most severely damaged. Moreover, heavy rainfall and floodinghave damaged 700 thousand hectares of agricultural land and resulted in 4600 billion USDlosses. In the construction sector, the country has suffered from 1,600 billion USD losses (Hamshahri Newspaper, 1398).
Conclusion
The present study have focused on synoptic and thermodynamic analysis of systems causing pervasive, heavy and hazardous precipitation onApril 5th and 11th in the western and south western regions of the country. The synoptic and thermodynamic analysis of maps indicated that the contrast between the influence of southern and western low pressure fronts such as Saudi Arabia, Sudan and the Mediterranean on the southwestern areas of the country and the cold high pressure frontover the Caspian Sea have caused a strong pressure gradientand formed a strong front condition over the country and the region under study at the sea level. In the middle and upper atmosphere, deep multiple amplitudetroughsformed over the North Pole passed through Russia as bipolar and low pressureblocks, cyclonic centressettled over the eastern Mediterranean regions and the eastern half of the trough formed as a result of blocking settledover the western and southwesternregions of the country. These have resulted in severe, and widespread negative omega and divergence of warm and humid southern weather over the country and the region.
Mahmood Davoodi; Naser Bay; Omid Ebrahimi
Volume 22, Issue 88 , January 2014, , Pages 100-105
Abstract
Traditional methods of climatic classification are very diverse. Despite traditional and comparative importance, these methods have weaknesses which impair their comprehensive performance. Natural potentials as the background of human activities form the basis and foundation of many environmental programs ...
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Traditional methods of climatic classification are very diverse. Despite traditional and comparative importance, these methods have weaknesses which impair their comprehensive performance. Natural potentials as the background of human activities form the basis and foundation of many environmental programs and land use plans. Sustainable development needs careful planning based on resource constraints and abundances, and local development potentials are determined by its climate. Due to the significant topographic diversity and geographic expansion of Iran, providing a logical classification based on this country's natural realities is quite difficult. Due to topographic diversity of Mazandaran province, its climatic classification is not easily executable. The present article seeks to determine the climate of Mazandaran province according to Litinsky model. We tried to use different methods for climatic classification of the province, yet finally we focused on Litinsky model and explained it. Litinsky model use three fundamental elements of temperature, precipitation and Berry coefficient. Then, it takes advantage of auxiliary indicators including adaptation, continuity of dry season and solar radiation condition to provide a comprehensive classification. To do so, data obtained from 10 synoptic and climatologic stations in Mazandaran during 1984-2005 statistical period was used in SPSS environment. Finally, climate of Mazandaran province stations were determined and proposed in table 4.
Manoochehr Farajzadeh; Ali Azizi; Hossein Soleymani
Volume 22, Issue 87 , November 2013, , Pages 2-13
Abstract
Direct influence of precipitation on human life and the role it plays in the development of different countries have resulted in an increase in using methods and algorithms for estimating precipitation. A few decades ago, traditional methods were used for predicting precipitation. Then, the presentation ...
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Direct influence of precipitation on human life and the role it plays in the development of different countries have resulted in an increase in using methods and algorithms for estimating precipitation. A few decades ago, traditional methods were used for predicting precipitation. Then, the presentation of meteorological satellite revolutionized this field. Considering the high dispersion of weather stations and rain gauges in developing countries like Iran, free access to images taken by sensors like AVHRR and MODIS is an appropriate opportunity to compensate these deficiencies. We can estimate the volume of water vapor ready to be transformed into precipitation using satellite images, water vapor absorption bands and thermal bands in any time, space, and scale. The algorithms used to estimate precipitation in satellite images are classified into three types- infrared, visible, microwave and a combination of the first two types-based on the sensors’ wave length. Methods based on infrared and visible waves have a good spatial and temporal resolution, while microwaves-based methods measure precipitation directly. Yet, these techniques contain many weaknesses, especially in low earth orbits. Combined techniques are used to compensate these weaknesses. Microwave images are not receivable in Iran, thus we cannot take advantage of microwaves and combined methods. As a result, the present article focuses mainly on visible and infrared wave length.
Fatemeh Zabol Abbasi; Morteza Asmari Sadabad
Volume 21, Issue 83 , November 2012, , Pages 70-72
Abstract
Precipitation and temperature can be considered among the most important climatic elements in any area. Phenomena like sudden increase or decrease in temperature and precipitation during a year or more can be considered a reason for climate change in the area. The present article applies usual time series ...
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Precipitation and temperature can be considered among the most important climatic elements in any area. Phenomena like sudden increase or decrease in temperature and precipitation during a year or more can be considered a reason for climate change in the area. The present article applies usual time series method to investigate annual and seasonal changes in temperature and precipitation in Bandar-e Lenge synoptic station during the statistical period (1966-2000). Analysis indicate that average precipitation in Bandar-e Lenge during the statistical time period is 154.7 mm. Standard deviation and coefficient of precipitation changes are respectively 96.7 and 63 percent. Seasonal trend of precipitation changes in the statistical period indicates that precipitation showed a decreasing trend in spring and summer and an increasing trend in autumn and winter. Seasonal distribution of precipitation in Bandar-e Lenge during the above mentioned period (spring to autumn) contains 7, 2, 21, 70 percent of the annual precipitation respectively. In order to determine annual temperature, frequency of decreasing periods, definite temperature increase and degree of changes in each thermal period were calculated. Temperature changes more severely in summer and autumn, while the frequency of changes in winter and spring is more orderly. Moreover, the statistical period of 1966-70 is introduced as the most arid period of the study years.
Omid Binesh; Ali Panahi
Volume 19, Issue 73 , May 2010, , Pages 60-63
Abstract
Iran is located in the belt of dry and semi-arid regions of the world. Due to its geographical situation, it has severe rainfall fluctuations, especially in arid areas. It is necessary to study this behavior for drainage basin operation, flood estimation for design and ... In this research, the IDF method ...
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Iran is located in the belt of dry and semi-arid regions of the world. Due to its geographical situation, it has severe rainfall fluctuations, especially in arid areas. It is necessary to study this behavior for drainage basin operation, flood estimation for design and ... In this research, the IDF method has been used to study the intensity-duration-frequency of precipitation, which shows the intensity of precipitation in a desired period of time with a specified return period, and it can be used to estimate the flow rate according to intensity of precipitation. Garmsar is also located in a hot, dry climatic zone. The recognition of precipitation regime as well as the intensity of showery and exceptional rainfall can be useful for future planning. By examining the IDF curves, it was found that the highest rainfall has been in April, March and May, and the lowest rainfall in September, August and June, respectively.
