Journal of Spatial Analysis

Urmia Lake is one of the largest hyper saline lakes in the world and largest inland lake in Iran which located in the north west of Iran, between the provinces of East Azerbaijan and West Azerbaijan. The lake basin is one of the most influential and valuable aquatic ecosystems in the country and registered as UNESCO Biosphere Reserve. In addition, it is very important in terms of water resources, environmental and economic. Unfortunately, lake water level has dramatically decreased in recent years, due to various reasons. This issue has created some problems for Local people, especially people living in rural area in east of the Lake. The results of this research are of great importance for regional authorities and decision-makers in strategic planning for people of inhabits in east coast village.

The present paper is an attempt to integrate a semi-automated Object-Based Image Analysis (OBIA) classification framework and a CA-Markov model to show impacts of Urmia Lake Retrogression On eastern coastal villages. OBIA present novel methods for image processing by means of integration remote sensing and GIS. Process and outcome of this methodology can be divided in three step including: Segmentation, Classification and Accuracy assessment.in the process of segmentation aims to create of homogeneous objects by considering shape, texture and spectral information. A necessary prerequisite for object oriented image processing is successful image segmentation. In our research the segmentation step was performed by applying multi-resolution segmentation and considering 0.2 for shape and 0.4 for the compactness. The scale of segmentation is also an important option which leads to determine the relative size of each object. Having great values for scale leads to create large objects while smaller value would result small objects respectively. In this study the scale parameter of 100 has been selected based on the size of objects in Scale of study area as well as spatial resolution of the satellite images were used for segmentation. In doing so, we employed spectral and visual parameters contains: texture, shape, color tone and etc. for developing object based rule-sets.  To determine the characteristics of the spectral data and geometric features classes the fuzzy based classification was performed by employing fuzzy operators including: or (max) operator with the maximum value of the return of the fuzzy, the arithmetic mean value of fuzzy and the geometric mean value of fuzzy, and (min). After this step, the validation process was performed by using overall accuracy and Kappa coefficient. Then, using the CA-Markov Model The trend of changes was predicted in the future (For 2020). Another way to predict changes in land use and cover, used the CA-Markov model. Markov chain analysis is a useful tool for modeling land use changes. Markov chain model consists of three step: First step Calculating the probability conversion using Markov chain analysis, second step, Calculating the Cover and land use maps competently on the basis of multi-criteria evaluation, third step, assign locations cover and land use simulation based on the CA position operator.

Results of Satellite image processing indicate that the area of garden, Farmland, Zones of muddy-salty (Saline soils), moist salt and newly formed salt have increased while area of Urmia lake has rapidly dropped between 1984 and 2015. The area of Urmia lake declined from 4904.51 square kilometers in 1984 to 676.79 square kilometers in 2015. The farmland area increased from 177.72 square kilometers in 1984 to 542.37 square kilometers in 2015. The garden area increased from 83.71 square kilometers in 1984 to 227.28 square kilometers in 2015. The moist salt area increased from 111.89 square kilometers in 1984 to 945 square kilometers in 2015. Zones of muddy-salty (Saline soils) area increased from 859.01 square kilometers in 1984 to 2986.5 square kilometers in 2015. The newly formed salt increased from 171.27 square kilometers in 1984 to 921.99 square kilometers in 2015. Markov chain model results indicate in 2020 the garden area will be 638 square kilometers, the moist salt area will be 717 square kilometers, Zones of muddy-salty (Saline soils) area will be 4127 square kilometers, the farmland area will be 644 square kilometers, the newly formed salt area will be 363 square kilometers and the Urmia lake area will be 118 square kilometers.

Nowadays, the severity of the drought hazard has reached a point that has affected all the rural and urban areas surrounding it. Increasing the resilience of villages via livelihood solutions, is one of the best strategies for reducing the vulnerability of villages against natural hazards such as drought. The eastern side of the Lake Urmia consists of the six cities of Osku, Azarshahr, Bonab, Shabestar, Ajabshir and Malekan. Totally, there are 199 villages in this region, which are affected by the drought of the Lake, directly and indirectly and according to the statistics, the quantitative and qualitative reduction in agricultural and livestock productions and soil quality, the incidence of respiratory diseases and … have risen sharply compared to the past and a number of villages have been evacuated. Also because of the lack of a coherent strategy, which should be taken by the planners and authorities, the important measures to revitalize the Lake has not been taken yet and the dimensions of the threat are increasing day by day.

Current study investigate the factors affecting the resilience of rural settlements of the eastern side of the Lake Urmia against Drought. This is an applied and analytic-explanatory research. The data is collected by questionnaire from the villagers living in rural areas of the six cities, which are the statistical population of the research and the total number of the villages estimated 199 with 232295 persons.

The standardized Perception Index (SPI) is used to estimate the varying degrees of the villages in the eastern side of the Lake Urmia. In the next step, the possession index for each of the villages was calculated and the studied villages were classified based on it. On this basis and by considering the four status of drought and the three levels of possession, after sorting the villages on the basis of these two indexes, 43 villages were chosen from different regions of the eastern side of the Lake as the first level of analysis, using systematic random selection. Also, to classify the villages in the regard of possessing of the development facilities, the composite indicators called Morris pattern and 47 existing items are used, which are calculated in 9 different indexes. Finally, the obtained information were analyzed using SPSS and GIS software.

Regarding to the research findings at the eastern side of the Lake and on the basis of Standardized Precipitation Index (SPI), about 78% of this area has been experiencing drought. Also, the status of the overall indicators of household's livelihood capital on the basis of the Normal Scale from 0 to 10 is 3.34, which shows the unfavorable status of this index. The results of the study in the field of the level of civil and institutional development showed that on the basis of the Normal scale from 0 to 10, civil development is 4.86 and institutional development is 3.69. Lastly, the research findings for the three levels of the sustainable development of the livelihood shows that the livelihood diversification is 3.61, in depth agriculture 3.24 and migration strategy is 3.02. The analysis of the results of the sustainable livelihood shows that the decrease of drought of the villages increases the diversity of the livelihood of the villagers. According to the results obtained, the mean of the resilience index of the investigated households on the basis of 0 to 10 equals to 4.86, which is close to the average level. The classified distribution of the resilience level and the focus of the more than of 56% of the households with average level of resilience confirms this situation. 30.26% of the households has low resilience and 15.64% has high resilience in the face of existing conditions. Upon this basis, the highest amount of the resilience equals to 5.38, which exists in the villages with severe drought conditions and by decrease of the drought, the resilience of household’s decreases. Finally it can be said that the villages with a long history of vulnerability from drought and also having more intense droughts, has a higher resilience level in dealing with the situation.

According to the results, the highest amount of vulnerability exists in the villages with low experience in dealing with the long-term conditions of drought, which their economic and social structures are not prepared to deal with the conditions. While the average amount of the livelihood capitals and the resilience of the studied statistical population do not show an appropriate conditions, but totally, the results and relationships of the studied variables conforms the role of possessing all dimensions of livelihood capital on taking appropriate approach to dealing with the conditions of drought in the Lake Urmia. In the field of taking the approaches of diversifying the livelihood resources of the villagers, there are several scientific and examined solutions, such as considering the education and awareness as a definite reality, also the knowledge and skills of the villagers in the fields of modifying the crop patterns, water saving strategies, the use of efficient products and making use of the other high-income jobs must be increased.

In the field of educational solutions, besides providing modern knowledge and international successful experiences, it must be possible to make use of the indigenous knowledge and experiences of the villagers.

Drought is a concept that is generally understood on a basic level, but is difficult to quantify. Palmer defined a drought as a meteorological phenomenon that is characterized by ‘‘prolonged and abnormal moisture deficiency. A drought can alternatively be broadly defined as a temporary, recurring reduction in the precipitation in an area.

Aridity and drought are not synonymous. Aridity is a measure of long-term average climatic conditions. Both humid and arid regions experience droughts. However, the inter-year variation in precipitation is greater in arid regions and there is a greater probability of below average precipitation in any particular year. Arid regions are thus more prone to droughts and may experience more severe impacts from droughts.

In this research was used temperature and precipitation monthly data of Urmia, Tabriz, saghez, Maragheh, and Mahabad station in statistically period 1985-2014. Run test was used to study the homogeneity of data. Randomness and homogeneity of data was approved.at a confidence level of %95. SEPI Index and ANFIS model was used for determining and forecasting drought in Urmia lake basin. SEPI index is more complete than SPI. Results of SEPI were used in ANFIS model.

Fuzzy index SEPI[1]: Standardized precipitation index and evapotranspiration (SEPI) to address some of the disadvantages of SPI index is provided. Evapotranspiration and precipitation index SPI index and SEI standardized integration is achieved. The index is the result of drought monitoring phase of architectural models using fuzzy logic in a fuzzy inference system is designed. How to design this model and determine SEPI is described below.

Fuzzy architecture drought monitoring: for derivatization indices SPI and SEI using Fuzzy Inference System, Due to the structure of fuzzy models were considered.

SPI index[2]: Standardized Precipitation Index is an indicator widely used in Drought Monitoring. This index is one of the few indicators drought monitoring and could even say the only indicator that the time scale is considered. Depending on the time scale to determine the effect of different sources of agricultural drought, hydrological and so determined. Time scale can be determined from one month to several years. SPI index is used to calculate the only element rainy climate. Monthly precipitation amounts for each station in the desired time scale is calculated.

SEI index[3]: Since the index SPI Single Entry, rain, The SPI index values under the influence of changes in temperature and evapotranspiration parameter that is powerful factor in the drought, it will not be. So to enter the effect of temperature and evapotranspiration in SPI, SEI (evapotranspiration index Standard) To calculate this index, before any measures should reference evapotranspiration for the period to be estimated.

define the rules for combining indicators SPI and SEI: Different classes index SPI and SEI rules or the same combination of conditional statements in the form if, as a class of SEPI index in the lead, is defined. This rule only a combination of different modes SPI and SEI indices that lead to SEPI index shows. In this regard, the rules can be combined to fit different for successive written and stored in the knowledge base. Since the output of the resultant composition, indices SPI and SEI are involved in determining the status of SEPI, Weight each of the indicators with regard to the effect of precipitation and temperature parameters on the severity of the drought was considered As a result, SPI indices and weights 0.667  and 0.333, respectively SEI were included in the calculations.

According to the results, according to the research, education Anfis model with 75 percent of the data series is well done SEPI and much has been done to ensure education is nearly 100 percent. So that the graphic maximum of 0.26 percent error in saghez station on a scale of 6 months and the lowest average error of 0.10 percent in Urmia station is on a scale of 6 months. In modeling, validation data, the average error modeling is naturally higher than the average training error. Most average forecast error saghez on a scale of 6 months at the station 0.34 percent and 0.10 percent, the lowest on a scale of Urmia station is 6 months. But the coding maximum of 0.65 percent error in saghez station on a scale of 6 months and the lowest average error of 0.32 percent in Tabriz station is on a scale of 6 months. SEPI index in the time scale of 6 and 12 months is used for investigate the characteristics  of adaptive neuro-fuzzy inference system in order to drought and drought forecasting model. According to the findings in this study, the frequency of drought in the stations of Urmia and Saghez and Maragheh on a scale of 6 months is more than the scale of 12 months in the basin of Lake Urmia but in Tabriz and Mahabad Stations situation is the vice versa. The drought in Urmia Lake basin is increasing trend but temperature has increasing trend with more intensity. The highest and lowest percentage of drought was seen in Urmia and Mahabad station respectively. The results of the forecasting of index by ANFIS model showed that the most training error is in Tabriz station (0.51) and the lowest training error is in Maragheh station (0.36) in a scale of 12 months in coding. In validation data modeling the average of modeling error is higher than the average training error naturally. According to the definition of drought SEPI was presented based on amounts of 0.73 or higher or mild drought to higher floors as dry conditions arise The scale of 6 months in Urmia station with 13.14 percent to 10.89 percent saghez station, Tabriz stations with 5.58 percent, with a 5.1% Mahabad station and Maragheh with the amount of 4.82 percent, the drought has occurred. The time scale of 12 months in Tabriz station by 9%, saghez station with 7.26 percent, with 6.11 percent of Urmia station, Maragheh with 5.5% and the amount of Mahabad stations with a 3.44 percent, from months of study in the series, drought has occurred.

Results of SPEI are:

1. Drought trend is increasing in urmia lake basin. Temperature has increasing trend extremely.
2. The highest percentage of drought is in Urmia station and its lowest is in Mahabad station.
3. Percent of frequency of drought in Urmia station, Saghez and Maragheh on a scale of 6 months is more than to 12 months, but the scale of Tabriz and Mahabad stations with the photos. Stations Tabriz and Mahabad is in the opposite situation.

Results of ANFIS Model are:

In study area and in ANFIS model whatever forecasting coming years is shorter; confidence of forecasting will be more.

Due to the errors amount obtained in model validation, in study area forecasting of drought by ANFIS model was done with confidence 94%.

     Environment, development and sustainability are the three significant issues of worldwide concern. Environmental vulnerability and assessment of natural and anthropogenic activities impacts represent a comprehensive evaluation approach. The main purpose of this study is to present a comprehensive and novel framework in order to environmental vulnerability assessment using by spatial data and techniques. The method of this research is analytical-descriptive. The basic premise is that the finding of this study can be applied in the local planning system and policy making process of environmental conservation particularly to cope with rapid environmental change. The environmental vulnerability is defined and governed by four factors: hydro-meteorology signatures, environmental attributes, human activities and natural hazard. Based on data availability and vulnerability status of different areas, there is no general rule for selecting how many variables are required to assess the environmental vulnerability. In this study, 18 variables were taken into account and organized into four aforementioned groups.  The process of environmental vulnerability index is proposed to integrate AHP approach, remote sensing indices and GIS techniques. The environmental vulnerability showed distinct spatial distribution in the study area. Furthermore, the distribution of heavy and very heavy vulnerability patterns mainly occur in low and medium lands where the human activities have been developing rapidly and is the nearest region to Urmia lake in the west region.