Journal of Spatial Analysis

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Groundwater resources are important sources for the supply of water in agriculture, industry and drinking in Ardabil plain, therefore underground water resources planning and sustainable management of these resources are important. The purpose of this study is grading the villages in the plain of Ardabil in underground water crisis and changes during the years 1360-1391. The information obtained from 39 wells, piezometers in plain of Ardabil. Using simple techniques and fuzzy cumulative weighting and interpolation methods, the piezometers interpolation of shallow water table and how it changes during the period is showd.

Introduction
     Groundwater is one of the main sources of drinking water supply for many people around the world, especially in rural areas. Groundwater can be contaminated by natural or human activities are numerous. All activities including residential, municipal, commercial, industrial and agriculture can affect groundwater quality. Groundwater contamination can result, such as the loss of a source of water supply, high cost of clearing the high cost of alternative water supply or cause potential health problems. Given the importance of determining the results of the plains of the country, the aim of this study was to determine changes in aquifer storage of Ardabil using statistics and analysis on multi-criteria decision-making and evaluation of groundwater is a crisis situation.

Data and Methods

     In this study, the data of piezometers wells in of Ardabil plain scattered through the city of Ardabil Regional Water Authority have been prepared. Also, the surface layers and point to the plains of Ardabil, political divisions and the location of wells, piezometers villages for final maps have been used. The data of deep wells, as well as cultivation of four major product with a high water requirement of wheat, barley, potatoes and forage to determine the relationship between ground water and water harvesting has been a drop in water table.

The study area

     Plain study area is located in the north-west of Iran in Ardabil province (Figure 2 and Figure 3).  The average height is about 1360 meters above sea level  It covers an area of approximately 820 square kilometers and is located in the Gharasoo watershed.

• Inverse Distance Weight;
• Global Polynomial Interpolation;
• Local Polynomial Interpolation;
• Radial Basis Functions;
• Straight Ranking;
• Fuzzy Normalized;
• Fuzzy multi-criteria decision-making;
• FSAW.

   The first step is to evaluate each process and required hydrological data collection, and the coordinatingits location. The geostatistical methods of IDW, GPI, LPI, and RBF in the ArcGIS software were used for  interpolating all existing data and a drop in water table in the area of standards for grades 10 class (raster) within restricted fields of Ardebil were determined.

    Finally, using simple collective weight, weight-bearing layers and layers of loss data water table for the years 60 and 90 is obtained. To get the final map of water table drops, the two layers are deducted and the final map of Ardabil plain water table drop that phase is obtained.

     Analysis showed the reduction of water table almost 47 percent in 1391 compared to 1360. As can be seen in Figures 12 and 13, maximum of 45 meters water table wells, piezometers in 1360 to more than 70 m in 1390 has come to reveal the deterioration of the aquifer Ardabil.

    Pholadloo_e_Shomali district with the highest concentration of deep wells in the near future to continue the removal of existing deep wells, groundwater resources will go into sharp decline.

    Sharghi Village goes to the crisis and in the meantime, the central Vilkij district includes the eastern part of the plain, the drop in water table aquifer at greatest risk to the two villages in East and Central Vilkij.

• Due to the limitations of traditional agricultural development potential ground water;
• Increase the efficiency of irrigation, changing crop patterns of water needed to fill low-power consumption;
• Efficient use of water resources and prevent unauthorized digging deep wells to exploit the nutritional front, especially in the East and Southeast plains.

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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.

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Among the important challenges facing water resources of the country, one can mention the phenomenon of climate change and its impacts. The General Circulation Models (GCMs) can provide the best information about the response to increasing the concentration of greenhouse gases. Since the outputs of this model do not have sufficient time and space accuracy for studies on the effects of climate change, the output data of small general circulation models need to be quantitative. In this study, the SDSM statistical magnitudes and the CanemS2 model for climate change assessment, which are presented in the fifth report of the IPCC Comes under three scenarios RCP2.6, RCP4.5 and RCP8.5. The daily minimum temperature, maximum and precipitation rates of the synoptic station of Shahrekord (Cold mountain region) and Bandar Anzali (very humid and temperate climatic zone) are utilized and the parameters are for the period of 2040-2011, 2070-2070, and 2071-2099. Is. The results of the study show that the SDSM model has high accuracy and high efficiency in the climatic zone of very humid and temperate (Bandar Anzali) relative to the cold cliff (Shahrekord). However, the model has an acceptable ability to simulate the parameters in both areas. Under all three scenarios, RCP will experience the minimum and maximum temperature and precipitation in both climatic zones in all three times, but the cold climatic zone will be more affected by the climate change phenomenon.

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Real-time detection of forest fire using NOAA/AVHRR data
Study area :(Kayamaki Wildlife Refuge)
 
Extended Abstract
Introduction
Land and forest fires are one of the most common problems in the world that cause various disturbances in forest and land efficiency. Real-time fire detection is crucial to prevent large-scale casualties. In order to identify early fire in areas where there is a high risk of fire, it is necessary to monitor these areas regularly. Forest monitoring is a technique used to detect fires in the past using traditional techniques such as surveillance, helicopter and aircraft. Today, satellite imagery is one of the most imperative and effective tools for detecting active fires in the world.
Materials and Methods
In this study, NOAA/AVHRR images were used for fire detection and MODIS products were applied for evaluation and validation.
Fire Detection Algorithms
There are several algorithms for detecting fires using satellite imagery. In this study, 3 algorithms of Giglio, extended and IGPP were used. The selection of these algorithms was due to the extensive background research in most of the previous studies that used them and the results of these algorithms, especially the IGPP, were far more than other algorithms.
Giglio Algorithm
Giglio et al., (1999) criticized Arino and Melinott (1993) threshold as too high for certain regions of the world such as tropical rain forests, temperate climates and marshes where the air temperature for small fires (100 m³) is usually between 308 and 314 degrees Kelvin. They believed that the smaller fires were not fully recognized by Arino and Melinott (1993) thresholds. They concluded that in suburban forests 60% of fires had temperatures below 320K of which 70% were in rainforests and 85% happened in the Savanna. Thus, the threshold cannot be applied on a large scale and it is only applicable for a regional scale.
IGBP Algorithm
The IGBP fire detection algorithm is implemented in two steps. The first step is the threshold test in which a pixel in micrometers (11.03 μm) minus the band 4 is greater than 8 degrees Kelvin, the desired pixel being considered as a potential fire pixel. Band 3 (3.9 μm) exceeds 311 K, and band 3 illumination temperature is 3.9.
Developed Algorithm
This algorithm is used to detect small and large fires (both at night and day).
 
Interpretation of the Results
After selecting fire detection algorithms, pre-processing (geometric, radiometric and atmospheric corrections), processing (applying fire relationships and fire formulas for fire detection) and post-processing (evaluating and validating the results), the fires were identified by the fire algorithms (images). Final results of fires identified for 2016 and 2017 (for 4 days) by fire algorithms indicate that fires identified by Giglio algorithm were 22 cases, those by IGPP algorithm were 27 cases and the ones by the developed algorithm were 15 cases. For this reason, the IGPP algorithm can be taken as the most appropriate algorithm in this study for fire detection using satellite imagery.
Evaluation of fires identified through MODIS products
To evaluate identified fires, after recognizing them with relevant algorithms, we used MODIS products for their evaluation (due to the lack of ground data on the days studied for evaluation). MODIS products were obtained from sites where the location of each fire was reported. For the evaluation of identified fires based on fire detection algorithms with MODIS products, 10 fire occurrences were used. The evaluation results express that out of 10 fires only 7 fires were recognized by the algorithms of MODIS products. 5 fire events were identified by Giglio algorithm (from 7 fires), 6 fires from IGBP (out of 7 fires), and 3 fire events from 7 extended algorithm were selected as fire pixels.
Comparison of the implications of the fire algorithms
The implications of fire occurrence algorithms indicate that the IGBP algorithm with 6 fires (out of 7 tested fires with error rate of 14% and with the number of fires detected (86%)), Giglio algorithm with 5 fires (out of 7 tested fires, with error rate of 28% and with the number of fires (72%)) and the developed algorithm with 3 fires (out of 7 fires tested with an error rate of 57% and with fire rate of 43%) have been identified. Therefore, it is concluded that the IGBP is the most appropriate algorithm for real-time fire detection, followed by Giglio and the developed algorithm in second and third orders, respectively.
Keywords:Real Time Fire Detection, Fire Algorithms, NOAA/AVHRR, Kiamaki Wildlife Refuge.
 

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