Zeinab Zaheri Abdehvand; Marzieh Mokarram; Fatemeh Meskini Vishkaei
Abstract
Extended Abstract
Introduction
Ecological agricultural zoning is a tool for proper assessment of land resources, better planning and management of cultivation in order to achieve sustainable agriculture. Due to the importance of Khuzestan province in the country's agriculture and the strategic ...
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Extended Abstract
Introduction
Ecological agricultural zoning is a tool for proper assessment of land resources, better planning and management of cultivation in order to achieve sustainable agriculture. Due to the importance of Khuzestan province in the country's agriculture and the strategic nature of wheat production, in this study, the zoning of wheat production potential in the DashtBagheh region of Khuzestan was done. Modern GIS technology is widely used in such studies to prepare land suitability. Separated agro-climatic zones can provide the ground for optimizing and expanding the growth of agricultural products (Balgaku, 2016). Cultivation of land can be attributed to the potential of the region in terms of food distribution and the availability of climatic factors. In a study using GIS and RS, Beijing region of China was divided into four regions in terms of winter wheat cultivation based on the weight of variables: appropriate, relatively appropriate, inappropriate and very appropriate (Wang et al., 2011).In another study evaluating arable lands such as wheat, barley and sunflower in Spain, environmental factors, topography and soil including altitude, slope, soil texture, temperature, rainfall, day length and the impact of each on this The plants were studied and then combined with the above data by weighing each layer in the GIS environment and finally mapped the susceptible areas (Khan et al., 2010). Due to the importance of the subject, the aim of this study is to use fuzzy methods and multi-criteria decision models (Analytic Hierarchy Process (AHP)) in order to identify suitable areas for wheat cultivation in Bagheh plain of Shousha city in Khuzestan province. It is worth mentioning that in this study, the most important parameters affecting wheat cultivation before entering the model were selected using statistical methods, which distinguishes it from previous studies.
Materials and methods
Climatic characteristics included average, minimum and maximum temperatures as well as annual rainfall. Also, environmental factors including topographic characteristics (slope) and soil characteristics (chemical and physical) were considered. Soil characteristics were determined from the data of 96 soil profiles obtained from semi-detailed studies in the region. Zoning of different soil characteristics and climatic variables was done the inverse distance weighting (IDW) method. Then, using membership functions, a fuzzy map of each of the effective parameters in determining the areas prone to wheat cultivation was prepared. Then, using the Analytic Hierarchy Process (AHP) model, the weight of each layer was determined and finally, in the GIS environment, a land suitability map was prepared for wheat cultivation. In this study, linear membership functions have been used. This function has four parameters that determine the shape of the function. Trapezoidal, triangular, S-shaped or L-shaped membership functions can be defined by selecting appropriate values for different states (Carter and Grime, 1994). Weighing to the layers was done to prepare the final map of land suitability. The weight parameter is an important parameter for relating the factors used in land suitability. Because each of the characteristics has a different effect on wheat cultivation, weighting was done using AHP method.the AHP is a method that makes it easy to weigh parameters. AHP relies On a pairwise comparison of each of the parameters. Each of the factors is in the range of 1 to 9 according to the importance of determining the suitable areas for wheat cultivation, according to Table 2.
Results
To prepare an interpolation map for each input data was used IDW method. The accuracy of the IDW method in mapping each of the variables showed that the climatic parameters have higher accuracy than the soil variables. Based on the evaluation statistics, the highest and lowest accuracy in climatic variables were obtained for the mean temperature (R2 = 0.99) and maximum temperature (R2 = 0.96), respectively. However, the highest interpolation accuracy in the studied soil properties was related to the percentage of exchangeable sodium (R2 = 0.81) and the lowest accuracy was observed in the interpolation and zoning of soil clay. The results of the AHP method showed that the greatest importance in preparing the land suitability map is related to rainfall with the highest weight and the least importance is related to the slope with the lowest weight. The results showed that the western part of the study area is suitable for wheat cultivation based on soil, climate and topographic characteristics of the area. It occupies about 46% of the total area of the study area (4220 hectares) and parts of the south and north of the study area have the most unsuitable conditions for wheat cultivation.
Conclusion
In this study, suitable areas for wheat cultivation were studied using the fuzzy AHP method in the GIS environment. For this purpose, the zoning map of each parameter was first determined using the IDW model method. Then, using membership functions, a fuzzy map of each of the effective parameters in determining the areas prone to wheat cultivation was prepared. Then, using the AHP model, the weight of each layer was determined and finally, in the GIS environment, a land suitability map was prepared for wheat cultivation. According to the results, it is clear that this method has high accuracy in determining areas prone to wheat cultivation.
Marzieh Mokarram; Saeed Negahban
Abstract
Extended Abstract
Introduction
Investigating the spatial andTranslation errorInvestigatingiiiii temporal variations of soil salinity plays a major role in managing the watershed and preventing the development of salinity (Mohammadi, 2007). Also, the study of groundwater salinity due to the complexity ...
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Extended Abstract
Introduction
Investigating the spatial andTranslation errorInvestigatingiiiii temporal variations of soil salinity plays a major role in managing the watershed and preventing the development of salinity (Mohammadi, 2007). Also, the study of groundwater salinity due to the complexity of hydrological processes, characteristics of the aquifer, and their variability is a difficult task. However, these problems exacerbate by external factors such as atmospheric conditions and human activities affecting the permeability and hydrological processes (Mirzaee and Hassan-Nia, 2013). Because of the costly nature of experiments involving salinity sampling, as well as the computational models not being calibrated and the complexity of these models in order to overcome these limitations and to determine salinity in the depths of the soil, determination of models consistent with natural behaviors and the use of existing models, Increase day by day. On the other hand, considering the fact that many lands are under cultivation in the northwest of Fars province, it is important to study the chemical properties of the soil and water in the region, including salinity.
There are various methods for studying the salinity of water and soil, for example, Syringes et al. (2006) predicted the salinity of soil profile and the drainage outlet in a research using neural networks in an experimental area in India. Arfin et al. (2003) used an artificial neural network model and linear regression model to predict the soil and water salinity. Topographic index is a measure of the extent of flow accumulation at the given point of the topographic surface. As catchment area increases and slope gradient decreases, topographic index increases. Like other combined morphometric variables, topographic index can be derived from a digital elevation model (DEM) by the sequential application of methods for local and nonlocal morphometric characteristics, followed by an arithmetic combination of the results of these calculations.
Materials & Methods
The studied watershed is located in the west of Shiraz, between the cities of Shiraz and Kazeroon. The most important urban center in this basin is the city of Bayza. The geo-location of the studied area is N 29° 12´to 29° 48´and E 52° 06´ to 52° 36´ (Figure 1). The area of the study region is 623.63 KM2. The highest and lowest altitudes in the study area are 1630 and 3083 meters respectively. The average temperature in the region is 16.8 degrees varying from 4.7 to 29.2. The study area is very rich for cultivating crops. It is also a very rich in terms of topography, geology and biodiversity. Regarding the presence of agricultural lands in this region as well as the significance of irrigation water quality and the type of soil in terms of electrical conductivity (EC), the study of the soil and water characteristics of the region is very important in terms of salinity.
The data used in this research include electrical conductivity of water and soil samples provided by Fars Agricultural Jihad Organization (2013). This region was selected considering the importance of the study region for agriculture. The zoning maps for each of them were prepared in the ArcGIS environment with the help of these sample points which were selected randomly. Then, the EC data of water and soil was homogenized and ranged from 0 to 1 with the help of membership functions. Finally, the relationship of the amount of water and soil salinity with the watershed rough terrain was investigated.
Discussion and Results
According to the interpolation maps, it was determined that the lowest and the highest values for water salinity in the study area were 0.42 and 3.07 respectively, while for soil salinity were 0.87 and 8.75 respectively. According to the salinity zoning map prepared for soil samples in the study area, it is determined that the highest soil salinity is in the southwest of the study area, while the north and center of the study area have lower soil salinity. Also, the results of water salinity obtained by IDW method showed that the highest salinity of water is in the north of the region, while the lowest salinity of water is observed in parts of the south of the study area. The fuzzy map values of the study area are between 0.08 to 0.99, that except for a very small part of the study area located in the southeast, the rest of the area contain saline water. Also, the results of soil salinity fuzzy map of the studied area showed that the soil salinity values were between 0.61 and 0.92. In fact, the soil in the study area has a lot of salinity.
Conclusion
After finalizing the fuzzy map of water and soil salinity by fuzzy method, the final salinity map was classified into four classes. Values less than 0.25, between 0.25 and 0.5, 0.5 to 0.75 and more than 0.75 were classified into inappropriate, moderate, good and very good grades, respectively. (The low values: < 0.25 (inappropriate for drinking), moderate: 0.25 – 0.50, high: 0.50 – 0.75, very high: > 0.75 (a-ppropriate for drinking)). Using fuzzy method for soil salinity, it was determined that 24.31% of the area was in poor class (inappropriate), 11.78 in the moderate class, 25.74 in the good class and 38.16% of the area was in the very good class, while for water salinity, it was found that 36.6% was in the moderate class, 31.69% in the good class and 31.65% was in the very good class. At the end, the relationship between the Landform map and the salinity map of the soil and water in the study area was determined. The results showed that salinity of the water in the valleys is very high, while soil salinity in the upstream drainage has shown the highest values. The results also showed that the minimum salinity of the soil and water are in the plains.
Marzeyeh Mokarram; Ali Darvishi; Saeed Negahban
Abstract
Extended Abstract
Introduction
Watershed is an area of land that surface water of rain and melting snow conduct towards a single point, which is usually out of the basin. Check of watershed is one of the main strategies for integrated management of natural resources and sustainable development. Recently, ...
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Extended Abstract
Introduction
Watershed is an area of land that surface water of rain and melting snow conduct towards a single point, which is usually out of the basin. Check of watershed is one of the main strategies for integrated management of natural resources and sustainable development. Recently, the availability of remote sensing (RS) data and Geographical information system (GIS) technologies has allowed for improved understanding of the morphometric properties and surface drainage characteristics of many watersheds in different parts of the world (Parveenet al., 2012; Nayar& Natarajan, 2013). For example, Shrimaliet al. (2001) presented a case study of the 42 km Sukhana lake catchment in the Shiwalik hills for the delineation and prioritization of soil erosion areas. In addition, Srinivasaet al. (2004) used GIS techniques for morphometric analysis of subwatersheds in the Pawagada area, Tumkur district, Karnataka. Nookaratnamet al. (2005) carried out a study on dam positioning through prioritization of microwatersheds using the sediment yield index (SYI) model and morphometric analysis. Khan et al. (2001), used RS and GIS techniques for watershed prioritization in the Guhiya basin and sub-watersheds in Odisha, India respectively.
Materials & Methods
The study area is one of the subwatersheds of the river of Urmia (Nazloochaei) that is located in North West of Iran with an area of 948.75 km2. The study area was selected for detailed morphometric analysis using Geography information system (GIS). The input data for morphometric analysis was DEM with resolution of 30 m from ASTER satellite. The steps of stream extraction consist of:
1. Extraction of drainage networks from the DEM using the flow direction method, which consists of the following steps (O’Callaghan & Mark, 1984):
i. Fill Sinks: A sink is an uncompleted value lower than the values of its neighborhood. To ensure proper drainage mapping, these sinks were filled by increasing elevations of sink points to their lowest outflow point.
ii. Calculate Flow Direction: Using the filled DEM produced in Step1, the flow directions were calculated using the eight-direction flow model, which assigns flow from each grid cell to one of its eight adjacent cells in the direction with the steepest downward slope.
iii. Calculate Flow Accumulation: Using the output flow direction raster created in Step2, the number of upslope cells flowing to a location was computed.
iv. Define Stream Network: The next step is to determine a critical support area that defines the minimum drainage area that is required to initiate a channel using a threshold value.
v. Stream Segmentation: After the extraction of drainage networks, a unique value was given for each section of the network associated with a flow direction.
Morphometric analysis of the study area consist of:
Stream number (Nu)
Nu is number of segments in order U
Stream order (U)
Cumulative length of streams (L), L = ∑Nu, L is calculated as the number of streams in each order and total length of each order is computed at sub-watershed level (Horton, 1945).
Bifurcation ratio (Rb)
Rb=Nu/N (u+1) N (u+1) = Number of segments of the next higher order (Schumms, 1956),
Watershed relief (Bb), Bb = Hmax – Hmin, Bb is defined as the maximum vertical distance between the lowest and the highest points of a sub-watershed. Hmax and Hmin are maximum and minimum elevations respectively (Schumms, 1956)
Drainage density (Dd)
Dd=Lu/A, A=Watershed area (km2), L (u) is total stream length (Horton, 1932)
Stream frequency (Fs), Fs = Nu/A, Fs is computed as the ratio between the total number of streams and area of the watershed (Horton, 1932)
Form factor (Rf)
Rf =A/Lb2, Rf is computed as the ratio between the watershed area and square of the watershed length. 𝐿 is the watershed length (Horton, 1932)
Circularity ratio (Rc)
Rc= 4π*A/P2, P is the watershed perimeter (km)
Elongation ratio (Re)
Re= (2/Lb)*(A/π) 0.5
Results and discussion
The results showed that according to the high number of streams (489 waterways), the existence of first, second and third degree streams, the high length of the streams, the high proportion of length of the streams in relation to the basin area, high coefficient of relief which indicates high elevations and slopes, the area is erodible and requires more management. Also, Landform studies in the studied area showed that with the help of morphometric characteristics, the sensitivity of landforms to erosion can be determined in the area. So, after the mapping of landforms using topographic position index (TPI), and considering the erosion-sensitive areas through morphometric characteristics, erosion-sensitive landforms in the study area were determined, So that the increase in the number of waterways and their length in the watershed indicates an increase in erosion. Comparing the map of the landforms and the map of the streams in the studied area, it was determined that class 4 (U-shaped valleys) and class III (high drainage) landforms have the highest erodibility. The results showed that, with increasing drainage density, the erodibility increases and the highest erodibility was observed in Class 4 (U-shaped valleys) and Class 6 landforms due to the high drainage density.
Conclusion
Ridge landforms such as those in high altitude (landforms in class 9 and 10), had the highest erosion and were therefore the most sensitive landforms. The drainage density features as the most important factor for determination of erosion and its relation to landforms were used. The results showed that by increasing the amount of drainage density the erosion increases which were for landforms Class 4 and Class 6. This study has demonstrated that morphometric characteristics can be used to predict other watershed characteristics.
Marziyeh Mokarram; Saeed Negahban
Abstract
Landform is a feature of land or landscape, the establishment of which is formed by natural processes that can be described and defined by index feature, and if detected, the landformprovidesinformation aboutits own structure along with its composition, texture, or integration. The existence of landforms ...
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Landform is a feature of land or landscape, the establishment of which is formed by natural processes that can be described and defined by index feature, and if detected, the landformprovidesinformation aboutits own structure along with its composition, texture, or integration. The existence of landforms variety and their diversity are mainly controlled by the change inthe shape and the position of the Earth. Therefore, the classification and identification of different areas with regard to their morphological characteristics is essential. This research attempts to classify different landforms in the southern city of Darab. This research is Descriptive-analytical based on quantitative, field, software and modeling methods in which the Topographic Position Index (TPI) method was used for the identification and classification of landforms of the study area. The input data in this model includes slope, transverse curvature, minimum and maximum curvatures. The results of the morphological classification of the study region showed that the region includes 10 types of landform (waterway, valleys of middle waterways, high drains, upstream, u-shaped valleys, small plains, open slopes, upper slopes, elevated ridges, middle slope ridges, mountain peaks). Most of the landform types in the study area are related to the waterways (32/19 %) and then, the peaks (25/36 %).
