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With the growth of the population, the development of industrial, agricultural and so on, the amount of gas demand has been increasing steadily. Supplying the required gas energy often involves the...

The study of landform can be used to predict specific solid rock and moisture conditions that exist in that landform. Landforms are of significance in engineering because they influence the quality and type of grading, pre-determine the drainage requirements, and the soil or rock conditions. They have added significance because there are an infinite number of duplicates of each type. Given that each of the landform units have different characteristics (soil, slope, elevation, vegetation etc.), classification of landforms is importance. Therefore, landforms are widely recognized as natural objects that partition the earth’s surface into essential spatial entities. Landform entities differ from one another in terms of specification such as shape, size, orientation, relief and contextual position. They also differ in terms of the physical processes that were involved in their formation and that continue to operate within them at the present time. This paper aims at providing a review of the landform characteristics and classification. Various definitions and attributes of landforms which focus on aspects of morphometry, geomorphometric context, terrain positions and scale are discussed as well. In addition, the methods developed for the landform classification are assessed.

The study of groundwater resources in relation to topography is important. Clearly, in different topography, depth of the water level is different. Therefore, the aim of this study is the determination of the relationship between landform classes with compound topographic index (CTI) and depth of the water for the Maharlou-Bakhtegan watershed, Fars Province, Iran. In order to evaluate the depth of the water for the study area, CTI and geomorphology (landforms) were derived from a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The results of landform classes extracted using topographic position index (TPI) showed that the largest landform is open slope, while the smallest are plains. It was found that CTI and depth of the water values are high in plain classes, while they are low in local ridges. High depth of the water were found to be mostly confined to the pit regions in the plain landform, because groundwater recharge occurs in the zones where standing water remains for sufficient long period of time and has favourable condition for recharge.

Soil genesis is highly dependent on landforms as they control the erosional processes and the soil physical and chemical properties. The relationship between landform classification and electrical conductivity (EC) of soil and water in the northern part of Meharloo watershed, Fars province, Iran, was investigated using a combination of a geographical information system (GIS) and a fuzzy model. The results of the fuzzy method for water EC showed 36.6 % of the land to be moderately land suitable for agriculture; high, 31.69 %; and very high, 31.65 %. In comparison, the results of the fuzzy method for soil EC showed 24.31 % of the land to be as not suitable for agriculture (low class); moderate, 11.78 %; high, 25.74 %; and very high, 38.16 %. In total, the land suitable for agriculture with low EC is located in the north and northeast of the study area. The relationship between landform and EC shows that EC of water is high for the valley classes, while the EC of soil is high in the upland drainage class. In addition, the lowest EC levels for soil and water are in the plains class.

In this study, the fuzzy analytic hierarchy process (AHP) is used to study the relationship between drinking water quality based on content of inorganic components and landform classes in the south of Firozabad, west of Fars province, Iran. For determination of drinking water quality based on content of inorganic components, parameters of calcium (Ca), chlorine (Cl), magnesium (Mg), thorium (TH), sodium (Na), electrical conductivity (EC), sulfate (SO4), and total dissolved solids (TDS) were used. It was found that 8.29 % of the study area has low water quality; 64.01 %, moderate; 23.33 %, high; and 4.38 %, very high. Areas with suitable drinking water quality based on content of inorganic components are located in parts of the south-eastern and south-western parts of the study area. The relationship between landform class and drinking water quality based on content of inorganic components shows that drinking water quality based on content of inorganic components is high in the stream, valleys, upland drainages, and local ridge classes, and low in the plain small and midslope classes. In fact we can predict water quality using extraction of landform classes from a digital elevation model (DEM) by the Topographic Position Index (TPI) method, so that streams, valleys, upland drainages, and local ridge classes have more water quality than the other classes. In the study we determined that without measurement of water sample characteristics, we can determine water quality by landform classes.

In this study, we investigate the use of morphometric parameters and fuzzy membership functions to perform landform classification for different case areas of Zagros Mountains from digital elevation models (DEMs). First, multiscale DEMs with scales of 5 to 45 cells are generated using the lifting scheme. The maximum curvature for the scale of five cells has the lowest standard deviation, and hence, is determined to be the characteristic scale. Data layers are produced from the DEM of this scale for slope, minimum and maximum curvatures, and plan and profile curvatures. The fuzzy membership rules for these data layers are used to determine the landform classes. Comparison of the results of landform classification using the fuzzy classification method and topographic position index (TPI) with the geology map of the study area show that the fuzzy classification method provides higher accuracy (81 %) as compared to TPI (42 %). This is because for the fuzzy classification method, sloping areas are separated into sloping and non-sloping areas, and the membership functions are defined to prevent landforms belonging to the sloping areas from being classified in the non-sloping areas and vice versa.

Morphometry research used geography information system (GIS) to explore alluvial fan morphology for determination of fans location and fast and accurate analysis. The aim of this study is to classify alluvial fans formed by high- gradient braided streams and torrents that discharge into the Oshtorankook altitudes in the Lorestan province, Iran. The morphology of the fans and their watershed is quantitatively described through estimated morphometric parameters. For relationships between geomorphological features of the fans and their drainage basins, self-organizing maps (SOM) were used. In SOM, according to both qualitative data and morphometric variables, the clustering tendency of alluvial fans was investigated using 15 alluvial fans parameters. The results of the analysis showed that several morphologically different fan types were recognized based on their geomorphological characteristics in the study area. A strong positive relationship was found between the drainage basin area and size of the fan with a simple power function. In addition, the relationship between fan slope and drainage area was found to be negative and moderately strong with a simple power function.

Analysis of surface textures of physiographic features extracted from multiscale digital elevation models via grey level co-occurrence matrix This paper is aimed at employing grey level co-occurrence matrix (GLCM) to analyse the surface textures of physiographic features extracted from multiscale digital elevation models (DEMs). Four GLCM parameters, energy, contrast, autocorrelation and entropy, are computed for horizontal (0°), vertical (90°) and diagonal (45 and 135°) cell pair orientations. For the respective DEMs and physiographic features, varying patterns are observed in the plots of the GLCM parameters due to varying surface profiles and the changes that occur over the scales. Due to the smoothing of the terrain during multiscaling, the features have increasing values of energy and entropy, and decreasing values of contrast and entropy, indicating decreasing roughness. Mountains have the highest roughness as compared to the other features over the scales, while basins have the lowest roughness. For each parameter, similar trends are observed in the plots for the four different cell pair orientations, indicating similar trends of change of surface texture in the different orientations over the scales. However, varying values are observed for the different orientations, depending on textural uniformity in the corresponding orientations. The results obtained demonstrate that GLCM can be an appropriate tool for classifying landforms from multiscale DEMs based on the different texture characteristics of the landforms.

In this study, fuzzy analytic hierarchy process (AHP) is used to study the relationship between drinking water quality and landform classes in south of Firozabad, east of Fars province, Iran. For determination of drinking water quality, parameters of calcium (Ca), chlorine (Cl), magnesium (Mg), thorium (TH), sodium (Na), electrical conductivity (EC), sulfate (So4) and total dissolved solids (TDS) were used. It was found that 8.29 % of the study area have low water quality; 64.01 %, moderate; 23.33 %, high; and very high, 4.38 %. Areas with suitable drinking water quality are located in parts of the southeast and southwest parts of the study area. The relationship between landform class and drinking water quality show that drinking water quality is high in the stream, valleys, upland drainages and local ridge classes, and low in the plain small and midslope classes.

In this research, the relationship between classes of landform, and electrical conductivity (EC) of soil and water in the Shiraz Plain, Fars province Iran was investigated using a combination of geographical information system (GIS) and fuzzy model. The results of the fuzzy method for water EC showed that 36.6 % of the land to be moderately land suitable for agriculture; high, 31.69 %; and very high, 31.65 %. In comparison, the results of the fuzzy method for soil EC showed that 24.31 % of the land to be as not suitable for agriculture (low class); moderate, 11.78 %; high, 25.74 %; and very high, 38.16 %. In the total, the land suitable for agriculture with low EC is located in the north and northeast of the study area. The relationship between landform and EC shows that EC of water is high for the valley classes, while EC of soil is high in the upland drainage class. In addition, the lowest EC for soil and water are in the plain small class.

In this study, Global positioning system (GPS) simulation is employed to study the effect of radio frequency interference (RFI) on the accuracy of two handheld GPS receivers; Garmin GPSmap 60CSx (evaluated GPS receiver) and Garmin GPSmap 60CS (reference GPS receiver). Both GPS receivers employ the GPS L1 coarse acquisition (C/A) signal. It was found that with increasing interference signal power level, probable error values of the GPS receivers increase due to decreasing carrier-to-noise density (C/N0) levels for GPS satellites tracked by the receivers. Varying probable error patterns are observed for readings taken at different locations and times. This was due to the GPS satellite constellation being dynamic, causing varying GPS satellite geometry over location and time, resulting in GPS accuracy being location/time dependent. In general, the highest probable error values were observed for readings with the highest position dilution of precision (PDOP) values, and vice versa. Defence Science Journal, 2012, 62(5), pp.338-347 , DOI:http://dx.doi.org/10.14429/dsj.62.1606

In this paper, a modification of the algorithm proposed by Ahmad Fadzil et al. (2011) for surface roughness computation from digital elevation models (DEMs) via multiscale analysis is presented. The new algorithm takes into account that the three predominant physiographic features of terrains (mountains, basins and piedmont slopes) have distinct curvature region distributions and hence, distinct roughness characteristics. To this end, the surface roughness of individual cells of DEMs is computed by identifying the curvature region distribution and roughness characteristics of each individual mountain, basin and piedmont slope region. The modified algorithm allows for the localisation and quantification curvature regions over varying scales for specific regions, providing a more appropriate surface roughness parameter.

The aim of this paper is to analyze topographic and aspect effects on vegetation indices in the Darab Mountain, Iran. Three commonly used vegetation indices, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and difference vegetation index (DVI), were computed from Landsat 8 ETM+ vegetation bands. Based on the results obtained by analyzing the vegetation indices, it was found that vegetation growth and vegetation indices increase with increasing elevation and aspect. The vegetation growth is highest between the elevations of 1500–3000 m, with the NDVI, EVI and DVI values being large. The best vegetation in this zone is distributed towards NW 300°.

The aim of this study is to cluster landforms in the west of the Fars province, Iran using self-organizing maps (SOM). In SOM, according to qualitative data, the clustering tendencies of landforms were investigated using six morphometric parameters, which were slope, profile, plan, elevation, curvature and aspect. First, topographic position index (TPI) was used to prepare the landform classification map. The results of SOM showed that there were five classes for landform classification in the study area. Cluster 5 corresponds to high slope, high elevation but with different of concavity and convexity that consist of ridge landforms. Cluster 3 corresponds to flat areas, possibly plantation areas, in medium elevation and almost flat terrain. Clusters 1, 2 and 4 correspond to channels with different slope conditions.

This study is aimed at evaluating the effect of Global Positioning System (GPS) satellite clock error using GPS simulation. Two conditions of tests are used; Case 1: All the GPS satellites have clock errors within the normal range of 0 to 7 ns, corresponding to pseudorange error range of 0 to 2.1 m; Case 2: One GPS satellite suffers from critical failure, resulting in clock error in the pseudorange of up to 1 km. It is found that increase of GPS satellite clock error causes increase of average positional error due to increase of pseudorange error in the GPS satellite signals, which results in increasing error in the coordinates computed by the GPS receiver. Varying average positional error patterns are observed for the each of the readings. This is due to the GPS satellite constellation being dynamic, causing varying GPS satellite geometry over location and time, resulting in GPS accuracy being location / time dependent. For Case 1, in general, the highest average positional error values are observed for readings with the highest PDOP values, while the lowest average positional error values are observed for readings with the lowest PDOP values. For Case 2, no correlation is observed between the average positional error values and PDOP, indicating that the error generated is random.

The aim of this study is to classify alluvial fans formed by high-gradient braided streams and torrents that discharge into the Oshtorankook altitudes in the Lorestan province, Iran. The morphology of the fans and their watershed is quantitatively described through estimated morphometric parameters. For relationships between geomorphological features of the fans and their drainage basins, self-organizingmaps (SOM) were used. In SOM, according to both qualitative data and morphometric variables, the clustering tendency of alluvial fans was investigated using 15 alluvial fans parameters. The results of the analysis showed that several morphologically different fan types were recognizedbased on their geomorphological characteristics in the study area. A strong positive relationship was found between the drainage basin area and size of the fan with a simple power function. In addition, the relationship between fan slope and drainage area was found to be negative and moderately strong with a simple power function.

The derivation of spatial significance is an important aspect of geospatial analysis and hence, various methods have been proposed to compute the spatial significance of entities based on spatial distances with other entities within the cluster. This paper is aimed at studying the spatial significance of mountain objects extracted from multiscale digital elevation models (DEMs). At each scale, the value of spatial significance index SSI of a mountain object is the minimum number of morphological dilation iterations required to occupy all the other mountain objects in the terrain. The mountain object with the lowest value of SSI is the spatially most significant mountain object, indicating that it has the shortest distance to the other mountain objects. It is observed that as the area of the mountain objects reduce with increasing scale, the distances between the mountain objects increase, resulting in increasing values of SSI. The results obtained indicate that the strategic location of a mountain object at the centre of the terrain is more important than its size in determining its reach to other mountain objects and thus, its spatial significance.

This paper is aimed at employing grey level co-occurrence matrix GLCM to analyse the surface textures of physiographic features extracted from multiscale digital elevation models DEMs . Four GLCM parameters, energy, contrast, autocorrelation and entropy, are computed for horizontal 0° , vertical 90° and diagonal 45 and 135° cell pair orientations. For the respective DEMs and physiographic features, varying patterns are observed in the plots of the GLCM parameters due to varying surface profiles and the changes that occur over the scales. Due to the smoothing of the terrain during multiscaling, the features have increasing values of energy and entropy, and decreasing values of contrast and entropy, indicating decreasing roughness. Mountains have the highest roughness as compared to the other features over the scales, while basins have the lowest roughness. For each parameter, similar trends are observed in the plots for the four different cell pair orientations, indicating similar trends of change of surface texture in the different orientations over the scales. However, varying values are observed for the different orientations, depending on textural uniformity in the corresponding orientations. The results obtained demonstrate that GLCM can be an appropriate tool for classifying landforms from multiscale DEMs based on the different texture characteristics of the landforms., Bu çalışmanın amacı çoklu ölçekli Sayısal Yükseklik Modellerinden SYMler çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin analizinde Gri Düzey Eş-Oluşum Matrisi GDEM ’nin kullanılmasıdır. Enerji, kontrast, otokorelasyon ve entropi olmak üzere dört GDEM parametresi yatay 0° , düşey, ve köşegen 45 and 135° yönler boyunca hücre çiftleri için hesaplanmıştır. Çeşitli yüzey profilleri ve farklı ölçeklerde oluşan değişimler nedeniyle fizyografik özellikler ve bunlara karşılık gelen SYMler için GDEM parametrelerinin çiziminde çeşitli örüntüler gözlemlenmektedir. Çoklu ölçeklendirme esnasında yeryüzündeki detayların yumuşatılması nedeniyle azalan engebeliliği gösterecek şekilde yüzey özellikleri artan enerji ve entropi değerlerine sahip olurken, azalan kontrast ve entropi değerleri oluşmaktadır. Farklı ölçeklerde farklı yüzey özellikleri ile karşılaştırıldıklarında dağlar en yüksek, havzalar ise en düşük engebelilik değerlerine sahip olmaktadır. Her bir parametre için dört farklı hücre çifti yönüne ait çizimlerde benzer eğilimler gözlenmektedir. Yani, farklı ölçeklerde, farklı yönlerde yüzey doku özelliklerindeki değişimde benzer eğilimler oluşmaktadır. Fakat, her bir yöndeki dokusal tekdüzeliğe bağlı olarak farklı yönler için değişen değerler gözlenmiştir. Elde edilen sonuçlar göstermektedir ki, GDEM, yeryüzü şekillerine ait farklı doku özelliklerine dayanan çoklu ölçekli SYMleri kullanarak sınıflandırma yapmak için uygun bir araçtır.