Geographic Data
Bahram Imani; Jafar Jafarzadeh
Abstract
Extended Abstract Introduction Assessments of water quality have recently developed and now include surface and groundwater pollution issues. Permanent changes occurring in the quality of groundwater, especially those affecting drinking water and salinization of water sources, are considered to be a ...
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Extended Abstract Introduction Assessments of water quality have recently developed and now include surface and groundwater pollution issues. Permanent changes occurring in the quality of groundwater, especially those affecting drinking water and salinization of water sources, are considered to be a serious threat to rural development. Unfortunately, many people lack enough knowledge about the importance of groundwater and the harmful effects of environmental pollution on these valuable resources. The present study has investigated the quality of potable groundwater in the rural parts of central Ardabil County using multi-criteria decision-making models and geostatistical analysis in GIS environment. Parameters such as EC, PH, SO4-, Cl-, Na and TH (in terms of CaCo3) have been used to create an overall picture of the quality of potable groundwater resources in Ardabil County based on which related zoning map was developed in geographic information system. The kriging interpolation method was also used to obtain the spatial distribution of the parameters and the simple additive weighting method was used to weigh and rank the layers. According to the final map of water quality, approximately 36% of the central Ardabil County (around 88 square kilometers, mainly in the southern part of the study area) has access to optimal quality of drinking water. On the other hand, low quality of drinking water is observed in the northern and northeastern parts which cover 46% of the study area (112 square kilometers). Moreover, a direct relationship is observed between the population density and the density of deep and semi-deep wells and the decrease in the quality of water. Materials and MethodsThe present study has applied library research and field study methods. Rstudio and Arc GIS 10 software were also used to perform related analyses.Study AreaCase study area includes 243 square kilometers of the central Ardabil County consisting of three cities and nineteen villages as illustrated in Figure 1.The following methods were used in this research:1- Direct rating2- Kriging interpolation3- Standardization method4- Simple weighing method5- Fuzzification of the final dataThe following parameters have also been used to assess the quality of drinking water:1- Electrical conductivity (EC)2- Chlorine level (Cl-)3-The amount of sulfate (SO4+)4- The amount of nitrate (Na)5- Total water hardness (TH)6- Water acidity level (PH)Results & DiscussionGroundwater chemical quality is primarily assessed using parameters such as changes in the amount of dissolved salts, and limitations on various uses of water especially water used for drinking. Table 1 shows different types of conventional kriging methods selected through the method test for the parameters. These can be obtained using the mutual evaluation method and RMS error. Factors affecting the quality of drinking water are then ranked and weighted according to the expert opinions. The final quality map is thus prepared. Layers are then standardized in accordance with data description and the results are presented in Table 1. It also exhibits maximum permissible and desirable level of non-toxic chemicals in drinking water in accordance with the Iranian Standards and Industrial Research Institute (ISIRI) and the World Health Organization (WHO) standard, along with the maximum permissible level of mineral substances in drinking water. Semivariograms used for kriging interpolation are also presented. Table 2 shows the RMS and RMSE errors as well as the average standard error of the water quality parameters in the study area. The interpolated primary layers are presented in Figure 3. The final map prepared for the quality of potable water in the study area indicates that the quality of groundwater in the northern part and a little section of the central part (46% or 112 square kilometers of the study area) is unfavorable. This includes 8 villages of the County. 6 villages have access to drinking water with semi-optimal quality and 5 villages are located in the optimal area of water quality. Accordingly, the quality of potable groundwater decreases drastically as we move towards the northern and northeastern parts of the study area, and the lowest quality of groundwater is observed in the most northerly part of the study area (covering 46% of the study area). Figure 4 shows the density of deep and semi-deep wells, the amount of annual harvest from rivers in the central part of Ardabil (in thousand cubic meters per year), the population density and industrial areas in this region. A direct relationship is therefore observed between population density, the density of existing wells, the level of water extraction from rivers and the sharp drop in the quality of groundwater. According to the reports prepared by Ardabil Regional Water Company, around 32 million cubic meters of water is annually needed to meet the drinking requirements of urban and rural uses, which can seriously damage the quality of underground water in the area.ConclusionAccording to the final map of groundwater quality, only 36% of the study area (88 square kilometers) has access to drinking water with favorable quality which can be a great concern for planners and managers. Finally, it is suggested to use geostatistical methods and geographic information system as a useful tool to assess the quality of underground water. These methods can gradually replace the old methods and thus prepare more accurate statistics, increase the efficiency of water-related projects, and reduce their cost.
Asghar Hosseini; Zahra Azizi; Saeed Sadeghian
Abstract
Introduction LiDAR (Light Detection and Ranging) employs pulse models which penetrates vegetation cover easilyand provides the possibility of retrieving data related to Digital Terrain Model (DTM).Pulses sent by the Lidar sensorhitdifferent geographical features on the surfaceground and scatter inall ...
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Introduction LiDAR (Light Detection and Ranging) employs pulse models which penetrates vegetation cover easilyand provides the possibility of retrieving data related to Digital Terrain Model (DTM).Pulses sent by the Lidar sensorhitdifferent geographical features on the surfaceground and scatter inall directions. Distance to the object is determined by recording the time between transmitted and backscattered pulses and by using the speed of light to calculate the distance traveled by the small portion of pulses backscattered. Most LiDAR receivers at least record the first and last backscattered pulses. The first backscattered pulses are used to produce Digital Surface Models (DSMs) and the last ones are used to produce DTMs. Despite the fact that these data can provide a valuable source for DTM generation, the volume of vegetation (vegetation density) in forest areas reducesthe accuracyof DTMs. Onthe other hand, ground surveying of forest areas is rather expensive and time consuming, especially in largerforests. Aerial images are also used as a source for DTM generation, but this approach requires a 60–80% overlap between images which along with canopy height reduce the potential of this method for DTM generation. Also, low spatial resolution of satellite images collected from forest areas increases errors in DTM generation to a large degree. The present study investigates the accuracy and precision of DTMsproduced from LiDAR data in a forest area. Furthermore, the effect of different methods of filtering and DTM interpolation was explored. Different methods of DTM generation were also closely analyzed and evaluated. Materials & Methods The case study area is located in Doroodforests, a part of Zagros forests, in the southeastern regions of Lorestan province in Iran (48°51’19’’E to 48°54’31’’E and 33°19’21’’N to 33°21’15’’N). Minimum and maximum altitude above sea level were 1143 and 2413m, respectively. The study area covers 100 hectares of mountains with an average slope of 38%. Approximately 50% of the area is covered by forests in which Brant’s oak (Quercusbrantii Lindley) is the most frequent species. LiDAR data were collected by the National Cartographic Center of Iran (NCC) in 2012 using a Laser scanner system (Litermapper 5600) fixed on an aircraft flying at an average altitude of 1000m. LiDAR data consisted of the first and last returns (backscattered pulses), distance and their intensity value. Collected data had an irregular structure and included an average of more than four points per square meter. A DTM was produced using a two-step filtering. First, a morphological filter removed most of non-ground points, and then a slope-based filter detected remaining points. Inforest areas with rough-surface, DTM was producedthrough processing ofLiDAR data with statistical methods likekriging and inverse distance weighting (IDW). These methods apply third and fourth power to detect and remove non-ground points. To assess the accuracy of DTMs produced by different approached, 5 percent of the LiDAR point cloudswererandomly separated as the test data. Amongst these data sets, 62 points with a suitable dispersion were selected and measured using a GPS-RTK. An error matrix, along with accuracy indices (including correlation and Root Mean Square Error (RMSE)) were calculated based on these data. Results & Discussion Results indicated that 44-degree slope is the best threshold for isolation of non-ground points and inverse distance weighting (IDW) is the best third power interpolation method with the highest correlation (0.9986) and the lowest RMSE (0.204 meter). Amongst the filtering methods, slope-based filter used for separation of ground and non-ground points had the best performance. Since this filter combines two parameters of slope and radius, it can remove cloud points related to the vegetation cover and results in high efficiency for steep forest areas. Slope-based filter is suitable for processing of near-surface vegetation, whilst statistical filter is well-suited for vegetation cover consisting of tall trees. Conclusion The present study proposed and investigated different scenarios for the production offorest areas’ DTM using LiDAR data and two interpolation methods. These algorithms were practicallyassessed using LiDAR data collected from Dorood forest areas. The best scenario was slope-based filter with inverse distance weighting (IDW) interpolation. Based on accurate assessment, this approach can produce reliable DTM in forest areas.
Abdolhossein ZarifianMehr; Laala Jahanshahloo; Hossein Zabihi; Bohloul Alijani
Abstract
Extended Abstract Introduction Obtaining reliable environmental values in vast geographic areas is usually costly and difficult; therefore, the ability to predict unknown values or in other words, the use of better interpolation methods is very important. Interpolation methods utilize a set of different ...
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Extended Abstract Introduction Obtaining reliable environmental values in vast geographic areas is usually costly and difficult; therefore, the ability to predict unknown values or in other words, the use of better interpolation methods is very important. Interpolation methods utilize a set of different mathematical and statistical models to predict the unknown values. The similarity of the unknown points to the nearest known points or the principle of the nearest neighbor is the basis of interpolation methods, and how this principle is used depends on the selected model. In a general classification, interpolation methods are divided into two large classes. The first method is deterministic, in which interpolation is carried out based on determining the level of sampled points and also based on the similarities such as Inverse Distance Weighting (IDW) method or Radial Basis Function (RBFs). In the second method, interpolation is probabilistic – geostatistical, that is done based on the statistical properties of the sampled points. On the other hand, due to the growing increase in the problems of urbanization and urban heat islands, current cities need to have a detailed planning for future developments and preserving the quality of urban environment. Also, the geometry of urban valleys, which is defined by changing the height, length and distance of buildings, has a significant impact on the energy exchange and thus, the temperature of urban areas. But, this temperature, in turn, depends on a number of geographical - geometric factors (such as SVF) and meteorological variables. The Sky View Factor (SVF), as one of the usual indicators of describing urban geometry that refers to the amount of sky observable from a point on the Earth, has become one of the most important predictors of UHI due to its applicability in the urban climate, its contribution to the spatial data, and the existence of available techniques. In the climatic studies, the SVF is also considered as an important geometric parameter due to its correlation with the local temperature performance and its potential importance in the urban design process.Although urban Climatologists know this indicator well, it is not that much known among the urban designers and planners. This issue has not progressed much in Iran and there are no reliable sources about it. Despite the fact that different methods and models have been introduced for interpolation of Point data, no specific method has been proposed for estimating this index. Hence, this study has empirically compared the interpolation models with an emphasis on the Empirical Bayesian Kriging (EBK). This comparison is important since EBK has automated the most difficult aspects of the construction of a kriging model. This is while in other Kriging methods, the parameters are adjusted manually to obtain accurate results. EBK automatically simulates and calculates these parameters through a setup process. In classical kriging, it is also assumed that the estimated semivariogram is a true semivariogram of the observed data. This means that the data are generated from Gaussian distribution with the correlation structure defined by the estimated semivariogram. This is a very strong assumption, and it rarely holds true in practice. Accordingly, measures should be taken to make the statistical model more realistic. Materials & Methods The present study is an applied research in terms of its objective and it is quantitative in terms of the data analysis method. The study area is district 6 of Shiraz Municipality (496 hectares). Due to the multiplicity of interpolation methods and techniques as well as kernel functions and model fit functions, about 138 interpolation scenarios arewereimplemented. Also, four indices of Root-Mean-Square (RMS), Mean Standardized (MS), Root-Mean-Square Standardized (RMSS) and Average Standard Error (ASE) have been used for evaluating the models. The input data (sample) contains 6157 points, measured at intervals of 30 m distances in the study area. These points are werecreated based on the SVF calculation software method and using the GIS base model in ArcGIS10.6. Results & Discussion Out of 138 scenarios, seven scenarios with the lowest RMS values arewereseparately examined in detail taking into account three other indicators. Another variable called “Neighborhood type” iswas added to the surveys in two standard and smooth modes. The results show that simple kriging and EBK have better results than the other models. Also, among the simple Kriging fitted models, the RQ model shows better results than other fitting models. Conclusion Based on the RMS index, EBK is one of the best reliable automatic interpolation models (ranked second) for estimating the SVF. In general, based on RMS, MS, RMSS, it is the best automatic interpolation model for estimating SVF.