Ali Kazemzadeh; Najmeh Neisany Samany; Ali Darvishi Boloorani; Ara Toomanian; Ahmad Pourahmad
Abstract
Extended abstract
Introduction
Life in the modern cities takes shape through interaction with various environmental, socio-economic, infrastructural, health, security, political and cultural conditions. The result of this interaction shapes the quality of urban life (QOUL). Quality of lifeis a complex ...
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Extended abstract
Introduction
Life in the modern cities takes shape through interaction with various environmental, socio-economic, infrastructural, health, security, political and cultural conditions. The result of this interaction shapes the quality of urban life (QOUL). Quality of lifeis a complex concept involving social, economic, environmental, physical, psychological and political aspects (El Din et al, 2013). In general, Quality of life (QOL) has been evaluatedbytwo objective and subjective points of view. Researches in this field, have mainly been conducted in the form of social studies and in the macro geographical scales of countries or cities,and less attention has been paid to the spatial differences of the life quality in the complex urban environments. In these studies, the principal components analysis (PCA) method has been the most common method used for combining and overlaying of the life quality indicators (Lo, 1998; Jun, 2006; Li and Weng, 2007; Motakan et al, 2010; HatamiNejad et al, 2014; Messer et al, 2014). But,Oneof the disadvantages of PCA is the possibility of deleting some of the useful information.Using Multi-Criteria Decision-Making (MCDM) and Fuzzy Logic methods can also be useful in spatial modeling of life quality. Moreover, QOL as one of the features of geographical environment is a dynamic concept. This means that this feature changesover time and location. The spatiotemporal modeling of this concept can help monitoringthe quality of urban life and planning for its improvement.
Data and Methods
This study offers a framework and process for spatiotemporal modeling of QOUL. For spatial modeling of QOUL, effective indiceswere taken into consideration at first. In this study,the indicators related to the urban quality of life were extracted in 3 three environmental, infrastructural/physical, and socio-economic dimensions.The Analytical Hierarchy Process (AHP) method was used for weighing the parameters(Uyan, 2013). Then, the indicators were combined with each other using the GammaFuzzyModel(Vafai, 2013) and Vikor-Fuzzy overlay technique(Huang et al, 2009). Furthermore, QOUL was modeled temporally due to the variability of environmental indicators and some of infrastructural / physical indicators during the seasons of the year. For this purpose,the cyclic model (developed based on the snapshot approach (Worboys and Duckham, 2004)) was used. In order to assess the developed framework, the quality of lifewasmodeled at urban blocks level in regions 3,6,11 of the city of Tehran.
Conclusion
The obtained results showed that applying multi-criteria decision-making and Fuzzy logicmodels in modeling of life quality is capable of showing the spatial difference oflife quality in urban environments. Based on the results of spatial modeling, the quality of life is more desirable in northern parts of the area (region 3) while the desirability decreases towards the southern areas (region 11). The study of Moran’s spatial autocorrelation index (greater than 0.35 for the results of both models and all seasons) emphasize on the non-randomness of the distribution method of the QOL feature in urban blocks and shows the existence of cluster pattern in the study area.The results of temporal modeling indicated that most of the blocks are more favorable in the spring and autumn seasons than in the winter and summer in terms of environmental conditions.
Mohsen Bakhtiari; Ali Darvishi Bolorani; Ataollah Abdollahi Kakroodi; Kazem Rangzan
Abstract
Extended Abstract Introduction Remote sensing has introduced new fields in monitoring and modeling environmental variables on different levels. One of the major advances of remote sensing is the use of quantitative algorithms to estimate the earth’s surface variables. Therefore, it can be regarded ...
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Extended Abstract Introduction Remote sensing has introduced new fields in monitoring and modeling environmental variables on different levels. One of the major advances of remote sensing is the use of quantitative algorithms to estimate the earth’s surface variables. Therefore, it can be regarded as a research and application framework in order to forecast and counteract devastating effects of environmental crisis. Meanwhile, capabilities of MODIS sensor in terms of its various products to estimate the environmental parameters have been explored more than any other remote sensing instruments. Optical remote sensing modeling techniques are robust and strong enough for modeling the relationship between land surface variables and the quantities measured by remote sensing data. Materials & Methods This research focuses on developing and introducing a new spectral index for modeling the Land Surface Temperature (LST) and vegetation, simultaneously. Land surface temperature is a key parameter in the balance physical processes of the Earth’s water and energy on different levels including from regional to local scales. On the other hand, studying the temporal and spatial variations of vegetation and temperature in different areas as an indicator showing the environmental conditions has the great importance for current and future behaviors of the surface. Therefore, combining these two parameters can lead to high synergies in the use of satellite data for studying the environmental status of the west Asia as the area is experiencing one of the most horrifying environmental degradations of the world. In order to evaluate the developed index, the spatial-temporal relationship of the Normalized Health Environmental Index (NHEI), in relation to the behavior of dust sources in the west Asia is investigated. The main steps of this study include the developing and introducing the remote sensing index that reflects the simultaneous behavior of environmental variables, trending the index based on its changes for the west Asia and finally applying that in studying dust sources of the studied region. The Normalized Health Environmental Index, (NHEI), is developed using MODIS products consisting of MYD11A2, MYD13A2 and MOD44W products. The developed index considers the spatiotemporal behavior of Land Surface Temperature (LST), and vegetation cover, simultaneously. This index is useful for monitoring the environmental health situation of lands by masking the surface water bodies. NEHI is a dimensionless parameter and the range of its values is between -1 to 1. The smaller values indicate that conditions in the region in terms of land surface temperature, vegetation, water and the environmental relevant phenomena are more critical. Results & Discussion NHEI was used to analyze the trend changes of the most important dust sources in the West Asia during last decade. Due to the high correlation between the results obtained from NHEI and the activities of the origins of dust storms, it can be adapted as a basis for modeling the behavior of these phenomena while such relationship has not been confirmed through applying the conventional indices such as NDVI. NHEI is developed and analyzed for 2002 to 2013. The trend of changes was detected by linear trending process and its relationship with dust sources has been evaluated. Since NHEI shows the changing trend of key elements of the environment, i.e. temperature, vegetation and humidity simultaneously, the results of trending reveal the general decrease of severity and extend of the index. While the distribution of dust storm hot spots in terms of the index values is showing more scattering for the whole of the west Asia. Although NHEI is not a pure physical parameter with certain and standard unit, however, because of reflecting the combined effects of NDVI and LST as well as its simplicity and strong correlation with environmental parameters, it can be used as a reliable reference index in the environment research at local and macro-scale. Then the values of NHEI within specific land covers were determined, so it has distinct values for different land covers. Conclusion This study emphasizes on NHEI capabilities in monitoring and modeling environmental variables associated with dust sources, therefore, the average of NHEI in dust sources individually and totally was significantly less and more critical than the value of NHEI in other areas of the study area. Generally, the results of this study can open a new horizon in the field of land surface variables modeling and investigation by developing new remote sensing indices especially in land degradation and dust storm investigations.
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.
