SHadman Darvishi; Karim Solaimani; Morteza Shabani
Abstract
Extended Abstract Introduction Urbanization is a continuous process and the spatial patternsof urban growth havealways played an important role in the transformation of human life throughout history. Urban growth has two dimensions: demographic and spatial, meaning that with increased urban population, ...
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Extended Abstract Introduction Urbanization is a continuous process and the spatial patternsof urban growth havealways played an important role in the transformation of human life throughout history. Urban growth has two dimensions: demographic and spatial, meaning that with increased urban population, the need for shelter increases and cities are faced with spatial growth. Expansion of cities in the spatial dimensions have several consequences,including changes in land use and land covers of areas surrounding cities.Land use change is currentlyone of the major concerns ofthe environmental approach, since land use changes in areas surrounding cities have led to changes in the economic structure of cities and the destruction of vegetation and agricultural lands as one of the main foundations of production in these areas. They have also seriously damaged other water resources, wildlife habitats, and resulted in the reduction of soil organic matter, changes in soil humidity and saltiness, increased energy consumption, increased urban heat islands, climate changes, as well as negative effects on the mental and physical health of urban residents. Nowadays, rapid growth in remote sensing technology and geographic information system, as well as the advancements in computer science and its application in environmental sciences and urban planning have created spatial modeling techniques such as Markov chain, Cellular Automata, intelligent neural networks and statistical models. Due to its dynamic nature, the capability of showing spatial distribution of land use changes, as well as its unique characteristics in modeling of natural and physical geographic featureson the ground and simpler adaptation with remote sensing data and GIS, a combination of Markov chain model and Cellular Automata are used as an important supporting toolfor decision making in urban planning and environmental sciences in many studies performedrecently. Over the past few decades, the population of Iranhas increased from 27 million in 1955 to 79 million in 2016. And according to the 2016census, 74 percent of the population lives in urban areas. In recent years, the population of Kurdistan province has experienced a 1.42% (2011 to 2016)average annual growth rate (especially in Baneh, Marivan and Saghez), which isaround 0.18% more than the average annual growth rate of the country (1.24%). Investigating census data shows that Baneh, Marivan and Saqezhave experienced a higher urban growth rate as compared to other cities in the province, and thus monitoring this growth and predicting its negative effects on the surrounding land use seems crucial.Destruction of vegetation and agricultural lands not only results in climate change, but also directly affect the lives of residents in the region. Therefore, understanding the growth rate is necessary for properplanning and managementofthese areas. Materials and Methodology Images received from Landsat in 1987, 2002 and 2017 were downloaded from the US Geological Surveywebsite and used in the present study. Google Earth images, land useand topography maps, and ground control points (GCP) were also used to perform imagepreprocessing, classification operations, and accuracy assessment. The study area includesBaneh, Marivan and Saqqez cities, which have recently experienced a high level of population growth. Considering the impact of population growth on increased rate of construction and physical development of urban areas, it is therefore necessary to study urban growth. In order to reduce the city’s impact on land use in future, it is necessary to modelurban growth. Using these models, planners can guide the urban development back to the optimal and appropriate routes and minimize the destruction of the land use.Image pre-processing in the present research was performed in ENVI5.3 environment. Then, using Maximum Likelihood algorithm, the images were categorized into five classes of water, residential areas, vegetation, agriculture and open spaces. Then, the overall accuracy of the classification maps was assessed using ground control points. To predict the urban growth, CA-Markov model was used in the IDRISI TerrSet software. Results and Discussion Findings indicate that the classified images have an accuracy of above 80%, and thus, land use maps of the study areas are valid.Investigations shows that the growth inMarivan and Baneh has most severely affected vegetation and agricultural land use. In the time period of 1987 to 2017, 897. 39 and 801 hectares of vegetation in Marivan and Banehhave been transformed into urban areas, respectively.During the same time period, 790.38 hectares of agricultural land in Marivan and 772.29 hectaresinBanehhave changed into urban areas. It is also important to note that unlike Saqez, the degradation of vegetation and agricultural lands in Marivan and Banehwas more severe than bare lands. In other words, bare landsinSaqez were more severely affected (as compared to vegetation and agricultural land), and about 1249,29 hectares of bare lands have turned into urban areas, while only 121.50 hectares of vegetation, and 509.04 hectaresof agriculture lands haveexperienced such a change.Also, results of the CA-Markov model showed that the growth of Baneh and Marivan cities in the 2017-2032 period will be in the Northeast and East directions, respectively. Results also indicate that this urban growth will affect agricultural and bare landsmore significantly. It is predicted that about 511.29 hectares of agricultural lands and 722.70 hectares of bare lands (in Baneh city) and 1080 hectares of agricultural lands and 2402.101 hectares of bare lands (in Marivan city) will turn into urban areas in this time period. Conclusion Based on the findings, it can be concluded that planning urban growth inthe study areas should be performed in a way that vegetation and especially the surrounding agricultural lands are preserved, and the negative effects of land use changesare minimized. Also,plannerscan apply the results of the present study in their future plansto guide the development of Baneh, Marivan and Saqeztoward optimal ways and reduce land use degradation.
Bahram Choubin; karim solaimani; Mahmoud Habibnejad Roshan; Arash Malekian
Abstract
Extended abstract
Introduction
Management of watersheds requires understanding of watershed conditions both in gauged and ungaugedbasins. The classification of watersheds by similarcharacteristics for the implementation of coordinated watershed operations and flood control as well as giving priority ...
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Extended abstract
Introduction
Management of watersheds requires understanding of watershed conditions both in gauged and ungaugedbasins. The classification of watersheds by similarcharacteristics for the implementation of coordinated watershed operations and flood control as well as giving priority to sub-basinsis of great importance. The need for a classification framework in hydrology is not an entirely new subject. In fact, this subject has long been discussed and several studies have also attempted to advance this idea. So far, no acceptedcomprehensive protocol has been presented for the classification of watersheds,and questions can be raised regarding why this has not happened. More efforts must be made in order to develop such a classification.Previous studies have used hard clustering methods more, for the classification of watersheds but, the present study used fuzzy approach as asoft method. In general, the purpose of this research is to focus on the characteristics of the watersheds including morphological characteristics, soil and land use for the identification of similar watersheds. These parameters can facilitate the watershed classification scheme and our understanding ofthe watershed conditions.
Materials & Methods
The dataset for this study includes is base maps (sub-watersheds boundary, streams and rivers, digital elevation model (DEM), soil and landuse which have been collected from Iran Water Resources Management Company. To classify the Karkheh watershed, 35 spatio-physical indices including topographic, morphological, landuse characteristics and soil parameters were considered. These indices have been calculated for each watershed. The dimension reduction of the variables was an important part, because 35 indices were quite large for the classification of 38 watersheds. Therefore, factor analysis for each group of indices wasusedseparately to reduce the number of variables.
After reducing the variables and selecting the final indices, the fuzzy clustering approach was conducted to classify the watersheds into homogenous groups. The number of optimal clusterswas determined through trial and error and the functions of partition coefficient and partition entropy evaluation.
Results & Discussion
Kaiser-Meyer-Olkin (KMO) test statistics for each group of the morphological, landuse and soil indices were 0.71, 0.69 and 0.76 respectively, indicating that the data was suitable for factor analysis. Factor analysis was conducted using Principle Component Analysis (PCA) method and the results revealed that among 35 spatio-physical indices, 9 indices (4 morphological indices, 3 land use indices and 2 soil parameters) had a higher load factor than other indices. Therefore, indices of the watershed surface, basin elongation, average length of drainage network and total topographic indexamong the morphological indices;percentage indices of rangelands, agricultural lands and wastelandsamong the land use indices; and indices of water holding capacity in the soil layer and saturated hydraulic conductivity among the soil parameters were selected as the ultimate criteria for grouping the watersheds.
Theselectedfactors were normalized between zero and one before the classification. Then, sub-watersheds were classified using fuzzy C-mean (FCM) approach. The trial and error method was used to find thenumber of optimum clusters. The maximum amount of evaluation function of partition coefficient equal to 0.76 and the minimum amount of partition entropy function equal to 0.49 occurred in three clusterstherefore,the number of optimum clusters equal to 3 clusters was determined through trial and error.The results of classification indicated that the triple groups included the sub-watersheds of the northeastern regions and parts of central regions of the Karkheh watershed (group 1), the northwestern- southeasternalong with the southern regions of Karkheh watershed (group 2) and the central regions and parts of southwestern regions of Karkheh watershed (group 3).
Conclusion
Watershed classification with similar characteristics can be used as a method for watershed management, flood control and giving the priority to critical sub-basins. However, watershed classification is only completedwhenit is understood why some catchments belong to certain groups of hydrological behavior, so as to be possible to classify gauged and ungaugedwatersheds through it.
Finally, it is important to remember that classification of watersheds is not the “be-all and end-all” of research on watersheds, but rather only a means towards achieving broader aims of planning and management of our ecosystems, environment, water resources, and other relevant earth systems and resources. However, watershed classification certainly allows us to study catchments more effectively and efficiently and develop more appropriate strategies in terms of simplification in models/model development, generalization in our modeling approach, and improvement in communication both within the hydrologic community and across disciplines, as much as possible.