Mohammad Hossein Rezaei Moghaddam; Davoud Mokhtari; Nasrin Samandar
Abstract
Land use change is one of the important factors in changing the hydrological flow, basin erosion and biodiversity destruction. Therefore, knowing the effect of land use change on discharge and suspended load is an inevitable necessity. The main purpose of this study is the efficiency test of the model ...
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Land use change is one of the important factors in changing the hydrological flow, basin erosion and biodiversity destruction. Therefore, knowing the effect of land use change on discharge and suspended load is an inevitable necessity. The main purpose of this study is the efficiency test of the model and its usability as a simulation of the process of land use change on discharge and sediment is from the soil and water assessment model (SWAT) and SUFI2 program. Model simulation was performed for 29 years from 1987 to 2015, the first 5 years of which were selected for model calibration and the last 5 years for model results validation. Four statistical indices, r_factor, P_factor Nash-Sutcliffe (NS) and coefficient of determination (R2), the ratio of squared root-to-standard deviation (RSR) and the percentage of skewness (PBIAS) were selected monthly to evaluate the model. The accuracy of monthly simulation using NS evaluation index in the calibration and validation stage for flow and suspended load is equal to 0.65 and 0.49, respectively. The results of the study were considered acceptable according to the interpretive domains used in previous studies and indicate the satisfactory efficiency of the SWAT model in simulating the components of the impact of land use change on sediment and discharge in the Ojan Chay Bostanabad watershed. The results showed that the height of surface runoff increased by 1.15 mm and the sediment concentration increased by 1.5 tons per hectare per year.
Geomorphology
fariba esfandiyari darabadi; hadi rafiei mahmoodjagh; roya farzaneh
Abstract
Today, the preparation of land use maps using remote sensing data is one of the most important methods for producing land use maps and land use assessment together.
Lack of proper infrastructure, non-observance of land development capability in selecting land uses and its unprincipled management leads ...
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Today, the preparation of land use maps using remote sensing data is one of the most important methods for producing land use maps and land use assessment together.
Lack of proper infrastructure, non-observance of land development capability in selecting land uses and its unprincipled management leads to the phenomenon of soil erosion.
The purpose of this study is to prepare land use maps using object-oriented method and to prepare soil zoning maps of Zarrineh Rud catchment for 2000 and 2018 Landsat satellite using WLC method. The results showed the detection of changes from object-oriented classification
The highest rate of change in rainfed and irrigated agricultural land uses has been faced with the largest increase in area in the region.
The trend of changes in barren land uses, rich and medium rangeland, has been decreasing over time, so that the area has decreased by 14.04, 10.66 and 5.73 percent, respectively.
Man-made use has been increasing almost uniformly over time, which has grown by 2.47% over 18 years.
The results obtained from the erosion zoning maps produced in 2000 and 2018 showed that there are two very high-risk and high-risk classes, each covering 15.29 and 27.51 percent of the area. These classes are mostly located in rainfed, irrigated, barren and medium range agricultural uses.
Hafez Mirzapour; Ali Haghizadeh; Naser Tahmasebipour; Hossein Zeinivand
Volume 6, Issue 20 , December 2019, , Pages 79-99
Abstract
1- IntroductionAccurate detection of changes land use in Accurate and timely, Basis for a better understanding of the relationships and interactions of human and natural phenomena to manage and provides better use of resources. Principal land use management requires accurate and timely information in ...
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1- IntroductionAccurate detection of changes land use in Accurate and timely, Basis for a better understanding of the relationships and interactions of human and natural phenomena to manage and provides better use of resources. Principal land use management requires accurate and timely information in the form of a map. Regarding the widespread and unsustainable changes in land use, including the destruction of natural resources in recent years, Investigating how landslide changes during time periods are essential for satellite imagery. Since conservation of natural resources requires monitoring and continuous monitoring of an area, Land-use change models are now used to identify and predict land-change trends and land degradation one of the most widely used models in predicting land use change is the Auto-Markov cell model. the aim of present study is to monitor land use changes in the past years and predict changes in the coming years in Badavar-Nurabad watershed in the Lorestan province with an area of 71600 hectares. 2- MethodologyThe Markov chain method analyzes a pair of land cover images and outputs a transition probability matrix, a transition area matrix, and a set of conditional probability images. The transition probability matrix shows the probability that one land-use class will change to the others . The transition area matrix tells the number of pixels that are expected to change from one class to the others over the specified period (Ahadnejad 2010). Automatic cells are models in which adjacent and continuous cells, such as cells that may include a quadrilateral network, change their state or attributes through simple application of simple rules. CA models can be based on cells that are defined in several dimensions. The rules for changing the state of a cell from one mode to another can be either a combination of growth or decrease, such as a change to a developed cell or without development. This change is the source of the change that occurs in the adjacent cell. Neighborhood usually occurs in adjacent cells or in cells that are close together(Ghorbani et al, 2013). In order to detect land use changes in the studied area, TM , ETM+ and OLI satellite images of Landsat were used during three time periods of 1991, 2004 and 2016. After applying geometric and atmospheric corrections to images, the land use map for each year was prepared using the maximum probability method. The Kappa coefficient for the classified images of 1991, 2004 and 2016was 0.81, 0.85 And 0.90 obtained. Then, to model land use changes using the Auto-Markov cell model for 2028 horizons, First, in the Idrisi Selva software using Markov chain, the map was selected as input from the years 1991 and 2004, the 12-year prediction of the changes was considered by 2016 to determine the likelihood of a change in application. Then, using the CA-Markov method, the data from the Markov chain and the map of 2016 were used as input data for the automated-Markov cell method. 3- ResultsAssessment of the match between simulated and actual map of 2016 with 0.97 kappa index showed that this model is an appropriate model for simulating of land use change. The results from monitoring satellite imagery that in 1991 to 2016, the extent of residential areas, land is Dry farming, garden and irrigated farming land added in front of vast pastures, shrubbery and other is reduced. After verifying the model's accuracy, a 2028 map was prepared to predict the changes over the coming years. Well as the results show that the vast pastures of the forecast is reduced in the amount of 659.89hectares and 395.47 hectares will be added to the extent of irrigated farming. 4- Discussion and conclusionThe results of the Auto-Markov cell model showed that if the current trend continues, the size of the ranges will decrease sharply. Comparison of simulated map of 2016 by model and actual map with Kappa index showed that Auto-Markov cell model is a suitable model for predicting land use change and can accurately assess the future status of land use and vegetation to predict. Therefore, it is suggested protective measures and make appropriate management decisions to control non-normative changes continue to apply more than ever.
Mahmood Khosravi; Taghi Tavousi; Kohzad Raeespour; Mahboobeh Omidi Ghaleh mohammadi
Volume 4, Issue 12 , December 2017, , Pages 25-44
Abstract
Extent Abstract Introduction In some parts of Iran, especially in its highlands, the predominant precipitation is snow. The large part of the snow cover is located in the mountainous and impassable areas. Consequently, it is almost impossible to study and investigate the snow point using traditional ...
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Extent Abstract Introduction In some parts of Iran, especially in its highlands, the predominant precipitation is snow. The large part of the snow cover is located in the mountainous and impassable areas. Consequently, it is almost impossible to study and investigate the snow point using traditional methods and snowflake stations. Chaharmahal-Bakhtiari province is one of the snowiest areas of Iran, and snowfall has a great role in the status of the water resources supplying the water of its central and southern regions, especially the Karun and Zayandeh Rood Rivers. Methodology Regarding the role and importance of Mount Zardkouh heights and its rivers in the region, the purpose of this study was to investigate the changes in the snow cover levels in Mount Zardukh altitudes. Therefore, remote sensing data, due to its provision of better results, is used with the aim of obtaining detailed information on snow cover. Today, remote sensing technology and revolutionary satellite imagery are created in the field of snow cover study so that wide-area snow measurements are dramatically more accurate over time. The occurrence of the recent droughts, the severe decrease of water resources, and the role and importance of snowfall in the supply of groundwater resources in mountainous areas needs to maximize the use of available resources by making the necessary arrangements. Discussion The process of these changes was measured using landsat satellite data, TM and ETM + sensors. In addition, the ndsi index was used to analyze the changes in the snow cover level of April (Farvardin) and September (Shahrivar), which were the peak months of the snow cover. The peak time of the snow cover melting in the region, Zardkouh Bakhtiari heights, during 1991, 2003, and 2011 (time spans of approximately 10 years) was also investigated to study the changes in the snow cover levels. Pre-processing steps including examining changes in the snow cover levels using the normalized differential snow index (NDSI) and corrections (radiometric, geometric, etc.), processing, classification, and after classification on the selected images using the ENVI software were taken. The NDSI index was applied based on the maximum snow cover per pixel of images (April & September). Conclusion Finally, the values, or maps, derived from the above indicators were classified into two classes of snow cover and snowless surfaces. After this classification, the areas of both classes were summed up for the investigation of the changes in snow cover and snowless cover during the studied years. The results showed that while the amount of the snow cover level in April 1991 was 1758.07 km2, it became 1128.43 km2 in April 2003. In other words, there was a decrease of 529.64 km2 between the years 1991 and 2003. In addition, it was 979.83 km2 in April 2011 and there was a decrease of 778.24 km2, compared to 1991. Moreover, while it was 802.86 km2 in September 1991, it became 615.83 km2 in September 2003. In other words, there was a decrease of 187.06 km2 between September 1991 and September 2003. In addition, it was 601.83 km2 in September 2011 and there was a decrease of 201.03, compared to September 1991.