Sahar Abdollahi; Hashem Dadashpoor
Introduction: Land use changes in coastal areas of Gilan Province in recent decades have caused problems such as forest and wetland degradation, soil erosion, biodiversity reduction, and increased environmental pollution. This region is important in terms of its unique features and human use of this ...
Introduction: Land use changes in coastal areas of Gilan Province in recent decades have caused problems such as forest and wetland degradation, soil erosion, biodiversity reduction, and increased environmental pollution. This region is important in terms of its unique features and human use of this environment for a variety of residential, industrial, and recreational activities. Therefore, it is necessary to be aware of the changes and factors influencing them, and predict the changes process in the future to prevent irreparable damages to the environment. The purpose of this study was to analyze the land use changes in Gilan Province during a 20-year period (1996-2016) and predict changes for the next 30 years based on the integration of the artificial neural network of multilayer perceptron and Markov chain model using the Land-change Modeler. Material and methods: Landsat 5 and 8 (TM) and (OLI-TIRS) satellite images were used for the years 1996, 2006 and 2016. Land cover maps were prepared for five classes in the forest, grass, agriculture, water, and residential resources using the Maximum Likelihood method. Land use changes and then modeling the transmission potential were explored using multilayer perceptron algorithm of artificial neural network using 13 independent variables and obtained 7 sub-models for modeling land use change for 2016 and then using Markov chain method, land use map for the year 2016 was predicted with a coefficient of Kappa 0.98. Finally, the land use pattern of Gilan Province was simulated for 2046. Results and discussion: The results obtained from the analyses of land use changes in the first period (1996-2006) indicated that residential land use with the 7702.72 hectares increased the most among other users. In these changes, agricultural land use had the largest share, where 7663 hectares of this land turned into residential areas. In the second period (2006-2016), residential land use, as in the previous period, with the annual change of 633.7 hectares, had the most significant change in this period. In the whole study period from 1996 to 2016, the residential land reached from 12157.57 hectares in 1996 to 26197.59 hectares in 2016, which agricultural lands had the largest share in the conversion of the built-up areas. Conclusion: The process of land use change suggests that this trend has begun from the past and will continue in the future. So, the results of the detection of changes from the predicting land use for the next 30 years would indicate an increase in residential use and a decrease in the area of agricultural lands, forests, and grasslands. According to these results, timely and accurate evaluation of these changes lead to better decision making and planning.
Fatemeh Rezaei; Samereh Falahatkar; Hashem Dadashpoor
Volume 16, Issue 2 , July 2018, , Pages 31-48
Introduction: Global warming is an absolute fact and an inevitable threat to life in the environment. Considering that urban areas are an important factor in the increase in CO2 gas emissions, the community needs to take action to reduce greenhouse gas emissions. Since the increase in the population ...
Introduction: Global warming is an absolute fact and an inevitable threat to life in the environment. Considering that urban areas are an important factor in the increase in CO2 gas emissions, the community needs to take action to reduce greenhouse gas emissions. Since the increase in the population is responsible for increasing the demand for housing and the rapid development of activity centres in the suburbs and the rapid growth of urban areas in Iran, we see the importance of fuel in sustainable development and the important and potential role of sustainable forms of urban development; the necessity of quantifying the relationship between the compactness of the urban form and CO2 emissions due to fossil fuel consumption is thus released. Materials and methods: The present study is designed in two phases. In the first phase, the changes of 15 urban forms in Guilan and Mazandaran Provinces are investigated according to the compactness dimension using landscape metrics (AI, PLADJ, PROXIM, COHESSION). OLI and TM Landsat satellite imagery from the years 2001 and 2015 was used to provide the urban maps. Also, a supervised classification based on the maximum likelihood algorithm was used for image classification. Post classification comparison was used for detection of change. In the second phase, after calculation of CO2 emissions the panel data analysis was used to calculate the relationship between time series variables of CO2 emissions and cross-section variables of landscape metrics. Results and discussion: The results of the landscape metrics show that, for all the urban forms studied during the period 1380-1394 (Persian calendar), the compactness in the class level in all urban areas of Guilan Province except for Bandar Anzali has shown a decreasing trend compared with Mazandaran Province, but an increasing trend of compactness was observed only in Ramsar, Behshahr and Amol in Mazandaran Province. Therefore, it can be said that the urban areas of Mazandaran Province are more compact than those of Guilan Province. According to the results, these four compactness variables (AI, PLADJ, PROXIM, COHESION) showed a negative correlation with CO2 emissions due to concentrations of gasoline and diesel oil at the urban class level. Among the metrics used, COHESIOM showed the highest correlations (8.79 and -10.17)) with carbon dioxide due to gasoline and diesel oil consumption, respectively. According to the results of the panel data analysis, the increase in the COHESION value of about one percent has caused CO2 emissions from gasoline to decrease by about nine percent, while a one percent increase in the COHESION value caused a 10% reduction in CO2 emissions from diesel oil. For example, if we consider that the amount of CO2 emissions from gasoline is 133.63 tons CO2 per hectare in Amol for 1394 and also, according to the results of the COHESION metrics which indicate that if the urban compactness increases by one percent CO2 emissions will be reduced by 8.8 percent, a reduction of 11.73 tons per hectare will occur. Conclusion: In sum, consideration of the urban form in the future planning of the northern cities development is recommended for creation of low carbon cities in Iran.