Journal of Radar and Optical Remote Sensing
Volume:4 Issue: 1, Winter 2021

In recent years, it has occurred in different regions of Iran, especially the plains, and in most regions has caused this phenomenon to become a major regional and country crisis. Kerman desert province is no exception to this rule and most of its plains and industrial areas have suffered from this phenomenon and have high subsidence rates. The present study investigated the occurrence of this phenomenon using radar interferometry technique and Sentinel 1 satellite images in the period of 2019 and 2020 in Bardsir plain of Kerman province. To investigate the rate of subsidence in the region, initial processing was performed in remote sensing software and GIS and two Goldstein and Adaptive filters were used to evaluate the obtained results. The results show that the Goldstein filter has subsidence values up to 10 cm in certain ranges and the uplift values up to about 6.5 cm and the Adaptive filter have given the subsidence values up to 9 cm in some ranges and the uplift values up to about 5.6 cm. The reason for the difference in values in the results of these two filters is that in the Goldstein filter, the amount of coherence increases by manipulating the phases, so the image is brighter, thus the situation in this filter improves. But this is not the case with the Adaptive filter, and the phases are not manipulated, and in some areas, the amount of blurriness is higher in different parts of the image.

Preparation of land use maps using traditional methods, in addition to spending a lot of time and money, is mainly about efficiency and it does not have the necessary accuracy. Today, satellite imagery and remote sensing techniques have a wide range of applications in all sectors, including agriculture, natural resources, and land use mapping, due to the provision of timely data and high analysis capabilities, variety of shapes, digitality, and the possibility of processing. Satellite imagery Landsat 8 for August 2020 was used, which after making the necessary corrections in the pre-processing stage, action experimentation or fusion of the desired image using the panchromatic band and spatial resolution of the image was increased from 30 meters to 15 meters. In the next step, four different classification methods, including backup vector machine, maximum probability, Mahalanoob distance, and minimum mean distance were compared. The results showed that the classification method of backup vector machine with average overall coefficients and kappa of 100 and 1, respectively, has higher accuracy than other methods. Priority accuracy of classification methods is in the form of backup vector machine, maximum probability, Mahalanoob distance, and minimum distance from the mean, respectively. Finally, by assessing the accuracy using user accuracy, producer accuracy, overall accuracy, kappa coefficient and error matrix, land use map was prepared in three separate classes.

The decrease in the level of Lake Urmia is evidence of climate change and anthropogenicity. This decrease in level has led to an increase in salt area, salt storms and salinization of groundwater. It is one of the major environmental challenges in northwestern Iran. Fluctuations in lake level, decrease in water level of plains and decrease in river discharge are evidences of hydrological changes in Urmia Lake basin. The present study tries to provide a clear picture of the water changes of Urmia Lake during the last three decades. Hydrological data and images of Landsat satellite for Urmia Lake basin in the period 1984-2017 were studied using remote sensing and statistical methods. The classification of satellite images was performed using the maximum likelihood method. According to the results, the highest decrease in the area of the lake between 2001 and 2013 happened. Also, the analysis of the results showed that along with the very important role of global warming on the water volume of Lake Urmia, humans have been able to be one of the most important regional factors in creating the challenge of Urmia Lake. In fact, the water problems of Urmia Lake, especially after 2001, are a combination of climatic and anthropogenic factors.

One of the main pillars of sustainable development in each country is the provision of adequate food at reasonable prices for the people of that community and, given the increasing population and the need for food, identifying and introducing favorable rice cultivation areas in each region is essential. For this purpose, two methods of hierarchical analysis (AHP) and a multilayer perceptron neural network (MLP) using Levenberg-Markov teaching algorithm were used in this study. The effective layers of rice cultivation were compiled and the required maps were compiled including twelve layers including land use map, average annual rainfall, average rainfall Spring season, average autumn rainfall, average temperature Spring season, average autumn temperature, slope, altitude, relative humidity, degree-day distance from the river. Analytic hierarchy model structure is used to determine the weight of layers by analyzing AHP questionnaires. Digital layers the environmental factors in the GIS environment were combined and integrated after assigning AHP weight to each layer. The grid structure is composed of twelve input layers above and eight intermediate layers and an output layer. Land zoning map of rice cultivars was obtained for both models. Thus, in the final map, the results of each of the two models, including five classes, very unfavorable, unfavorable, relatively favorable and favorable, are respectively 22, 43, 25, 7 and 3 percent for the network and results from the hierarchical model are 15, 20, 25, 22, and 18 the total area of the city. The results show that the neural network model is more accurate than the hierarchical model. The total regression coefficient of ninety-four percent of the network, which is the result of all data in the network, indicates the high efficiency of the multilayer perceptron neural network in this zoning.

The use of accurate lithological maps is inevitable in the preparation of rock unit’s erosion susceptibility maps. In this study, rock unit outcrops in the Soh Basin (50 km Northern Isfahan) were extracted using nonlinear correlation analysis of satellite data. Moreover, rock unit’s erosion susceptibility such as marl, shale, and quaternary deposits and resistant rock units such as sandstone and limestone were extracted based on soil erosion intensity factors. The lithology of the basin was studied using the virtual variables method. Initially, rock units, as a virtual independent variable, and the PC1 (the first principal component) of ETM+ multispectral bands were analyzed by a multiple linear regression model. Afterward, rock units were analyzed in logistic regression analysis as virtual dependent variables. The results revealed that logistic regression analysis is a suitable model for rock unit’s extraction.

Fast and unorganized urban development increases the number of abandoned lands and brownfields within the cities. Revitalization of these lands is one of the key factors to achieve urban sustainability. Most researches on this field have mainly considered a single brownfield site for redevelopment on the bases of local neighborhood demand and characteristics. The current paper proposes a brownfields land use change modeling process in a larger scale perspective rather than local aspects. The proposed model is a statistical-based weights-of-evidence (WoE) approach in the GIS environment. The changes probability of brownfield sites of the Qazvin city to residential land use was predicted using several urban development parameters. Next, the predicted map was aggregated with the existing brownfields map in order to evaluate by Master Plan of the Qazvin city. In this manner, existing brownfield sites are project according to planning strategies. Results indicate that according to potential and suitability of the site and neighborhood properties, each brownfield can serve the community as single or mixture of several land use types. It is concluded that the application of land use change modeling techniques in GIS environment can provide a strong tool for brownfields redevelopment planning and strategies.