فهرست مطالب hamid amounia

Quantitative and qualitative monitoring of vegetation is one of the main applications of remote sensing knowledge. Estimating the area of vegetation and agricultural lands in the traditional way is time consuming. Remote sensing is of great importance for providing up-to-date information, duplicate coverage, sensing in different spectral ranges, quantitative and digital sensing of surface phenomena. This study was conducted to estimate the level of vegetation and agricultural lands of Babolsar county through Sentinel 2 satellite images using NDVI index.

The present research method is descriptive-analytical based on the data of Sentinel 2 satellite images of 2019. These images were extracted from the USGS website. After extraction, the images are processed in remote sensing software systems and geographic information systems, and the required results have been extracted.

After receiving the Sentinel 2 satellite images, the bands 2, 3, 4, 8, which have a spatial resolution of 10 meters, after preprocessing and atmospheric corrections, based on IDL programming language, using NDVI index, Sentinel 2 ROI were obtained, then output. Threshold was calculated between a maximum of 1 and a minimum of 0.5 and with a Shape file output in ENVI and Arc Map software, the area of vegetation and the area under cultivation of agricultural lands in Babolsar county were calculated and concluded on the date of obtaining satellite images. Discussion and

The results showed that the area of arable land and vegetation of Babolsar city is estimated to be more than 52%. Overall accuracy of 95% and kappa coefficient of 89% by evaluating the output of NDVI index showed that Sentinel 2 images can be used to extract vegetation and arable lands.

Estimating the level of vegetation and arable land in the traditional way takes time. One of the effective tools in estimating the level of vegetation is the use of remote sensing techniques. This technique is of great importance in order to provide up-to-date information, time series of data, measurement in different spectral ranges. The purpose of this research is to estimate the level of vegetation in Babolsar and evaluate the accuracy of the NDVI index. In this research, Landsat 8 satellite image data from 2019 was used. The process of pre-processing, processing and verification has been done in ENVI5.3 software, and area maps and calculations have been done in ArcGIS10.8 software. The results showed that the vegetation area of the city is estimated at 11474.37 hectares. The evaluation of the accuracy of the used index also showed that by using Landsat 8 images, it is possible to calculate the level of vegetation with an overall accuracy of 92% and a Kappa coefficient of 87%.

Background and Objective:

Today, land use change in many countries has become an important challenge that has many effects on the environment. Accordingly, the study of land use change at different scales is one of the important issues in the proper management of natural resources and environmental change at various levels. Therefore, being aware of land use changes and investigating their causes and factors in several time periods, and predicting land use changes in the future can be properly planned to reduce adverse effects, which has been considered by planners and city managers. They help in land use planning. Also, converting land uses to each other and changing the use of vegetation is known as an important issue. Therefore, the purpose of this study is to monitor and predict land use changes and land cover in Abbasabad urban area in the future; Using these changes, appropriate management measures can be taken to preserve and rehabilitate lands.

Materials and Methods:

 A combination of an automated cell model and Markov chain in the Abbasabad urban area was used to predict land use change; The relevant images were taken from the TM and OLI sensors of the Landsat 8 and 5 satellites at the USGS site. Four user classes, including zone class built with code number 1, vegetation class with code number 2, water resources class with code number 3, and barren land class with code number 4, were separated for Abbasabad urban area. Obtained USGS. In order to extract land use classes, after checking several methods, object-oriented classification method and support vector machine (SVM) algorithm were used due to better efficiency. Evaluation of Babian satellite imagery classification The overall accuracy and kappa coefficient were performed for three periods of time. Each of these classified maps was evaluated by drawing an error matrix. 250 sample points were used to prepare this matrix. The type of sampling was stratified sampling. Also, to determine land use changes in 2030, classified maps were used and with the help of TerrSet software, changes made in classes and their percentages were obtained, and using the CA-MARKOV model, changes of different classes based on matrices. The possibility of transfer was predicted.

Results and Discussion :

The results during 1997, 2006, and 2017 show that the constructed area has an increasing trend and the uses of vegetation, barren lands, and water resources have a decreasing trend and 23279 hectares of lands in the region are built area dedicated. The kappa coefficient calculated for 1997, 2006, and 2017 is 0.86, 0.89, and 0.89, respectively. Markov chain forecasting model with 85% accuracy stated that the trend of land use change for 2030 will be the same as in previous years, and this indicates that the conversion and change of land uses will proceed as before, and it is necessary to mention this point that the identical uses of vegetation to vegetation cover the largest area during the years 2006 to 2017, and this shows that in this area, vegetation is still stable and has undergone less changes.

Conclusion :

The output of the 13-year forecast map for 2030 in this study indicates the appropriate accuracy of the CA-MARKOV model. In addition, this output shows that this method can be trusted for short-term planning. These forecast maps can be a good guide for managers and urban planners. To achieve better results, it is recommended to use a combination of automated cell model and Markov chain to monitor and predict changes nationwide. The results of this study, in addition to helping to reduce the volume of input data, but also in the processing of classified images and in predicting them for the future.

Coastal areas are of special importance to humans due to their many uses. Changes in the coastline are inevitable despite human beings and their effects such as land-use changes, and therefore it is necessary to study the relationship between coastline changes and land-use changes. Babolrood coastline with a length of about 9 km in most of its area intersects with the city of Babolsar, which causes the impact of human activities on changes in the coastline in this area. The fluctuating behavior of the Caspian Sea and the rate of regression and advancement of the coastline in this area can be affected by human activities and changes in coastal land use. The data used in this study include the level of the Caspian Sea and Landsat images on TM and OLI sensors. The length of the 42-year study period from 1976 to 2018 has been determined, which is in line with the trend of changes in the level of the Caspian Sea. The analytical research method is a comparison between sea level data and information and shoreline changes and land use maps. The system (DSAS) in ArcGIS10.7 and ENVI5.3.1 has been used to calculate land-use changes. After calculating the shoreline changes and calculating the NSM, the shoreline change diagram was extracted and compared with the land-use changes. The results show that human activities above sea level have been effective in shoreline changes, the most important of which is the development of coastal structures and structures beyond the natural shoreline.

The position of the coastline depends on interactions between waves, tides, rivers, storms, geological structures and physical processes .Recognizing the spatial variations of the coastline, one can understand the dynamics of coastal zones and morphodynamic processes It is absolutely necessary to manage shore line changes. The Caspian Sea has created a unique environment with special characteristics due to frequent fluctuations and periodic changes, such as in closed lake environments. Due to this change in the level of the water base, we see large changes in the coastline in the coastal areas of the Caspian Sea. The sea shows a decrease of about 3 meters in the 48-year period (1929-1977) and has experienced an increase of about 3 meters in a period of 18 years (1995-1997). Given the awareness of the changes in the Caspian Sea, we need a method that clearly shows these changes in time and place. The purpose of this research is to investigate the 60-year variation of coastal lines using remote sensing data in the Caspian region.

The research method was descriptive-analytic based on remote sensing data and aerial photographs using the GIS. The aerial photographs of 1956 and Landsat satellite images were used in 1994, 1976, and 2017. These images and images were originally prepared in Envi5.3. ArcGIS 10.5 ArcGIS 10.5 transcoded transects and their statistical calculations. The statistical methods used in this study to determine the coastline variation in the DSAS plug-in are End-End Rate Method (EPR) and Linear Regression Rate (LRR).

In this paper, maps and diagrams required for analysis have been extracted based on the drawing of coastlines and calculations in the DSAS plug-in. From this, one can assess and analyze how the 60-year change process was taking place in the study area.In the study area, by plotting the base line and transects 50 meters apart, the maximum, the lowest and the average changes during the time period are calculated. The map of variations in two methods shows the amount of variation along the coastline. In this study, the coastline coastal speed and coastline rate for the Haraz delta range is -0.15 m and for Babolrood range is +0.05 m and Talar range is +/16 + m.In these maps, the variation in estrus varies from one side to another. In the Bablrood Delta, the seaward section has advanced to the sea, and in the Haraz and Talar deltas, this is reversed. In the EPR method, as we can see, erosion occurred in the Haraz delta around the estuary of the river, and as much as the river estuary refers to both sides of the east and west, we see a replacement of sediment instead of erosion. In the Bablrood Delta, there is almost a picture of the Haraz Delta, but Talar River delta conditions are slightly similar to those of the Haraz, with variations in the Haraz Delta quite typical. In the LRR output map, the results are similar to those of the EPR method, with the exception that the erosion intensity in the Talar River is less than that of Talar. Because the LRR method uses all coastlines drawn for computation, the result of this map can be more accurate.

In this research, using transects method, the statistical characteristics of coastline changes during the 60-year period have been studied. According to the results of the maps, the general trend of erosion in the studied area is toward coastal erosion. Depending on the basis of the LRR method, the largest changes in the delta of the Babolrood River are observed at 5.9 m, while the adjacent delta of this quantity does not reach 2m. This difference indicates a remarkable sea in the Babolrood Delta, which can be considered a human factor especially in recent years, among the most important reasons. But comparing the results between the maximum and minimum changes, as shown in Table 3, shows a high degree of variation in these three delta. The result of the use of DSAS outbound maps is an important consideration and that in spatial analysis, the use of such single-dimensional Negri instrument will be transformed into a multi-dimensional view, and in this study we observed that during a coastline interval, 60-year-old change can be quite a photo.

One of the fundamental factors in urban planning is the issue of hazards performance evaluation, prediction and control. Our country is located in one of the riskiest areas of the planet in terms of natural hazards and its diversity. Today, natural hazard extend their threats more to the cities and harsh human environments and make their development more vulnerable. This is very evident in a dense metropolis of Tehran, especially in the mountainous region of the district 1.
This research seeks to approach the issue of sustainable urban development in the district 1 of Tehran metropolis with natural hazard approach especially geomorphological hazards. in the present study, at first using library studies, we attempted to evaluate the concept of sustainable urban development and natural hazards and in the next step, field studies were conducted for a comprehensive understanding of the region and with the help of network analysis method, research variables were analyzed and zoned in accordance with the research objectives and ultimately, the assessment of sustainable urban development was defined and explained.
Findings: The research has been designed around two main sections. The issue of zoning of natural hazards, particularly a range of issues was analyzed and in the next step, the sustainable urban development was assessed in the context of natural hazards.
As the study results from hazard zonation and assessment indicate, the region is at a high risk level of 73.11%. The issues such as the weight of urban construction and the natural hazards (poor housing planning), the problems of urban worn out buildings, natural hazards, non-standard gradients and their damage, and ultimately, the slippery landslide of residential areas all indicate that it has to be more careful in the management of land usages and turn up to replace residential usages with low-risk usages such as green space.