چکیده:
پایش ویژگیهای مختلف نواحی ساحلی یکی از عوامل اساسی در جهت استفادهی بهینه از این منابع طبیعی و مدیریت پایدار آنها میباشد. هدف تحقیق پایش تغییرات ، شناخت و تعیین مناطق حساس به تغییرات خطساحلی و تحلیل این تغییرات برپایه ژئومورفولوژی میباشد. برای این منظور ابتدا به کمک نقشهها و مدارک موجود منطقه مورد مطالعه شناسایی شده و سپس از طریق تصاویر ماهوارهای با سنجندههای TM , ETMو OLI در بازه زمانی سالهای 1986 تا 2014، تغییرات خط ساحلی با استفاده از روشهای مبتنی بر طبقهبندی حداکثر احتمال، بررسی شدهاند. لازم به ذکر است میزان دقت کاپا و دقت کلی طبقهبندی حداقل %94 و 97% به ترتیب می باشد. در ادامه با استفاده از تکنیک مقایسه پس از طبقه بندی به پایش تغییرات پرداخته شد. نتایج حاکی از این است که محدوده مورد مطالعه در طی 28 سال گذشته، دارای تغییرات چشمگیری به صورت پسروی و پیشروی خط ساحل بوده است. طوریکه در طول دوره اول (1986-1994) 9 کیلومترمربع کلاس خشکی به کلاس آب و 68 کیلومترمربع کلاس آب به خشکی تبدیل، در طول دوره دوم (1994-2001) 19 کیلومترمربع خشکی به آب و 17 کیلومترمربع آب به خشکی تبدیل و در دوره سوم (2001-2008) 43 کیلومترمربع کلاس خشکی به کلاس آب و 3 کیلومترمربع کلاس آب به خشکی تبدیل و در دوره آخر (2008-2014) 65 کیلومترمربع کلاس خشکی به کلاس آب و 30 کیلومترمربع کلاس آب به کلاس خشکی تبدیل شده است. در نهایت مناطق حساس به تغییرات در خط ساحلی تعیین، و با تهیه نقشه ژئومورفولوژی آن منطقه تحلیل شد.
Aim of this article is investigation of changes of the shoreline using satellite images and interpreting these changes on the basis of geomorphology. To achieve this purpose, Landsat images of four periods (1994-1986, 1994-2001, 2001-2008, and 2008-2014) are investigated. Studies indicate huge changes of the territory. In the first period, 900 hectare of the land has changed to the water and 6800 hectare of the water has changed to the land. 1900 hectare of land has changed to the water and 1700 hectare of the water has changed to the land, during the second period. Over the third period, 4300 hectare of the land changed to the water and 300 hectare of the water changed to the land. 6500 hectare of the land changed to the water and 300 hectare of the water changed to the land, during the last period. Finally, sensitive areas to shoreline change are determined. Providing the geomorphological map of the area, the changes were studied and it was understood that changes of the shoreline don't have any regular pattern. The irregularities happen in the swamp areas. In other word, in the swamp areas, shoreline experienced both regression and propagation. Introduction Shore systems are so active and changes over them happen so fast due to the interaction of the two dynamic systems. (Sea and the land) About 70 percent of the world's shores undergo permanent erosion and regression of the shoreline. Today, remote sensing data are of the most applicable sources of the information to investigate the shore landforms, tidal surfaces, shoreline changes, depth of the water and anything of the category. Lots of people have dedicated their investigation to these subjects among whom Makota et.al(2004), Chalabi et.al (2006), Li (2011), chenta mansilvan et.al (2013) may be named. Aim of this article is investigation of the shoreline changes of the Jask area. To achieve this purpose, firstly proper timed satellite images were collected and changes of the shoreline were studied. Quantity measurements evaluated the displacement of the shoreline to the area of changes, direction of the changes and displacements of the shoreline of the area in a twenty eight years period. At the end, geomorphological map of the area was provided and the area was analyzed. Methodology This research has several steps which are name below: Collecting the proper data for the research Pre-processing of the images and the data Processing of the data and applying the changes detecting algorithms Post-processing the results Evaluating the results of the different methods, indicating the changes and getting the different maps of the seasonal and periodic changes of the shoreline. Analyzing these changes on the basis of the geomorphological map of the interested area. In the post-processing section, monitored comparison after classification method is used. In this method, classification is done for each of the images individually. Therefore, in this research, most probable algorithm is applied for the classification. In this research, due to the aim of the research and data available, two classes including water and soil are used. Knowing these two classes for a long period, shoreline changes can be investigated and relation and correlations between the shoreline changes with the water and land coverage may be shown. Results and Discussions After classifying the images, investigating of the changes starts. With intersecting these images two by two, classification results are investigated. Tables, images and circular charts present the from-to information of the class changes of the land usage of the area from 1986 to 2014. Using these information one can investigate the relationships between the different class changes trend. To analyze the shoreline changes, images of the previous step having two classes of water and soil, and intersected two by two should be investigated periodically. Charts of the figure 10 present the alternating the two classes of water and soil in a 28 years period. As it shows, changes do not have a regular pattern. However, it can be understood that in each period, larger area of the water has changed to the soil. In general, most of the changes were about the regression of the shoreline. In other words, changes of the classes in the third and fourth period were more than the other two periods. Secondly, class changes of these two periods present more soil classes changed to the water. Lastly, the most sensitive and stable areas of shoreline changes were evaluated. Conclusion Aim of this article is investigation of changes of the shoreline using satellite images and interpreting these changes on the basis of geomorphology. Evaluation of the shoreline changes using image classification and running change detection, is better than other methods since this method presents the from-to information of the each classes in each period and changed of the classes to each other in detailed. (Area, pixel and changes percentage) Analyzing the results of the classification method, it may be understood that to evaluate shoreline changes, classification of the satellite images can be a proper method to present the changes of the shoreline. Results indicated that due to the area's condition no exact trend of the shoreline change could be achieved. Changes of the two first periods were about the propagation of the shoreline, however in the other two periods the changes were of the reversed form. Also, it could be indicated that the swap areas were the sensitive areas to the shoreline changes and most of the irregularities of the changes were about these swaps.
خلاصه ماشینی:
براي اين منظور ابتدا به کمک نقشه ها و مدارک موجود منطقه مورد مطالعـه شناسـايي شـده و سپس از طريق تصاوير ماهواره اي با سنجنده هاي ETM ,TM و OLI در بازه زمـاني سـال - هاي ١٩٨٦ تا ٢٠١٤، تغييرات خط ساحلي با استفاده از روش هاي مبتني بر طبقه بندي حـداکثر احتمال ، بررسي شده اند.
در ادامه با استفاده از اندازه گيري هاي کمي ، ميزان جابه جايي خطوط ساحلي از نظر تعداد پيکسل ، درصد تغييرات ، مساحت تغييرات ، جهت تغييرات محدوده مورد مطالعه در محدوده زماني ۲۸ ساله مورد مقايسه و ارزيابي قرار مي گيرد و در نهايت با تهيه نقشه ژئومورفولوژي منطقه به تحليل اين تغييرات پرداخته مي شود.
الف ) تصوير، ب ) اطلاعات from-to ، و ج ) نمودار مساحت (درصد) ميزان تبديلات دو کلاس خشکي و آب حاصل از detection change منطقه مورد مطالعه از دوره ٢٠٠٨-٢٠١٤ / (به تصویر صفحه مراجعه شود) با توجه به نمودارهاي موجود در شکل ۱۰ که ميزان تبديلات مساحت طبقۀ آب و طبقۀ خشکي به هم و نحوه تغييرات را در فاصله زماني ۲۸ ساله مشخص کرده است .
Monitoring Coastline Change Using Remote Sensing and GIS Technologies.
Coastline change detection with satellite remote sensing for environmental management of the Pearl River Estuary, China.
, 2004, Monitoring shoreline change using remote sensing and GIS: a case study of Kunduchi area, Tansania, western Indian ocean J.
Monitoring of shoreline changes using remote sensing (case study: coastal city of Bandar Abbas).
Monitoring Coastline Change Using Remote Sensing and GIS Technology: A case study of Acıgöl Lake, Turkey.