طبقه بندی و ارزیابی تغییرات کاربری اراضی با استفاده از تصاویر ماهواره ای لندست (مطالعه موردی: آبخوان دشت قزوین)

تغییرات کاربری اراضی بر اثر فعالیت های انسانی از موضوعات مهم در برنامه ریزی های منطقه ای و توسعه ای است. برای استفاده مناسب از زمین، آگاهی از تغییرات کاربری اراضی و نوع استفاده انسان از سرزمین لازم و ضروری است، این امر با آشکارسازی تغییرات کاربری اراضی میسر می شود. از اینرو بررسی تغییرات کاربری اراضی نقش اساسی در مطالعات زیست محیطی ایفا می کند. امروزه فن آوری سنجش از دور و سیستم اطلاعات جغرافیایی، به طور موثر جهت شناسایی و تعیین مقدار تغییرات کاربری اراضی و اثرات آن بر محیط زیست استفاده می شوند. این پژوهش با هدف بررسی تغییرات تبدیل کاربری ها با استفاده از فن آوری سنجش از دور و تصاویر ماهواره ای برای چهار دوره زمانی 5 ساله، از سال 1378 تا 1398 در سطح آبخوان دشت قزوین که بخشی از حوزه آبخیز قزوین می باشد انجام شده است. جهت تهیه نقشه های کاربری اراضی سال های 1378، 1383، 1388، 1393 و 1398 از تصاویر ماهواره ای tm و oli ماهوارهای لندست 5 و 8 استفاده شد. در ادامه تصاویر ماهواره ای به روش طبقه بندی نظارت شده و با استفاده از الگوریتم حداکثر احتمال تشابه با دقت بسیا خوبی در پنج کلاس کاربری طبقه بندی شدند. در نهایت نقشه های کاربری اراضی حاصله مورد مقایسه قرار گرفتند. تجزیه و تحلیل تغییرات نشان داد که مساحت اراضی مرتعی در طول سال های مورد مطالعه روند کاهشی و سایر کاربری ها روند افزایشی را طی نموده است، بطوریکه وسعت اراضی با کاربری زراعی آبی، مناطق مسکونی و صنعتی، اراضی دیم و رها شده و اراضی شوره زار و بایر به ترتیب به میزان 14.24درصد، 38.8درصد، 25.37درصد و 8.37 درصد افزایش یافته ولی اراضی با کاربری مرتع به میزان 21.16 درصد کاهش یافته است.

Classification and Assessment of the land use changes using Landsat satellite imagery (Case Study: Qazvin plan’s Aquifer)

IntroductionOne of the main issues in regional planning and development is land use change by human activities. It can be argued that human actions can lead to significant changes in current state of earth rsquo;s surface. Changes in surface cover (land cover change) may in turn lead to alternations in balance of energy, water, and geochemical fluctuations at local, regional or global levels. Land use mapping using remotesensing data is one of the newest and most widely used methods for the provision of land use map, and making a comparison between the existent usages.Therefore, Considering the benefits and potentials of satellite data, this technology can be of great help in identifying and detecting these changes. Materials and MethodsProcessing satellite images and performing supervised classification helps to extract information from these images. This study was carried out for assess changes in landuse from 1999 to 2019 in the Qazvin plan rsquo;s Aquifer. Qazvin plan rsquo;s Aquifer is located at the North West part of Iran and the Qazvin province. The land uses observed in visit the area included: 1irrigated agricultural lands, 2residential and industrial areas, 3rangelands, 4dry and abandoned lands and 5saltmarsh and barren lands. In this study, ENVI 5.3 software was used for processing five selected imageries in this project (1999,2004,2009,2014 and 2019).For this purpose, Landsat5 Thematic Mapper (1999, 2004, and 2009) and Landsat8 Operation Land Imager Sensor (2014, and 2019) satellite images were used for the land use change analysis with 30m spatial resolution, which were taken from the United States Geological Survey (USGS; https://glovis.usgs.gov/), and after correcting geometric and radiometric in the preprocessing stage, Maximum Likelihood Classification (MLC) algorithm as a supervised classification method has been used to identify and detect land use changes. Also, The overall accuracy test used to determine the accuracy of produced maps. Results and DiscussionDetection of land use change is one of the most important applications of remote sensing data. The ability to periodically repeat over time, this data can be used to identify and investigate variable and dynamic phenomena in the environment. Different land use classes had been recognized and used as the base map. The result showed that, the area of rangeland lands has decreased from 1999 to 2019 and other uses have increased. so that the area of irrigated agricultural lands, residential and industrial areas, dry and abandoned lands and saltmarsh and barren lands have increased by 14.24%, 38.8%, 25.37% and 8.37%, respectively, but rangelands decreased by 21.16%. Overall accuracy and Kappa statistics were extracted from the error matrix. supervised classification accuracy for the 5 different time frames (1999,2004,2009,2014 and 2019) found from accuracy assessment showed that the highest accuracy was found for 2019 supervised classification (96.28% accuracy). Kappa value is also used to check accuracy in classification and having a Kappa value (0.81 -1.00) denotes almost perfect match between the classified and referenced data. The Kappa coefficient for land use in 1999, 2004, 2009, 2014 and 2019 were 87%, 86%, 91%, 89%, and 94%, respectively. Also,The results showed that the extraction of adequate samples from different classes of land use would increase the possibility of correct distinction of image pixels received from the satellite and accurate extraction of land use classes. Thus, obtaining accurate results from the classification of images via the maximum likelihood method is depending on adequate and appropriate training samples.ConclusionThe present study confirm that remote sensing is an important technology for extracting land use maps and detecting land use changes. Changes detection is made possible by this technology in less time and with better accuracy. Land use changes is one of the most important factors of environmental changes. Such changes is often the result of human intervention, in addition to the negative effects on the environment, will increase the damage to natural disasters. The quantification of land use changes is very useful for environmental management groups, policy makers and for public to better understand the surrounding. Hence, the researchers emphasize the need for the planning to managing natural resources and monitoring environmental changes.