ارزیابی آماری از انتقالات کاربری اراضی و پوشش زمین در حوضه آبخیز دریاچه ارومیه با استفاده از ماتریس انتقال

اهداف: پایش تغییرات کاربری ها و درک پویایی آن در یک حوضۀ آبخیز، از جایگاه خاصّی در مدیریت پایدار آن حوضه برخوردار است. هدف تحقیق حاضر، استفاده از سنجش از دور و gis جهت تهیۀ نقشۀ تغییرات و شناسایی انتقالات کاربری اراضی و پوشش زمین با به کارگیری ماتریس انتقال و تصاویر ماهوارۀ لندست در حوضۀ آبخیز دریاچۀ ارومیه می باشد.روش: جهت انجام تحقیق، از تصاویر ماهوارۀ لندست در دورۀ زمانی 2015 ـ 1988 استفاده گردید. بدین منظور پس از انجام پیش پردازش های موردنظر، جهت انجام طبقه بندی از روش های ماشین بردار پشتیبان و روشیءگرا استفاده و سپس اعتبارسنجی گردیدند. همچنین جهتِ برآورد میزان انتقالات و دیگر ویژگی های حوضۀ آبخیز دریاچۀ ارومیه، ابتدا ماتریس انتقالی استخراج شده و سپس طبقه بندی شئ گرا بین دوره های زمانی 2015ـ1988 ارائه شد. سپس با استفاده از فرمول های موردنظر، میزان پایداری، افزایش، کاهش، تغییرات کل، تغییرات خالص و مبادلۀ همزمان کاربری های اراضی و پوشش زمین مشخّص گردید.یافته ها/ نتایج: پس از ارزیابی صحت، صحت کلّی برای نقشه های حاصل از ماشین بردار پشتیبان و روش شئ گرا به ترتیب برابر با 94 و 92 درصد و مقدار کاپای آنها به ترتیب 92 و 89 برآورد شد که نشان دهندۀ برتری روش شئ گرا در مقایسه با روش ماشین بردار پشتیبان است. در کل، هر دو روشِ طبقه بندی توانستند صحت قابل قبولی برای نقشه های کاربری اراضی و پوشش زمین ارائه دهند. نتایج حاصل از انتقالات نشان داد به طور میانگین، 59 درصد از چهرۀ زمین در حوضۀ آبخیز دریاچۀ ارومیه در فاصلۀ زمانی 2015ـ 1988 پایداری پوشش داشته است، که بیشترین میزان این تداوم براساس مقدار این کاربری در فاصلۀ زمانی 2015ـ1988 مربوطه به مناطق مسکونی میباشد. حدود 14 درصد از سطح حوزۀ آبخیز دریاچۀ ارومیه به صورت تبادل همزمان بوده است. همچنین سطوح آبی حوضۀ آبخیز دریاچۀ ارومیه در دورۀ زمانی فوق، بیشترین ازدست دادگی و کمترین تبادل همزمان را تجربه کرده است.نتیجه گیری: حوضۀ آبخیز دریاچۀ ارومیه در این فاصلۀ زمانی (2015ـ1988) تغییرات و انتقالات شدیدی را تجربه کرده است، تاجایی که تنها 59 درصد از چهرۀ زمین، ثابت مانده و قسمت های دیگر، انواعی از انتقال ها را تجربه کرده اند. همچنین سطوح آبی و سپس مراتع، بیشترین آسیبپذیری را تجربه کرده اند که نشان از افزایش اراضی فاقد پوشش و اراضی زراعی (کشاورزی) میباشد. این تجزیه وتحلیل ما را به سنجش و تجسّم میزان انتقالات عمدۀ lulc درجهت برنامه ریزیِ آیندۀ حوضۀ آبخیز دریاچۀ ارومیه توصیه می کند.

A Statistical Assessment of Land Use and Land Cover Transitions of the Urmia Lake Basin Using Transition Matrix

Extended Abstract1 Introduction Monitoring land use changes and understanding its dynamics in a watershed is a special issue in the sustainable management of watershed basins. In recent decades, rapid changes in land use and land cover in Urmia Lake basin is accompanied by important consequences such as the destruction of natural resources, environmental pollution, and rapid growth of cities. Understanding the ratio of changes as well as the systematic and random transitions of Land Use and Land Cover (LULC) over time can be used to determine the extent of degradation, manage these changes in a proper direction, predict future changes, and act properly. Detecting and modeling land use changes by using remotely sent data and GIS technology can provide a solid understanding of changes in land use and therefor can offer suitable management practices. The aim of the present study is the use of remote sensing and GIS for mapping land use and land cover changes and identification of their transitions using a transfer matrix and Landsat images in Urmia Lake basin. Therefor, the performance of pixelbased and objectoriented methods of land use and land cover classification specified in Urmia Lake basin is going to be compared. Also the spatial and temporal dynamics of LULC is going to be analysed for this basin. Another objective of this research is to identify the ratio of vulnerabilities of each land use relative to other land uses.2 Theoretical FrameworkLandcover transitions can be classified into random and systematic changes. Random transitions are those influenced by coincidental or unique processes of change; for instance, the rapid and abrupt changes of land transformations in an ecosystem depending on resilience and feedback mechanisms. On the other hand, Systematic transitions are those due to regular or common processes of change. (Tucker et al., 1991; Lambin et al., 2003).3 Methodology In this study, Landsat TM and ETM + images were used for the period of 1988 to 2015. First, some image preprocessing techniques were done including reducing the brightness of water, strips of pixels in images, and removing the cloud spots. The area of Urmia Lake basin is 5786 Km2 and contains eight Landsat image frames. So, for each period, 8 images were obtained and mosaicked based on band to band method and the coordinate of the region. After running the required preprocessing on the images, training samples were obtained and the images were classified based on vector machine and objectbased classification methods. Then, the results of the classification were validated. Based on field observations and vegetation map of Urmia lake basin, the training samples were obtained from 6 land uses including the residential area, forests, agricultural fields, rangelands, water resources, and bare lands. Bias, Gama, Kernel functions, and pyramid levels and Penalty Pyramid Parameters were obtained from a cyclic kernel function for Support Vector Machine. Also, effective parameters of objectoriented approach were obtained including the window width, weighted mean factor, weighted variance factor with error, and similarity tolerance. In order to evaluate the results, Kappa coefficient and overall accuracy were used. For estimating the rate of transmission and other characteristics of the watershed of Urmia Lake, the transition matrix of objectoriented classification method was extracted for 19882015. After that, by using appropriate formula, the rate of gain, loss, persistence, net change, and swap (simultaneous exchange) was calculated for each land use/land cover. The swap represented changes in the location between land covers, whereas the net change was associated with a measurable irreversible change in the surface of one land cover to another; having these two components of change allowed the actual spatial dynamics of LULC change to be determined in the study area. In this manner, it was possible to determine the total change in LULC between 1988 and 2015 and highlight the land cover types that exhibited the greatest variation.4 Results & Discussion The overall accuracy of Support Vector Machine (SVM) and objectoriented approach were 94 and 92 respectively. Also, Kappa coefficient was 92 and 89 respectively, showing that although both methods show acceptable results, the objectoriented approach is stronger. The results of the transactions showed that coverage has been persisted in the 59 percent of the land in the catchment area of Lake Urmia during the period of 1988 to 2015, most of which was related to the residential areas. About 14 percent of the basin has experienced swap. Water resources has experienced the most loss and the less swap. For residential areas, agricultural fields, and water resources classes, the ratio of gain to persistence is more than 1 indicating that the amount of gains are due to the persistence of these classes. The ratio of net changes to persistence was negative for the classes of forest, rangelands, and water resources. The net reduction of water resources was almost contrary to the persistence of this landscape. Also, the net reduction of agricultural fields was almost the same but half the net gain.5 Conclusions & Suggestions Urmia Lake basin has experienced rapid changes and transitions during 19882015; that is, only 59 percent of the land uses have been stable while the other areas have experienced a kind of transition. Due to the reduction of water resources and rangelands, bare lands and agricultural fields have been increased. The results should be noticed for an integrated watershed management of the basin.