بررسی عوامل تهدید جنگل های حرا به کمک داده های دورسنجی

جنگل‌های حرا از منابع مهم و میراث با ارزش طبیعی هستند که نقش ارزنده‌ای در حفظ اکوسیستم ساحلی داشته و مکانی ایده آلی برای حمایت از اجزای شبکه های غذایی در دریا محسوب می‌شوند. با این‌حال به دلیل رشد سریع جمعیت، برنامه‌ریزی ضعیف و توسعه اقتصادی ناهماهنگ این جنگل‌ها در معرض تخریب هستند. این مطالعه با هدف بررسی روند کاهش پوشش گیاهی جنگل های حرا و شناسایی عوامل تخریب کننده این نواحی در منطقه حفاظت شده حرا صورت گرفته است. بدین منظور تصاویر ماهواره‌ای لندست 8 سنجنده oli مربوط به سال 2015 و تصاویر سنجنده etm+ در سال 2001 برای این جنگلها دریافت گردید و چهار روش پایش تغییر بر روی این تصاویر اعمال شد. روش‌های پایش تغییر مورداستفاده در این مطالعه شامل روش‌های مقایسه پیکسل به پیکسل به‌صورت تفاضل، نسبت‌گیری و رگرسیون و مقایسه پس از طبقه‌بندی بوده‌اند. بر همین اساس مناطق دارای تغییرات کاهشی، افزایشی و بدون تغییر مورد بازدید میدانی قرار گرفت و جهت ارزیابی دقت روش‌های پایش تغییر، پس از برداشت واقعیات زمینی که از طریق بازدید میدانی و تصاویر ماهواره‌ای به دست آمد، دقت کل و ضرایب کاپا تعیین شد و مشخص گردید روش مقایسه پس از طبقه بندی با دقت کلی 93% و کاپای بیش از 9/0 بیشترین دقت را در آشکارسازی تغییرات دارد. پس از آن روش تفاضل با آستانه دو برابر انحراف معیار با کاپا 76/0 و دقت کلی 82% بالاترین دقت را نشان داد. همچنین بررسی های میدانی نشان داد قاچاق سوخت و ریزش ترکیبهای نفتی بخصوص گازوئیل به درون آب و چرای مستقیم شتر از حرا مهمترین عوامل تخریب این جنگلها به شمار می روند. در پایان پس از بررسی میزان تخریب این مناطق به‌منظور احیاء و بازسازی اولویت‌بندی شدند. نتایج این پژوهش نشان می دهد در صورتیکه از فن سنجش از دور به صورت علمی بهره برداری شود، می توان به راحتی در شناسایی عوامل تهدید جنگلهای حرا و اولویت بندی پروژه های احیائی از آن استفاده نمود.

Investigating the threats of mangrove forests with the help of remotely sensed data

Investigating the threats of mangrove forests with the help of remotely sensed data Behzad Rayegani: Assistant Professor of College of Environment, Department of Environment Mangroves are a group of trees and shrubs that live in the coastal intertidal zone. Mangrove forests are very important because they are known as natural heritage and crucial in protecting coastal ecosystems. Mangrove forests stabilize the coastline, reducing erosion from storm surges, currents, waves, and tides. The intricate root system of mangroves also makes these forests attractive to fish and other organisms seeking food and shelter from predators. So, they are ideal places to support the elements of seafood networks. However, these forests are in danger of degradation because of rapid population growth, poor planning and unsustainable economic development. In the process of regenerating an ecosystem, it is necessary to identify the precursors of the threat, to consider the means to eliminate these threats. Therefore, identifying the threatening factors of the mangrove forest ecosystem is the first step in the restoration and protection of the ecosystem.This study aims to investigate the change and the destruction in Mangrove forests and to identify threatening forces in the Hara Protected Area. Remote sensing is now widely used in studies of ecosystem changes because its information is available for the past, and there are many highlydeveloped techniques for change detection through remote sensing. Therefore, in order to identify the threatening factors of mangrove forests, remote sensing techniques were used to identify changed areas during a 15year period. Images of ETM+ and OLI sensor from 2001 to 2015 were collected in the Hara Protected Area (Khorekhoran International Wetland). Given that we have used the multipledate remote sensor data in this study, it was necessary to use absolute atmospheric correction methods for radiometric harmonization of data. So, with the aid of the ERDAS IMAGINE 2014 software, the Atmospheric and Topographic Correction (ATCOR) model was applied to all data. Subsequently, due to the difference in radiometric resolution of the OLI sensor with the ETM+ sensor, the output of ATCOR of both sensors was stretched into 8bit data in order to eliminate the existing divergence in radiometric resolution. Also, based on spatial information, one of the image of OLI sensor at the current time was corrected geometrically, and then other images were registered to this image to eliminate geometric errors. There are many ways to detect changes with the help of remote sensing data, but we used two widely used techniques in this study: 1) postclassification comparison; 2) Change detection techniques of Algebra. Totally four different change detection methods were applied to these images. Change detection techniques of Algebra image method include image difference, image ratio, regression and postclassification comparison were used. At first, with the knowledge of the studied area, by combining the two supervised and unsupervised classification (hybrid method), the pixels that were known as mangrove forests were identified in both time periods of study. Then pixels with decreasing trend were determined by postclassification comparison method. From the image of the mangrove forests with the logic of Boolean (OR), a mask of mangrove was obtained, which showed the areas of mangroves during the two periods. This mask was used to make the second group of methods for determining changes (Algebra method) applied to the data. By doing this, in all algebra methods, the histogram showed the normal distribution. Finally, the vegetation spectral indices were applied to the data and their coefficient of variation was obtained in the Boolean mask area. Among these indices, NDVI showed better performance, so the algebra operation was used for this index. Accordingly, areas with decrease, increase and no change trends were visited and then overall accuracy and kappa coefficients were determined.The results showed that the method of post classification comparison has the highest accuracy in the monitoring of vegetation changes in mangrove forests. This method with a total accuracy of over 93% and a kappa of more than 0.9 showed the highest accuracy in the detection methods of the changes, therefore, in the final examination and prioritization of the regions, this method was used. The surveys showed that the smuggling of fuel due to pour gasoline into the water and camel grazing are the most important destructive factors in the mangrove forest. After determining the rate degradation in four regions, these regions were ranked in order to carry out reclamation and restoration projects.In the case of intelligent use of the capabilities of remote sensing, one can easily identify the threatening factors of an ecosystem. In the case of mangroves, the only limiting factor is tidal conditions. It is therefore recommended that, as in this study, images are chosen to determine the changes that are in a same tidal state Keywords: Remotely Sensed change detection, Image Algebra Change Detection, Postclassification comparison, Determination of thresholds