ذخیره برف در حوزه‌های کوهستانی از منابع آب مهم است. طبق بررسی‌های انجام‌ شده حدود 60 درصد آب‌های سطحی و 57 درصد آب‌های زیرزمینی کشور در مناطق برف‌گیر جریان دارند. در سال‌های اخیر اهمیت و کاربردهای داده‌های روزنه ترکیبی راداری (SAR) با توجه به مزیت‌های عمده‌ای نسبت به سایر سیستم‌های سنجش ‌از دور دارند رو به فزونی بوده است. در این پژوهش ابتدا با استفاده از تولیدات ماهواره مودیس (MODIS) و الگوریتم Snow map نقشه سطح پوشش برف و سپس با استفاده از دوازده تصویر راداری سنجنده TerraSAR-X و تکنیک راداری تداخل سنجی (DInSAR) ‌به گونه‌ای که ابتدا یک تصویر به عنوان تصویر پایه و بقیه تصاویر نسبت به تصویر اول تداخل سنجی انجام شد سپس بین مناطقی که پوشش برف بود مقدار جا به جای بدست آمده آن ها نشان‌دهنده تغییرات عمق برف است و در مرحله بعد نقشه عمق برف و نقشه حجم برف مهرماه 1391 تا اردیبهشت 1392 استخراج شد. با استفاده از رگرسیون خطی بین نقشه عمق برف که از تکنیک تداخل سنجی تولید شده و داده های عمق آب معادل برف که از ایستگاه‌های زمینی برداشت شده بودند نقشه عمق آب معادل برف تولید شد که نتایج بیان گر دقت کلی 3/91 درصد و ضریب کاپای 45/84 نقشه سطح برف و نقشه عمق برف با ضریب تعمیم 85 درصد و RMSe برابر 78/2 است برای محاسبه عمق آب معادل برف با استفاده از همبستگی بین داده‌های عمق برف مستخرج از DInSAR و داده‌های زمینی عمق آب معادل برف یک رابطه خطی با ضریب تعمیم 77/0 و RMSe برابر 97/2 محاسبه شد که این نتیجه که از لحاظ آمار در سطح 99 درصد معنی‌دار بود. تفاصيل المقالة

Fuzzy classification techniques have been developed recently to estimate the classcomposition of image pixels, but their output provides no indication of how theseclasses are distributed spatially within the instantaneous field of view represented bythe pixel. Super-resolution land-cover mapping is a promising technology forprediction of the spatial distribution of each land-cover class at the sub-pixel scale.This distribution is often determined based on the principle of spatial dependence andfrom land-cover fraction images derived with soft classification technology. As such,while the accuracy of land cover target identification has been improved using fuzzyclassification, it remains for robust techniques that provide better spatial representationof land cover to be developed. An approach was adopted that used the output from afuzzy classification to constrain a Hopfield neural network formulated as an energyminimization tool. The network converges to a minimum of an energy function. Thisenergy minimum represents a “best guess” map of the spatial distribution of classcomponents in each pixel. The technique was applied to remote sensing imagery(MODIS & OLI images), and the resultant maps provided an accurate and improvedrepresentation of the land covers. Low RMSE, high accuracy. By using a Hopfieldneural network, more accurate measures of land cover targets can be obtained, The Hopfield neural network used in this way represents a simple, robust, and efficienttechnique, and results suggest that it is a useful tool for identifying land cover targetsfrom remotely sensed imagery at the sub-pixel scale. The present research purpose wasevaluation of HNN algorithm efficiency for different land covers (Land, Water,Agriculture land and Vegetation) through Area Error Proportion, RMSE andCorrelation coefficient parameters on MODIS & OLI images and related ranking,results of present super resolution algorithm has shown that according to precedence,most improvement in feature’s recognition happened for Water, Land, Agricultureland and ad last Vegetation with RMSEs 0.044, 0.072, 0.1 and 0.108. تفاصيل المقالة

Land use planning is the most important issue of sustainable urban development and a major priority in urban planning. This study was conducted with the aim of investigating the extent of physical expansion of Yazd city, identifying the land use changes to urban use and determining the city growth directions during the years 2013 to 2018, using Landsat 8 satellite imagery over a time period of 5 years for Yazd city. Using the technique of detection of changes by comparative method after classification of images, changes in green space and moor lands were identified for urban use. Then, regional statistics techniques were used to determine the extent of city expansion and the nature of changes were obtained in different geographical directions. From 2013 to 2018, based on the results, 24.73% of the moor lands and 32.22% of the green area has had urban use. The growth of the city during this period were towards the East and South directions in the central and southern sectors in the marginal sector. The greatest amounts of green space changed to urban use were in the central and peripheral sectors, and the largest conversions of the moor land to urban use were in the central eastern region and in the southern marginal sector. The results showed that inappropriate distribution of applications in some parts of the city disrupted the spatial distribution of the land use. تفاصيل المقالة