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بررسی تراکم و تنوع پرندگان در دو پهنه جنگلی حفاظتی و تفرجی (مطالعه موردی: استان گلستان) The Survey of Density and Diversity of Birds in Two Protected and Recreational Forest Areas (A Case Study: Golestan Province, Iran) 1 1 به‌منظور مقایسه تراکم و تنوع پرندگان در دو پهنه جنگلی مختلف، جنگل حفاظت‌شده شصت‌کلاته و جنگل تفرجی النگدره، گونه‌های پرنده و متغیرهای محیطی درون هر یک از 100 نقطه نمونه‌برداری و به شعاع 25 متر ثبت شدند. محور اول آنالیز تطبیقی متعارف دو گروه از پرندگان را از هم تفکیک کرد. گروه نخست شامل پرندگانی نظیر دارکوب سیاه، دارکوب خال‌دار بزرگ، کمرکلی جنگلی، توکای سیاه، سسک دم‌پهن بود که همبستگی مثبتی با تعداد درختان مرده افتاده با ارتفاع کمتر از هفت متر، درجه پوسیدگی درختان خشک افتاده، تراکم تاج‌پوشش درختان، عمق لاشبرگ و درصد پوشش علفی در جنگل شصت‌کلاته داشتند. گروه دوم، شامل سهره سرسیاه، کلاغ ابلق و سینه سرخ، همبستگی مثبتی را با تعداد درختان زنده با قطر برابر سینه 100-50 سانتی‌متر، تعداد درختان زنده با ارتفاع بیشتر از 15 متر، تعداد درختان مرده ایستاده با ارتفاع 15-7 متر و موقعیت تاج پوشش خشکه‌دار در پارک جنگلی النگدره نشان دادند. با توجه به نتایج آنالیز زوجی آنوسیم در فصل پاییز و زمستان، از نظر ترکیب گونه‌ای بین دو منطقه جنگل شصت‌کلاته و پارک جنگلی النگدره اختلاف معنی‌داری (0/001= P) مشاهده شد.  2 Density and diversity of birds in two different forest zones, namely, Shasta Kalateh Protected Forest and Alangdareh Recreational Forest, were estimated and compared. Birds and environmental variables were recorded within 25 m radius in 100 sampling points. The first axis of Canonical Component Analysis segregated two main groups of birds. The first group consisted of Black Woodpecker, Great Spotted Woodpecker, Nuthatch, Blackbird, and Cettis Warbler; these had a positive correlation with logs less than 7 m in height, the degree of the decay of logs, the density of canopy cover, litter depth, and the percentage of grass cover in Shasta Kalateh Forest. The second group including Bullfinch, Hooded Crow, and Robin had a positive correlation with the number of trees with the dbh of 50- 100 cm, the number of trees more than 15 m in height, the number of snags with 7-15 m in height, and the position of the canopy cover of snags in Alangdareh Forest Park. Also, according to the results of the analysis of similarity (ANOSIM) in the autumn and winter, there was a significant difference between Shasta Kalateh Forest and Alangdareh Forest (P=0.001).  1 16 2019/07/7 1398/4/16 2019/09/25 1398/7/3 ملیحه بروغنی M. Borooghny گرگان-میدان بسیج-دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران mburooghny1994@yahoo.com حسین وارسته مرادی H. Varasteh Moradi گرگان-میدان بسیج-دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران varasteh@gau.ac.ir علیرضا میکاییلی A. Mikaeili گرگان-میدان بسیج-دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران amikaeili@gmail.com علی اکبر محمدعلی پورملکشاه A. A. Mohamadali Pourmalekshah گرگان-میدان بسیج-دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران pourmalekshah@gmail.com Bird community Recreational activity Shasta Kalateh Forest Alangdareh Recreational Forest جامعه پرندگان فعالیت تفرجی جنگل شصت‌کلاته جنگل تفرجی النگدره 1. Atkinson, P. L. 2003. 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بررسی اثرات طرح جنگلداری بر شاخص‌های تنوع گونه درختی پهنه‌بندی شده به‌روش کریجینگ (پژوهش موردی: طرح جنگلداری واتسون در شرق مازندران) Investigating the Effects of Forestry Plans on Tree Diversity Indices Mapped by Kriging Method (A Case Study: Watson Forestry Plan in the Eastern of Mazandaran) 1 1 طرح‌های جنگلداری، با برداشت درختان و نهال‌کاری گونه‌های مختلف در تنظیم تنوع گونه‌های درختی تأثیرگذار بوده‌اند و بررسی تغییرات تنوع گونه‌های درختی و حفظ پایداری توده با استفاده از آماربرداری طرح‌های جنگلداری می‌تواند ابزاری مناسب به‌منظور تصمیم‌گیری‌هـا در مدیریت جنگل‌هــا به‌شمار آید. برای بررسی تغییرات تنوع گونه‌های درختی در جنگل می‌توان از شاخص‌هایی نظیر شاخص شانون- وینر و سیمپسون استفاده کرد. در این تحقیق از آماربرداری‌های طرح جنگلداری سری واتسون در سال 1383 و 1393 استفاده شد. در این سال‌ها 369 قطعه نمونه دایره‌ای شکل به شعاع 10 آر با شدت 3/3 درصد و با شبکه آماربرداری 200×150 متر با GPS برداشت شد. شاخص‌های سیمپسون و شانون- وینر در قطعات نمونه محاسبه و سپس با استفاده از بررسی هیستوگرام، نمودار Q-Q، سمی‌واریوگرام و RMSE، نرمال بودن داده‌ها و کارایی درون‌یابی به‌روش کریجینگ بررسی شد و نقشه زمین آمار شاخص‌های گفته شده در سال 1383 و 1393 و نیز نقشه تغییرات آنها تهیه شد. نتایج نشان داد، طی دوره 10 ساله اجرای طرح جنگلداری، شاخص‌های تنوع گونه‌ای سیمپسون و شانون-وینر در حدود 30 درصد سطح سری بهبود‌یافته و حدود 60 درصد عرصه بدون تغییر و حدود 11 درصد منطقه نیز کاهش تنوع گونه‌ای داشته است. 2 Forestry plans have been influential on tree species and planting in different ways, regulating tree species diversity. Investigation of the changes in tree species diversity and preservation livestock sustainability by using forestry inventory can be an appropriate tool for decision makers in management. Indicators such as the Shannon Wiener and Simpson indices can be used to study the variations in tree species diversity in the forest. In this research, the  Watson series forestry inventory was used in 2004 and 2014. In the aforementioned years, 369 circular sampling plots were taken with an intensity of 3.3% and using a 200 * 150 m inventory grid with GPS. The Simpson and Shannon Wiener indices were calculated in sampling plots pieces; then, Kriging ability was investigated  using histogram, QQ plot, semivariogram and RMSE, and data normalization. The land maps of these indices were analyzed in 2004, 2014; also, their change detections were prepared. The results showed that during the 10-year period of the implementation of the forestry plan, the Simpson and Shannon Wiener species diversity indices were increased by 30%, and about 60% of the area remained unchanged; also, about 11% of the area had reduced species diversity. 17 30 2019/07/72019/02/17 1397/11/28 2019/09/252019/11/2 1398/8/11 علیرضا حسین پور A. Hoseinpour دانشگاه علوم کشاورزی و منابع طبیعی ساری ایران arhoseinpour88@gmail.com حمید جلیلوند H. Jalilvand دانشگاه علوم کشاورزی و منابع طبیعی ساری ایران hj_458_hj@yahoo.com مریم نیک نژاد M. Niknejad دانشگاه علوم کشاورزی و منابع طبیعی ساری ایران maryam612niknejad@yahoo.com هاتف پرینژاد H. parynejad دانشگاه علوم کشاورزی و منابع طبیعی ساری ایران hp_ef52@yahoo.com امیر سوادکوهی A. Savadkohi دانشگاه علوم کشاورزی و منابع طبیعی ساری ایران savadkohi_st59@yahoo.com Shannon Wiener Index Simpson Index Variogram Interpolation Inventory Grid شاخص شانون- وینر شاخص سیمپسون واریوگرام درون‌یابی شبکه‌ آماری 1. Aertsen, W., V. Kint, K. 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تهیه نقشه پراکنش شدت خشکیدگی جنگل‌های بلوط زاگرس با استفاده از آمار مکانی و شبکه عصبی مصنوعی Mapping Dieback Intensity Distribution in Zagros Oak Forests Using Geo-statistics and Artificial Neural Network 1 1 اولین و اساسی‌ترین امر در مبارزه با خشکیدگی جنگل، اطلاع از چگونگی پراکنش مکانی و شدت خشکیدگی در جنگل است. با توجه به اهمیت موضوع در این مطالعه کارایی دو روش آمار مکانی و شبکه عصبی مصنوعی در تهیه نقشه شدت خشکیدگی جنگل در بخشی از جنگل‌های شهرستان ایلام مورد مطالعه قرار گرفت. برای نمونه‌برداری از روش تصادفی سیستماتیک استفاده شد. به این صورت که پس از پیاده کردن شبکه آماربرداری با ابعاد 200 × 250 متر و تعیین مرکز پلات‌ها با استفاده از GPS، در 100 قطعه نمونه مستطیلی شکل به مساحت 1200 متر‌مربعی درصد خشکیدگی درختان اندازه‌گیری و ثبت شد. همچنین یک نمونه ترکیبی خاک از مرکز و چهار گوشه هر قطعه نمونه برداشت و پس از انتقال به آزمایشگاه خصوصیات فیزیکی و شیمیایی آن اندازه‌گیری شد. پس از بررسی نرمال بودن داده‌ها، نقشه خشکیدگی با استفاده از روش‌های مختلف زمین‌آمار و شبکه عصبی مصنوعی تهیه شد. نتایج نشان داد که بهترین روش برای تهیه نقشه شدت خشکیدگی جنگل، شبکه عصبی پرسپترون چند لایه (MLP) با صحت 85 درصدی است. همچنین نتایج نشان داد که خشکیدگی بلوط دارای همبستگی مثبت با شیب منطقه و وزن مخصوص ظاهری خاک و همبستگی منفی با رطوبت و ماده آلی خاک است. 2 The first and most important issue in forest drought management is knowledge of the location and severity of forest decline. In this regard, we used geostatistics and artificial neural network methods to map the dieback intensity of oak forests in the  Ilam province, Iran. We used a systematic random sampling with a 250 × 200 m grid to establish 100 plots, each covering 1200 m2. The percentage of the declined trees in each plot was measured and recorded. Also, a composite soil sample was extracted from the center and the four corners of each plot in order to determine their physical and chemical properties. After examining the normality of the data, the dieback intensity map was made using interpolation methods and the artificial neural network. The results showed  that the best method for dieback intensity estimation was the artificial neural network with an accuracy of 85 %, by using the multilayer perceptron algorithm. Oak decline was found to be mainly related to the slope, soil moisture, soil organic content and soil bulk density. 31 44 2019/07/72019/02/172019/06/16 1398/3/26 2019/09/252019/11/22019/11/4 1398/8/13 فرشته مظفری F. Mozafari دانشگاه ایلام ایران karamshahi64@yahoo.com عبدالعلی کرمشاهی A. Karamshahi دانشگاه ایلام ایران a.karamshahi@ilam.ac.ir مهدی حیدری M. Heydari دانشگاه ایلام ایران m.heidari@ilam.ac.ir امید کرمی O. karami دانشگاه آزاد ایران karamshahi64@yahoo.com Oak decline Geostatistics Artificial Neural Network Zagros زوال بلوط زمین‌آمار شبکه عصبی مصنوعی زاگرس 1. Alison, B. T., J. M. Kenneth, C. L. Burras, G. B. Donald and M. D. Philip. 2005. Improving map accuracy of soil variables using soil electrical conductivity as a covariate. Precision Agriculture 6: 225-270.##2. Benediktsson, J. A., P. H. Swain and O. K. Erosy. 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data. 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(In Farsi).##22. Karami, O., A. Fallah, S. H. Shataei and H. Latifi. 2018. Assessment of geostatistical and interpolation methods for mapping forest dieback intensity in Zagros forests. Caspian Journal of Environmental Sciences 16(1): 73-86.##23. Loukas, A., L. N. R. Vasiliades and N. R. Dalezios. 2003. Intercomparison of meteorological drought indices for drought assessment and monitoing in Greece. Proceeding of the 8 International Conference on Environmental Science and Technology. Lemons Island and Greece, 8-10 September.##24. Nasernia, E., M. Nouri-Khajavi and M. Rezaee. 2018. Milling tool wear prediction by feed motor current signal using MLPs and ANFIS. Journal of Aerospace Mechanics 15(1): 51-62. (In Farsi).##25. Pahlavan Rad, M. R. and A. A. Dehghani, 2015. The prediction of spatial variation of soil salinity and clay using geostatistics and artificial neural networks. Soil Management and Sustainable Production 6(1): 247-254. (In Farsi).##26. Pourreza, M., S. M. 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ارزیابی چندزمانه تغییرات جنگل‌های مانگرو در مناطق ساحلی بوشهر با استفاده از تصاویر ماهواره لندست Multi-Temporal Assessment of Mangrove Forests Change in the Coastal Areas of Bushehr Region Based on Landsat Satellite Imagery 1 1 اطلاعات مداوم و دقیق در مورد تغییرات کاربری/ پوشش اراضی برای هر نوع برنامه توسعه پایدار، که در آن کاربری/پوشش اراضی به‌عنوان یکی از معیارهای ورودی اصلی است، بسیار مهم است. در این مطالعه نقشه تغییرات پوشش جنگل‌های مانگرو در سواحل استان بوشهر با اعمال طبقه‌بندی نظارت شده روی سه تصویر لندست 1986، 1998 و 2018 تهیه شد. نتایج طبقه‌بندی نظارت شده تصاویر با استفاده از بارزسازی تصویر و تفسیر چشمی بهبود یافت. تفسیر تصویری نه تنها در افزایش دقت طبقه‌بندی تصاویر لندست مفید بود، بلکه در تشخیص پوشش جنگل حرا مفید است. نتایج روش مقایسه پس از طبقه‌بندی سطح جنگل حرا منطقه رویشگاهی عسلویه در دوره زمانی 1986 تا 2018 نشان می‌دهد که مساحت این اراضی 68/9 هکتار با 43/8 درصد تغییرات افزایش داشته است که این افزایش با نرخ سالانه 1/4 درصد بوده است. این تغییرات در منطقه رویشگاهی دیر نشان می‌دهد که مساحت اراضی تحت پوشش جنگل حرا 5/2 هکتار با 0/8 درصد تغییرات افزایش داشته است. در منطقه رویشگاهی مله گنزه مساحت اراضی تحت پوشش جنگل حرا 26/8 هکتار با 6/3 درصد تغییرات افزایش داشته است که این افزایش با نرخ سالانه 0/2 بوده است. 2 Continual access to precise information about the land use/land cover (LULC) changes of the Earth’s surface is extremely important for any sustainable development program in which LULC serves as one of the major input criteria. In this study, a supervised classification was applied to three Landsat images collected in 1986, 1998and 2018, providing mangrove forests change data in the coastal area of the  Bushehr province. The supervised classification results were further improved by employing image enhancement and visual interpretation. Visual interpretation was not only useful in increasing the classification accuracy of the Landsat images, but also in identifying the mangrove forest cover. Post-classification comparisons of the classified images of the mangrove forest area of the Assaluyeh habitat zone during a period from 1986 to 2018 indicated that the area of these lands had been increased by 68.94 hectares, with a percentage change of 43.85%, which was an increase of 1.41% per year. These changes in the Deir habitat zone showed that the area of the land covered by the mangrove forest was 5.25 hectares, with a percentage change of 0.87. In the Mela Ganza habitat zone, the area of the land covered by the mangrove forest was 26.82 hectares, with a percentage change of 6.33, which was an increase in the annual rate of 0.20. 45 62 2019/07/72019/02/172019/06/162019/07/5 1398/4/14 2019/09/252019/11/22019/11/42019/11/20 1398/8/29 طیبه طباطبایی T. Tabatabaie استادیار گروه محیط زیست، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران ایران tabatabaie20@yahoo.com فاضل امیری F. Amiri دانشیار گروه منابع طبیعی و محیط زیست، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران ایران fazel16760@gmail.com Change detection Land use/Land cover Image enhancement Visual interpretation Mangrove forest پایش تغییرات کاربری/ پوشش اراضی بارزسازی تصویر تفسیر چشمی جنگل حرا 1. Abdu, H. A. 2019. Classification accuracy and trend assessments of land cover-land use changes from principal components of land satellite images. International Journal of Remote Sensing 40(4): 1275-1300.##2. Adhikary, P. P., D. Barman, M. Madhu, C. J. Dash, P. Jakhar, H. 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پایش و پیش‌بینی تغییرات پوشش و کاربری اراضی تالاب بین‌المللی شادگان ایران Monitoring and Prediction of Land Use/Cover Changes in Shadegan International Wetland, Iran 1 1 به‌منظور پایش و ارزیابی پیامدهای اکولوژیک ناشی از مداخلات انسانی، کمی‌سازی تغییرات کاربری و پوشش اراضی ضروری است. شرایط اکولوژیک و کیفیت آب تالاب به ویژگی‌های سیمای سرزمین از جمله نوع و نسبت کاربری و پوشش اراضی در بالادست و پیرامون تالاب مرتبط است. در مطالعه حاضر، کاربری و پوشش اراضی تالاب بین‌المللی شادگان ایران برای سال‌های 2001، 2014 و 2017 با استفاده از تصاویر ماهواره‌ای لندست و الگوریتم شبکه عصبی تهیه و تغییرات رخ داده در این بازه زمانی آشکارسازی شد. سپس با استفاده از ابزار مدل‌ساز تغییر سرزمین در نرم‌افزار Idrisi TerrSet، کاربری و پوشش اراضی و تغییرات آن با استفاده هفت متغیر مستقل تا سال 2030 شبیه‌سازی شد. نتایج نشان داد که بین سال‌های 2001 تا 2017، وسعت پوشش آب تالاب حدود 48200 هکتار افزایش و طبقات مربوط به کاربری و پوشش اراضی شوره‌زار و پوشش گیاهی حدود 50000 هکتار کاهش یافته‌اند، اما از آنجایی که این افزایش آب تالاب ناشی از ورود زهاب و پساب صنایع مختلفی از جمله: کشت و صنعت نیشکر و... است، این افزایش به‌طور قابل توجهی باعث تغییر کیفیت آب و ترکیب گونه‌های گیاهی تالاب می‌شود که پهنه‌بندی این تغییرات نیازمند بررسی‌های بیشتر و مطالعات در مقیاس خرد است.   2 Quantifying land use/land cover changes is essential to monitor and assess the ecological consequences of human disturbances. Ecological condition and water quality of wetlands are highly related to the landscape characteristics, including land use/land cover (LULC) types and their fractions in the upland and the surrounding landscape. The changing characteristics of LULC in Shadegan International Wetland, Khouzestan Province, Iran, were detected in this study by using the Landsat Satellite images of the years 2001, 2014, and 2017, which were classified using the Artificial Neural Network algorithm. Then by using Land Change Modeler (LCM) in the TerrSet IDRISI software, the future of LULC changes was simulated using six independent variables and the Markov chain method. The results of this study showed that from 2001 to 2017, about 48200 ha of the wetland water was increased and around 50000 ha of saline soils and vegetation area was decreased. However, since this water increase in the wetland was due to the entry of drainage and wastewater, particularly from sugarcane cultivation around the wetland, this increase could significantly alter the hydrology, the water quality of wetland and also, the plant species composition, as compared to historical conditions; mapping these changes requires further investigations and fine scale monitoring studies. 63 76 2019/07/72019/02/172019/06/162019/07/52019/01/15 1397/10/25 2019/09/252019/11/22019/11/42019/11/202019/12/4 1398/9/13 زهرا اصغری پوده Z. asghari poudeh دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان ایران za.asghari71@gmail.com امید قدیریان بهارانچی O. ghadirian baharanchi دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان ایران omidghadirian90@gmail.com شکوفه نعمت الهی shekoofeh nematallahi دانشکده منابع طبیعی-دانشگاه صنعتی اصفهان ایران Shekoofenematallahy@yahoo.com سیما فاخران S. fakheran دانشکده منابع طبیعی-دانشگاه صنعتی اصفهان ایران fakheran@cc.iut.ac.ir سعید پورمنافی S. pourmanafi دانشکده منابع طبیعی-دانشگاه صنعتی اصفهان ایران spmanafi@yahoo.com Land use change Wetland Land Change Modeler prediction تغییرات کاربری اراضی تالاب مدل‌سازی تغییر سرزمین پیش‌بینی 1. Abedi, Z. and M. Ahmadian. 2008. Research project on environmental valuation of Shadegan wetland. Department of Environmental Protection. Iran. (In Farsi)##2. Arkhi, S. 2014. Prediction of spatial land use changes based on LCM in a GIS environment (A case study of Sarabeleh (Ilam), Iran. Journal of Research and Technology for Protection and Protection of Forests and Rangelands of Iran 12(1): 19-1. (In Farsi)##3. Asghari Pudeh, Z., M. Shafieizadeh, S. Fakheran Esfahani, and P. Gilani. 2015. Evaluation and zoning of spatial temporal changes of dust storms using DSI index in Khuzestan province, Second National Conference on Climate Change and Sustainable Development in Agriculture. Natural Resources, Isfahan, October 2015. (In Farsi)##4. Bayat, R., S. Parsley. B. Red and A. M. Charkhebi. 2016. Studying the effect of dust on vegetation changes (case study: Shadegan wetland, Khuzestan). Remote Sensing and Geographic Information System in Natural Resources 7(2): 17-32. (In Farsi)##5. Bhatti, S., S. N. K. Tripathi, V. Nitivattananon. I. A. Rana and C. Mozumder. 2015. A multi-scale modeling approach for simulating urbanization in a metropolitan region. Habitat International 50: 354-365. ##6. Eastman, J. and J. Toledano. 2018. A Short Presentation of the Land Change Modeler (LCM). Geomatic Approaches for Modeling Land Change Scenarios, Springer 499-505. ##7. Hejazi, S. and R. M. Goodarzi. 2011. Investigation and evaluation of geographic and environmental impacts of tourism using AHP model (case study: Shadegan International Wetland). Quarterly Journal of Ahvaz Islamic Azad University. Ahvaz 3(9): 59-70. (In Farsi)##8. Hosseini, S. M., S. M. B. Nabavi, A. O., Rajabzadeh, B. Omidvar. 2010. Comparison of Shadegan Wetland Conservation Values Change (IUCN, IMO, Slam and Price) during the 60s to 80s. Quarterly Journal of Ecology of Wetland of Islamic Azad University, Ahvaz 1(4): 21-37. (In Farsi)##9. Jafari Azar, A., T. M. Sabzeqbaee and S. Dashti. 2017. Application of multi-criteria decision-making methods in environmental risk assessment (case study: the international wetland of Shadegan, Khur e Omayyeh and Khur e Mousa Estuary). Geography and environmental hazards 24: 97-119. (In Farsi)##10. Lotfi, A. 2018. Shadegan Wetland (Islamic Republic of Iran). PP. 1566-1575. In: Finlayson, C. M., R. Milton, C. Prentice and N. C. Davidson (Eds.), The Wetland Book. Vol 2. Springer Netherlands, Netherlands..##11. Mitsch, W. and J. G. Gosselink. 2016. Wetlands, Van Nostrand Reinhold .6th edition, New York, 772 p.##12. Monetazerhojat, A., B. Mansouri and M. Ghorbannezhad. 2015. Economic valuation of the Shadegan wetland. Quarterly Journal of Quantitative Economics 12(1): 55-77##13. Mozumder, C. and N. K. Tripathi. 2014. Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network. International Journal of Applied Earth Observation and Geoinformation 32: 92-104. ##14. Nasiri, V., A. A. Darvishsefat, R. A. Rafiee, A. Shirvany and M. A. Hemat. 2019. Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis (case study: Arasbaran region, Iran). Journal of Forestry Research 3: 943-957.##15. Pordel, F., A. Ebrahimi and Z. Azizi. 2015. A Review of Correction Methods in Multi-Time Satellite Images. ##First International Comprehensive Conference on the Environment. Tehran. Center for Iranian Development Conferences, Conference: Comprehensive International Congress on environment. Tehran-Iran. (In Farsi).##16. Rahimi-Baluchi, L. and B. Malik Mohammadi. 2014. Assessment of environmental risks in Shadegan wetland. Journal of Ecology 1: 101-112. (In Farsi)##17. Rojas, C., J. Pino, C. Basnou and M. Vivanco. 2013. Assessing land-use and-cover changes in relation to geographic factors and urban planning in the metropolitan area of Concepción (Chile). Implications for biodiversity conservation. Applied Geography 39: 93-103. ##18. Roy, H. G., D. M. Fox and K. Emsellem. 2014. Predicting land cover change in a Mediterranean catchment at different time scales. International Conference on Computational Science and Its Applications, Springer. ##19. Sabzghabaee, Gh. and N. Yousefi Khanqah. 2015. Detection and prediction of the land use change process in Hooralazim wetland using remote sensing and GIS. MSc. thesis, Faculty of Agriculture and Natural Resources. University of Technology Khatam Al Anbia Behbahan. (In Farsi)##20. Sugumaran, R., J. Harken and J. Gerjevic. 2004. Using remote sensing data to study wetland dynamics in Iowa. Iowa Space Grant (Seed) Final Technical Report, University of Northern Iowa, Cedar Falls, p. 1-17.##21. Ziaeyan Firoozabadi, P. and S. Saroue. 1382. The Evaluation of the Capacity of GIS Surveying Technologies in the Preparation of a Land Cover Map for Shadegan Wetland. Journal of Applied Geosciences Research 2(2):73-83.##22. https://earthexplorer.usgs.gov/## ##

تأثیر شکل و اندازه پلات در تعیین الگوی پراکنش گونه Olivier Astragalus verus The Effects of Plot Shape and Size on Determining the Distribution Pattern of Astragalus verus Olivier 1 1 الگوی پراکنش گیاهان یکی از خصوصیات مهم جوامع گیاهی است که بررسی و تعیین آنها در مطالعات اکولوژی و برنامه‌های نمونه‌برداری حائز اهمیت است. این تحقیق به‌منظور بررسی کارایی شکل و اندازه‌های مختلف پلات برای تفکیک الگوی پراکنش گیاه Astragalus verus Olivier با استفاده از شاخص‌های پراکندگی انجام شد. در این تحقیق مختصات هـر یک از پایه‌های گون خاردار و مرز منطقه مطالعه در مراتع مایان (خراسان رضوی) توسط دوربین دیجیتال برداشت و به‌کمک نرم‌افزار R نقشه دیجیتال پراکنش گون‏ها در منطقه مورد نظر رسم شد. اشکال مختلف پلات به‌ترتیب مربع، مستطیل پهن و مستطیل کشیده با سطوح مختلف پلات 1، 2، 4 و 8 مترمربع در‌نظر گرفته شد که در مجموع 12 پلات با ابعاد 3×4 مورد بررسی قرار گرفت. شاخص‌های پراکندگی مطالعه شده شامل شاخص پراکنش، گرین، کپه‌ای لوید، موریسیتا و موریسیتای استاندارد بودند. با توجه به نتایج به‌دست آمده در این پژوهش مشخص شد که شاخص موریسیتا و موریسیتای استاندارد در تمام شکل‌ها و اندازه‌های پلات، یک نوع الگو را نشان دادند. این دو شاخص‌ تحت تأثیر اندازه و شکل پلات قرار نداشته و الگوی پراکنش را بهتر نشان می‌دهند. همچنین این مطالعه نشان داد که دو شاخص موریستا و موریستای استاندارد نسبت به شاخص گرین و لوید از دقت بالاتری برخوردارند.  2 The distribution pattern of plants is an important characteristic of plant communities, being of critical importance in ecological studies and sampling plans. This study was designed to investigate the efficiency of different shapes and sizes of quadrants to delineate the spatial patterns of Astragalus verus Olivier by using dispersion indices. At Mayan Rangeland (Khorasan Razavi), a digital camera and R software were used to locate the coordinates of individual plants and to demarcate the boundary of the study area. We used plots of different shapes, square, wide rectangular and long rectangular, with the areas of 1, 2, 4, and 8 m2, for a total of 12 combinations of shapes and sizes. The distribution indices used included Green, Lioyd, Morisita, and the Standardized Index of Morisita. The results of the study showed that the standard Morisita's index and Morisita’s index had the same distribution patterns in all shapes and sizes of plots. These two indices were not affected by plot size and shape in displaying the distribution pattern. The current study alsoshowed that Morisita’s and standard Morisita’s indices were more precise in comporison with Green and Lioyd indeces. 77 89 2019/07/72019/02/172019/06/162019/07/52019/01/152019/04/7 1398/1/18 2019/09/252019/11/22019/11/42019/11/202019/12/42019/12/9 1398/9/18 زهرا زنگنه Z. Zangane دانشگاه فردوسی مشهد ایران zahra.zangane@yahoo.com کمال ناصری K. Naseri دانشگاه فردوسی مشهد ایران klnaseri@um.ac.ir فریدون ملتی F. Melati دانشگاه فردوسی مشهد ایران melati@um.ac.ir منصور مصداقی M. Mesdaghi دانشگاه فردوسی مشهد ایران mmesdagh@yahoo.com نفیسه فخارایزدی N. Fakhar Izadi دانشگاه فردوسی مشهد ایران nfakhar93@gmail.com Dispersion indices Plot shape and size Degree of species clumping R software شاخص‌های پراکندگی شکل و اندازه پلات درجه تجمع گونه نرم‌افزار R 1. Arzani, H. and M. Abedi. 2015. Rangeland Assessment: Vegetation Measurement. University of Tehran Press, Iran, 346 p.##2. Baranian, E., M. Basiri, H. Bashari and M. Tarkesh. 2011. 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Oikos 118(4): 495-502.##18. Logan, M. 2010. Biostatistical Design and Analysis Using R: A Practical Guide.Wiley-Blackwell, 546 p.##19. Ludwig, J. A. and J. F. Reynolds. 1988. Statistical Ecology. Wiely - Interscience Pub., USA, 337 p.##20. Mohebbi, Z., M. A. Zare Chahouki, A. Tavili, M. Jafari and A. Fahimipour. 2012. Comparing the efficiency of distance and quadrate indices in determining Artemisia sieberi and Astragalus ammodendron distribution pattern in Markazi province. Watershed Management Research (Pajouhesh and Sazandegi) 94: 27-35. (In Farsi).##21. Masoumi, A. S. 2000a. Astragalus of Iran. 1 st edition, Forest and Rangeland Research Institute, Vol.4, pp. 558. (In Farsi).##22. Moghaddam M. R., 2001. Quantitative Plant Ecology. Tehran University Press, 285 p. (In Farsi).##23. Moghaddam, M. R., 2005. Terrestrial Plant Ecology. Tehran University Press, 701 p. (In Farsi).##24. Mosai Sanjaraii, M. and M. Basiri, 2006. Comparison and evaluation of indices of dispersion pattern of plants on Artimisia siberi shrub lands in Yazd provice. Agriculture and Natural Resources Journals 40: 483-495. (In Farsi). ##25. Malhado, A. C. M. and J. M. Petrere. 2004. Behavior of dispersion indices in pattern detection of a population of Angico, Anadenanthera peregrina (Leguminoceae). Brazilian Journal of Biology 64: 243-249.##26. Myers, J. H. 1978. Selecting a measure of dispersion. Environment Entomology 7: 619-621.##27. Stoll, P. and E. Bergius. 2005. Pattern and process: Competition causes regular spacing of individuals within plant populations. Journal of Ecology 93: 395-403.##28. Zare Chahouki, M. A. and A. Tavili. 2018. Evaluating the efficiency of distance and quadrate indices in determining the distribution pattern of rangeland species of arid areas. (case study: Nir region in Yazd province). Scientific-Research Journal of Rangeland 101-112. (In Farsi).##29. Zare Chahouki, M. A., J. Imani and H. Arzani. 2012.Comparison of the efficiency of quadrate and spatial index for determination of distribution pattern for Bromus tomentellus, Festuca ovina and Prangos ferulacea. (case study: Saral rangeland in Kordestan province). Pajouhesh and Sazandegi 65-71. (In Farsi).## ##