نوع مقاله : مقاله پژوهشی
نویسنده
مؤسسه تحقیقات آب- پژوهشکده منابع آب- تهران- ایران
چکیده
هدف اصلی تحقیق حاضر، پهنه‌بندی تمامی عرصه‌های منابع طبیعی کشور با اولویت مناطق بیابانی و نیمهبیابانی با استفاده از شاخص‌ها و معیارهای قابل استخراج از اطلاعات سنجش از دوری و با بهره‌گیری از تکنیک‌های نوین طبقه‌بندی تصاویر ماهواره‌ای است. بر این اساس، پهنه‌های بیابانی، نیمهبیابانی و اراضی کویری و نمکی کشور به همراه سایر عرصه‌های منابع طبیعی (همانند اراضی جنگلی، مرتعی، پهنه‌های آبی و اراضی کشاورزی) با استفاده از سری زمانی تصاویر ماهواره MODIS و با استخراج شاخص‌ها و پارامترهای مختلفی همانند آلبدو، شاخص تفاضل نرمال شده گیاهی، دمای سطح زمین در طول روز و شب به همراه اختلاف دمای روز و شب مورد بررسی و مطالعه قرار گرفت. در این روش برخلاف روش‌های کلاسیک طبقه‌بندی که تنها بر استفاده از یک تصویر ماهواره‌ای و ویژگی‌هایی همانند تراکم پوشش گیاهی و یا دمای سطح زمین استوار هستند، نحوه رفتار پوشش‌های مختلف منابع طبیعی در گذر زمان در شاخص‌ها و معیارهای قابل استخراج از تصاویر ماهواره‌ای مورد بررسی قرار خواهد گرفت. بر این اساس، رفتار زمانی هر کدام از عرصه‌های منابع طبیعی یاد شده در طول سال 2019 میلادی با استفاده از معیارهای سنجش از دوری نام برده شده مورد بررسی و تحلیل قرار گرفت. جهت طبقه‌بندی کشور با استفاده از شاخص‌های یاد شده، از روش طبقه‌بندی شی پایه و با بهره‌گیری از تکنیک کمترین فاصله بر اساس منطق فازی استفاده گردید. بر اساس نتایج بدست آمده می‌توان عنوان کرد به ترتیب در حدود 2/41، 8/14 و 9/3 درصد از مساحت کشور (مجموعاً در حدود 60 درصد از خاک کشور) به توسط اراضی بیابانی، نیمهبیابانی و نمک‌زارها پوشیده شده است که با لحاظ نمودن درصد مساحت اراضی کوهستانی سنگلاخی (3/11 درصد) می‌توان عنوان کرد که در حدود 2/71 درصد از خاک کشور فاقد شرایط زیستی (لم‌یزرع) مناسب برای فعالیت‌های کشاورزی و یا موارد مشابه با آن هستند. برطبق انتظار، اراضی بیابانی و نیمهبیابانی در مناطق مرکزی، شرقی، جنوب شرقی و جنوبی کشور تمرکز یافته است و در مناطق شمالی، شمال غربی و غربی کشور هیچ نشانه‌ای از چنین مناطقی یافت نگردید.
کلیدواژه‌ها
عنوان مقاله [English]
Delineating natural resources of Iran by focusing on desert and semi-desert areas using indices and criteria that can be extracted from satellite images
نویسنده [English]
- Neamat Karimi
Water Research Institute- Water resources research department- Tehran- Iran
چکیده [English]
The main objective of the present study is to delineate all-natural resources of Iran with the priority of desert and semi-desert areas using indicators and criteria extracted from remote sensing data and new satellite image classification techniques. Accordingly, desert, semi-desert, and salinity areas of Iran in conjunction with other natural resources areas (such as forests, rangelands, water bodies, and farmlands) were studied using time-series MODIS satellite images and different indices and parameters such as Albedo, NDVI, and surface temperature during day and night along with the temperature difference between day and night. Here, unlike the classical classification methods, based on using one-single satellite image and features such as vegetation density or surface temperature, the behavior of different natural resources over time, extracted from satellite images, was analyzed. Accordingly, the temporal behavior of each of the mentioned natural resource areas during 2019 was studied and analyzed using the remote sensing criteria. The basic object classification method was used to classify the country using the mentioned indicators using the least distance technique based on fuzzy logic. Based on results, about 41.2%, 14.8%, and 3.9% of Iran (totally about 60% of Iran) are classified as desert, semi-desert, and salty areas, respectively. By considering the percentage of rocky mountainous areas (11.3%), about 71.2% of Iran has no biological conditions (unsuitable for agricultural activities). As expected, desert and semi-desert areas are concentrated in central, eastern, southeastern, and southern regions of Iran, and no signs of such areas are found in the northern, northwestern, and western of the country.
کلیدواژه‌ها [English]
- Remote sensing
- desert and semi desert areas
- NDVI
- Albedo
- object oriented classification
- Afrasinei, G. M., Melis, M.T., Buttau, C., Arras, C., Pistis, M., Zerrim, A., Guied, M., Ouessar, M., Essifi, B., Zaied, M.B., Jlali, A., Jarray, H. and Ghiglieri, G., 2017. Classification methods for detecting and evaluating changes in desertification-related features in arid and semiarid environments. Euro-Mediterranean Journal for Environmental Integration, 2: 1-19.
- Al-Ali, Z.M., Abdullah, M.M., Asadalla, N.B. and Gholoum, M., 2020. A comparative study of remote sensing classification methods for monitoring and assessing desert vegetation using a UAV-based multispectral sensor. Journal of Environmental Monitoring and Assessment, 192: 1-14.
- Attarpour Fard, H., Hasani, M. and Khosroshahi, M., 2004. Identification and separation of geomorphological deserts in Tehran. Iranian Journal of Range and Desert Research, 11(3): 275-286.
- Baatz, M., Benz, U., Dehghani, S., Heynen, M., HÖltje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., Weber, M. and Wilihauck, G., 2004. ECognition Professional: User guide 4. Munich: Definiens-Imaging.
- Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I. and Heynen, M., 2004. Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58: 239–258.
- Carleer, A.P., Debeir, O. and Wolff, E., 2005. Assessment of very high spatial resolution satellite image segmentations. Photogrammtric Engineering & Remote Sensing, 71(11): 1285–1294.
- Dehvari, A. and Heck, R. J., 2009. Comparison of object-based and pixel based infrared airborne image classification methods using DEM thematic layer. Journal of Geography and Regional Planning, 2(4): 86-96.
- Ekhtesasi, M. R. and Mohajeri, S., 1995. Classification of the type and severity of land desertification in Iran (ICD). Second Conference on the Issues of Desert Regions in Kerman. Iran, 10-13 November: 56-63.
- Farahani, E., Khosroshahi, M., Hosseni, M. and Loghmani, S., 2008. Determiation of desert domain in Tehran province by use of vegetative cover. Iranian Journal of Range and Desert Research, 14(4): 490-499.
- Fayez Bakhsh Waqif, K.H. and Khajvandi, M., 2007. Evaluation of environmental strategies for sustainable development of Iran. 6th Symposium of Iranian Society of Environmentalists. Tehran, Iran.
- Ge, G., Shi, Z., Zhu, Y., Yang, X. and Hao, Y., 2020. Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms. Global Ecology and Conservation, 22: e00971.
- Goudarzi, M., Farahpour, M. and Mosavi, A., 2006. Land cover and rangeland classification map using Landsat satellite image (TM) (Case study) Namrood watershed. Iranian Journal of Range and Desert Research, 13(3): 265-277.
- Ildoromi, A.R. and Moradi, M., 2017. Assessment of wind erosion intensity by IRIFR.E.A Model (Case Study: Qahavand, Hamedan). Journal of Geography and Planning, 21(60): 43-60.
- Kahkhakohan, M.S., Forfoie, A.R., Mousavi, S.H. and Vali, A., 2019. Assessment of drought dynamics in Sistan and Baluchestan province using MODIS satellite data (2000-2015). Iranian Journal of Range and Desert Research, 26(3): 754-771.
- Karimi, N. and Namdari, S., 2019. Estimation of severity and extent of desertification in Iran using Landsat satellite images and spectral mixture analyses methods during 1984 and 2015. Iranian Journal of Range and Desert Research, 26(2): 500-515.
- Ketting, R.L. and Landgrebe, D.A., 1976. Classification of multispectral image data by extraction and classification of homogeneous objects. IEEE Transactions on Geoscience Electronics, 14(2): 19-26.
- Khamchin Moghaddam, M.F. and Pazhand, H., 2009. Criticising de marttone regionalization method according to linear moments for maximum daily precipitation in Iran. Information Technology on Engineering Design, 2(2): 93-103.
- Khosroshahi, M., Hassani, M. and Karami, S.A., 2003. Development of new method for recognition of Tehran deserts based on climatological criteria (case study: Tehran province). Iranian Journal of Range and Desert Research, 10(4): 40-56.
- Khosroshahi, M., Kali, R.A. and Marandy, H.M., 2011a. Comparison of geological and climatological deserts domain of Iran. Iranian Journal of Range and Desert Research, 18(2): 336-352.
- Khosroshahi, M., Mahmoudi, F. and Kashki, M. T., 2011b. Determination of desert areas in Iran on the bases of geological effected factors. Journal of Geosciences, 20(80): 15-22.
- Laliberte, A.S., Fredrickson, E.L. and Rango, A., 2007. Combining decision trees with hierarchical object-oriented image analysis for mapping arid rangelands. Photogrammetric Engineering and Remote Sensing, 73(2): 197-207.
- Ministry of Energy., 1987. The comprehensive water plan of the country includes the appearance of the plains of Iran, surface and underground water resources of the country's areas.
- Myint, S.W., Giri, C.P. and Wang, L., 2008. Identifying mangrove species and their surrounding land use and land cover classes using an object-oriented approach with a lacunarity spatial measure. Journal of GIS cience & Remote Sensing, 45(2): 188-208.
- Papoli Yazdi, M.H., 1996. The significance of ecological consideration in living environment. Journal of Geographical Researches, 36(2): 6887-6906.
- Radoux, J. and Defourny, P., 2008. Quality assessment of segmentation results devoted to object-based classification. In T. Blaschke, S. Lang, & G.J. Hay (Eds.), Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Springer, Berlin, 271p.
- Sarabi, S., Heshmatpour, A., Komaki, C.B. and Tahmasebi, A., 2015. The assessment of relationship between MODIS vegetation indices and drought on northern rangelands of Golestan province. Iranian Journal of Range and Desert Research, 22(2): 392-405.
- Schmidt, H. and Karnieli, A., 2000. Remote sensing of the seasonal variability of vegetation in a semi-arid environment. Journal of Arid Environments, 45: 43-59.
- Shamsipur, A.A., Alavipanah, K. and Mohammadi, H., 2010. Efficiency of vegetation and thermal indices of NOAA-AVHRR satellite in ecological drought analysis of Kashan region. Iranian Journal of Range and Desert Research, 17(3): 445-465.
- Silver, M., Tiwari, A. and Karnieli, A., 2019. Identifying vegetation in arid regions using object-based image analysis with RGB-Only aerial imagery. Journal of Remote Sensing, 11: 22-35.
)