تعیین مناسب‌ترین روش تهیه نقشه تیپ در جنگل‌های زاگرس مرکزی با استفاده از تصاویر ماهواره لندست 8

نوع مقاله: علمی - پژوهشی

نویسندگان

1 کارشناسی ارشد جنگلداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران.

2 استادیار، گروه جنگلداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران.

3 دانشیار، گروه جنگلداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران.

چکیده

نقشه تیپ جنگل یکی از ضروری‌ترین نقشه‌های موضوعی برای مدیریت اکوسیستم جنگل است. تهیه نقشه تیپ با استفاده از روش‌های میدانی یا عکس‌های هوایی، سخت و با صرف زمان و هزینه زیاد همراه است. در مقابل، داده‌های ماهواره‌ای با ویژگی‌های خاص خود مانند دید وسیع و یکپارچه، پوشش تکراری، فراهم آوردن داده‌های بهنگام و استفاده از قسمت‌های مختلف طیف الکترومغناطیسی جهت ثبت خصوصیات پدیده‌ها، امکان مناسبی را در این زمینه فراهم می‌کنند. این پژوهش با هدف تهیه نقشه تیپ بخشی از جنگل‌های زاگرس مرکزی (ذخیره‌گاه جنگلی چهارطاق) با داده‌های سنجنده OLI ماهواره لندست هشت مربوط به شهریورماه 1395 انجام شد. نقشه واقعیت زمینی از طریق پیمایش زمینی بر اساس محاسبه تراکم درختان غالب و سطح تاج‌پوشش درختان با بهره‌گیری از اطلاعات نوع گونه، موقعیت و مساحت تاج‌پوشش درختان تهیه شد. به‌منظور افزایش قدرت تفکیک مکانی داده­‌های چند طیفی، فنون مختلف ادغام روی تصاویر اعمال شد. بهترین نتیجه حاصل از خوارزمی حداکثر احتمال، مقادیر شاخص کاپا و صحت کلی برابر 57/0 و 63 درصد را در مقایسه با نقشه واقعیت زمینی بر اساس تراکم درختان در منطقه نشان داد. نتایج نشان داد تصاویر این سنجنده با توجه به تنوع زیاد گونه‌های گیاهی منطقه، قابلیت متوسطی برای تهیه نقشه تیپ جنگل­ را دارند.

کلیدواژه‌ها


عنوان مقاله [English]

Determination of the most suitable method for forest type mapping in central Zagros using landsat-8 satellite Images

نویسندگان [English]

  • Yasaman Lohrabi 1
  • Mozhgan Abasi 2
  • Ali Soltani 3
  • Hamid reza Riyahi bakhtyari 2
1 M.Sc. student of of Forestry, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, I. R. Iran.
2 Assistant Professor, Department of Forest sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, I. R. Iran.
3 Associate Professor, Department of Forest sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, I. R. Iran.
چکیده [English]

The forest type map is one of the most important thematic maps for forest ecosystem management. Forest mapping using field methods or aerial photos is labor-intensive and time consuming. In contrast, satellite data with its own characteristics like large and repetitive coverage, update and useful information in various wavelengths provides a good opportunity in this regard. This research was carried out with the aim of providing forest type map of central Zagros forests (Chahartagh forest reservoir), of Iran, using the Landsat 8 Operational Land Imager (OLI) data, in August 2016.Two ground-truth maps based on tree density and tree crown area were prepared by field surveying. Moreover, ancillary data such as tree species, location and crown area was taken. In order to increase the spatial resolution of multispectral bands, various image fusion techniques were applied. The best result obtained by the maximum likelihood algorithm with kappa coefficient and overall accuracy values of 57 and 63%, respectively. Due to high species diversity in this area the results showed that the OLI images have a moderate capability to produce forest type maps in Zagros forest.

کلیدواژه‌ها [English]

  • Landsat-8 images
  • Chahartagh forest reservoir
  • Classification
  • Forest type map
- Ahmadisani, N., 2005. Feasibility the capability of ASTER satellite imagery for mapping density in Zagros forests. Msc thesis. Faculty of Natural Resources. University of Tehran.  Tehran, Iran, 85 p. (In Persian)

- Amini, M. R., 2006. Feasibility the trend of changes in forest area and its relation with physiographic and human factors using satellite imagery and GIS. Msc thesis. Faculty of Natural Resources. University of Gorgan. Gorgan, Iran, 144 p. (In Persian)

- Bhandari, S. P. & Y. A. Hussin, 2003. A comparison of sub-pixel and maximum likelihood classification of landsatetm+ images to detect illegal logging in the tropical rain forest of Berau. Proceedings of Map Asia Conference, east Kalimantan, Indonesia.

- Chrysafis, I., G. Mallinis, I. Gitas & M. Tsakiri-Strati, 2017. Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method, Remote Sensing of Environment, 199: 154-166.

- Congalton, R. G., K. Birch, R. Jones & J. Schriever, 2002. Evaluating remotely sensed techniques for mapping riparian vegetation, Computers and Electronics in Agriculture, 37(1-3): 113-126.

- Darvishsefat, A. A., M. Abbasi & M. R. Marvi mohajer, 2009. Investigating the Possibility of Preparing the Fagus Orientalis Map Using ETM + Data (Case Study: Kheyrod Forest of Noshahr), Forestry Journal of Iran, Iranian Foresters Association, 1(2): 105-113. (In Persian)

- Darvishsefat, A. A., P. Fatehi, A. Khalilpour & A. Farzaneh, 2004. Comparison of SPOT5 and Landsat7 for forest area mapping. Proceedings of XX the ISPRS Congress, Istanbul, Turkey.

- Das, S. & T. P. Singh, 2013. Mapping Vegetation and Forest Types using Landsat TM in the Western Ghat Region of Maharashtra, India, International Journal of Computer Applications, 76(1): 30-37.

- Ebrahimi, G. h., 2011, Mapping of forest type using by satellite data. Msc theseis. Faculty of Natural Resources. University of Kordestan.  Kordestan, Iran, 68 p. (In Persian)

- Gao, T., J. Zhu, X. Zheng, G. Shang, L. Huang & S. Wu, 2015. Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests, Remote Sensing, 7(2): 1702-1720.

- Gunlu, A., F. Sivrikaya, E. Z. Baskent, S. Keles, G. Çakir & A. İ. Kadiogullari, 2008. Estimation of stand type parameters and land cover using Landsat-7 ETM image: A case study from Turkey, Sensors, 8(4): 2509-2525.

- Hamidi, S. K., M. Namiranian, J. Feghhi & M. Shabani, 2015. Comparison of land inventory and using of Ikonos image in Google Earth database to estimate quantity characteristics of urban forest (Case study: Iran; Sari city, Forest Research and Development, 1(1): 85-94. (In Persian)

- Held, A., C. Ticehurst, L. Lymburner & N. Williams, 2003. High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing, International Journal of Remote Sensing, 24(13): 2739-2759.

- Jahanbazi, H., A. Karouri, M. Talebi & M. Khoshnevis, 1999. Investigation of Juniperuspolycarpus ecophysiological studies in Chaharmahal va Bakhtiari province. Final report of the research project of Natural Resources and Animal Sciences Research Center of Chaharmahal and Bakhtiari province, 78 p. (In Persian)

- Joibary, S. S., A. A. Darvishsefat & T. W. Kellenberger, 2007. Forest type mapping using incorporation of spatial models and ETM+ data, Pakistan journal of biological sciences: PJBS, 10(14): 2292-2299.

- Ke, Y., J. Im, J. Lee, H. Gong & Y. Ryu, 2015. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations, Remote Sensing of Environment, 164: 298-313.

- Latifi, H. & D. Oladi, 2006. Evaluating Landsat ETM+ Data Capability to Produce Forest Cover Type Maps in the Timberline of Northern Forests of Iran, Taiwan Journal of Forest Science, 21(3): 363-75.

- Basham May, A. M., J. E. Pinder & G. C. Kroh, 1997. A comparison of Landsat Thematic Mapper and SPOT multi-spectral imagery for the classification of shrub and meadow vegetation in northern California, USA, International Journal of Remote Sensing, 18(18): 3719-3728.

- Mei, A., C. Manzo, G. Fontinovo, C. Bassani, A. Allegrini & F. Petracchini, 2016. Assessment of land cover changes in Lampedusa Island (Italy) using Landsat TM and OLI data, Journal of African Earth Sciences, 122: 15-24.

- Minaei, M. & W. Kainz, 2016. Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran, ISPRS International Journal of Geo-Information, 5(5):57-69.

- Najafzadeh, A., M. Erfanian, A. Alijanpour & S. Babaei Hessar, 2017. Recovering missing pixels for a Landsat SLC-off image using Weighted Linear Regression and accuracy assessment of land cover map (Case study: Khoy region, Northwest Iran), Journal of Forest Research and Development, 3(3): 275-289. (In Persian)

- Naseri, F., 2003, Classification of forest types and estimation of their quantitative characteristics using satellite data in dry and semi-arid forests. PhD thesis. Faculty of Natural Resources. University of Tehran. Tehran, Iran, 155 p. (In Persian)

- Palsson, F., J. R. Sveinsson, J. A. Benediktsson & H. Aanæs, 2010. Image fusion for classification of high resolutionn images based on mathematical morphology. Proceedings of In Geoscience and Remote Sensing Symposium (IGARSS), pp. 492-495.

- Parma, R. & Sh. Shataei, 2013, Comparison of Forest Types Using Artificial Neural Network and ETM + Data, Journal of Natural Ecosystems of Iran, 3(3): 13-15. (In Persian)

- Quintano, C., A. Fernández-Manso & O. Fernández-Manso, 2018. Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity, International Journal of Applied Earth Observation and Geoinformation, 6(4): 221-225.

- Raeesei, F., A. Asadi & J. Mohammadi, 2005, Long-term grazing effect on the dynamics of litter carbon in Sabzkouh rangeland ecosystems of Chaharmahal and Bakhtiari province, Journal of Agricultural Science and Technology, 9(3): 1-11. (In Persian)

- Yang, X., N. Rochdi, J. Zhang, J. Banting, D. Rolfson, C. King, K. Staenz, S. Patterson & B. Purdy, 2014. Mapping tree species in a boreal forest area using RapidEye and Lidar data. Proceedings of Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International.

 - Sarouei, S., 2003, Investigating the possibility of forest classification using by density in Zagros forests using satellite data. Msc thesis. Faculty of Natural Resources. University of Tehran.  Tehran, Iran, 122 p. (In Persian)

- Shaban, M. A. & O. Dikshit, 2002. Evaluation of the merging of SPOT multispectral and panchromatic data for classification of an urban environment, International Journal of Remote Sensing, 23(2): 249-262.

 - Shataei, Sh., 2003. Investigating the possibility of preparing forest types maps using satellite data (Case study of Kheyroudkenar Forest and Research Forest of Noshahr). PhD thesis. Faculty of Natural Resources. University of Tehran. Tehran, Iran, 202 p. (In Persian)

- Sivrikaya, F., S. Keleş, G. Çakır, E. Z. Başkent & S. Köse, 2006. Comparing accuracy of classified Landsat data with land use maps reclassified from the stand type maps. Proceedings of7th International Syposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, pp. 643-652.

- Wang, L., W. P. Sousa & P. Gong, 2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery, International Journal of Remote Sensing, 25(24): 5655-5668.

- Wenbo, W., Y. Jing & K. Tingjun, 2008. Study of remote sensing image fusion and its application in image classification: The International Archives of the Photogrammetry, The international archives of the photogrammetry, Remote Sensing and Spatial Information Sciences, 3(B7): 1141-1146.

- Xiao, X., S. Boles, J. Liu, D. Zhuang & M. Liu, 2002. Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data, Remote Sensing of Environment, 82(2-3): 335-348.

- Yu, Q., P. Gong, N. Clinton, G. Biging, M. Kelly & D. Schirokauer, 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery, Photogrammetric Engineering & Remote Sensing, 72(7): 799-811.

- Zhang, Y., X. Li, F. Ling, P. M. Atkinson, Y. Ge, L. Shi & Y. Du, 2017. Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping, International Journal of Applied Earth Observation and Geoinformation, 63: 129-142.

- Zhu, X. & D. Liu, 2014. Accurate mapping of forest types using dense seasonal Landsat time-series, ISPRS Journal of Photogrammetry and Remote Sensing, 9(6): 1-11.