| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,219 |
| تعداد مقالات | 35,690 |
| تعداد مشاهده مقاله | 53,565,211 |
| تعداد دریافت فایل اصل مقاله | 92,844,421 |
شناسایی خشکیدگی تودههای بلوط با استفاده از فنآوری سنجش از دور (مطالعه موردی: بخشی از جنگلهای استان لرستان) | ||
| تحقیقات حمایت و حفاظت جنگلها و مراتع ایران | ||
| دوره 18، شماره 2 - شماره پیاپی 36، اسفند 1399، صفحه 244-253 اصل مقاله (918.12 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfrpr.2021.352283.1451 | ||
| نویسندگان | ||
| افسانه محمدی1؛ مهتاب پیرباوقار* 2؛ نقی شعبانیان3 | ||
| 1دانش آموخته کارشناسی ارشد گروه جنگلداری، دانشکده منابع طبیعی دانشگاه کردستان، سنندج، ایران | ||
| 2نویسنده مسئول، دانشیار، گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه کردستان، سنندج، ایران | ||
| 3دانشیار، گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه کردستان، سنندج، ایران | ||
| چکیده | ||
| بحران زوال یا خشکیدگی درختان بلوط در جنگلهای زاگرس یکی از مشکلاتی است که در سالهای اخیر با آن روبهرو بودهایم. اولین گام در مدیریت این بحران، تهیه نقشه مناطق مبتلا و طبقهبندی شدت ابتلای جنگلها به پدیده خشکیدگی است. هدف این پژوهش بررسی قابلیت دادههای سنجنده OLI ماهواره لندست8 در تهیه نقشه خشکیدگی تودههای بلوط ایرانی در استان لرستان است. علاوه بر باندهای اصلی و باندهای ادغام شده با باند پانکروماتیک 15 متری سنجنده OLI، شاخصهای گیاهی مناسب و مولفههای حاصل از تحلیل مولفههای اصلی نیز با استفاده از باندهای اصلی و ادغام شده، ایجاد شدند. به منظور ایجاد نقشه واقعیت زمینی، تعداد 150 قطعه نمونه مربعی در منطقه پیاده شد. طبقهبندی داده-ها به روش نظارتشده و با استفاده از الگوریتمهای حداقل فاصله از میانگین، حداکثر احتمال و شبکه عصبی مصنوعی در ابتدا با پنج کلاسه خشکیدگی انجام شد. به دلیل تفکیکپذیری کم بین برخی از کلاسهها، این کلاسهها با هم ادغام شدند و طبقهبندی در گام دوم با سه کلاسه خشکیدگی و نهایتا با دو کلاسه (سالم، خشکیده) انجام شد. بالاترین صحت و ضریب کاپا با پنج کلاسه خشکیدگی به ترتیب معادل 53 درصد و 43/0، با سه کلاسه خشکیدگی معادل 75 درصد و 64/0 و با دو کلاسه خشکیدگی معادل 91 درصد و 71/0 با استفاده از باندهای ادغام شده و روش شبکه عصبی مصنوعی به دست آمد. نتایج به دست آمده بیانگر کارایی بالای دادههای سنجنده OLI در تفکیک مناطق سالم و خشکیده و قابلیت کم تا متوسط آن در تفکیک شدتهای مختلف زوال بلوط است. | ||
| کلیدواژهها | ||
| زوال بلوط؛ سنجنده OLI؛ شاخصهای گیاهی؛ طبقهبندی؛ واقعیت زمینی؛ جنگلهای زاگرس | ||
| عنوان مقاله [English] | ||
| Detection of Oak Stands Dieback Using Remote Sensing (Case study: Some Parts of Lorestan Province Forests) | ||
| نویسندگان [English] | ||
| Afsaneh Mohammadi1؛ Mahtab Pir Bavaghar2؛ Naghi Shabanian3 | ||
| 1Department of Forestry, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran | ||
| 2Department of Forestry, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran | ||
| 3Department of Forestry, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran | ||
| چکیده [English] | ||
| The crisis of decline or drying of oak trees in the Zagros forests is one of the problems we have faced in recent years. The first step in managing this crisis is to map the affected areas and classify the severity of deforestation. The present study aimed to evaluate the Landsat OLI capability to map oak stands dieback in the Koohdasht city of Lorestan province. In addition to the main bands and fused bands with the 15-meter panchromatic band of the OLI sensor, suitable vegetation indices and the first components from PCA were also applied in the claasification. 150 square sample plots with dimensions of 30 × 30 meters were recorded to produce ground truth map. Data classification was done using minimum distance to mean, maximum likelihood and artificial neural networks classifiers in 5 classes of dieback and accuracy assessment was done using ground truth map. Because of the low separability of some classes, these classes were merged. Finally, classification with three and two classes (healthy, dieback) was performed. The highest overall accuracy of 53%, 75% and 91% and Kappa coefficient of 0.43, 0.64 and 0.71 was obtained using fused bands and artificial neural networks classifier for five, three and two dieback classes, respectively. The results demonstrated high performance of Landsat 8-OLI for mapping of healthy and oak dieback areas, but low to moderate functionality for identification of the intensity of oak decline in the study area. | ||
| کلیدواژهها [English] | ||
| oak dieback, OLI sensor, vegetation indices, classification, ground truth, Zagros forests | ||
| مراجع | ||
|
Accettura, M., Bauch, T., Raqueño, N., Mallia, J. and Salvaggio, C. 2018. Hyperspectral detection of methane stressed vegetation. In: Proc.SPIE (Vol. 10664). -Adams, J.B., Sabol, D. E, Kapos, V., Almeida Filho, R., Roberts, D. A., Smith, M. O. and Gillespie, A. R. 1995. Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment, 52: 137–154. -Amer, R., Kusky, T., Reinert, P.C. and Ghulam, A. 2009. Image processing and analysis using Landsat ETM imagery for lithological mapping at Fawakhir, central eastern desert of Egypt. In: R. G. Congalton, ed. ASPRS 2009 Annual Conference. Baltimore, Maryland, USA: ASPRS. -Attarchi, S. and Gloaguen, R. 2014. Classifying complex mountainous forests with L-Band SAR and Landsat data integration: A comparison among different machine learning methods in the Hyrcanian Forest. Remote Sensing of Environment, 6: 3624–3647. -Bazrafkan, A., Pir Bavaghar, M. and Fathi, P. 2014. Capability of Liss III data for forest canopy density mapping in Zagros forests (Case study: Marivan Forests). Iranian Journal of Forest, 6: 387–401. -Cano, F., Cerrillo, R.M.N., Ferrer, A.G. and de la Orden, M.S. 2006. Detection of Forest Decline Using IKONOS Sensor for Cork Oak (Quercus suber L.) Woods in South Spain. Geocarto International, 21: 13–18. -Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37: 35-46. -Dennison, P., Nagler, P., Hultine, K., Glenn, E. and Ehleringer, J. 2009. Remote monitoring of tamarisk defoliation and evapotranspiration following leaf beetle attack. Remote Sensing of Environment, 113: 1462–1472. -Fallah, A. and Haidari, M. 2018. Investigating the Oak Decline in different Crown-Dimensions in Middle Zagros Forests (Case Study: Ilam), Ecology of Iranian Forests, 6(12): 9-17. -Gambarova, Y., Gambarov, A., Rustamov, V. and Zeynalova, M. 2010. remote sensing and GIS as an advance space technologies for rare vegetation monitoring in Gobustan state national park, Azerbaijan. Journal of Geographic Information System, 2: 93–99. -Gooshbor, L., Pir Bavaghar, M., Amanollahi, J. and Ghobari, H. 2016. Monitoring Infestations of Oak Forests by Tortrix viridana. Plant Protection Science, 52: 270–276. -Huete, A.R. 1998. A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25(3): 295-309. -Jones, C., Song, C. and Moody, A. 2015. Where’s woolly? An integrative use of remote sensing to improve predictions of the spatial distribution of an invasive forest pest the Hemlock Woolly Adelgid. Forest Ecology and Management, 358: 222–229. -Knipling, E.B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1: 155–159. -Li, G., Lu, D., Moran, E. and Hetrick, S. 2011. Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery. International Journal of Remote Sensing, 32: 8207–8230. -Liu, C., Sung, P.-S. and Liu, S.-R. 2016. A review of plant spectral reflectance response to water physiological changes. Chinese Journal of Plant Ecology. -Liu, X.-H., Skidmore, A.K. and van Oosten, H. 2003. An experimental study on spectral discrimination capability of a backpropagation neural network classifier. International Journal of Remote Sensing, 24: 673–688. -Mahdavi, A. and Falah Shamsi, S.R. 2012. Mapping forest cover change, using aerial photography and IRS-LISSIII imagery (case study: Ilam Township). Journal of Wood and Forest Science Technology, 19: 77–91. -Mas, J.F. and Flores, J.J. 2008. The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29: 617–663. -Mora, B., Fournier, R.A. and Foucher, S. 2012. Mapping the health of mature deciduous forest stands by fusing multisource geospatial data with Dempster’s combination rule. International Journal of Remote Sensing, 33: 1139–1163. -Parma, R. and Shataee, S. 2010. Capability study on mapping the diversity and canopy cover density in Zagros forests using ETM+ images (case study Ghalajeh forests, Kirmanshah province). Iranian Journal of Forest, 2: 231–242. -Peng, C. and Wen, X. 1999. Recent applications of artificial neural networks in forest resource management: an overview. Environmental Decision Support Systems and Artificial Intelligence. Menlo Park, CA. -Rullán-Silva, C., Olthoff, A.E., Pando, V., Pajares, J.A. and Delgado, J. A. 2015. Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain. Forest Ecology and Management, 347: 200–208. -Sangüesa-Barreda, G., Camarero, J.J., García-Martín, A., Hernández, R. and de la Riva, J. 2014. Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. Forest Ecology and Management, 320: 171–181. -Stone, C. and Mohammed, C. 2017. Application of remote sensing technologies for assessing planted forests damaged by insect pests and fungal pathogens: a review. Current Forestry Reports, 3: 75–92. -Tian, J. and Philpot, W.D. 2015. Relationship between surface soil water content, evaporation rate, and water absorption band depths in SWIR reflectance spectra. Remote Sensing of Environment, 169: 280–289. -Tian, Z., XiaoLi, Z., HongWei, L. and XiaoJie, P. 2010. Application of remote sensing technology in monitoring forest diseases and pests. Plant Diseases and Pests, 1: 57–62. -Townsend, P.A., Singh, A., Foster, J.R., Rehberg, N.J., Kingdon, C.C., Eshleman, K.N. and Seagle, S.W. 2012. A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sensing of Environment, 119: 255–265. -Tso, B. and Mather, P.M. 2009. Classification Methods for Remotely Sensed Data. New York: CRC Press. -Wang, C., Lu, Z. and Haithcoat, T.L. 2007. Using Landsat images to detect oak decline in the Mark Twain National Forest, Ozark Highlands. Forest Ecology and Management, 240: 70–78. -Wang, Y., Huang, W., Liu, Y. and Hu, L. 2011. Classification of Forest Land Information Using Environment Satellite (HJ-1) Data. International Journal of Intelligent Systems and Applications, 3: 33–40. -Yao, L., Fei, L., Gui, S., Liu, Q. and Liu, G. 2009. Remote sensing for monitoring on the health of artificial Robinia Pseudoacacia Forests in the Yellow River Delta. In: 2009 17th International Conference on Geoinformatics. | ||
|
آمار تعداد مشاهده مقاله: 786 تعداد دریافت فایل اصل مقاله: 701 |
||