پایش و برآورد وسعت مناطق گرفتار حریق بخشی از کوهستان زاگرس با استفاده از تصاویر ماهوارهای لندست (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
جنگل ها ازنظر تأمین تعادل اجتماعی و زیست محیطی جایگاه فوق العاده مهمی در اکوسیستم دارد. بزرگ ترین خطر برای چنین جنگل هایی، آتش سوزی است. هرساله سطوح زیادی از جنگل های منطقه زاگرس در غرب ایران، دستخوش آتش سوزی می شود و از بین می رود؛ بنابراین برآورد شکل گیری و ویژگی های رفتاری آتش برای مقابله با آن بسیار مهم است. بهره گیری از داده ها و تصاویر ماهواره ای مناسب و در دسترس، اطلاعات مفیدی را درباره شرایط پیش و پس از آتش سوزی عرصه های جنگلی فراهم می کند. این پژوهش، با هدف برآورد مساحت مناطق گرفتار حریق و آگاهی از کارایی، قابلیت داده های ماهواره لندست و شاخص های NBR و dNBR در تشخیص، ارزیابی و تهیه نقشه جنگل های سوخته شده منطقه زاگرس انجام شد. برای این منظور، پس از تهیه تصاویر لازم، آتش سوزی های اتفاق افتاده در خردادماه سال 1399 در جنگل های زاگرس با بهره گیری از تکنیک های سنجش از دور و انجام پردازش های لازم بررسی شد. نتایج به دست آمده نشان دهنده آن بود که شاخص های NBR و dNBR اطلاعات مناسبی را درخصوص تأثیر آتش سوزی و روند تغییرات آن در اختیار قرار می دهد؛ همچنین 13685 هکتار از جنگل های زاگرس در این آتش سوزی طعمه حریق شده اند.Monitoring and Estimating the Fire-Affected Areas of the Zagros Mountains Using Landsat Satellite Images
Forests have an extremely important place in the ecosystem in terms of providing social and environmental balances. Fire is the greatest danger to forests. Therefore, estimating fire formation and its behavioral characteristics is very important to deal with this issue. Every year, large areas of forests in the Zagros region in western Iran are burned and destroyed. The extent and distribution of fires, the mountainous terrain, and the difficulty of crossing the predominantly forested areas of the Zagros Mountain Range have hampered managers’ abilities to obtain quantitatively reliable information about the burned areas, levels of damage, as well as their statistics and factual information. Utilizing appropriate and available satellite data and images can provide useful information about pre- and post-forest fire conditions. The aim of this study was to estimate fire affected areas and be aware of the efficiency and capability of Landsat satellite data and NBR and dNBR indices in identifying, evaluating, and mapping the burned forests of Zagros region. For this purpose, after preparing the required images, the fires that occurred in June 2016 in the forests of Zagros were investigated by using remote sensing techniques and the necessary processing. The results showed that NBR and dNBR indices provided good information about the impact of fire and its changes. 13685 hectares of Zagros forests were burned in this fire in 2016. Introduction Due to the location of Iran in the dry belt of the Earth and the high pressure area of the subtropical region, weather conditions are perfect for the occurrence of unexpected events. According to the surveys, our country is among the 10 most accident-prone countries in the world. One of these incidents that happen abundantly in Iran is the phenomenon of fire in forests and pastures. Forests are an important part of the Earth's ecosystem. They are a great resource for various purposes, including a genetic reservoir, water reservoir, carbon source, and a source of energy storage in nature. They play an essential role in improving the environment and keeping it in balance. At the same time, they are an important natural resource for proper development in the social economy; yet, this huge resource has been endangered by fires. Every year, thousands of hectares of forests are burned in different regions. Forest fire with natural or human origin has direct or indirect harmful and destructive effects on human life. In addition to the destruction of the environment and its pollution, it causes the destruction of wood reserves, livestock, agricultural and grazing lands, buildings, and human lives and property, besides having many economic, social, and psychological consequences. Methodology In this research, Landsat 8 Satellite images were used, which were obtained from the USGS (United States Geological Survey) website. Landsat 8 is the 8 th satellite of this series. The most important role of Landsat program is monitoring and ensuring that the resources necessary for human livelihood, such as food, water, and forests will continue to exist. Landsat 8 satellite images were used by the OLI sensor to extract the land use map and the TIRS sensor was used to extract the surface temperature of the ground and fire-affected areas. In this study, the fires that occurred in Kermanshah, Ilam, and Kurdistan provinces were selected from among the fires in the forests and pastures of Zagros in June 2019. The fire in Kermanshah Province started on Thursday, June 8, 2019, and because it was not constrained in time, it spread to the borders of Ilam and Kurdistan provinces on June 9, 2019. The data used were case-by-case with descriptive information due to the extent of fires during this time period and limited access to satellite images. First, the areas with fires were identified and then, Landsat 8 Satellite images were prepared on a case-by-case basis before and after these two fires. Discussions According to the main objective of the study --determination of fire-affected areas -- after separation and identification of fire areas through the used indicators, classification and separation of the burnt areas from other areas were done. In this regard, the basic pixel method and the maximum likelihood algorithm were used for classification. The 3 classes of fire-affected areas, residential areas, and other land uses were used in the classification process. Using the results obtained from the classification map (Fig. 6), the extent of the fire areas in the study area was estimated to be 13,685 hectares. Conclusions The changes after the fire and burn severity were analyzed using the NBR index. As a general conclusion, it could be said that according to the accuracy of the classification results in distinguishing the burned areas from other areas with high confidence, the power and capability of Landsat Satellite images and NBR and dNBR indices in separating and distinguishing the burned forest areas could be emphasized. Based on the results of the fire intensity maps obtained from this study, it could be claimed that the spatial and spectral resolutions of Landsat images could be a good enough for preparing correct statistics and information about fire areas, especially for preparing a map of burned areas in the Zagros forests of Iran. According to the results obtained from the maps of ground surface temperatures related to before and after the fire, which indicated an increase of 9◦C in the studied area, and by extracting the fire-affected areas by using the NBR and dNBR indices and simultaneously performing fire classification via the supervised method (maximum likelihood algorithm), as well as aligning these areas, it was possible to estimate the size of the areas affected by fire in the study area (13,685 hectares) with high confidence. Keywords : fire, NBR and dNBR indices, classification, Zagros References -Aboelnour, M., & Engel, B. (2018). Application of remote sensing techniques and Geographic Information Systems to Analyze Land Surface Temperature in Response to Land Use/Land Cover Change in Greater Cairo Region, Egypt. Journal of Geographic Information System , 10, 57-88. - Ardakani, A., Valadanzooj, M., Mansourian, A. (2010). Spatial Analysis of Fire Potential in Iran Different Region by Using RS and GIS. Journal of Environmental Studies , 35(52), 25-34. - Chen, Y., Lara, M.J., & Hu, F.S. (2020). A robust visible near-infrared index for fire severity mapping in Arctic tundra ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing , 159, 101–113. - Darvishi, S., Solaimani, K., Rashidpour, M. (2019). Impact of vegetation indices and urban surface characteristics on land surface temperature changes (Case study: Sanandaj city). Journal of RS and GIS for Natural Resources , 10(1), 17-35. - Gondwe, S.V.C., Muchena, R., & Boys, J. (2018). Detecting land Use and Land Cover and Land Surface Temperature Change in Lilongwe City, Malawi. Journal of Remote Sensing and GIS , 9(2), 17-26. - Hussaina, M., Chen, D., Cheng, A., Wei, H.,& Stenley, D. (2013). Change Detection fromRemotely SensedImages: From Pixel basedto Object-based Approaches .Journal of Photogrammetry and RemoteSensing , 80, 91–106. - Kakehmami, A., Ghorbani, A., AsghariSarasekanrood, S., Ghale, E., Ghafari, S. (2020). Study of the relationship between land use and vegetation changes with the land surface temperature in Namin County. Journal of RS and GIS for Natural Resources , 11(2), 27-48. - Kaufman, Y.J., Ichoku, C., & Giglio, L. (2003). Fire and Smoke Observation from the Earth Observation System MODIS Instrument-Products, Validation, and Operational Use. International Journal of Remote Sensing , 24(8), 1765-1781. - Kurnaz, B., Bayik, C., & Abdikan, S. (2019). Determination of Forest Fire Area by Using Satellite Images: Muğla Case. 3rd International Conference on Advanced Engineering Technologies , 19-21. - Labib, S. M., & Harris, A. (2018). The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method. European Journal of Remote Sensing , 51(1), 231-240. - Niu, R., & Zhai, P. (2012). Study on forest fire danger over Northern China during the recent 50 years. Climatic Change , 111(3-4), 723-736. - Novelli, A., Aguilar, M.A., Nemmaoui, A., Auilar, F.J., & Tarantino, E. (2016). Performance evaluation of object based greenhouse detection fromSentinel-2 MSI and Landsat 8 OLI data: A case study from Almería(Spain). International Journal of Applied Earth Observation and Geoinformation , 52, 403–411. - Parks, S.A., Dillon, G.K., & Miller, C. (2014). A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio. Remote Sensing , 6, 1827-1844. - Quintano, C., Fernández-Manso, A., & Fernández-Manso, O. (2018). Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. International Journal Applied Earth Observation Geoinformation , 64, 221–225. - Sobrino, J.A., Jiménez-Muñoz, J.C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Journal of Remote Sensing of Environment , 90(4), 434-440. - Veysi, S., Naseri, A., Hamzeh, S., Moradi, P. (2016). Estimation of sugarcane field temperature using Split Window Algorithm and OLI LandSat 8 satellite images. Journal of RS and GIS for Natural Resources , 7(1), 27-40. - Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. Journal of Geoscience and Remote Sensing , 34(4), 892-905.
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