نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی فتوگرامتری، دانشگاه صنعتی نوشیروانی بابل

2 استادیار گروه مهندسی نقشه‌برداری، دانشگاه صنعتی نوشیروانی بابل

چکیده

مطالعات متعددی که طی چندین دهة اخیر درمورد پدیدة آتش‌سوزی انجام شده، مجموعة گسترده‌ای از داده‌های ورودی و روش‌های اجرا و ارزیابی را فراهم کرده است. بااین‌حال این مجموعة گستردة نتایج و تحقیقات، به‌صورت ساختاریافته، به‌منظور ارائة نقشة راه به کاربران جدید این حوزه و راهنمایی در زمینة کاربردها و شرایط گوناگونی فراهم آمده است که تا کنون تحلیل نشده‌اند. به‌عبارتی دیگر، خلأ تحقیقی منسجم درمورد عملکرد نسبی فرایندهای گوناگون سنجش از دور در این حوزه، به‌منظور تولید اطلاعات متفاوت و مرتبط با کاربری‌ها، احساس می‌شود. برای رفع این خلأ، در این تحقیق، تحلیلی نسبتاً جامع از مطالعات انجام‌شده دربارة آتش‌سوزی در نشریات سنجش از دور صورت پذیرفته‌ است. چند عامل کلی مورد ارزیابی در مطالعات پیش، حین و پس از آتش‌سوزی، تغییر در داده‌های ورودی، بررسی الگوریتم‌ها و توسعة آنها بودند زیرا تحلیلگران می‌توانند این عوامل را کنترل کنند تا دقت نهایی تحلیل‌ها و نتایج حاصل را بهبود بخشند. یکی از مسائل مهم در موضوع آتش‌سوزی، پس از شناسایی و کشف آتش، با توجه به تغییرات دائمی ایجادشده در ساختار و ترکیب پوشش گیاهی، بررسی نحوة بازیابی پوشش گیاهی و میزان رشد آن طی سالیان پس از آتش‌سوزی است. براساس بررسی انجام‌‌شده دربارة مطالعات آتش‌سوزی در کشور، حدود 48% از این پژوهش‌ها به شناسایی و گسترش آتش‌سوزی و حدود 52% دیگر به احیا و بازیابی پرداخته‌اند. در بررسی تحقیقات دربارة مطالعات شناسایی، مشخص شد که تقریباً 5% از آنها با استفاده از روش‌های یادگیری و 43% دیگر با روش‌های سنتی انجام شدند. درعین‌حال از سهم مرتبط با مطالعات احیا نیز، تقریباً 21% به بررسی پوشش گیاهی و 31% به بررسی خاک زیر سطح آتش پرداختند. یافته‌های این تحقیق می‌تواند به محققان، برای تصمیم‌گیری در انتخاب داده‌ها و الگوریتم‌های مورد استفاده، با توجه به هدف مطالعه، در شاخه‌های گوناگون مطالعات مرتبط با آتش‌سوزی کمک مؤثری برساند. بااین‌حال تحلیلگران می‌توانند، علاوه‌بر این دستورالعمل‌های کلی، ترجیحات شخصی یا مزایای الگوریتم ویژه‌ای را که ممکن است به برنامه‌ای خاص مربوط باشد، در نظر بگیرند.

کلیدواژه‌ها

عنوان مقاله [English]

A Review of Remote Sensing Methods in Identifying and Monitoring Forest Fires

نویسندگان [English]

  • Zohreh Roodsarabi 1
  • ali Sam Khaniani 2
  • Abbas Kiani 2

1 M.Sc. Student of Photogrammetry Engineering, Babol Noshirvani University of Technology, Babol

2 Assistant Prof., Civil Engineering Dep., Babol Noshirvani University of Technology, Babol

چکیده [English]

Numerous studies on the phenomenon of fire over the past several decades have provided an extensive set of input data and implementation and evaluation methods. However, this vast array of results and research is structured to provide a roadmap to new users in the field and guidance on various applications and conditions that have not yet been analyzed. In other words, the absence of coherent research on the relative performance of different remote sensing processes in the fire is felt to produce various products or the resulting utilities. To fill this gap, a relatively comprehensive analysis of fire studies in remote sensing publications has been performed in this study. Some of the general factors evaluated in the pre, during, post-fire studies were the manipulation of input data, the review of algorithms, and their development, as these are factors that can be controlled by analysts to improve the Final accuracy of analyzes and results. One of the important issues in the field of fire after the identification and discovery of fire, due to the permanent changes in the structure and composition of vegetation, is to study how vegetation is restored and its growth rate during the years after the fire. According to a study of fire studies in the country, about 48% of them are related to the identification and spread of fire and the remaining 52% are related to resuscitation and recovery. In a review of research related to identification studies, it was found that approximately 5% of its share was done using learning methods and the remaining 43% was done using traditional methods. At the same time, of the study-related share of Resuscitation studies approximately 21% to examine vegetation and 31% of the soil under the fire surface. The findings of this study can be useful in helping researchers to make decisions in the selection of data and algorithms used according to the purpose of study, in different branches of studies associated with fire. However, in addition to these general guidelines, an analyst can consider personal preferences or the benefits of a particular algorithm that may be relevant to a particular program.

کلیدواژه‌ها [English]

  • Fire
  • Photogrammetry and remote sensing
  • Satellite imagery
  • Environmental parameters
  • Vegetation recovery
Akbarzadeh, J., 2013, Comparison and Evaluation of Conventional Fire Detection Methods and Investigation the Influence of Temperature and Humidity in Accuracy of Detecting Permanent Fire Points Using MODIS, K.N Toosi University of Technology, Faculty of Geodesy and Geomatics, Tehran.
Akther, M.S. & Hassan, Q.K., 2011, Remote Sensing-Based Assessment of Fire Danger Conditions over Boreal Forest, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4, P. 992-999.
Alexandridis, A., Vakalis, D., Siettos, C.I. & Bafas, G.V., 2008, A Cellular Automata Model for Forest Fire Spread Prediction: The Case of the Wildfire That Swept through Spetses Island in 1990, Applied Mathematics and Computation, 204, PP. 191-201.
Alhaj Khalaf, M.W., Shataee Jouibary, S. & Jahdi, R., 2020, Ability and Sensitivity Study of Spectral Indices for Wildfire Severity Mapping (Case Study: Arabdagh-Golestan Reforestations), Forest and Wood Products, 73, PP. 97-110.
Allen, J.L. & Sorbel, B., 2008, Assessing the Differenced Normalized Burn Ratio’s Ability to Map Burn Severity in the Boreal Forest and Tundra Ecosystems of Alaska’s National Parks, International Journal of Wildland Fire, 17, PP. 463-475.
 
Alley, W.M., 1985, The Palmer Drought Severity Index as a Measure of Hydrologic Drought 1, Jawra Journal of the American Water Resources Association, 21, PP. 105-114.
Alonso-Canas, I. & Chuvieco, E., 2015, Global Burned Area Mapping from ENVISAT-MERIS and MODIS Active Fire Data, Remote Sensing of Environment, 163, PP. 140-152.
Amiro, B., MacPherson, J. & Desjardins, R., 1999, Boreas Flight Measurements of Forest-Fire Effects on Carbon Dioxide and Energy Fluxes, Agricultural and Forest Meteorology, 96, PP.199-208.
Andersen, H.-E., Strunk, J., Temesgen, H., Atwood, D. & Winterberger, K., 2012, Using Multilevel Remote Sensing and Ground Data to Estimate Forest Biomass Resources in Remote Regions: A Case Study in The Boreal Forests of Interior Alaska, Canadian Journal of Remote Sensing, 37, PP. 596-611.
Ardakani, A.S., Zoej, M.J.V., Mohammadzadeh, A. & Mansourian, A., 2010, Spatial and Temporal Analysis of Fires Detected by Modis Data in Northern Iran from 2001 To 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4, PP. 216-225.
Aricak, B., Kucuk, O. & Enez, K., 2014, Determining a Fire Potential Map Based on Stand Age, Stand Closure and tree Species, Using Satellite Imagery (Kastamonu Central Forest Directorate Sample), Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 35, PP. 101-108.
Askari, T., 2013, Assessment Pattern and Models of Forest Fire Spread Using Remote Sensing Data, K.N. Toosi University of Technology, Faculty of Geodesy and Geomatic, Tehran.
Azari, O., 2016, Evaluation of Forest Fires Detection Methods Using MODIS Satellite Images, K.N.Toosi University of Technology, Faculty of Geodesy and Geomatics, Tehran.
Azari, O. & Mohammadzadeh, A., 2016, Fire Detection in Remote Sensing from the Perspective of Some Satellite Sensor, Geospatial Engineering Journal, 7, PP. 15-23.
Baheri, H., 2016, Long-Term Effect of Fire on Vegetation and Regeneration of Beech (Fagus Orientalis L.) in the Mountain Forests (Case Study: Lesakoti Tenkaben), Gilan University, Faculty of Natural Resources.
Banj Shafiei, A., 2007, Effects of Fire On Ecological Properties of Forest, Watershed of Golband, 45, Series No.4 Chelir, Tarbiat Modares University, Faculty of Agriculture, Tehran.
Banj Shafiei, A., Akbarinia, M., Jalali, S.G., Azizi, P. & Hosseini, S.M., 2007, The Effects of Fire on Forest Structure: Case Study in Chelir, Kheyroudkenar, (Watershed Number 45 Golband, Nowshahr), Pajouhesh & Sazandegi, 76, PP. 112-105.
Bannari, A., Asalhi, H. & Teillet, P.M., 2002, Transformed Difference Vegetation Index (TDVI) for Vegetation Cover Mapping, IEEE International Geoscience and Remote Sensing Symposium, IEEE, PP. 3053-3055.
Barani Fard, H.A., 2015, The Study of The Wood Sensetivity of Some Regional Hardwood and Softwood Trees against Fire in The Forests of Golestan Province, Gorgan University of Agricultural Sciences and Natural Resources, Faculty of Forest Sciences.
Barrett, K. & Kasischke, E.S., 2013, Controls on Variations in MODIS Fire Radiative Power in Alaskan Boreal Forests: Implications for Fire Severity Conditions, Remote Sensing of Environment, 130, PP. 171-181.
Barrett, K., Kasischke, E., McGuire, A., Turetsky, M. & Kane, E., 2010, Modeling Fire Severity in Black Spruce Stands in the Alaskan Boreal Forest Using Spectral and Non-Spectral Geospatial Data, Remote Sensing of Environment, 114, PP. 1494-1503.
Bashmaghi, M., 2015, The Effect of Fire Intensity and Frost on the Soil Seed Bank in Different Times of Fire (Case Study of Golestan National Park), Tarbiat Modares University, Faculty of Natural Resources and Marine Sciences, Tehran.
Bianco, V., Mazzeo, P., Paturzo, M., Distante, C. & Ferraro, P., 2020, Deep Learning Assisted Portable IR Active Imaging Sensor Spots and Identifies Live Humans through Fire, Optics and Lasers in Engineering, 124, P. 105818.
Boby, L.A., Schuur, E.A., Mack, M.C., Verbyla, D. & Johnstone, J.F., 2010, Quantifying Fire Severity, Carbon, and Nitrogen Emissions in Alaska's Boreal Forest, Ecological Applications, 20, PP. 1633-1647.
Boles, S.H. & Verbyla, D.L., 2000, Comparison of Three AVHRR-Based Fire Detection Algorithms for Interior Alaska, Remote Sensing of Environment, 72, PP. 1-16.
Bond, W.J. & Van Wilgen, B.W., 2012, Fire and Plants, Springer Science & Business Media.
Bourgeau-Chavez, L., Harrell, P., Kasischke, E. & French, N., 1997, The Detection and Mapping of Alaskan Wildfires Using a Spaceborne Imaging Radar System, International Journal of Remote Sensing, 18, PP. 355-373.
Bourgeau-Chavez, L., Kasischke, E., Brunzell, S., Mudd, J. & Tukman, M., 2002, Mapping Fire Scars in Global Boreal Forests Using Imaging Radar Data, International Journal of Remote Sensing, 23, PP. 4211-4234.
Bourgeau‐Chavez, L.L., Kasischke, E.S., Riordan, K., Brunzell, S., Nolan, M., Hyer, E., Slawski, J., Medvecz, M., Walters, T. & Ames, S., 2007, Remote Monitoring of Spatial and Temporal Surface Soil Moisture in Fire Disturbed Boreal Forest Ecosystems with ERS SAR Imagery, International Journal of Remote Sensing, 28, PP. 2133-2162.
Brang, P., Schönenberger, W., Frehner, M., Schwitter, R. & Wasser, B., 2006, Management of Protection Forests in the European Alps: An Overview, For. Snow Landsc. Res. Citeseer.
Brown, C. & Johnstone, J., 2011, How Does Increased Fire Frequency Affect Carbon Loss from Fire? A Case Study in the Northern Boreal Forest, International Journal of Wildland Fire, 20, PP. 829-837.
Brown, M.E., Lary, D.J., Vrieling, A., Stathakis, D. & Mussa, H., 2008, Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS, International Journal of Remote Sensing, 29, PP. 7141-7158.
Caccamo, G., Bradstock, R., Collins, L., Penman, T. & Watson, P., 2015, Using MODIS Data to Analyse Post-Fire Vegetation Recovery in Australian Eucalypt Forests, Journal of Spatial Science, 60, PP. 341-352.
Cardil, A., Monedero, S., Ramírez, J. & Silva, C.A., 2019, Assessing and Reinitializing Wildland Fire Simulations through Satellite Active Fire Data, Journal of Environmental Management, 231, PP. 996-1003.
Catchpole, W., 2002, Fire Properties and Burn Patterns in Heterogeneous Landscapes, "Flammable Australia: The Fire Regimes and Biodiversity of a Continent" (Book Chapter), PP. 49-75.
Celik, T. & Demirel, H., 2009, Fire Detection in Video Sequences Using a Generic Color Model, Fire Safety Journal, 44, PP. 147-158.
Certini, G., 2005, Effects of Fire on Properties of Forest Soils: A Review, Oecologia, 143, PP. 1-10.
Çetin, A.E., Dimitropoulos, K., Gouverneur, B., Grammalidis, N., Günay, O., Habiboǧlu, Y.H., Töreyin, B.U. & Verstockt, S., 2013, Video Fire Detection–Review, Digital Signal Processing, 23, PP. 1827-1843.
Chamani Takaldani, B., 2013, Agent Based Simulation for Helping Forest Fire Control Management, K.N Toosi University of Technology, Faculty of Geodesy and Geomatics, Tehran.
Chen, K., Blong, R. & Jacobson, C., 2001, MCE-RISK: Integrating Multicriteria Evaluation and GIS for Risk Decision-Making in Natural Hazards, Environmental Modelling & Software, 16, PP. 387-397.
Chen, T.-H., Wu, P.-H. & Chiou, Y.-C., 2004, An Early Fire-Detection Method Based on Image Processing, 2004 International Conference on Image Processing, 2004, ICIP'04. IEEE, PP. 1707-1710.
Chen, X., Vierling, L., Deering, D. & Conley, A., 2005, Monitoring Boreal Forest Leaf Area Index Across a Siberian Burn Chronosequence: A MODIS Validation Study, International Journal of Remote Sensing, 26, PP. 5433-5451.
Chen, T.-H., Yin, Y.-H., Huang, S.-F. & Ye, Y.-T., 2006, The Smoke Detection for Early Fire-Alarming System Base on Video Processing, 2006 International Conference on Intelligent Information Hiding and Multimedia. IEEE, PP. 427-430.
Cheng, Y., Huang, Y., Pang, B. & Zhang, W., 2018, ThermalNet: A Deep Reinforcement Learning-Based Combustion Optimization System for Coal-Fired Boiler, Engineering Applications of Artificial Intelligence, 74, PP. 303-311.
Chowdhury, E.H. & Hassan, Q.K., 2013, Use of Remote Sensing-Derived Variables in Developing a Forest Fire Danger Forecasting System, Natural Hazards, 67, PP. 321-334.
Chu, T. & Guo, X., 2014, Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review, Remote Sensing, 6, PP. 470-520.
Chuvieco, E., Englefield, P., Trishchenko, A.P. & Luo, Y., 2008, Generation of Long Time Series of Burn Area Maps of the Boreal Forest from NOAA–AVHRR Composite Data, Remote Sensing of Environment, 112, PP. 2381-2396.
Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martín, M.P., Vilar, L., Martínez, J., Martín, S. & Ibarra, P., 2010, Development of a Framework for Fire Risk Assessment Using Remote Sensing and Geographic Information System Technologies, Ecological Modelling, 221, PP. 46-58.
Cochrane, M.A., 2003, Fire Science for Rainforests, Nature, 421, PP. 913-919.
COFOM, 2006, Incendios Forestales, Guia Practica para Comunicadores, Zapopan, Mexico.
Çolak, E. & Sunar, F., 2020, Evaluation of Forest Fire Risk in the Mediterranean Turkish Forests: A Case Study of Menderes Region, Izmir, International Journal of Disaster Risk Reduction, 101479.
Countryman, C.M., 1972, The Fire Environment Concept, Pacific Southwest Forest and Range Experiment Station.
Croft, T.A., 1973, Burning Waste Gas in Oil Fields, Nature, 245, PP. 375-376.
Daneshrad, A., 1985, The Effects of Forest Destruction on the Occurrence of Diseases, Olive Magazine, 361, P. 11.
Dı́az-Delgado, R. & Pons, X., 2001, Spatial Patterns of Forest Fires in Catalonia (NE of Spain) along the Period 1975–1995: Analysis of Vegetation Recovery after Fire, Forest Ecology and Management, 147, PP. 67-74.
Duffy, P.A., Epting, J., Graham, J.M., Rupp, T.S. & McGuire, A.D., 2007, Analysis of Alaskan Burn Severity Patterns Using Remotely Sensed Data, International Journal of Wildland Fire, 16, PP. 277-284.
Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z.-L., Quayle, B. & Howard, S., 2007, A Project for Monitoring Trends in Burn Severity, Fire Ecology, 3, PP. 3-21.
Ekinci, H., 2006, Effect of Forest Fire on Some Physical, Chemical and Biological Properties of Soil in Çanakkale, Turkey, International Journal of Agriculture and Biology, 8, PP. 102-106.
Epting, J. & Verbyla, D., 2005, Landscape-Level Interactions of Prefire Vegetation, Burn Severity, and Postfire Vegetation over a 16-Year Period in Interior Alaska, Canadian Journal of Forest Research, 35, PP. 1367-1377.
Epting, J., Verbyla, D. & Sorbel, B., 2005, Evaluation of Remotely Sensed Indices for Assessing Burn Severity in Interior Alaska Using Landsat TM and ETM+, Remote Sensing of Environment, 96, PP. 328-339.
Erickson, H.E. & White, R., 2008, Soils under Fire: Soils Research and the Joint Fire Science Program, US Department of Agriculture, Forest Service, Pacific Northwest Research Station.
Eshaghi, M.A. & Shataee Jouibary, S., 2016, Preparation Map of Forest Fire Risk Using SVM, RF and MLP Algorithms (Case Study: Golestan National Park, Northeastern Iran), Journal of Wood & Forest Science and Technology, 23.
Eva, H. & Lambin, E.F., 2000, Fires and LandCover Change in the Tropics: A Remote Sensing Analysis at the Landscape Scale, Journal of Biogeography, 27, PP. 765-776.
Fan, Q., Wang, C., Zhang, D. & Zang, S., 2017, Environmental Influences on Forest Fire Regime in the Greater Hinggan Mountains, Northeast China. Forests, 8, P. 372.
Fathi, F., 2015, Evaluation The Effects of Environmental Parameters on the Spatial Accuracy of Fires Fixed Discovered by MODIS Satellite Imagery (Case Study: Southern Iran and Part of Iraq Country), K.N. Toosi University of Technology, Faculty of Geodesy and Geomatics, Tehran.
Firoz, A., Goparaju, L., Qayum, A. & Quli, S., 2017, Forest Fire Trend Analysis and Effect of Environmental Parameters: A Study in Jharkhand State of India Using Geospatial Technology, World Scientific News, 90, PP. 31-50.
Flasse, S. & Ceccato, P., 1996, A Contextual Algorithm for AVHRR Fire Detection, International Journal of Remote Sensing, 17, PP. 419-424.
Fornacca, D., Ren, G. & Xiao, W., 2018, Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China, Remote Sensing, 10, P. 1196.
Fraser, R. & Li, Z., 2002, Estimating Fire-Related Parameters in Boreal Forest Using SPOT VEGETATION, Remote Sensing of Environment, 82, PP. 95-110.
French, N.H., Kasischke, E.S., Hall, R.J., Murphy, K.A., Verbyla, D.L., Hoy, E.E. & Allen, J.L., 2008, Using Landsat Data to Assess Fire and Burn Severity in the North American Boreal Forest Region: An Overview and Summary of Results, International Journal of Wildland Fire, 17, PP. 443-462.
Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux, J.-M., Moreau, E. & Fnaiech, F., 2016, Convolutional Neural Network for Video Fire and Smoke Detection, IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, PP. 877-882.
Furyaev, V.V., Vaganov, E.A., Tchebakova, N.M. & Valendik, E.N., 2001, Effects of Fire and Climate on Successions and Structural Changes in the Siberian Boreal Forest, Eurasian Journal of Forest Research, 2, PP. 1-15.
Gao, B.-C., 1996, NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space, Remote Sensing of Environment, 58, PP. 257-266.
Garakhanzhadeh, A., 2016, Comparing Ordered Weighted Average (OWA) & Artificial Neural Networks (ANN) Approaches for Forest Fire Risk Mapping (Case Study: Ramsar County's Eastern Forests), Tabriz University, Faculty of Planning and Environmental Sciences.
Garavand, S., 2013, Evaluating the Probability of Occurrence and Calculating the Risk of Fire in the Natural Areas of Lorestan Province, Shahrekord University, Faculty of Natural Resources and Earth Sciences.
García-Llamas, P., Suárez-Seoane, S., Taboada, A., Fernández-Manso, A., Quintano, C., Fernández-García, V., Fernández-
Guisuraga, J.M., Marcos, E. & Calvo, L., 2019, Environmental Drivers of Fire Severity in Extreme Fire Events that Affect Mediterranean Pine Forest Ecosystems, Forest Ecology and Management, 433, PP. 24-32.
George, C., Rowland, C., Gerard, F. & Balzter, H., 2006, Retrospective Mapping of Burnt Areas in Central Siberia Using a Modification of the Normalised Difference Water Index, Remote Sensing of Environment, 104, PP. 346-359.
Ghaemi Rad, T., 2014, Review and Evaluate Different Approaches to Simulate Forest Fire Spreading Using Cellular Automata, K.N. Toosi University of Technology, Faculty of Geodesy and Geomatics, Tehran.
Gholami Gohareh, R., 2010, Influense of Surface Fire in Range and Reclaimed Forest Lands of Kodir Area on Temporal Variation of Infiltration, Runoff and Sediment, Tarbiat Modares University, Faculty of Natural Resources.
Giglio, L., Descloitres, J., Justice, C.O. & Kaufman, Y.J., 2003, An Enhanced Contextual Fire Detection Algorithm for MODIS, Remote Sensing of Environment, 87, PP. 273-282.
Giglio, L., Van der Werf, G., Randerson, J., Collatz, G. & Kasibhatla, P., 2006, Global Estimation of Burned Area Using MODIS Active Fire Observations, Atmospheric Chemistry and Physics, 6, PP. 957-974.
Giglio, L., Loboda, T., Roy, D.P., Quayle, B. & Justice, C.O., 2009, An Active-Fire Based Burned Area Mapping Algorithm for the MODIS Sensor, Remote Sensing of Environment, 113, PP. 408-420.
Gitelson, A.A. & Merzlyak, M.N., 1998, Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves, Advances in Space Research, 22, PP. 689-692.
Goetz, S.J., Sun, M., Baccini, A. & Beck, P.S., 2010, Synergistic Use of Spaceborne Lidar and Optical Imagery for Assessing Forest Disturbance: An Alaska Case Study, Journal of Geophysical Research: Biogeosciences, 115.
Gongalsky, K.B. & Persson, T., 2013, Recovery of Soil Macrofauna after Wildfires in Boreal Forests, Soil Biology and Biochemistry, 57, PP. 182-191.
Guide, U.P., 2017, Landsat Surface Reflectance Derived Spectral Indices, Department of the Interior US Geological Survey, Version 3.
Hadi, D.P., 2008, A RS/GIS–Based Multi-Criteria Approaches to Assess Forest Fire Hazard in Indonesia, Citeseer.
Hamedi, A. & Esmaeili, A., 2016, Diagnosing the Damage of Zagros Forests after a Low Severity Fire Using Landsat TM and Modis Data, National Geomatics Conference.
Hansen, M.C. & Loveland, T.R., 2012, A Review of Large Area Monitoring of Land Cover Change Using Landsat Data, Remote Sensing of Environment, 122, PP. 66-74.
Hassan, A.H.B.M., 2008, Early Detection of Potential Forest Fires Using Satellite Remote Sensing Techniques, A Thesis Submitted In Fulfillment of The Requirements For The Award of the degree of Master of Science (Remote Sensing).
Hassanpur, M., 2019, Fire Detection Using Convolutional Neural Network and Support Vector Machine Pras Razavi Institute of Higher Education, Department of Computer Engineering, Gonabad.
Hawbaker, T.J., Radeloff, V.C., Syphard, A.D., Zhu, Z. & Stewart, S.I., 2008, Detection Rates of the MODIS Active Fire Product in the United States, Remote Sensing of Environment, 112, PP. 2656-2664.
Heward, H., Smith, A.M., Roy, D.P., Tinkham, W.T., Hoffman, C.M., Morgan, P. & Lannom, K.O., 2013, Is Burn Severity Related to Fire Intensity? Observations from Landscape Scale Remote Sensing, International Journal of Wildland Fire, 22, PP. 910-918.
Hicke, J.A., Asner, G.P., Kasischke, E.S., French, N.H., Randerson, J.T., James Collatz, G., Stocks, B.J., Tucker, C.J., Los, S.O. & Field, C.B., 2003, Postfire Response of North American Boreal Forest Net Primary Productivity Analyzed with Satellite Observations, Global Change Biology, 9, PP. 1145-1157.
Holden, Z., Smith, A., Morgan, P., Rollins, M. & Gessler, P., 2005, Evaluation of Novel Thermally Enhanced Spectral Indices for Mapping Fire Perimeters and Comparisons with Fire Atlas Data, International Journal of Remote Sensing, 26, PP. 4801-4808.
Hollingsworth, T.N., Johnstone, J.F., Bernhardt, E.L. & Chapin III, F.S., 2013, Fire Severity Filters Regeneration Traits to Shape Community Assembly in Alaska’s Boreal Forest, PloS one, 8.   
Horng, W.-B., Peng, J.-W. & Chen, C.-Y., 2005, A New Image-Based Real-Time Flame Detection Method Using Color Analysis, Proceedings, 2005 IEEE Networking, Sensing and Control, 2005. IEEE, PP. 100-105.
Hoy, E.E., French, N.H., Turetsky, M.R., Trigg, S.N. & Kasischke, E.S., 2008, Evaluating the Potential of Landsat TM/ETM+ Imagery for Assessing Fire Severity in Alaskan Black Spruce Forests, International Journal of Wildland Fire, 17, PP. 500-514.
Huete, A., 1988, A Soil-Adjusted Vegetation Index (SAVI), Remote Sensing of Environment, 25(3), PP. 295-309.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G., 2002, Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices, Remote Sensing of Environment, 83, PP. 195-213.
Isabel, M.d.P.M., 1999, Cartografía e Inventario de Incendios Forestales en la Península Ibérica a Partir de Imágenes NOAA-AVHRR, Universidad de Alcalá.
Jafari Goldarag, Y., 2012, Assessment and Evaluation of Effective Environmental Parameters on Forest Fire Occurrence Based on Remote Sensing Technology (Case Study: Iran North Forest), K.N Toosi University of Technology Faculty of Geodesy and Geomatics, Tehran.
Jain, T.B., Graham, R.T. & Pilliod, D.S., 2004, Tongue-Tied: Confused Meanings for Common Fire Terminology Can Lead to Fuels Mismanagement, Wildfire, July/August: 22-26, PP. 22-26.
Johnson, D.W. & Curtis, P.S., 2001, Effects of Forest Management on Soil C and N Storage: Meta Analysis, Forest Ecology and Management, 140, PP. 227-238.
Johnstone, J.F. & Kasischke, E.S., 2005, Stand-Level Effects of Soil Burn Severity on Postfire Regeneration in a Recently Burned Black Spruce Forest, Canadian Journal of Forest Research, 35, PP. 2151-2163.
Justice, C., Giglio, L., Korontzi, S., Owens, J., Morisette, J., Roy, D., Descloitres, J., Alleaume, S., Petitcolin, F. & Kaufman, Y., 2002, The MODIS Fire Products, Remote Sensing of Environment, 83, PP. 244-262.
Karafyllidis, I. & Thanailakis, A., 1997, A Model for Predicting Forest Fire Spreading Using Cellular Automata, Ecological Modelling, 99, PP. 87-97.
Kasischke, E.S. & French, N.H., 1995, Locating and Estimating the Areal Extent of Wildfires in Alaskan Boreal Forests Using Multiple-Season AVHRR NDVI Composite Data, Remote Sensing of Environment, 51, PP. 263-275.
Kasischke, E.S., Turetsky, M.R., Ottmar, R.D., French, N.H., Hoy, E.E. & Kane, E.S., 2008, Evaluation of the Composite Burn Index for Assessing Fire Severity in Alaskan Black Spruce Forests, International Journal of Wildland Fire, 17, PP. 515-526.
Kasischke, E.S., Loboda, T., Giglio, L., French, N.H., Hoy, E., de Jong, B. & Riano, D., 2011a, Quantifying Burned Area for North American Forests: Implications for Direct Reduction of Carbon Stocks, Journal of Geophysical Research: Biogeosciences, 116.
Kasischke, E.S., Tanase, M.A., Bourgeau-Chavez, L.L. & Borr, M., 2011b, Soil Moisture Limitations on Monitoring Boreal Forest Regrowth Using Spaceborne L-Band SAR Data, Remote Sensing of Environment, 115, PP. 227-232.
Kaufman, Y.J., Justice, C., Flynn, L., Kendall, J., Prins, E., Giglio, L., Ward, D.E., Menzel, P. & Setzer, A., 1998a, Potential Global Fire Monitoring from EOS-MODIS, Journal of Geophysical Research, 103(D24), PP. 32215-32238.
Kaufman, Y.J., Justice, C.O., Flynn, L.P., Kendall, J.D., Prins, E.M., Giglio, L., Ward, D.E., Menzel, W.P. & Setzer, A.W., 1998b, Potential Global Fire Monitoring from EOSMODIS, Journal of Geophysical Research: Atmospheres, 103, PP. 32215-32238.
Keeley, J.E., 2009, Fire Intensity, Fire Severity and Burn Severity: A Brief Review and Suggested Usage, International journal of wildland fire, 18, PP. 116-126.
Khanmohammadi, M., 2014, Spatial and Temporal Analysis of Forest Fire Hazard in the East Hyrcanian Iran, Semnan University, Faculty of Desertology.
Ko, B.C., Cheong, K.-H. & Nam, J.-Y., 2009, Fire Detection Based on Vision Sensor and Support Vector Machines, Fire Safety Journal, 44, PP. 322-329.
Lentile, L.B., Holden, Z.A., Smith, A.M., Falkowski, M.J., Hudak, A.T., Morgan, P., Lewis, S.A., Gessler, P.E. & Benson, N.C., 2006, Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects, International Journal of Wildland Fire, 15, PP. 319-345.
Leon, J.R.R., Van Leeuwen, W.J. & Casady, G.M., 2012, Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments, Remote Sensing, 4, PP. 598-621.
Li, X., Jin, H., Wang, H., Wu, X., Huang, Y., He, R., Luo, D. & Jin, X., 2020., Distributive Features of Soil Carbon and Nutrients in Permafrost Regions Affected by Forest Fires in Northern Da Xing’anling (Hinggan) Mountains, NE China, Catena, 185, P. 104304.
Liu, T., Marlier, M.E., Karambelas, A., Jain, M., Singh, S., Singh, M.K., Gautam, R. & DeFries, R.S., 2019a, Missing Emissions from Post-Monsoon Agricultural Fires in Northwestern India: Regional Limitations of MODIS Burned Area and Active Fire Products, Environmental Research Communications, 1, P. 011007.
Liu, X.-p., Zhang, G.-q., Lu, J. & Zhang, J.-q., 2019b, Risk Assessment Using Transfer Learning for Grassland Fires, Agricultural and Forest Meteorology, 269, PP. 102-111.
Lizundia-Loiola, J., Otón, G., Ramo, R. & Chuvieco, E., 2020, A Spatio-Temporal Active-Fire Clustering Approach for Global Burned Area Mapping at 250 m from MODIS Data, Remote Sensing of Environment, 236, P. 111493.
Loboda, T., O'neal, K. & Csiszar, I., 2007, Regionally Adaptable dNBR-Based Algorithm for Burned Area Mapping from MODIS Data, Remote Sensing of Environment, 109, PP. 429-442.
Loboda, T.V., Hoy, E.E., Giglio, L. & Kasischke, E.S., 2011, Mapping Burned Area in Alaska Using MODIS Data: A Data Limitations-Driven Modification to the Regional Burned Area Algorithm, International Journal of Wildland Fire, 20, PP. 487-496.
Loboda, T.V., Zhang, Z., O'Neal, K.J., Sun, G., Csiszar, I.A., Shugart, H. & Sherman, N., 2012, Reconstructing Disturbance History Using Satellite-Based Assessment of the Distribution of Land Cover in the Russian Far East, Remote Sensing of Environment, 118, PP. 241-248.
Loboda, T.V., French, N.H., Hight-Harf, C., Jenkins, L. & Miller, M.E., 2013, Mapping Fire Extent and Burn Severity in Alaskan Tussock Tundra: An Analysis of the Spectral Response of Tundra Vegetation to Wildland Fire, Remote Sensing of Environment, 134, PP. 194-209.
Lozano, F.J., Suárez-Seoane, S. & de Luis, E., 2007, Assessment of Several Spectral Indices Derived from Multi-Temporal Landsat Data for Fire Occurrence Probability Modelling, Remote Sensing of Environment, 107, PP. 533-544.
Lutz, J.A., Key, C.H., Kolden, C.A., Kane, J.T. & van Wagtendonk, J.W., 2011, Fire Frequency, Area Burned, and Severity: A Quantitative Approach to Defining a Normal Fire Year, Fire Ecology, 7, PP. 51-65.
Maeda, E.E., Formaggio, A.R., Shimabukuro, Y.E., Arcoverde, G.F.B. & Hansen, M.C., 2009, Predicting Forest Fire in the Brazilian Amazon Using MODIS Imagery and Artificial Neural Networks, International Journal of Applied Earth Observation and Geoinformation, 11, PP. 265-272.
Magnussen, S. & Wulder, M.A., 2012, Post-Fire Canopy Height Recovery in Canada’s Boreal Forests Using Airborne Laser Scanner (ALS), Remote Sensing, 4, PP. 1600-1616.
Maleki, M., 2016, Modeling Occurrence and the Spread of Forest Fire Using Cellular Automata Approach (Case Study: Arasbaran Protected Area), Tabriz University, Faculty of Planning and Environmental Sciences.
Mansori, A., 2012, Investigating and Prioritizing the Factors Affecting Fire and Determining the Risky and Critical Points of the Forest (Case Study Series 2 of Nau Islam Gilan), Gilan University, Faculty of Agriculture and Natural Resources.
Marbach, G., Loepfe, M. & Brupbacher, T., 2006, An Image Processing Technique for Fire Detection in Video Images, Fire Safety Journal, 41, PP. 285-289.
Martín, M., Gómez, I. & Chuvieco, E., 2005, Performance of a Burned-Area Index (BAIM) for Mapping Mediterranean Burned Scars from MODIS Data, Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment. Paris, Universidad de Zaragoza, GOFC GOLD, EARSeL, PP. 193-198.
Matricardi, E.A., Skole, D.L., Pedlowski, M.A., Chomentowski, W. & Fernandes, L.C., 2010, Assessment of Tropical Forest Degradation by Selective Logging and Fire Using Landsat Imagery, Remote Sensing of Environment, 114, PP. 1117-1129.
Mazrae, M., 2012, Short Time Effect of Forest Fire on Soil Carbon Sequestration Potential, Gorgan University of Agricultural Sciences and Natural Resources, Faculty of Forestry.
McCullough, D.G., Werner, R.A. & Neumann, D., 1998, Fire and Insects in Northern and Boreal Forest Ecosystems of North America, Annual Review of Entomology, 43, PP. 107-127.
McElhinny, C., Gibbons, P., Brack, C. & Bauhus, J., 2005, Forest and Woodland Stand Structural Complexity: Its Definition and Measurement, Forest Ecology and Management, 218, PP. 1-24.
Merino-de-Miguel, S., Huesca, M. & González-Alonso, F., 2010, Modis Reflectance and Active Fire Data for Burn Mapping and Assessment at Regional Level, Ecological Modelling, 221, PP. 67-74.
Mesdaghi, M., 2001, Vegetation Description and Data Analysis: A Practical Approach, Academic Jihad Publications, Ferdowsi University of Mashhad.
Michalek, J., French, N., Kasischke, E., Johnson, R. & Colwell, J., 2000, Using Landsat TM Data to Estimate Carbon Release from Burned Biomass in an Alaskan Spruce Forest Complex, International Journal of Remote Sensing, 21, PP. 323-338.
Mohammadi, F., 2012, Preparation of Forest Fire Risk Map (Case Study: Sarovabad Forests in Kurdistan Province), University of Kurdistan, Faculty of Agriculture and Natural Resources.
Mölders, N. & Kramm, G., 2007, Influence of Wildfire Induced Land-Cover Changes on Clouds and Precipitation in Interior Alaska—A Case Study, Atmospheric Research, 84, PP. 142-168.
Morisette, J.T., Giglio, L., Csiszar, I., Setzer, A., Schroeder, W., Morton, D. & Justice, C.O., 2005, Validation of MODIS Active Fire Detection Products Derived from Two Algorithms, Earth Interactions, 9, PP. 1-25.
Movaghati, S., Samadzadegan, F. & Azizi, A., 2008, An Agent-Based Algorithm for Forest Fire Detection, ISPRS Congress Beijing, PP. 631-634.
Murphy, K.A., Reynolds, J.H. & Koltun, J.M., 2008, Evaluating the Ability of the Differenced Normalized Burn Ratio (dNBR) to Predict Ecologically Significant Burn Severity in Alaskan Boreal Forests, International Journal of Wildland Fire, 17, PP. 490-499.
Najafi, A., 2014, Investigation and Modeling Effects of Forest Fires in the Landscape Using RS and GIS (Case Study: Protected Area of Baghe-Shadi Khatam), Yazd University, Faculty of Natural Resource and Desert studies.
Nayebi, A.H., Eslamlo, A. & Naderi, S., 2018, Recognizing and Fighting Different Types of Forest Fires.
Nazari, C., 2015, Effect of Fire on Some Soil Physical, Chemical and Biological Properties in a Northern Zagros Oak Forest (Case Study: Marivan Forests), University of Kurdistan, Faculty of Agriculture and Natural Resources.
Nepstad, D.C., Verssimo, A., Alencar, A., Nobre, C., Lima, E., Lefebvre, P., Schlesinger, P., Potter, C., Moutinho, P., Mendoza, E., 1999, Large-Scale Impoverishment of Amazonian Forests by Logging and Fire, Nature, 398, PP. 505-508.
Nesterov, V., 1949, Forest Fires and Methods of Fire Risk Determination, Russian, Goslesbumizdat, Moscow.
Norouzi, M., 2011, Effect of Fire on Some Physico-Chemical and Micromorphological Properties of Forest Soils in Guilan Province, Gilan University, Faculty of Agricultural Sciences.
Palacios-Orueta, A., Chuvieco, E., Parra, A. & Carmona-Moreno, C., 2005, Biomass Burning Emissions: A Review of Models Using Remote-Sensing Data, Environmental Monitoring and Assessment, 104, PP. 189-209.
Paz, S., Carmel, Y., Jahshan, F. & Shoshany, M., 2011, Post-Fire Analysis of Pre-Fire Mapping of Fire-Risk: A Recent Case Study from Mt. Carmel (Israel), Forest Ecology and Management, 262, PP. 1184-1188.
Peng, Y. & Wang, Y., 2019, Real-Time Forest Smoke Detection Using Hand-Designed Features and Deep Learning, Computers and Electronics in Agriculture, 167, P. 105029.
Pflugmacher, D., Cohen, W.B. & Kennedy, R.E., 2012, Using Landsat-Derived Disturbance History (1972–2010) to Predict Current Forest Structure, Remote Sensing of Environment, 122, PP. 146-165.
Pinty, B. & Verstraete, M., 1992, GEMI: A Non-Linear Index to Monitor Global Vegetation from Satellites, Vegetatio, 101, PP. 15-20.
Polat, S., 2017, Mapping of Distribution of Forest Fire Risk in Forests by Using Neural Networks and Geographic Information System (Case Study: Ilam Forests), Malayer University, Faculty of Natural Resources and Environment.
Pommerening, A., 2002, Approaches to Quantifying Forest Structures, Forestry: An International Journal of Forest Research, 75, PP. 305-324.
Potapov, P., Hansen, M.C., Stehman, S.V., Loveland, T.R. & Pittman, K., 2008, Combining MODIS and Landsat Imagery to Estimate and Map Boreal Forest Cover Loss, Remote Sensing of Environment, 112, PP. 3708-3719.
Powell, S.L., Cohen, W.B., Healey, S.P., Kennedy, R.E., Moisen, G.G., Pierce, K.B. & Ohmann, J.L., 2010, Quantification of Live Aboveground Forest Biomass Dynamics with Landsat Time-Series and Field Inventory Data: A Comparison of Empirical Modeling Approaches, Remote Sensing of Environment, 114, PP. 1053-1068.
Preisler, H.K. & Westerling, A.L., 2007, Statistical Model for Forecasting Monthly Large Wildfire Events in Western United States, Journal of Applied Meteorology and Climatology, 46, PP. 1020-1030.
Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. & Sorooshian, S., 1994, A Modified Soil Adjusted Vegetation Index, Remote Sensing of Environment, 48, PP. 119-126.
Rahimi, H., 2013, Capability of TM Images in Detection of Burned Forests (Case Study: Qoori Qaleh Area in Kermanshah Province), University of Kurdistan, Faculty of Resources Department of Forestry.
Roberts, G. & Wooster, M., 2014, Development of a Multi-Temporal Kalman Filter Approach to Geostationary Active Fire Detection & Fire Radiative Power (FRP) Estimation, Remote Sensing of Environment, 152, PP. 392-412.
Röder, A., Hill, J., Duguy, B., Alloza, J.A. & Vallejo, R., 2008, Using Long Time Series of Landsat Data to Monitor Fire Events and Post-Fire Dynamics and Identify Driving Factors, A Case Study in the Ayora Region (Eastern Spain), Remote Sensing of Environment, 112, PP. 259-273.
Roy, D.P., 2000, The Impact of Misregistration upon Composited Wide Field of View Satellite Data and Implications for Change Detection, IEEE Transactions on Geoscience and Remote Sensing, 38, PP. 2017-2032.
Roy, P., 2003, Forest Fire and Degradation Assessment Using Satellite Remote Sensing and Geographic Information System, Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, PP. 361-400.
Ruiz, J.A.M., Riaño, D., Arbelo, M., French, N.H., Ustin, S.L. & Whiting, M.L., 2012, Burned Area Mapping Time Series in Canada (1984–1999) from NOAA-AVHRR LTDR: A Comparison with other Remote Sensing Products and Fire Perimeters, Remote Sensing of Environment, 117, PP. 407-414.
Rundel, P., 1981, Fire as an Ecological Factor, Physiological Plant Ecology I. Springer, PP. 501-538.
Running, S.W., Justice, C., Salomonson, V., Hall, D., Barker, J., Kaufmann, Y., Strahler, A.H., Huete, A., Muller, J.-P. & Vanderbilt, V., 1994, Terrestrial Remote Sensing Science and Algorithms Planned for EOS/MODIS, International Journal of Remote Sensing, 15, PP. 3587-3620.
Saglam, B., Bilgili, E., Dincdurmaz, B., Kadiogulari, A.I. & Küçük, Ö., 2008, Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery, Sensors, 8, PP. 3970-3987.
Saha, A.K., Arora, M.K., Gupta, R.P., Virdi, M. & Csaplovics, E., 2005, GISBased Route Planning in LandslideProne Areas, International Journal of Geographical Information Science, 19, PP. 1149-1175.
Salamati, H., Mostafalou, H., Mastoori, A., Honardoost, F., 2011, Evaluation and Provision of Forest Fire Risk Map Using GIS in Golestan Forests, Proceeding of the First International Conference on Fire in Natural Resources, Gorgan, Iran, PP. 26-28.
Samui, P., 2008, Support Vector Machine Applied to Settlement of Shallow Foundations on Cohesionless Soils, Computers and Geotechnics, 35, PP. 419-427.
Sarkargar Ardakani, A., 2010, Analysis of Spectral-Spatial Features of Fire and Background Targets for Identification and Discrimination in Remote Sensing Data, K.N. Toosi University of Technology, Faculty of Geodesy and Geomatics, Tehran.
Schroeder, W., Prins, E., Giglio, L., Csiszar, I., Schmidt, C., Morisette, J. & Morton, D., 2008, Validation of GOES and MODIS Active Fire Detection Products Using ASTER and ETM+ Data, Remote Sensing of Environment, 112, PP. 2711-2726.
Sedighi Pashaki, M., 2011, Study of Effect Fires on Composition of Plant Spicies under Story and Physical and Chemical Charactristice Soil in Forests Guilan Provience (Case Study: Saravan), Gilan University, Faculty of Agriculture and Natural Resources.
Senici, D., Chen, H.Y., Bergeron, Y. & Cyr, D., 2010, Spatiotemporal Variations of Fire Frequency in Central Boreal Forest, Ecosystems, 13, PP. 1227-1238.
Sharifi Niaraq, J. & Imani, A.A., 1999, Investigating the Effect of Fire on Changes in Vegetation Cover and Species Composition in Semi-Steppe Pastures of Ardabil Province (Case Study in Khalkhal Research), Iran's Natural Resources, 59, PP. 517-526.
Sharma, N. & Hussin, Y.A., 1996, Spatial Modelling for Forest Fire Hazard Prediction, Management and Control in Corbett National Park, India, Faculty of Geo-Information Science and Earth ObservationDepartment of Natural ResourcesUT-I-ITC-FORAGES.
Sharma, J., Granmo, O.-C., Goodwin, M. & Fidje, J.T., 2017, Deep Convolutional Neural Networks for Fire Detection in Images, International Conference on Engineering Applications of Neural Networks, Springer, PP. 183-193.
Sharples, J.J., McRae, R.H., Weber, R. & Gill, A.M., 2009a, A Simple Index for Assessing Fire Danger Rating, Environmental Modelling & Software, 24, PP. 764-774.
Sharples, J.J., McRae, R.H., Weber, R. & Gill, A.M., 2009b, A Simple Index for Assessing Fuel Moisture Content, Environmental Modelling & Software, 24, PP. 637-646.
Sirimongkonlertkul, N. & Phonekeo, V., 2012., Remote Sensing and GIS Application Analysis of Active Fire, Aerosol Optical Thickness and Estimated PM10 in the North of Thailand and Chiang Rai Province, APCBEE Procedia, 1, PP. 304-308.
Sitanggang, I.S., Razali Yaakob, N.M. & Ainuddin, A., 2020, Based on Physical, Socio-Economic, and Peatlands Data, Discovering New Roads to Development, 19.
Smith, A.M., Wooster, M.J., Drake, N.A., Dipotso, F.M., Falkowski, M.J. & Hudak, A.T., 2005, Testing the Potential of Multi-Spectral Remote Sensing for Retrospectively Estimating Fire Severity in African Savannahs, Remote Sensing of Environment, 97, PP. 92-115.
Smith, A., Drake, N., Wooster, M., Hudak, A., Holden, Z. & Gibbons, C., 2007, Production of Landsat ETM+ Reference Imagery of Burned Areas within Southern African Savannahs: Comparison of Methods and Application to MODIS, International Journal of Remote Sensing, 28, PP. 2753-2775.
Sobhani, S. & Amini, A.S., 2019, An Analysis of Forest Fire Monitoring and Modeling Methods Using Remote Sensing Data, The First International Conference and the Fourth National Conference on Conservation of Natural Resources and Environment.
Sohrabi, F., 2014, Effect of Fire Influence on Some Plant Cover Properties in Western Zagros Ranges (Case Study: Rangeland of Taze Abad Serias - Paveh), Birjand University, Faculty of Natural resources and the Environment.
Sripada, R.P., Heiniger, R.W., White, J.G. & Meijer, A.D., 2006, Aerial Color Infrared Photography for Determining Early inSeason Nitrogen Requirements in Corn, Agronomy Journal, 98, PP. 968-977.
Swetnam, T.W. & Betancourt, J.L., 1990, Fire-Southern Oscillation Relations in the Southwestern United States, Science, 249, PP. 1017-1020.
Thornthwaite, C.W., 1948, An Approach toward a Rational Classification of Climate, Geographical Review, 38, PP. 55-94.
Töreyin, B.U., Cinbis, R.G., Dedeoglu, Y. & Cetin, A.E., 2007, Fire Detection in Infrared Video Using Wavelet Analysis, Optical Engineering, 46, P. 067204.
 
Torres, J.G., Zavala, L.M.M., Crocci, N.B. & López, A.J., 2010, Acidez y Capacidad de Intercambio Catiónico en los Suelos Afectados por Incendios. Métodos de Determinación e Interpretación de Resultados, Actualización en métodos y técnicas para el estudio de los suelos afectados por incendios forestales, Cátedra Divulgación de la Ciencia, PP. 327-347.
Trigg, S. & Flasse, S., 2001, An Evaluation of Different Bi-Spectral Spaces for Discriminating Burned Shrub-Savannah, International Journal of Remote Sensing, 22, PP. 2641-2647.
Uyeda, K.A., Stow, D.A., Roberts, D.A. & Riggan, P.J., 2017, Combining Ground-Based Measurements and MODIS-Based Spectral Vegetation Indices to Track Biomass Accumulation in Post-Fire Chaparral, International Journal of Remote Sensing, 38, PP. 728-741.
Vakili, A., 2016, Developing a Spatio-Temporal Model For Forest Fire Risk With Combining Daily, Seasonal Models Using, Remote Sensing And GIS Kerman Graduate University of Industrial and Advanced Technology, Faculty of Civil Engineering and Mapping.
Van Leeuwen, W.J., 2008, Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data, Sensors, 8, PP. 2017-2042.
Varela, M., Faria, S., Campos, I., Caria, M., Ferreira, R., Machado, A., Martins, M., Pinto, R., Prats, S. & Esteves, V., 2012, Effects of Wildfire on Soil Organic Carbon Export by Runoff in Central Portugal, EGU General Assembly Conference Abstracts, P. 9971.
Vasilakos, C., Kalabokidis, K., Hatzopoulos, J. & Matsinos, I., 2009, Identifying Wildland Fire Ignition Factors through Sensitivity Analysis of a Neural Network, Natural Hazards, 50, PP. 125-143.
Vega-García, C., Woodard, P.M. & Lee, B.S., 1993, Geographic and Temporal Factors that Seem to Explain Human-Caused Fire Occurrence in Whitecourt Forest, Alberta, In ‘GIS’93: 7th Annual Symposium on Geographic Information Systems in Forestry, Environment and Natural Resources Management’, 15-18 February 1993, Vancouver, British Columbia, Canada, PP. 115-119.
Veraverbeke, S., Verstraeten, W.W., Lhermitte, S. & Goossens, R., 2010, Evaluating Landsat Thematic Mapper Spectral Indices for Estimating Burn Severity of the 2007 Peloponnese Wildfires in Greece, International Journal of Wildland Fire, 19, PP. 558-569.
Veraverbeke, S., Harris, S. & Hook, S., 2011a, Evaluating Spectral Indices for Burned Area Discrimination Using MODIS/ASTER (MASTER) Airborne Simulator Data, Remote Sensing of Environmen, 115, PP. 2702-2709.
Veraverbeke, S., Lhermitte, S., Verstraeten, W.W. & Goossens, R., 2011b, Evaluation of Pre/Post-Fire Differenced Spectral Indices for Assessing Burn Severity in a Mediterranean Environment with Landsat Thematic Mapper, International Journal of Remote Sensing, 32, PP. 3521-3537.
Viana-Soto, A., Aguado, I., Salas, J. & García, M., 2020, Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests, Remote Sensing, 12, P. 1499.
Wang, W., Qu, J.J., Hao, X., Liu, Y. & Sommers, W.T., 2007, An Improved Algorithm for Small and Cool Fire Detection Using MODIS Data: A Preliminary Study in the Southeastern United States, Remote Sensing of Environment, 108, PP. 163-170.
Wang, L., Qu, J.J. & Hao, X., 2008, Forest Fire Detection Using the Normalized Multi-Band Drought Index (NMDI) with Satellite Measurements, Agricultural and Forest Meteorology, 148, PP. 1767-1776.
Wang, W., Qu, J.J., Hao, X. & Liu, Y., 2009, Analysis of the Moderate Resolution Imaging Spectroradiometer Contextual Algorithm for Small Fire Detection, Journal of Applied Remote Sensing, 3, P. 031502.
Wang, J., Zhou, M., Xu, X., Roudini, S., Sander, S.P., Pongetti, T.J., Miller, S.D., Reid, J.S., Hyer, E. & Spurr, R., 2020, Development of a Nighttime Shortwave Radiative Transfer Model for Remote Sensing of Nocturnal Aerosols and Fires from VIIRS, Remote Sensing of Environment, 241, P. 111727.
Westerling, A.L., Gershunov, A. & Cayan, D.R., 2003, Statistical Forecasts of the 2003 Western Wildfire Season Using Canonical Correlation Analysis, Experimental Long-Lead Forecast Bulletin, 12, P. 2.
Wolfe, R.E., Nishihama, M., Fleig, A.J., Kuyper, J.A., Roy, D.P., Storey, J.C. & Patt, F.S., 2002, Achieving Sub-Pixel Geolocation Accuracy in Support of MODIS Land Science, Remote Sensing of Environment, 83, PP. 31-49.
Wooster, M.J., Xu, W. & Nightingale, T., 2012, Sentinel-3 SLSTR Active Fire Detection and FRP Product: Pre-Launch Algorithm Development and Performance Evaluation Using MODIS and ASTER Datasets, Remote Sensing of Environment, 120, PP. 236-254.
Wulder, M.A., Han, T., White, J.C., Sweda, T. & Tsuzuki, H., 2007, Integrating Profiling LIDAR with Landsat Data for Regional Boreal Forest Canopy Attribute Estimation and Change Characterization, Remote Sensing of Environment, 110, PP. 123-137.
Wulder, M., White, J., Alvarez, F., Han, T., Rogan, J. & Hawkes, B., 2009, Characterizing Boreal Forest Wildfire with Multi-Temporal Landsat and LIDAR Data, Remote Sensing of Environment, 113, PP. 1540-1555.
Yang, J., Pan, S., Dangal, S., Zhang, B., Wang, S. & Tian, H., 2017, Continental-Scale Quantification of Post-Fire Vegetation Greenness Recovery in Temperate and Boreal North America, Remote Sensing of Environment, 199, PP. 277-290.
Yosefi, A., 1961, Fire in the Forest, Its Causes and Ways to Fight It, National Forestry Organization, 117.
Zaitsev, A.S., Gongalsky, K.B., Persson, T. & Bengtsson, J., 2014, Connectivity of Litter Islands Remaining after a Fire and Unburnt Forest Determines the Recovery of Soil Fauna, Applied Soil Ecology, 83, PP. 101-108.
Zare Maivan, H. & Memariani, F., 2002, Natural Revegetation Pattern in Fire - Damaged Parts of Golestan Forest in I.R. Iran, Pajouhesh & Sazandegi, 54, PP. 34-39.
Zhang, Q., Pavlic, G., Chen, W., Latifovic, R., Fraser, R., & Cihlar, J., 2004, Deriving Stand Age Distribution in Boreal Forests Using SPOT VEGETATION and NOAA AVHRR imagery, Remote Sensing of Environment, 91, PP. 405-418.
Zheng, Z., Huang, W., Li, S. & Zeng, Y., 2017, Forest Fire Spread Simulating Model Using Cellular Automaton with Extreme Learning Machine, Ecological Modelling, 348, PP. 33-43.
Zohdi, T., 2020, A Machine-Learning Framework for Rapid Adaptive Digital-Twin Based Fire-Propagation Simulation in Complex Environments, Computer Methods in Applied Mechanics and Engineering, 363, P. 112907.