Journal of Spatial Analysis Environmental Hazards
تحلیل فضایی مخاطرات محیطی
Journal of Spatial Analysis Environmental Hazards
Literature & Humanities
http://jsaeh.khu.ac.ir
1
admin
2423-7892
2588-5146
10.61186/jsaeh
fa
jalali
1399
2
1
gregorian
2020
5
1
7
1
online
1
fulltext
fa
شناسایی بلادرنگ آتش سوزی جنگل و مراتع با استفاده از داده های NOAA/AVHRR منطقه مورد مطالعه(پناهگاه حیات وحش کیامکی)
Real-time detection of wildlife using NOAA/AVHRR data Study area :(Kayamaki Wildlife Refuge)
تخصصي
Special
كاربردي
Applicable
<strong>آتش سوزی جنگل در سال های اخیر توجه زیادی به تغییرات اقلیمی و اکوسیستم داشته است.</strong> <strong>سنجش از دور، یک روش سریع و ارزان برای تشخیص و نظارت بر آتش سوزی جنگل ها در مقیاس وسیع است. هدف از این پژوهش شناسایی آتش­سوزی جنگل و مراتع با استفاده از سنجنده­ </strong><strong><span dir="LTR">NOAA/AVHRR</span></strong> <strong>در پناهگاه حیات وحش کیامکی می­باشد.جهت انجام تحقیق، ابتدا تاریخ آتش­سوزی­های رخ داده از محصولات </strong><strong><span dir="LTR">MODIS</span></strong><strong> استخراج گردید. سپس تصاویر سنجنده مورد نظر براساس تاریخ­ آتش­سوزی­های رخ داده تهیه شد. بعد از انجام پیش پردازش تصاویر، با استفاده از الگوریتم­های توسعه یافته، گیگلیو و </strong><strong><span dir="LTR">IGBP</span></strong><strong> اقدام به شناسایی آتش­سوزی گردید. نتایج الگوریتم­های شناسایی آتش­سوزی با محصولات </strong><strong><span dir="LTR">MODIS</span></strong><strong> مورد ارزیابی قرار گرفتند. نتایج نشان داد که شناسایی آتش­سوزی</strong> <strong>با استفاده از الگوریتم </strong><strong><span dir="LTR">IGBP</span></strong><strong> نسبت به الگوریتم­های توسعه یافته و گیگلیو بهتر است. بدین صورت که الگوریتم </strong><strong><span dir="LTR">IGBP</span></strong><strong> با تعداد آتش­سوزی شناسایی شده برابر با 6 پیکسل از</strong> <strong>7</strong> <strong>پیکسل آتش­سوزی شناسایی شده توسط محصولات </strong><strong><span dir="LTR">MODIS</span></strong><strong>، الگوریتم گیگلیو با تعداد آتش­سوزی شناسایی شده برابر با 5 پیکسل از 7 پیکسل آتش­سوزی شناسایی شده توسط محصولات </strong><strong><span dir="LTR">MODIS</span></strong><strong> و الگوریتم توسعه یافته تعداد آتش­سوزی شناسایی شده برابر با 3 پیکسل از 7 پیکسل آتش­سوزی شناسایی شده توسط محصولات </strong><strong><span dir="LTR">MODIS</span></strong><strong> را شناسایی کرد. همچنین الگوریتم </strong><strong><span dir="LTR">IGBP</span></strong> <strong>با میزان خطای 14% و با تعداد آتش­سوزی شناسایی 86%، الگوریتم گیگلیو با میزان خطای 28% و تعداد آتش­سوزی شناسایی شده 72% و الگوریتم توسعه یافته با میزان خطای 57% و تعداد آتش­سوزی شناسایی شده 43% را نشان داد.</strong>
<strong>Real-time detection of forest fire using NOAA/AVHRR data</strong><strong><span dir="RTL"></span></strong><br>
<strong>Study area<span dir="RTL"> :</span>(Kayamaki Wildlife Refuge</strong><strong>)</strong><strong><span dir="RTL"></span></strong><br>
<br>
<strong>Extended Abstract</strong><br>
<strong>Introduction</strong><br>
Land and forest fires are one of the most common problems in the world that cause various disturbances in forest and land efficiency. Real-time fire detection is crucial to prevent large-scale casualties. In order to identify early fire in areas where there is a high risk of fire, it is necessary to monitor these areas regularly. Forest monitoring is a technique used to detect fires in the past using traditional techniques such as surveillance, helicopter and aircraft. Today, satellite imagery is one of the most imperative and effective tools for detecting active fires in the world<span dir="RTL">.</span><span dir="RTL"></span><br>
<strong>Materials and Methods</strong><br>
In this study, NOAA/AVHRR images were used for fire detection and MODIS products were applied for evaluation and validation<span dir="RTL">.</span><span dir="RTL"></span><br>
<em>Fire Detection Algorithms</em><br>
There are several algorithms for detecting fires using satellite imagery. In this study, 3 algorithms of Giglio, extended and IGPP were used. The selection of these algorithms was due to the extensive background research in most of the previous studies that used them and the results of these algorithms, especially the IGPP, were far more than other algorithms<span dir="RTL">.</span><span dir="RTL"></span><br>
<em>Giglio Algorithm</em><br>
Giglio et al., (1999) criticized Arino and Melinott (1993) threshold as too high for certain regions of the world such as tropical rain forests, temperate climates and marshes where the air temperature for small fires (100 m<sup>3</sup>) is usually between 308 and 314 degrees Kelvin. They believed that the smaller fires were not fully recognized by Arino and Melinott (1993) thresholds. They concluded that in suburban forests 60% of fires had temperatures below 320K of which 70% were in rainforests and 85% happened in the Savanna. Thus, the threshold cannot be applied on a large scale and it is only applicable for a regional scale<span dir="RTL">.</span><span dir="RTL"></span><br>
<em>IGBP Algorithm</em><br>
The IGBP fire detection algorithm is implemented in two steps. The first step is the threshold test in which a pixel in micrometers (11.03 μm) minus the band 4 is greater than 8 degrees Kelvin, the desired pixel being considered as a potential fire pixel. Band 3 (3.9 μm) exceeds 311 K, and band 3 illumination temperature is 3.9<span dir="RTL">.</span><span dir="RTL"></span><br>
<em>Developed Algorithm </em><br>
This algorithm is used to detect small and large fires (both at night and day).<span dir="RTL"></span><br>
<br>
<strong>Interpretation of the Results</strong><br>
After selecting fire detection algorithms, pre-processing (geometric, radiometric and atmospheric corrections), processing (applying fire relationships and fire formulas for fire detection) and post-processing (evaluating and validating the results), the fires were identified by the fire algorithms (images). Final results of fires identified for 2016 and 2017 (for 4 days) by fire algorithms indicate that fires identified by Giglio algorithm were 22 cases, those by IGPP algorithm were 27 cases and the ones by the developed algorithm were 15 cases. For this reason, the IGPP algorithm can be taken as the most appropriate algorithm in this study for fire detection using satellite imagery<span dir="RTL">.</span><span dir="RTL"></span><br>
<em>Evaluation of fires identified through MODIS products</em><br>
To evaluate identified fires, after recognizing them with relevant algorithms, we used MODIS products for their evaluation (due to the lack of ground data on the days studied for evaluation). MODIS products were obtained from sites where the location of each fire was reported. For the evaluation of identified fires based on fire detection algorithms with MODIS products, 10 fire occurrences were used. The evaluation results express that out of 10 fires only 7 fires were recognized by the algorithms of MODIS products. 5 fire events were identified by Giglio algorithm (from 7 fires), 6 fires from IGBP (out of 7 fires), and 3 fire events from 7 extended algorithm were selected as fire pixels<span dir="RTL">.</span><span dir="RTL"></span><br>
<em>Comparison of the implications of the fire algorithms</em><br>
The implications of fire occurrence algorithms indicate that the IGBP algorithm with 6 fires (out of 7 tested fires with error rate of 14% and with the number of fires detected (86%)), Giglio algorithm with 5 fires (out of 7 tested fires, with error rate of 28% and with the number of fires (72%)) and the developed algorithm with 3 fires (out of 7 fires tested with an error rate of 57% and with fire rate of 43%) have been identified. Therefore, it is concluded that the IGBP is the most appropriate algorithm for real-time fire detection, followed by Giglio and the developed algorithm in second and third orders, respectively<span dir="RTL">.</span><span dir="RTL"></span><br>
<strong>Keywords:</strong>Real Time Fire Detection, Fire Algorithms, NOAA/AVHRR, Kiamaki Wildlife Refuge<span dir="RTL">.</span><br>
شناسایی آتشسوزی در زمان واقعی, الگوریتمهای آتشسوزی, NOAA/AVHRR, پناهگاه حیات وحش کیامکی
Real-time fire detection, fire algorithms, NOAA / AVHRR, Kayamaki Wildlife Refuge.
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http://jsaeh.khu.ac.ir/browse.php?a_code=A-10-828-1&slc_lang=fa&sid=1
firuz
aghazadeh
فیروز
آقازاده
f.aghazadeh95@ms.tabrizu.ac.ir
100319475328460011272
100319475328460011272
Yes
Tabriz University
دانشگاه تبریز
hashem
rostamzadeh
هاشم
رستم زاده
h_rostamzadeh@tabrizu.ac.ir
100319475328460011273
100319475328460011273
No
Tabriz University
دانشگاه تبریز
khalil
valizadeh kamran
خلیل
ولیزاده کامران
valizadeh@tabrizu.ac.ir
100319475328460011274
100319475328460011274
No
Tabriz University
دانشگاه تبریز