Volume 7, Issue 1 (5-2020)
Journal of Spatial Analysis Environmental Hazards 2020, 7(1): 1-14 |
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aghazadeh F, rostamzadeh H, valizadeh kamran K. Real-time detection of wildlife using NOAA/AVHRR data Study area :(Kayamaki Wildlife Refuge). Journal of Spatial Analysis Environmental Hazards 2020; 7 (1) :1-14
URL: http://jsaeh.khu.ac.ir/article-1-2916-en.html
URL: http://jsaeh.khu.ac.ir/article-1-2916-en.html
1- Tabriz University , f.aghazadeh95@ms.tabrizu.ac.ir
2- Tabriz University
2- Tabriz University
Abstract: (3928 Views)
Real-time detection of forest fire using NOAA/AVHRR data
Study area :(Kayamaki Wildlife Refuge)
Extended Abstract
Introduction
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.
Materials and Methods
In this study, NOAA/AVHRR images were used for fire detection and MODIS products were applied for evaluation and validation.
Fire Detection Algorithms
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.
Giglio Algorithm
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 m3) 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.
IGBP Algorithm
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.
Developed Algorithm
This algorithm is used to detect small and large fires (both at night and day).
Interpretation of the Results
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.
Evaluation of fires identified through MODIS products
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.
Comparison of the implications of the fire algorithms
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.
Keywords:Real Time Fire Detection, Fire Algorithms, NOAA/AVHRR, Kiamaki Wildlife Refuge.
Study area :(Kayamaki Wildlife Refuge)
Extended Abstract
Introduction
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.
Materials and Methods
In this study, NOAA/AVHRR images were used for fire detection and MODIS products were applied for evaluation and validation.
Fire Detection Algorithms
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.
Giglio Algorithm
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 m3) 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.
IGBP Algorithm
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.
Developed Algorithm
This algorithm is used to detect small and large fires (both at night and day).
Interpretation of the Results
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.
Evaluation of fires identified through MODIS products
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.
Comparison of the implications of the fire algorithms
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.
Keywords:Real Time Fire Detection, Fire Algorithms, NOAA/AVHRR, Kiamaki Wildlife Refuge.
Type of Study: Applicable |
Subject:
Special
Received: 2019/02/22 | Accepted: 2019/12/6 | Published: 2020/06/16
Received: 2019/02/22 | Accepted: 2019/12/6 | Published: 2020/06/16
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