Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data
"> Figure 1
<p>Flowchart of the proposed algorithm for smoke detection.</p> "> Figure 2
<p>True-color composition RGB images of three cases used to extract seasonal samples: (<b>a</b>) Smoke plumes emitted from fire happened in Daxing’anling area, China in autumn, (<b>b</b>) Smoke plumes emitted from major fires in northeastern Asia in spring, (<b>c</b>) Smoke plumes emitted from fires in Russia in summer.</p> "> Figure 3
<p>Samples used for spectral analysis are extracted in this area during the forest fire happened on 28 June 2010, (<b>a</b>) True-color composition RGB image acquired over Daxing’anling area, China on 28 June 2010, (<b>b</b>) The extracted smoke samples are shown in the bright white area, (<b>c</b>) The extracted cloud samples are marked with bright white color.</p> "> Figure 4
<p>Response curves of the four cover types.</p> "> Figure 5
<p>Normalized distances between smoke and cloud, smoke and water, smoke and vegetation in reflectance of MODIS bands 1–8.</p> "> Figure 6
<p>3D scatter plot between smoke and cloud in <span class="html-italic">BT11</span>, <span class="html-italic">BTD (3.7–12)</span> and <span class="html-italic">R26</span>.</p> "> Figure 7
<p>The structure of BP neural networks that designed for identifying smoke plumes. S means smoke, C represents cloud whereas W/V is underlying surface.</p> "> Figure 8
<p>The evolution of BPNN performance with epoch.</p> "> Figure 9
<p>The decrease of accuracy and the increase of error by comparing the tests in different ways (one is that the training samples and testing samples extracted in the same season; the other is that the training samples extracted in summer while the testing samples extracted in spring).</p> "> Figure 10
<p>(<b>a</b>) True-color composition RGB image acquired over Daxing’anling area, China on 16 October 2004, (<b>b</b>) True-color composition RGB image acquired over Daxing’anling area, China and Amur region, Russia on 29 April 2009, (<b>c</b>) True-color composition RGB image acquired over Ryazan region, Russia on 29 July 2010, Panels (<b>d</b>) and (<b>e</b>) are the results of panel (a) and the rectangle area in (a) by using the algorithm, Panels (<b>f</b>) and (<b>g</b>) are the results of panel (b) and the rectangle area in (b), Panels (<b>h</b>) and (<b>i</b>) are the results of panel (c) and the rectangle area in (c).</p> "> Figure 11
<p>Smoke plumes detection by using the improved algorithm in Russia on 3 August 2012 (summer): (<b>a</b>) True-color composition RGB image of MODIS bands 1, 4 and 3 covering the detected area, (<b>b</b>) The rectangle area shown in panel (<b>a</b>), (<b>c</b>) The detected result of panel (<b>a</b>) and panel (<b>d</b>) is the result of rectangle area. The smoke plumes are depicted in red color.</p> "> Figure 12
<p>Smoke plumes detection in western Quebec, Canada on 19 June 2013 (spring): (<b>a</b>) True-color composition RGB image of MODIS bands 1, 4 and 3 covering the detected area, (<b>b</b>) The rectangle area shown in panel (a), (<b>c</b>) The detected result of panel (a) and panel (<b>d</b>) is the result of rectangle area, the smoke plumes are depicted in red color.</p> "> Figure 13
<p>Smoke plumes detection by using the algorithm in Greece on 24 August 2007 (summer): (<b>a</b>) True-color composition RGB image of MODIS bands 1, 4 and 3 covering the detected area, (<b>b</b>) The rectangle area shown in panel (a), (<b>c</b>) The detected result of panel (a) and panel (<b>d</b>) is the result of rectangle area, the smoke plumes are depicted in red color.</p> "> Figure 14
<p>Smoke plumes detection by using the algorithm around the Alice Spring (Australia) on 30 September 2011 (spring): (<b>a</b>) True-color composition RGB image of MODIS bands 1, 4 and 3 covering the detected area, (<b>b</b>) The rectangle area shown in panel (<b>a</b>), (<b>c</b>) The detected result of panel (<b>a</b>) and panel (<b>d</b>) is the result of rectangle area, the smoke plumes are depicted in red color.</p> "> Figure 15
<p>Smoke plumes detection by using the multi-threshold method in different locations. Panel (<b>a</b>) and Panel (<b>b</b>) are the results of two groups of thresholds in Russia on 3 August 2012. Panel (<b>c</b>) and Panel (<b>d</b>) are the results of two groups of thresholds in Quebec, Canada on 19 June 2013. Panel (<b>e</b>) and Panel (<b>f</b>) are the results of two groups of thresholds in Greece on 24 August 2007.</p> ">
Abstract
:1. Introduction
2. Data Source
Band | Bandwidth * | Signal to Noise Radio | Spatial Resolution | Primary Use |
---|---|---|---|---|
1 | 620~670 | 128 | 250 m | Land/Cloud/Aerosols Boundaries |
2 | 841~876 | 201 | 250 m | Land/Cloud/Aerosols Boundaries |
3 | 459~479 | 243 | 500 m | Land/Cloud/Aerosols Properties |
4 | 545~565 | 228 | 500 m | Land/Cloud/Aerosols Properties |
5 | 1230~1250 | 74 | 500 m | Land/Cloud/Aerosols Properties |
6 | 1628~1652 | 275 | 500 m | Land/Cloud/Aerosols Properties |
7 | 2105~2155 | 110 | 500 m | Land/Cloud/Aerosols Properties |
8 | 405~420 | 880 | 1000 m | Ocean Color/Phytoplankton/Biogeochemistry |
9 | 438~448 | 838 | 1000 m | Ocean Color/Phytoplankton/Biogeochemistry |
19 | 915~965 | 250 | 1000 m | Atmospheric Water Vapor |
20 | 3.66~3.84 | 0.050 | 1000 m | Surface/Cloud Temperature |
26 | 1.36~1.39 | 1504 | 1000 m | Cirrus Clouds Water Vapor |
31 | 10.78~11.28 | 0.05 | 1000 m | Surface/Cloud Temperature |
32 | 11.77~12.27 | 0.05 | 1000 m | Surface/Cloud Temperature |
3. Algorithm
- (1)
- Extraction of training samples: Extraction of seasonal training samples of different cover types by multi-threshold method;
- (2)
- Spectral analysis for selecting feature vectors: Spectral analysis of different cover types and selecting feature vectors for input layer of BPNN;
- (3)
- Training of BPNN and Elimination of noise pixels.
3.1. Extraction of Training Samples
3.1.1 Multi-Thresholds for Various Cover Types
3.1.2. Seasonal Training Sample Set
3.2. Spectral Analysis for Selecting Feature Vectors
- (1)
- Spectral analysis of potential smoke plumes and underlying surface;
- (2)
- Spectral analysis of smoke and cloud.
3.2.1. Spectral Analysis of Potential Smoke Plumes and Underlying Surface
3.2.2. Spectral Analysis of Smoke and Cloud
3.3. Training of BPNN and Elimination of Noise Pixels
Cover Types | Smoke | Underlying Surface | Cloud |
---|---|---|---|
Desired Output | 1 | 0 | −1 |
4. Results and Discussion
4.1. Accuracy Evaluation of the Algorithm
Cover Types | Smoke | Underlying Surface | Cloud | Omission Error | Commission Error |
---|---|---|---|---|---|
Smoke | 296 | 18 | 0 | 1.66% | 5.73% |
Underlying Surface | 5 | 521 | 4 | 3.34% | 1.70% |
Cloud | 0 | 0 | 296 | 1.35% | 0 |
Overall Accuracy | 97.63% | ||||
Kappa Coefficient | 96.29% |
Cover Types | Smoke | Underlying Surface | Cloud | Omission Error | Commission Error |
---|---|---|---|---|---|
Smoke | 1178 | 228 | 248 | 10.08% | 28.78% |
Underlying Surface | 132 | 1151 | 1062 | ||
Cloud | 0 | 1 | 0 | ||
Overall Accuracy | 58.23% | ||||
Kappa Coefficient | 36.92% |
4.2. Seasonal Applicability of the Algorithm
4.3. Robustness of the Algorithm
4.4. Results of the Multi-Threshold Method
5. Conclusions
Acknowledgements
Author Contributions
Conflicts of Interest
References
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Li, X.; Song, W.; Lian, L.; Wei, X. Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data. Remote Sens. 2015, 7, 4473-4498. https://doi.org/10.3390/rs70404473
Li X, Song W, Lian L, Wei X. Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data. Remote Sensing. 2015; 7(4):4473-4498. https://doi.org/10.3390/rs70404473
Chicago/Turabian StyleLi, Xiaolian, Weiguo Song, Liping Lian, and Xiaoge Wei. 2015. "Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data" Remote Sensing 7, no. 4: 4473-4498. https://doi.org/10.3390/rs70404473