A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature Threshold
Satellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algor...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/14/3/477 |
| _version_ | 1797611656517255168 |
|---|---|
| author | Yunhong Ding Mingyang Wang Yujia Fu Lin Zhang Xianjie Wang |
| author_facet | Yunhong Ding Mingyang Wang Yujia Fu Lin Zhang Xianjie Wang |
| author_sort | Yunhong Ding |
| collection | DOAJ |
| description | Satellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algorithms are mainly based on a fixed brightness temperature threshold to distinguish wildfire pixels and non-wildfire pixels, which reduces the applicability of the algorithm in different space–time regions. This paper presents an adaptive wildfire detection algorithm, DBTDW, based on a dynamic brightness temperature threshold. First, a regression dataset, MODIS_DT_Fire, was constructed based on moderate resolution imaging spectroradiometry (MODIS) to determine the wildfire brightness temperature threshold. Then, based on the meteorological information, normalized difference vegetation index (NDVI) information, and elevation information provided by the dataset, the DBTDW algorithm was used to calculate and obtain the minimum brightness temperature threshold of the burning area by using the Planck algorithm and Otsu algorithm. Finally, six regression models were trained to establish the correlation between factors and the dynamic brightness temperature threshold of wildfire. The root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the regression performance. The results show that under the XGBoost model, the DBTDW algorithm has the best prediction effect on the dynamic brightness temperature threshold of wildfire (leave-one-out method: RMSE/MAE = 0.0730). Compared with the method based on a fixed brightness temperature threshold, the method proposed in this paper to adaptively determine the brightness temperature threshold of wildfire has higher universality, which will help improve the effectiveness of satellite remote fire detection. |
| first_indexed | 2024-03-11T06:31:48Z |
| format | Article |
| id | doaj.art-36a4ba684618449db07bff912cc0465d |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-11T06:31:48Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-36a4ba684618449db07bff912cc0465d2023-11-17T11:09:08ZengMDPI AGForests1999-49072023-02-0114347710.3390/f14030477A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature ThresholdYunhong Ding0Mingyang Wang1Yujia Fu2Lin Zhang3Xianjie Wang4College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaSchool of Physics, Harbin Institute of Technology, Harbin 150001, ChinaSatellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algorithms are mainly based on a fixed brightness temperature threshold to distinguish wildfire pixels and non-wildfire pixels, which reduces the applicability of the algorithm in different space–time regions. This paper presents an adaptive wildfire detection algorithm, DBTDW, based on a dynamic brightness temperature threshold. First, a regression dataset, MODIS_DT_Fire, was constructed based on moderate resolution imaging spectroradiometry (MODIS) to determine the wildfire brightness temperature threshold. Then, based on the meteorological information, normalized difference vegetation index (NDVI) information, and elevation information provided by the dataset, the DBTDW algorithm was used to calculate and obtain the minimum brightness temperature threshold of the burning area by using the Planck algorithm and Otsu algorithm. Finally, six regression models were trained to establish the correlation between factors and the dynamic brightness temperature threshold of wildfire. The root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the regression performance. The results show that under the XGBoost model, the DBTDW algorithm has the best prediction effect on the dynamic brightness temperature threshold of wildfire (leave-one-out method: RMSE/MAE = 0.0730). Compared with the method based on a fixed brightness temperature threshold, the method proposed in this paper to adaptively determine the brightness temperature threshold of wildfire has higher universality, which will help improve the effectiveness of satellite remote fire detection.https://www.mdpi.com/1999-4907/14/3/477dynamic brightness temperature thresholdadaptive wildfire detectionremote sensingMODIS_DT_Fire |
| spellingShingle | Yunhong Ding Mingyang Wang Yujia Fu Lin Zhang Xianjie Wang A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature Threshold Forests dynamic brightness temperature threshold adaptive wildfire detection remote sensing MODIS_DT_Fire |
| title | A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature Threshold |
| title_full | A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature Threshold |
| title_fullStr | A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature Threshold |
| title_full_unstemmed | A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature Threshold |
| title_short | A Wildfire Detection Algorithm Based on the Dynamic Brightness Temperature Threshold |
| title_sort | wildfire detection algorithm based on the dynamic brightness temperature threshold |
| topic | dynamic brightness temperature threshold adaptive wildfire detection remote sensing MODIS_DT_Fire |
| url | https://www.mdpi.com/1999-4907/14/3/477 |
| work_keys_str_mv | AT yunhongding awildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT mingyangwang awildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT yujiafu awildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT linzhang awildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT xianjiewang awildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT yunhongding wildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT mingyangwang wildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT yujiafu wildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT linzhang wildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold AT xianjiewang wildfiredetectionalgorithmbasedonthedynamicbrightnesstemperaturethreshold |