Keywords
How to Cite
Abstract
With satellite remote sensing technology blooming, satellite remote sensing has become a common tool to detect forest fires, and played an important role in forest fire monitoring. This paper sort the research status and progress on satellite remote sensing monitoring for forest fires to provide directions and insights for subsequent research and applications. Through reviewing the literature on satellite remote sensing monitoring for forest fires, we present satellites and sensors for forest fire monitoring, describe forest fire monitoring methods through brightness temperature detection and smoke detection, and summarize current problems of satellite remote sensing monitoring of forest fires. Despite forest fire satellite remote sensing monitoring algorithms are becoming increasingly mature, it is not without problems such as slow migration of cloud detection algorithms, difficulties in unifying spatial and temporal characteristics, and difficulties in detecting small fires and low-temperature fires. Finally, in response to the problems identified, we list some recommendations with a view to providing useful references for future research on forest fire monitoring with satellite remote sensing.
References
Abid, F. (2021). A survey of machine learning algorithms based forest fires prediction and detection systems. Fire Technology, 57(2), 559–590. https://doi.org/10.1007/s10694-020-01056-z
Arino, O., Casadio, S., & Serpe, D. (2012). Global night-time fire season timing and fire count trends using the ATSR instrument series. Remote Sensing of Environment, 116, 226–238. https://doi.org/10.1016/j.rse.2011.05.025
Arino, O., Rosaz, J., & Atlas, F. (1999). 1997 and 1998 world atsr fire atlas using ers-2 atsr-2 data. Proceedings of the joint fire science conference.
Arrue, B. C., Ollero, A., & Dios, J. R. M. d. (2000). An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems and their Applications, 15(3), 64–73. https://doi.org/10.1109/5254.846287
Ba, R., Chen, C., Yuan, J., Song, W., & Lo, S. (2019). SmokeNet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention. Remote Sensing, 11(14), 1702. https://doi.org/10.3390/rs11141702
Barmpoutis, P., Stathaki, T., Dimitropoulos, K., & Grammalidis, N. (2020). Early fire detection based on aerial 360-degree sensors, deep convolution neural networks and exploitation of fire dynamic textures. Remote Sensing, 12(19), 3177. https://doi.org/10.3390/rs12193177
Bing, F., Jin, Y., Zhang, W., Xu, N., Yu, T., Zhang, L., & Pei, Y. (2023). Research progress of remote sensing image cloud detection based on machine learning. Remote sensing technology and application, 38(1), 129–142. https://doi.org/10.11873/j.issn.1004-0323.2023.1.0129
Chen, J., Zheng, W., Wu, S., Liu, C., & Yan, H. (2022). Fire monitoring algorithm and its application on the GEO-KOMPSAT-2A geostationary meteorological satellite. Remote Sensing, 14(11), 2655. https://doi.org/10.3390/rs14112655
Chrysoulakis, N., Herlin, I., Prastacos, P., Yahia, H., Grazzini, J., & Cartalis, C. (2007). An improved algorithm for the detection of plumes caused by natural or technological hazards using AVHRR imagery. Remote Sensing of Environment, 108(4), 393–406. https://doi.org/10.1016/j.rse.2006.11.024
Chung, Y. S., & Le, H. V. (1984). Detection of forest-fire smoke plumes by satellite imagery. Atmospheric Environment, 18(10), 2143–2151. https://doi.org/10.1016/0004-6981(84)90201-4
Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., & Oliva, P. (2020). Satellite remote sensing contributions to wildland fire science and management. Current Forestry Reports, 6(2), 81–96. https://doi.org/10.1007/s40725-020-00116-5
Dozier, J. (1981). A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of Environment, 11, 221–229. https://doi.org/10.1016/0034-4257(81)90021-3
Eckmann, T. C., Roberts, D. A., & Still, C. J. (2008). Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS. Remote Sensing of Environment, 112(10), 3773–3783. https://doi.org/10.1016/j.rse.2008.05.008
Fang, Z. (2014). The evolution of meteorological satellites and the insight from it. Advances in Meteorological Science and Technology, 4(06), 27–34. https://doi.org/10.3969/j.issn.2095-1973.2014.06.003
Feng, L., & Zhou, W. (2023). The forest fire dynamic change influencing factors and the impacts on gross primary productivity in China. Remote Sensing, 15(5), 1364. https://doi.org/10.3390/rs15051364
Ferrare, R. A., Fraser, R. S., & Kaufman, Y. J. (1990). Satellite measurements of large-scale air pollution: Measurements of forest fire smoke. Journal of Geophysical Research: Atmospheres, 95(D7), 9911–9925. https://doi.org/10.1029/JD095iD07p09911
Flannigan, M. D., & Haar, T. H. V. (1986). Forest fire monitoring using NOAA satellite AVHRR. Canadian Journal of Forest Research, 16, 975–982. https://doi.org/10.1139/x86-171
Flasse, S. P., & Ceccato, P. (1996). A contextual algorithm for AVHRR fire detection. International Journal of Remote Sensing, 17(2), 419–424. https://doi.org/10.1080/01431169608949018
Gao, J., Wang, K., Tian, X., & Chen, J. (2018). A BP-NN based cloud detection method for FY-4 remote sensing images. Journal of Infrared and Millimeter Waves, 37(4), 477–485. https://doi.org/10.11972/j.issn.1001-9014.2018.04.016
Giglio, L. (2007). Characterization of the tropical diurnal fire cycle using VIRS and MODIS observations. Remote Sensing of Environment, 108(4), 407–421. https://doi.org/10.1016/j.rse.2006.11.018
Giglio, L., Csiszar, I., Restás, Á., Morisette, J. T., Schroeder, W., Morton, D., & Justice, C. O. (2008). Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER). Remote Sensing of Environment, 112(6), 3055–3063. https://doi.org/10.1016/j.rse.2008.03.003
Giglio, L., Descloitres, J., Justice, C. O., & Kaufman, Y. J. (2003). An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87(2), 273–282. https://doi.org/10.1016/S0034-4257(03)00184-6
Giglio, L., & Schroeder, W. (2014). A global feasibility assessment of the bi-spectral fire temperature and area retrieval using MODIS data. Remote Sensing of Environment, 152, 166–173. https://doi.org/10.1016/j.rse.2014.06.010
Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178, 31–41. https://doi.org/10.1016/j.rse.2016.02.054
He, L., & Li, Z. (2011). Enhancement of a fire-detection algorithm by eliminating solar contamination effects and atmospheric path radiance: Application to MODIS data. International Journal of Remote Sensing, 32(21), 6273–6293. https://doi.org/10.1080/01431161.2010.508057
He, L., & Li, Z. (2012). Enhancement of a fire detection algorithm by eliminating solar reflection in the mid-IR band: Application to AVHRR data. International Journal of Remote Sensing, 33(22), 7047–7059. https://doi.org/10.1080/2150704X.2012.699202
He, Q., & Liu, C. (2008). Improved algorithm of self-adaptive five detection for MODIS data. Journal of Remote Sensing(3), 448–453. https://doi.org/10.11834/jrs.20080361
He, R., Zhao, F., Zeng, Y., Zhou, R., Shu, L., & Ye, J. (2022). Application of multisource remote sensing imagery to forest fire monitoring. World Forestry Research, 35(2), 59–63. https://doi.org/10.13348/j.cnki.sjlyyj.2021.0097.y
He, X., Feng, X., Han, Q., Kang, N., Guo, Q., & Peng, Y. (2020). Advances of the geostationary meteorological satellite in the world: A review. Advances in Meteorological Science and Technology, 10(1), 22–29+41. https://doi.org/10.3969/j.issn.2095-1973.2020.01.005
Howard, J., Murashov, V., & Branche, C. M. (2018). Unmanned aerial vehicles in construction and worker safety. American journal of industrial medicine, 61(1), 3–10. https://doi.org/10.1002/ajim.22782
Hua, L., & Shao, G. (2017). The progress of operational forest fire monitoring with infrared remote sensing. Journal of Forestry Research, 28(2), 215–229. https://doi.org/10.1007/s11676-016-0361-8
Jeppesen, J. H., Jacobsen, R. H., Inceoglu, F., & Toftegaard, T. S. (2019). A cloud detection algorithm for satellite imagery based on deep learning. Remote Sensing of Environment, 229, 247–259. https://doi.org/10.1016/j.rse.2019.03.039
Justice, C. O., Giglio, L., Korontzi, S., Owens, J., Morisette, J. T., Roy, D. P., Descloitres, J., Alleaume, S., Petitcolin, F., & Kaufman, Y. J. (2002). The MODIS fire products. Remote Sensing of Environment, 83(1), 244–262. https://doi.org/10.1016/S0034-4257(02)00076-7
Kang, Y., Jang, E., Im, J., & Kwon, C. (2022). A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency. GIScience & Remote Sensing, 59(1), 2019–2035. https://doi.org/10.1080/15481603.2022.2143872
Kaufman, Y. J., Tucker, C. J., & Fung, I. (1990). Remote sensing of biomass burning in the tropics. Journal of Geophysical Research: Atmospheres, 95(D7), 9927–9939. https://doi.org/10.1029/JD095iD07p09927
Kennedy, P. J., Belward, A. S., & Gregoire, J. M. (1994). An improved approach to fire monitoring in West Africa using AVHRR data. International Journal of Remote Sensing, 15(11), 2235–2255. https://doi.org/10.1080/01431169408954240
Lee, T. F., & Tag, P. M. (1990). Improved detection of hotspots using the AVHRR 3.7-um channel. Bulletin of the American Meteorological Society, 71(12), 1722–1730. https://doi.org/10.1175/1520-0477(1990)071<1722:IDOHUT>2.0.CO;2
Li, X., & Jia, J. (2018). How the smallest clairvoyance in meteorological satellite was tempered ?—The preparation of the infrared-detector chips of FY-4A multiple channel scanning radiation imager. Chinese Journal of Nature, 40(2), 90–101. https://doi.org/10.3969/j.issn.0253-9608.2018.02.002
Li, X., Song, W., Lian, L., & Wei, X. (2015). Forest fire smoke detection using back-propagation neural network based on MODIS data. Remote Sensing, 7(4), 4473–4498. https://doi.org/10.3390/rs70404473
Li, X., Wang, J., Song, W., Ma, J., Telesca, L., & Zhang, Y. (2014). Automatic smoke detection in MODIS satellite data based on K-means clustering and fisher linear discrimination. Photogrammetric Engineering & Remote Sensing, 80(10), 971–982. https://doi.org/10.14358/PERS.80.10.971
Li, X., Zhang, G., Tan, S., Yang, Z., & Wu, X. (2023). Forest fire smoke detection research based on the random forest algorithm and sub-pixel mapping method. Forests, 14(3), 485. https://doi.org/10.3390/f14030485
Li, Y., Zhang, X., Wu, H., Gao, P., & Xia, D. (2007). An enhanced contextual fire detection algorithm based on remote sensing images. Journal of Image and Graphics, 12(9), 1627–1632. https://doi.org/10.11834/jig.20070922
Li, Z., Kaufman, Y. J., Ichoku, C., Fraser, R., & Yu, X. (2001). A review of AVHRR-based active fire detection algorithms: Principles limitations and recommendations. In: Ahern FJ, Goldammer JG, Justice CO (eds), Global and regional vegetation fire monitoring from space: planning and coordinated international effort (pp. 199–225).
Li, Z., Nadon, S., & Cihlar, J. (2000). Satellite-based detection of Canadian boreal forest fires: Development and application of the algorithm. International Journal of Remote Sensing, 21(16), 3057–3069. https://doi.org/10.1080/01431160050144956
Lin, Z., Chen, F., Li, B., Yu, B., Shirazi, Z., Wu, Q., & Wu, W. (2017). FengYun-3C VIRR active fire monitoring: Algorithm description and initial assessment using MODIS and landsat data. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6420–6430. https://doi.org/10.1109/TGRS.2017.2728103
Ling, F., Du, Y., Xiao, F., Xue, H., & Wu, S. (2010). Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images. International Journal of Remote Sensing, 31(19), 5023–5040. https://doi.org/10.1080/01431160903252350
Liu, S., Li, X., Qin, X., Sun, G., & Liu, Q. (2020). Adaptive threshold method for active fire identification based on GF-4 PMI data. Journal of Remote Sensing, 24(3), 215–225. https://doi.org/10.11834/jrs.20208297
Lu, N., & Gu, S. (2016). Review and prospect on the development of meteorological satellites. Journal of Remote Sensing, 20(5), 832–841. https://doi.org/10.11834/jrs.20166194
Lv, D., Wang, P., Qiu, J., & Tao, S. (2003). An overview on the research progress of atmospheric remote sensing and satellite meteorology in China. Chinese Journal of Atmospheric Sciences, 27(4), 552–566. https://doi.org/10.3878/j.issn.1006-9895.2003.04.09
Maeda, E. E., Formaggio, A. R., Shimabukuro, Y. E., Arcoverde, G., & Hansen, M. C. (2009). Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks. International Journal of Applied Earth Observation Geoinformation, 11(4), 265–272. https://doi.org/10.1016/j.jag.2009.03.003
Nakayama, M., Maki, M., Elvidge, C. D., & Liew, S. C. (1999). Contextual algorithm adapted for NOAA-AVHRR fire detection in Indonesia. International Journal of Remote Sensing, 20(17), 3415–3421. https://doi.org/10.1080/014311699211444
Peng, G., Shen, W., Hu, D., Li, J., & Chen, Y. (2008). Method to identify forest fire based on smoke plumes mask by using modis data. Journal of Infrared and Millimeter Waves, 27(3), 185–189. https://doi.org/10.3321/j.issn:1001-9014.2008.03.007
Peterson, D., Wang, J., Ichoku, C., Hyer, E., & Ambrosia, V. (2013). A sub-pixel-based calculation of fire radiative power from MODIS observations: 1: Algorithm development and initial assessment. Remote Sensing of Environment, 129, 262–279. https://doi.org/10.1016/j.rse.2012.10.036
Pozo, D., Olrno, F. J., & Alados-Arboledas, L. (1997). Fire detection and growth monitoring using a multitemporal technique on AVHRR mid-infrared and thermal channels. Remote Sensing of Environment, 60(2), 111–120. https://doi.org/10.1016/S0034-4257(96)00117-4
Pu, R., Gong, P., Li, Z., & Scarborough, J. (2004). A dynamic algorithm for wildfire mapping with NOAA/AVHRR data. International Journal of Wildland Fire. Journal of the International Association of Wildland Fire, 13(3), 275–285. https://doi.org/10.1071/WF03054
Qin, X., Chen, X., Zhong, X., Zu, X., Sun, G., & Yin, L. (2015). Development of forest fire early warning and monitoring technique system in China. FOREST Resources MANAGEMENT(6), 45–48. https://doi.org/10.13466/j.cnki.lyzygl.2015.06.009
Qin, X., Li, X., Liu, S., Liu, Q., & Li, Z. (2020). Forest fire early warning and monitoring techniques using satellite remote sensing in China. Journal of Remote Sensing, 24(5), 511–520. https://doi.org/10.11834/jrs.20209135
Qin, X., & Yi, H. (2004). A method to ldentify forest fire based on MODIS data. Fire Safety Science(2), 83–89+60. https://doi.org/10.3969/j.issn.1004-5309.2004.02.005
Rafik, G., Jmal, M., Mseddi, W. S., & Attia, R. (2020). Recent advances in fire detection and monitoring systems: A review. Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications
Santos, S. M. B. d., Bento-Gonçalves, A., & Vieira, A. (2021). Research on wildfires and remote sensing in the last three decades: A bibliometric analysis. Forests, 12(5), 604. https://doi.org/10.3390/f12050604
Sathishkumar, V. E., Cho, J., Subramanian, M., & Naren, O. S. (2023). Forest fire and smoke detection using deep learning—¬based learning without forgetting. Fire Ecology, 19(1), 9. https://doi.org/10.1186/s42408-022-00165-0
Schroeder, W., Oliva, P., Giglio, L., & Csiszar, I. A. (2014). The new VIIRS 375m active fire detection data product: Algorithm description and initial assessment. Remote Sensing of Environment, 143, 85–96. https://doi.org/10.1016/j.rse.2013.12.008
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(5), 2711–2726. https://doi.org/10.1016/j.rse.2008.01.005
Segal-Rozenhaimer, M., Li, A., Das, K., & Chirayath, V. (2020). Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN). Remote Sensing of Environment, 237, 111446. https://doi.org/10.1016/j.rse.2019.111446
Setzer, A. W., & Pereira, M. C. (1991). Amazonia biomass burnings in 1987 and an estimate of their tropospheric emissions. Ambio, 20(1), 19–22. https://doi.org/10.2307/4313765
Shu, L., Wang, M., Zhao, F., Li, H., & Tian, X. (2005). Comparison and application of satellites in forest fire monitoring. World Forestry Research(6), 49–53. https://doi.org/10.13348/j.cnki.sjlyyj.2005.06.008
Sun, F., Li, X., Li, Z., & Qin, X. (2020). Near-real-time forest fire monitoring system with medium and high spatial resolutions. Journal of Remote Sensing, 24(5), 543–549. https://doi.org/10.11834/jrs.20209137
Sun, W., Yang, G., Chen, C., Chang, M., Huang, K., Meng, X., & Liu, L. (2020). Development status and literature analysis of China’s earth observation remote sensing satellites. Journal of Remote Sensing, 24(5), 479–510. https://doi.org/10.11834/jrs.20209464
Sun, Z., Shen, W., Wei, B., Liu, X., Su, W., Zhang, C., & Yang, J. (2010). Object-oriented land cover classification using HJ-1 remote sensing imagery. Science China Earth Sciences, 53(1), 34–44. https://doi.org/10.1007/s11430-010-4133-6
Tang, S., Qiu, H., & Ma, G. (2016). Review on progress of the Fengyun meteorological satellite. Journal of Remote Sensing, 20(5), 842–849. https://doi.org/10.11834/jrs.20166232
Tian, Y. P., Wu, Z. C., Li, M. Z., Wang, B., & Zhang, X. D. (2022). Forest fire spread monitoring and vegetation dynamics detection based on multi-source remote sensing images. Remote Sensing, 14(18), 4431. https://doi.org/10.3390/rs14184431
Tsagkatakis, G., Aidini, A., Fotiadou, K., Giannopoulos, M., Pentari, A., & Tsakalides, P. (2019). Survey of deep-learning approaches for remote sensing observation enhancement. Sensors, 19(18), 3929. https://doi.org/10.3390/s19183929
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(1), 031502. https://doi.org/10.1117/1.3078426
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(2), 163–170. https://doi.org/10.1016/j.rse.2006.11.009
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, 236–254. https://doi.org/10.1016/j.rse.2011.09.033
Wu, X., Lu, X., & Leung, H. (2020). A motion and lightness saliency approach for forest smoke segmentation and detection. Multimedia Tools Applications, 79, 69–88. https://doi.org/10.1007/s11042-019-08047-5
Xie, Y., Qu, J. J., Xiong, X., Hao, X., Che, N., & Sommers, W. (2007). Smoke plume detection in the eastern United States using MODIS. International Journal of Remote Sensing, 28(10), 2367–2374. https://doi.org/10.1080/01431160701236795
Xie, Z., Song, W., Ba, R., Li, X., & Xia, L. (2018). A spatiotemporal contextual model for forest fire detection using Himawari-8 satellite data. Remote Sensing, 10(12), 1992. https://doi.org/10.3390/rs10121992
Xu, G., & Zhong, X. (2017). Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8. Remote Sensing Letters, 8(11), 1052–1061. https://doi.org/10.1080/2150704X.2017.1350303
Xu, H., Zhang, G., Zhou, Z., Zhou, X., & Zhou, C. (2022). Forest fire monitoring and positioning improvement at subpixel level: Application to Himawari-8 fire products. Remote Sensing, 14(10), 2460. https://doi.org/10.3390/rs14102460
Xu, W., Wooster, M. J., He, J., & Zhang, T. (2020). First study of Sentinel-3 SLSTR active fire detection and FRP retrieval: Night-time algorithm enhancements and global intercomparison to MODIS and VIIRS AF products. Remote Sensing of Environment, 248, 111947. https://doi.org/10.1016/j.rse.2020.111947
Xu, W., Wooster, M. J., Polehampton, E., Yemelyanova, R., & Zhang, T. (2021). Sentinel-3 active fire detection and FRP product performance—Impact of scan angle and SLSTR middle infrared channel selection. Remote Sensing of Environment, 261, 112460. https://doi.org/10.1016/j.rse.2021.112460
Yi, H., Ji, P., He, X., & Zhang, Y. (1996). Study on monitoring and early alarm technique of forest fire using satellite data. Remote sensing technology and application, 11(1), 40-46. https://doi.org/10.11873/j.issn.1004-0323.1996.1.40
Yin, J., He, R., Zhao, F., & Ye, J. (2023). Research on forest fire monitoring based on multi-source satellite remote sensing images. Spectroscopy and Spectral Analysis, 43(3), 917–926. https://doi.org/10.3964/j.issn.1000-0593(2023)03-0917-10
Yin, Z., Chen, F., Lin, Z., Yang, A., & Li, B. (2020). Active fire monitoring based on FY-3D MERSI satellite data. Remote sensing technology and application, 35(5), 1099–1108.
Yu, J., Li, Y., Zheng, X., Zhong, Y., & He, P. (2020). An effective cloud detection method for Gaofen-5 images via deep learning. Remote Sensing, 12(13), 2106. https://doi.org/10.3390/rs12132106
Zhang, D., Huang, C., Gu, J., Hou, J., Zhang, Y., Han, W., Zhang, Y., Han, W., Dou, P., & Feng, Y. (2023). Real-Time wildfire detection algorithm based on VIIRS fire product and Himawari-8 data. Remote Sensing, 15(6), 1541. https://doi.org/10.3390/rs15061541
Zhang, N., Sun, L., & Sun, Z. (2022). GF-4 satellite fire detection with an improved contextual algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 163–172. https://doi.org/10.1109/JSTARS.2021.3132360
Zhang, Q. X., Lin, G. H., Zhang, Y. M., Xu, G., & Wang, J. J. (2018). Wildland forest fire smoke detection based on faster R-CNN using synthetic smoke images. Procedia Engineering, 211, 441–446. https://doi.org/10.1016/j.proeng.2017.12.034
Zhang, T., Wooster, M. J., & Xu, W. (2017). Approaches for synergistically exploiting VIIRS I- and M-Band data in regional active fire detection and FRP assessment: A demonstration with respect to agricultural residue burning in Eastern China. Remote Sensing of Environment, 198, 407–424. https://doi.org/10.1016/j.rse.2017.06.028
Zheng, W., Shao, J., Wang, M., & Liu, C. (2013). Dynamic monitoring and analysis of grassland fire based on multi-source satellite remote sensing data. Journal of Natural Disasters, 22(3), 54–61. https://doi.org/10.13577/j.jnd.2013.0308
Zheng, Y., Zhang, G., Tan, S., Yang, Z., Wen, D., & Xiao, H. (2023). A forest fire smoke detection model combining convolutional neural network and vision transformer. Frontiers in Forests and Global Change, 6. https://doi.org/10.3389/ffgc.2023.1136969
Zhou, X., Feng, D., Xie, Y., Tao, Z., Lv, T., & Wang, J. (2021). Radiometric Cross-Calibration of GF-4/IRS Based on MODIS measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6807–6814. https://doi.org/10.1109/JSTARS.2021.3091977
Zhou, X., & Wang, X. (2006). Validate and lmprovement on arithmetic of ldentifying forest fire based on EOS-MODIS data. Remote sensing technology and application, 21(3), 206–211. https://doi.org/10.3969/j.issn.1004-0323.2006.03.007
Zhukov, B., Lorenz, E., Oertel, D., Wooster, M., & Roberts, G. (2006). Spaceborne detection and characterization of fires during the bi-spectral infrared detection (BIRD) experimental small satellite mission (2001–2004). Remote Sensing of Environment, 100(1), 29–51. https://doi.org/10.1016/j.rse.2005.09.019
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2023 Ying Zheng, Gui Zhang, Sanqing Tan, Lanbo Feng