A REVIEW ON METHODS FOR DETECTING STUBBLE RESIDUE BURNING USING SATELLITE REMOTE SENSING
Keywords:
Stubble Burning; Environment; GIS; Remote Sensing; Satellite.Abstract
Stubble residue burning is a significant environmental issue, contributing to air pollution, greenhouse gas emissions, and public health hazards. Satellite remote sensing has emerged as a vital tool for detecting and monitoring stubble burning events over large areas. This paper reviews the various methods used for detecting stubble residue burning through satellite remote sensing. Various methods discussed include thermal anomaly detection, smoke plume identification, spectral analysis and the role of machine learning. A comparative analysis of these methods is provided, focusing on their accuracy, resolution, computational requirements and ability to capture the spatial and temporal dynamics of stubble burning. Sentinel-2 MSI and MODIS data is used to detect and visualize the fire for state of Punjab region.
References
Chen, Z., et al. (2023). Global smoke aerosol detection using MODIS and VIIRS data. Atmospheric Environment, 280(1), 225-240.
Dasgupta, S., Banerjee, R., & Pal, P. K. (2022). Supervised machine learning for detecting agricultural fires using Sentinel-2 data. IEEE Access, 10, 52785-52799.
Giglio, L., et al. (2023). Characterization of the tropical diurnal fire cycle using MODIS and VIIRS observations. Remote Sensing of Environment, 261, 112321.
Houghton, R. A., et al. (2022). Thermal anomaly detection for biomass burning. Earth Systems Science Data, 14(5), 2293-2307.
Mandal, N., Mittal, P., &Saxena, S. (2022). Satellite-based monitoring of crop residue burning in Punjab and Haryana: Recent advances and prospects. Journal of Environmental Management, 308, 1-12.
Roy, D., et al. (2023). Texture-based smoke detection using satellite imagery and deep learning techniques. IEEE Transactions on Geoscience and Remote Sensing, 61(4), 1-15.
Schroeder, W., et al. (2022). The VIIRS active fire product: Algorithm enhancements and global assessment. Remote Sensing of Environment, 258, 112-149.
Shukla, S., et al. (2021). An integrated MODIS and VIIRS approach for the detection and monitoring of agricultural residue burning. Remote Sensing, 13(18), 1-14.
Stroppiana, C., Grippa, M., &Castelli, F. (2022). Using Sentinel-2 and Sentinel-3 data for monitoring stubble burning in India. Remote Sensing, 14(5), 12-25.
Stroppiana, C., Grippa, M., Verhegghen, A., &Castelli, F. (2022). Stubble burning detection using Sentinel-3 imagery. Remote Sensing, 12(3), 1-16.
Verma, S. P., & Singh, A. K. (2022). Assessment of burned area using Sentinel-2 time series data: A case study in Indian agricultural lands. Remote Sensing, 14(5).
Wang, T., et al. (2023). Deep learning-based stubble burning detection with Sentinel and VIIRS data fusion. Remote Sensing, 15(2), 1-18.
Zheng, Y. Q., et al. (2022). Advances in burned area detection using Sentinel-2 and machine learning algorithms. ISPRS Journal of Photogrammetry and Remote Sensing, 191, 123-137.
