Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Identification and characterization of spatio-temporal hotspots of forest fires in South Asia

  • 44 Accesses

  • 1 Citations

Abstract

Forest fire is considered as one of the major threats to global biodiversity and a significant source of greenhouse gas emissions. Rising temperatures, weather conditions, and topography promote the incidences of fire due to human ignition in South Asia. Because of its synoptic, multi-spectral, and multi-temporal nature, remote sensing data can be a state of art technology for forest fire management. This study focuses on the spatio-temporal patterns of forest fires and identifying hotspots using the novel geospatial technique “emerging hotspot analysis tool” in South Asia. Daily MODIS active fire locations data of 15 years (2003–2017) has been aggregated in order to characterize fire frequency, fire density, and hotspots. A total of 522,348 active fire points have been used to analyze risk of fires across the forest types. Maximum number of forest fires in South Asia was occurring during the January to May. Spatial analysis identified areas of frequent burning and high fire density in South Asian countries. In South Asia, 51% of forest grid cells were affected by fires in 15 years. Highest number of fire incidences was recorded in tropical moist deciduous forest and tropical dry deciduous forest. The emerging hotspots analysis indicates prevalence of sporadic hotspots, followed by historical hotspots, consecutive hotspots, and persistent hotspots in South Asia. Of the seven South Asian countries, Bangladesh has highest emerging hotspot area (34.2%) in forests, followed by 32.2% in India and 29.5% in Nepal. Study results offer critical insights in delineation of fire vulnerable forest landscapes which will stand as a valuable input for strengthening management of fires in South Asia.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  1. Andela, N., Morton, D. C., Giglio, L., Chen, Y., Van Der Werf, G. R., Kasibhatla, P. S., ... & Bachelet, D. (2017). A human-driven decline in global burned area. Science, 356(6345), 1356–1362.

  2. ArcGIS (2016). ArcGIS Help 10.4. Accessed on 10th March, 2018. Retrieved from http://desktop.arcgis.com/en/arcmap/

  3. Bass, C.A. (2017). Emerging hotspot analysis of Florida manatee (Trichechus manatus latirostris) mortality (1974-2012). Master’s thesis. Nova Southeastern University. Retrieved from NSUWorks (456).

  4. Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martín, M. P., Vilar, L., Martínez, J., Martín, S., Ibarra, P., de la Riva, J., Baeza, J., Rodríguez, F., Molina, J. R., Herrera, M. A., & Zamora, R. (2010). Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling, 221, 46–58.

  5. Dawson, T. P., Butt, N., & Miller, F. (2002). The ecology of forest fires. ASEAN Biodiversity, 1, 18–21.

  6. Dwyer, E., Pereira, J. M., Grégoire, J. M., & DaCamara, C. C. (2000). Characterization of the spatio-temporal patterns of global fire activity using satellite imagery for the period April 1992 to March 1993. Journal of Biogeography, 27(1), 57–69.

  7. FAO (2009). Asia Pacific Forestry Sector Outlook Study II. Pakistan Forestry Outlook Study, Working Paper No. APFSOS II/WP/2009/28, Bangkok, pp. 55–53.

  8. Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.

  9. Giglio, L., Csiszar, I., & Justice, C. O. (2006). Global distribution and seasonality of active fires as observed with the Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) sensors. Journal of Geophysical Research – Biogeosciences, 111(G2), 1–12. https://doi.org/10.1029/2005JG000142.

  10. Goldammer, J.G. and C. de Ronde (Eds.), (2004). Wildland fire management handbook for Sub-Sahara Africa. Cape Town: Oneworldbooks. Global Fire Monitoring Center. ISBN 1-919833-65-X.

  11. Harris, N. L., Goldman, E., Gabris, C., Nordling, J., Minnemeyer, S., Ansari, S., Lippmann, M., Bennett, L., Raad, M., Hansen, M., & Potapov, P. (2017). Using spatial statistics to identify emerging hot spots of forest loss. Environmental Research Letters, 12(2), 024012.

  12. Hiremath, A. J., & Sundaram, B. (2005). The fire-Lantana cycle hypothesis in Indian forests. Conservation and Society, 3, 26–42.

  13. Holdsworth, A. R., & Uhl, C. (1997). Fire in the eastern Amazonian logged rain forest and the potential for fire reduction. Ecological Applications, 7, 713–725.

  14. Holloway, J., & Mengersen, K. (2018). Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sensing, 10(9), 1365.

  15. IMD (2009). http://www.imd.gov.in/doc/warm2009. (accessed on 29th March 2018)

  16. IPCC. (2007). Fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press.

  17. Kerr, J. T., & Ostrovskym, M. (2003). From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18, 299–305.

  18. Matin, M. A., Chitale, V. S., Murthy, M. S., Uddin, K., Bajracharya, B., & Pradhan, S. (2017). Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data. International Journal of Wildland Fire, 26(4), 276–286.

  19. Narendran, K. (2001). Forest fires – origins and ecological paradoxes. Resonance, 6, 34–41.

  20. Pausas, J. G., & Keeley, J. E. (2009). A burning story: the role of fire in the history of life. Bioscience, 59, 593–601.

  21. Peres, C. A., Barlow, J., & Laurance, W. F. (2006). Detecting anthropogenic disturbance in tropical forests. Trends in Ecology & Evolution, 21(5), 227–229.

  22. Reddy, C. S., Jha, C. S., Manaswini, G., Alekhya, V. V. L. P., Pasha, S. V., Satish, K. V., Diwakar, P. G., & Dadhwal, V. K. (2017a). Nationwide assessment of forest burnt area in India using Resourcesat-2 AWiFS data. Current Science, 112(7), 1521–1532.

  23. Reddy, C. S., Alekhya, V. V. L. P., Saranya, K. R. L., Athira, K., Jha, C. S., Diwakar, P. G., & Dadhwal, V. K. (2017b). Monitoring of fire incidences in vegetation types and protected areas of India: implications on carbon emissions. Journal of Earth System Science, 126, 11. https://doi.org/10.1007/s12040-016-0791-x.

  24. Reddy, C. S., Saranya, K. R. L., Pasha, S. V., Satish, K. V., Jha, C. S., Diwakar, P. G., Dadhwal, V. K., Rao, P. V. N., & Krishna Murthy, Y. V. N. (2018a). Assessment and monitoring of deforestation and forest fragmentation in South Asia since the 1930s. Global and Planetary Change, 161, 132–148.

  25. Reddy, C. S., Faseela, V. S., Unnikrishnan, A., & Jha, C. S. (2018b). Earth observation data for assessing biodiversity conservation priorities in South Asia. Biodiversity and Conservation, 28, 2197–2219. https://doi.org/10.1007/s10531-018-1681-0.

  26. Reddy, C. S., Satish, K. V., & Prasada Rao, P. V. V. (2018c). Significant decline of forest fires in Nilgiri biosphere reserve. Remote Sensing Applications: Society and Environment, 11, 172–185.

  27. Saranya, K. R. L., Reddy, C. S., Prasada Rao, P. V. V., & Jha, C. S. (2014). Decadal time scale monitoring of forest fires in Similipal Biosphere Reserve, India using remote sensing and GIS. Environmental Monitoring and Assessment, 186, 3283–3296.

  28. Satendra, & Kaushik, A. D. (2012). Forest fire disaster management. Delhi: India disaster report. National Institute of Disaster Management.

  29. Zhu, Y., & Newsam, S. (2016). Spatio-temporal sentiment hotspot detection using geotagged photos. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic information systems (p. 76). ACM.

Download references

Acknowledgments

This work has been carried out as part of ISRO’s National Carbon Project. We thank ISRO-DOS Geosphere Biosphere Programme for the financial support. We are grateful to Shri Santanu Chowdhury, Director, NRSC, Hyderabad and Dr. V.K. Dadhwal, Project Director, National Carbon Project and Director, Indian Institute of Space Science and Technology, Thiruvananthapuram, for suggestions and encouragement. We are grateful to NASA for providing access to the MODIS data.

Author information

Correspondence to C. Sudhakar Reddy.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Terrestrial and Ocean Dynamics: India Perspective

Electronic supplementary material

ESM 1

(DOCX 4951 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Reddy, C.S., Bird, N.G., Sreelakshmi, S. et al. Identification and characterization of spatio-temporal hotspots of forest fires in South Asia. Environ Monit Assess 191, 791 (2019). https://doi.org/10.1007/s10661-019-7695-6

Download citation

Keywords

  • Forest
  • Frequency
  • Density
  • Emerging
  • Hotspots, MODIS