| Autor: |
Reddy CS; National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad, Telangana, 500 037, India. drsudhakarreddy@gmail.com., Bird NG; National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad, Telangana, 500 037, India.; Centre for Spatial Information Technology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana, 500 085, India., Sreelakshmi S; National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad, Telangana, 500 037, India.; Indian Institute of Information Technology and Management, Thiruvananthapuram, Kerala, 695 581, India., Manikandan TM; National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad, Telangana, 500 037, India., Asra M; National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad, Telangana, 500 037, India., Krishna PH; National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad, Telangana, 500 037, India.; Arid Zone Regional Centre, Botanical Survey of India, Jodhpur, Rajasthan, 342 008, India., Jha CS; National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad, Telangana, 500 037, India., Rao PVN; National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad, Telangana, 500 037, India., Diwakar PG; Indian Space Research Organisation, Antariksh Bhavan, New BEL Road, Bengaluru, Karnataka, 560 231, India. |
| Abstrakt: |
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. |
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