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<p style="margin-right:0cm"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">During 2003–2017, a total of 5,20,861 active forest fire events were detected in India. According to the report of the Forest Survey of India, over 54% of the forest cover in India is exposed to occasional fire.</span></span></p>
<p style="margin-right:0cm"> </p>
<p style="margin-right:0cm"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Background</strong></span></span></p>
<p style="margin-right:0cm"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The study used remote sensing-based models to measure primary productivity over an area and also looked at burn indices, which help to demarcate the forest fire burn scars using satellite imagery.</span></span></p>
<p style="margin-right:0cm"> </p>
<p style="margin-right:0cm"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Details</strong></span></span></p>
<ul>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The normalized burn ratio is an effective burn index commonly used to identify burnt regions in large fire zones. In normal conditions, healthy vegetation exhibits a very high reflectance in the near-infrared spectral region and considerably low reflectance in the shortwave infrared spectral region. These conditions get dismantled and reversed if a fire occurs.</span></span></p>
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<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The spectral differences between healthy vegetation and burnt forest areas can easily be identified and highlighted by remote sensing burn indices. It can be a promising tool for land resource managers and fire officials.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The team notes that the States of northeast India, Madhya Pradesh, Odisha, Chhattisgarh, Himachal Pradesh, and Uttarakhand are the most fire-prone in India.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Previous studies using forecasting models and <em>in-situ </em>observations in western Himalaya have shown a sharp increase of carbon monoxide, nitrogen oxides, and ozone during high fire activity periods. </span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The current paper noted very high to high carbon emissions in the eastern Himalayan states, western desert region, and lower Himalayan region.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The occurrence of high fire intensity at the low altitude Himalayan hilly regions may be due to the plant species (pine trees) in the area and proximity to villages. Villages make them more susceptible to anthropogenic activities like forest cover clearance, grazing, and so on.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Studies have shown that the sharp increase in average and maximum air temperature, decline in precipitation, change in land-use patterns have caused the increased episodes of forest fires in most of the Asian countries.</span></span></p>
</li>
<li>
<p><span style="font-size:11.0pt"><span style="font-family:"Calibri","sans-serif"">The team plans to further work on the prediction of forest fires with the support of advanced machine learning models and AI-based techniques.</span></span></p>
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<p style="margin-right:0cm"> </p>
<p style="margin-right:0cm"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Background</strong></span></span></p>
<p style="margin-right:0cm"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The study used remote sensing-based models to measure primary productivity over an area and also looked at burn indices, which help to demarcate the forest fire burn scars using satellite imagery.</span></span></p>
<p style="margin-right:0cm"> </p>
<p style="margin-right:0cm"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Details</strong></span></span></p>
<ul>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The normalized burn ratio is an effective burn index commonly used to identify burnt regions in large fire zones. In normal conditions, healthy vegetation exhibits a very high reflectance in the near-infrared spectral region and considerably low reflectance in the shortwave infrared spectral region. These conditions get dismantled and reversed if a fire occurs.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The spectral differences between healthy vegetation and burnt forest areas can easily be identified and highlighted by remote sensing burn indices. It can be a promising tool for land resource managers and fire officials.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The team notes that the States of northeast India, Madhya Pradesh, Odisha, Chhattisgarh, Himachal Pradesh, and Uttarakhand are the most fire-prone in India.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Previous studies using forecasting models and <em>in-situ </em>observations in western Himalaya have shown a sharp increase of carbon monoxide, nitrogen oxides, and ozone during high fire activity periods. </span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The current paper noted very high to high carbon emissions in the eastern Himalayan states, western desert region, and lower Himalayan region.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">The occurrence of high fire intensity at the low altitude Himalayan hilly regions may be due to the plant species (pine trees) in the area and proximity to villages. Villages make them more susceptible to anthropogenic activities like forest cover clearance, grazing, and so on.</span></span></p>
</li>
<li>
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Studies have shown that the sharp increase in average and maximum air temperature, decline in precipitation, change in land-use patterns have caused the increased episodes of forest fires in most of the Asian countries.</span></span></p>
</li>
<li>
<p><span style="font-size:11.0pt"><span style="font-family:"Calibri","sans-serif"">The team plans to further work on the prediction of forest fires with the support of advanced machine learning models and AI-based techniques.</span></span></p>
</li>
</ul>
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