Journal Article

Forest fire pattern and vulnerability mapping using deep learning in Nepal

Panthi, Saroj, Poudel, Shobha, Mishra, Bhogendra, Ghimire, Bhoj Raj

2023

Overview

Background In the last two decades, Nepal has experienced an increase in both forest fire frequency and area, but very little is known about its spatiotemporal dimension. A limited number of studies have researched the extent, timing, causative parameters, and vulnerability factors regarding forest fire in Nepal. Our study analyzed forest fire trends and patterns in Nepal for the last two decades and analyzed forest fire-vulnerability risk based on historical incidents across the country. Results We analyzed the spatial and temporal patterns of forest fires and the extent of burned area using the Mann-Kendall trend test and two machine-learning approaches maximum entropy (MaxEnt), and deep neural network (DNN). More than 78% of the forest fire burned area was recorded between March and May. The total burned area has increased over the years since 2001 by 0.6% annually. The forest fire-vulnerability risk obtained from both approaches was categorized into four classes—very high, high, low, and very low. Conclusions Although burned area obtained from both models was comparable, the DNN slightly outperformed the MaxEnt model. DNN uses a complex structure of algorithms modeled on the human brain that enables the processing of the complex relationship between input and output dataset, making DNN-based models recommended over MaxEnt. These findings can be very useful for initiating and implementing the most suitable forest management intervention.

• Publisher: Springer International Publishing,Springer Nature B.V
• Subject: Algorithms / Artificial neural networks / Biomedical and Life Sciences / Deep learning / Ecology / Emergency communications systems / Emissions / Entropy / Forest & brush fires / Forest fires / Forest management / Forestry / Forests / Generalized linear models / Learning algorithms / Life Sciences / Machine learning / Maximum entropy / Neural networks / Original Research