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Temporal assessment of forest cover dynamics in response to forest fires and other environmental impacts using AI

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Abstract

The rapid reduction of forests due to environmental impacts such as deforestation, global warming, natural disasters such as forest fires as well as various human activities is an escalating concern. The increasing frequency and severity of forest fires are causing significant harm to the ecosystem, economy, wildlife, and human safety. During dry and hot seasons, the likelihood of forest fires also increases. It is crucial to accurately monitor and analyze the large-scale changes in the forest cover to ensure sustainable forest management. Remote sensing technology helps to precisely study such changes in forest cover over a wide area over time. This research analyzes the impact of forest fires over time, identifies hotspots, and explores the environmental factors that affect forest cover change. Sentinel-2 imagery was utilized to study changes in Brunei Darussalam’s forest cover area over five years from 2017 to 2022. An object-based approach, Simple Non-Iterative Clustering (SNIC), is employed to cluster the region using NDVI values and analyze the changes per cluster. The results indicate that the area of the clusters reduced where fire incidence occurred as well as the precipitation dropped. Between 2017 and 2022, the increased forest fires and decreased precipitation levels resulted in the change in cluster areas as follows: 66.11%, 69.46%, 68.32%, 73.88%, 77.27%, and 78.70%, respectively. Additionally, hotspots in response to forest fires each year were identified in the Belait district. This study will help forest managers assess the causes of forest cover loss and develop conservation and afforestation strategies.

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Acknowledgements

The research work is conducted in the NUST-Coventry Internet of Things Lab (NCIL) at NUST-SEECS, Islamabad, Pakistan.

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  1. School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, 44000, Pakistan

    Shehla Noor, Rafia Mumtaz & Muhammad Ajmal Khan

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  1. Shehla Noor
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Contributions

(1) Shehla Noor: conceptualization, methodology, validation, investigation, writing—original draft, writing—review and editing, software, visualization. (2) Rafia Mumtaz: conceptualization, methodology, validation, investigation, writing—original draft, writing—review and editing, visualization, supervision. (3) Muhammad Ajmal Khan: conceptualization, methodology, investigation, writing—review and editing.

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Correspondence to Rafia Mumtaz.

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Noor, S., Mumtaz, R. & Khan, M.A. Temporal assessment of forest cover dynamics in response to forest fires and other environmental impacts using AI. Environ Monit Assess 196, 893 (2024). https://doi.org/10.1007/s10661-024-12992-6

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