A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires
- Title
- A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires
- Creator
- Tiwari A.; Nanjundan P.; Kumar R.R.; Soni V.K.
- Description
- Background: Wildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock. Results: The study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10?16), along with a positive correlation (r ? 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10?4kgcarbonh?1), methane (CH4; ~ 200 10?9mol fraction in dry air), carbon monoxide (CO; 2000 1015moleculescm?2 total column), and formaldehyde (HCHO; 3500 1013moleculescm?2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5). Conclusions: We believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies. The Author(s) 2024.
- Source
- Fire Ecology, Vol-20, No. 1
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Aerosol; Biomass burning; Climate change; Natural hazards; Wildfires
- Coverage
- Tiwari A., Christ University, Lavasa, Pune, 412112, India, India Meteorological Department, Shimla Office, Shivajinagar, Pune, 411005, India; Nanjundan P., Christ University, Lavasa, Pune, 412112, India; Kumar R.R., India Meteorological Department, Shimla Office, Shivajinagar, Pune, 411005, India; Soni V.K., India Meteorological Department, Mausam Bhawan, Lodhi Road, New Delhi, 110003, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 19339747
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Tiwari A.; Nanjundan P.; Kumar R.R.; Soni V.K., “A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/12638.