Forest Fire Risk Assessment for Sikkim using Earth Observation (EO) Datasets and Multi Criteria Decision Making Technique
Abstract
Forest fires significantly influence the whole ecosystem by increasing the mortality rate of vegetation and by regulating the exchange of carbon, water, and other particulate matter between land and atmosphere. Recent climate change and anthropogenic activities are increasing the incidents of forest fires, degrading rich forest biodiversity and, their functioning. It is therefore of paramount importance to design effective strategies for protecting the forest areas. Understanding the spatial distribution of forest fires and identification of the Forest Fire Risk (FFR) zones is urgently needed to propose effective forest fire management strategies by advising efficient and practical mitigation measures.
A protocol has been developed in this work to produce the FFR maps for the entire Sikkim state using the earth observation datasets and multi-criteria decision-making technique, i.e., AHP (Analytical Hierarchy Process) in a GIS (Geographic Information System) framework. We selected 9 different parameters (vegetation type, vegetation density, land surface temperature, elevation, slope, aspect, and distance from settlements, river, and roads) based on the understanding of the factors influencing the spatial distribution of forest fires in the region. Our results show that more than 50% area of all the districts is under high risk zones except North Sikkim, which lies at an altitude of 500m to 8056m and is mostly covered with snow. The model showed an accuracy of 82.36%, which implies that a large number of past forest fire incidence overlay the high risk zone of the state. Further analysis concluded that moderate dense forest of this region is more prone to fire, whereas aspect and human density differentiate very high and high risk zones. This model has provided a geographical representation of fire ignition probability and identifies high-risk areas in different regions.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMNH0330001L
- Keywords:
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- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES;
- 0240 Public health;
- GEOHEALTH;
- 4328 Risk;
- NATURAL HAZARDS;
- 4332 Disaster resilience;
- NATURAL HAZARDS