Year: 2025
Type: article
Abstract: We present a study that addresses the critical need for a prototype Decision Support System for forest fire information and management in Uttarakhand, India. The study’s main objective was to carry out statistical analysis of large fire incident datasets to understand trends of fires in the region and develop essential spatial decision support tools. These tools address the necessary fire management decision-making along with comprehensive datasets that can enable a decision maker to exercise better management. Moreover, this DSS addresses three major components of forest fire decision support: (i) pre-fire (forest information visualization) tools, (ii) during-fire terrain-based spatial decision support tools, and (iii) post-fire restoration tools. The efforts to develop this DSS included satellite lidar dataset-based fuel load estimations, the Keetch–Byram Drought Index, and the integration of spatial tools that ensure better spatial decisions in fire suppression planning. In addition, based on the bibliographic literature, the study also uses ecological and community-based knowledge, including financial aspects, for fire prevention and post-fire restoration planning. The development of this DSS involves an open-source R Shiny framework, enabling any decision maker at the execution or planning level to access these key datasets and simulate the spatial solutions cost-effectively. Hence, this study aimed to internalize key decision support tools and datasets based on extensive statistical analysis for data-driven forest fire planning and management.
Source: Fire
DOI: 10.3390/fire8040149
Authors: Neelesh Yadav, Shrey Rakholia, Peter Moore, Laura Patricia Ponce-Calderón, Mithun Kumar S R, Reuven Yosef
Language: English
Topic: Fire effects on ecosystems
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409257060