# Multispectral Indices for Wildfire Management

arXiv: https://arxiv.org/abs/2309.01751 · HTML: https://mlsanity.com/paper/2309.01751/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 149 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors](https://mlsanity.com/paper/2105.01998/) (2021)

## Built on by (most cited first, 1 of 1)

- [Data Science for Geographic Information Systems](https://mlsanity.com/paper/2404.03754/) (2024)

## Abstract (arXiv)

The increasing frequency and severity of wildfires necessitates advanced methods for effective surveillance and management, as traditional ground-based techniques often struggle to adapt to rapidly changing fire behavior and environmental conditions. This study investigates the use of multispectral aerial and satellite imagery for wildfire management through an assessment of current literature and two practical case studies. We evaluate several multispectral indices for their ability to extract environmental features critical for analyzing wildfire behavior, including vegetation, water bodies, and artificial structures. Our results highlight NVDI for vegetation, MNDWI for water features, and MSR for artificial structures as particularly effective for segmentation and feature extraction. The application of these indices enhances wildfire data processing and supports improved monitoring, risk assessment, and response strategies, demonstrating the potential of multispectral imagery to complement traditional wildfire monitoring and management approaches.