Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring

TL;DR

This paper introduces Hyper-Drive3D, a novel hyperspectral and LiDAR system mounted on an UGV, designed to accurately map forest biomass and assess wildfire risk through spectral analysis and 3D moisture data extraction.

Contribution

The paper presents a new integrated hyperspectral-LiDAR system for wildfire risk assessment, including a framework for moisture data extraction and 3D projection, tested in real forest environments.

Findings

Effective classification of forest vegetation spectral signatures

Enhanced wildfire risk prediction in diverse forest conditions

Validated system performance through field trials

Abstract

With the rapid increase in wildfires in the past decade, it has become necessary to detect and predict these disasters to mitigate losses to ecosystems and human lives. In this paper, we present a novel solution -- Hyper-Drive3D -- consisting of snapshot hyperspectral imaging and LiDAR, mounted on an Unmanned Ground Vehicle (UGV) that identifies areas inside forests at risk of becoming fuel for a forest fire. This system enables more accurate classification by analyzing the spectral signatures of forest vegetation. We conducted field trials in a controlled environment simulating forest conditions, yielding valuable insights into the system's effectiveness. Extensive data collection was also performed in a dense forest across varying environmental conditions and topographies to enhance the system's predictive capabilities for fire hazards and support a risk-informed, proactive forest management strategy. Additionally, we propose a framework for extracting moisture data from hyperspectral imagery and projecting it into 3D space.