森林バイオマス計測のための地上ハイパースペクトルイメージングによる山火事リスクモニタリング
Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring
UGVに搭載したスナップショットハイパースペクトルカメラとLiDARで森林植生の分光特性を解析し、山火事の燃料となる危険区域を3D空間で可視化するシステムを提案・実証した。
著者: Nathaniel Hanson, Sarvesh Prajapati, James Tukpah, Yash Mewada, Taşkın Padır
分類: cs.RO
原文アブストラクト
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.
