用无人机搭载高光谱与激光雷达，精准识别森林易燃区域。
Forest Biomass Mapping with Terrestrial Hyperspectral Imaging for Wildfire Risk Monitoring
- 结合高光谱成像与激光雷达，通过植被光谱特征分析风险区。
- 在不同地形和环境下采集数据，提升火险预测准确性。
- 可将湿度信息投影至三维空间，支持主动森林管理。
过去十年野火频发，亟需有效检测与预警以减少对生态与人类生命损失。本文提出一种新型系统 Hyper-Drive3D，集成快照式高光谱成像与激光雷达（LiDAR），安装于无人地面车（UGV）上，用于识别林内可能成为火灾燃料的区域。该系统通过分析植被光谱特征实现更精确分类。我们在模拟森林环境的受控场地开展实地测试，验证系统有效性；同时在复杂地形与多变环境的密林中进行大规模数据采集，增强其火险预测能力，支持基于风险的主动森林管理策略。此外，我们提出一种从高光谱影像提取湿度信息并投影至三维空间的框架。
原文摘要 · Abstract (English)
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.
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