An Agentic Approach to Generate Conversational Narratives on the Immersive Forest

Wildfires are becoming increasingly destructive and costly each year, affecting lives, damaging infrastructure, and degrading ecosystems. To address this growing threat, the fire and land management community need smarter, data-driven tools to understand the landscape and plan their essential prescribed burns that reduce hazardous fuels. With the ultimate goal to minimize the devastation of wildfires by enabling proactive and data-driven fuel management at landscape scale, this paper presents an approach that builds heterogeneous remote sensing data into a temporal-spatial knowledge graph, then queries it with a Large Language Model (LLM) based agent, providing a natural language interface to an extensive system of granular landscape knowledge and metrics. We demonstrate how we build knowledge graphs from LiDAR-derived vegetation metrics using GraphDB enabling precise location and time-based insights. We present a user-facing system intended to respond to queries about the effects of prescribed burns over time. Built around an LLM Agent (e.g., OpenAI, LLaMA) orchestrated with LangChain and LangGraph, the system allows users to interact with complex fire and fuel data through a natural language chat interface. It also includes a web search tool for retrieving external fire-related content to enrich responses. While this work operates as a standalone knowledge system, it was first envisioned as a method of generating conversational narratives while navigating a virtual 3D forest environment in our prototype immersive visualization application called Immersive Forest.