Understanding spatiotemporal relationships in environmental monitoring is crucial for effective decision making and analysis. In this work, we leverage knowledge graphs (KGs) to model such relationships using measurements collected from field sensors. Specifically, we present datasets from a prescribed fire at Sedgwick Reserve, Santa Barbara, and introduce an ontology that defines key relationships between entities in these datasets. To facilitate data integration and analysis, we developed an ingestion pipeline that structures the information into a KG and facilitates querying along spatial and temporal edges. We evaluate our implementation with two tasks, the ability for spatial relationships to improve vegetation metric prediction, and the use of the inferred relationships between carbon emission data and vegetation metrics to estimate prescribed fire outcomes. Our approach highlights the potential of knowledge graphs in environmental data management, offering a scalable and interactive framework for analysis and decision support.

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.

Wildland fire modeling tools can ingest high resolution 3D vegetation models as inputs. However, data used to build the surface fuels in these models is often at a 30-meter resolution, which does not necessarily provide sufficient detail for accurate modeling of fires. Terrestrial laser scans are increasingly being used to collect detailed vegetation data that could be integrated with new approaches to fuel and fire modeling, but manual segmentation of scans is not scalable beyond a small number of scans. There is a need to automatically segment these high resolution point clouds as they are collected in the field, such that they may be leveraged by fuel and fire models for wildland fire response and mitigation and other applied climate science. This paper summarizes our early work on a labeling, visualization and machine learning pipeline for detailed segmentation of fuels. Specific contributions are: (1) a labeling approach involving 3 dimensional segmentation of point clouds using a point cloud processing engine; (2) a visualization approach using a computer graphics engine; and (3) early results from a deep learning modeling approach for fuel segmentation by category (live and dead) and size class (1, 10, 100 and 1000 hour fuels).

Increasing social acceptance of prescribed burns is an important element of ramping up these controlled burns to the scale required to effectively mitigate destructive wildfires through reduction of excessive fire fuel loads. As part of a Design Challenge, students created concept designs for physical or virtual installations that would increase public understanding and acceptance of prescribed burns as an important tool for ending devastating megafires. The proposals defined how the public would interact with the installation and the learning goals for participants. This poster provides an overview of the virtual reality (VR) pipeline created to develop working prototypes of the immersive experiences and VR games that were proposed by the finalists in the design challenge.