Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
(N/A)
Non Technical Summary
Starting on February 24, 2024, the Panhandle wildfire quickly spread within days, making it the largest wildfire in Texas history. The scale of the destruction has been enormous, with many people facing the loss of their homes and other structures, livestock, and forage, and while the fire is now contained, the damages are expected to grow as additional assessments are conducted.The immediate concern post-disaster is to ensure the safety and well-being of the affected communities, for which a clear understanding of the extent and severity of damage is imperative. Despite the known urgency, a comprehensive assessment of the Panhandle wildfire damages is still far from complete, andthe full scope of the current Panhandle wildfire structure damage and forage loss still is not known. There is an urgent and critical need for developing innovative technologies and an effective decision support tool that can enable rapid and comprehensive damage assessment in response to large wildfires and similar future disasters in Texas.To address those challenges, this project aims to develop and implement innovative technological approaches and an effective decision support tool and protocol for rapid disaster response in Texas. This project will develop a hyper-resolution structure damage classification map and fine-resolution forage loss and recovery datasets based on remote sensing observations. This project will also develop and implement a decision support tool for Texas A&MDisaster Assessment and Recovery (DAR) unit to support rapid assessment and recovery efforts for the Panhandle wildfire and similar future disasters in Texas. This project will lead toa significant improvement in the effectiveness of rapid response to disasters such as the Panhandle wildfire across Texas.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
Our over-arching goals are to (1) develop cutting-edge technologies and tools to enable rapid and cost-effective assessment of wildfire damage of agricultural infrastructure and forage loss at regional scales in Texas, and (2) develop and implement a decision support tool and protocol for rapid response to large wildfire in Texas. We will(1) utilize hyper-resolution remote sensing and advanced deep learning models to generate hyper-resolution structure damage classification maps for the entire Texas Panhandle wildfire-impacted region, (2) develop fine-resolution maps of forage loss and their recovery over time, (3) seek input from key stakeholder groups and compile a comprehensive list of damage information needed for effective disaster assessment and response, (4) develop a decision support tool based on the needs assessment and the new technologies developed, and (5) engage stakeholders in outreach efforts to promote awareness and access to the damage information generated, to support disaster preparedness, response, and recovery.
Project Methods
We will use the following methods to achieve our goals and generate our expected products and outcomes: (1) develop Transformer deep learning-based structure damage identification models by leveraging hyper-resolution remote sensing data; (2) generate fine-resolution maps of the forage loss and recovery based on the satellite image time-series analysis; (3)conduct surveys and focus groups to seek input from key stakeholder groups to build a comprehensive list of specific damage information needed for disaster response actions immediately following a rangeland wildfire or similar disasters; (4)develop a decision support tool, namely Rapid Assessment Protocol for Disaster Response (RapDR), for the Disaster Assessment and Recovery (DAR) unit to support rapid assessment and recovery efforts after rangeland wildfires and similar disasters. This tool will be designed based on the damage information needs assessment and include a GIS database with pre-processed and updated key spatial data layers necessary for disaster response planning and assessment, and protocols to produce decision support information/maps for stakeholders from the products generated by the hyper-resolution satellite imagery and deep learning models; and (5)develop Extension and outreach programming on the RapDR, its damage and monitoring information products, and methods for accessing the information products, as well as roles and resources of different agencies and organizations involved in disaster response and relief. We will conduct this programming for personnel of related agencies and organizations to enhance their preparedness, and potentially their coordination, for effective rapid response to disasters. We will also design and conduct similar outreach programming for landowners and youth in areas being affected or with the potential to be affected by wildfire, to both promote awareness and access to the damage information that may be useful to them and enlist their participation in collecting onsite damage information and submitting using designated apps. We will deliver these programs in both in-person and virtual formats and will develop social media campaigns with key elements of these programs to broaden the reach.