Recipient Organization
UNIV OF HAWAII
3190 MAILE WAY
HONOLULU,HI 96822
Performing Department
(N/A)
Non Technical Summary
In early August 2023, a devastating fire disaster struck Maui, Hawaii, fueled by severe drought and Hurricane Dora. The serious destruction, loss of life, and widespread damage to agricultural areas, prompted a state of emergency declaration by Hawaii and federal major disaster declaration by President Biden. The National Agricultural Statistics Service (NASS), as a USDA data arm, is obligated to provide timely and accurate disaster impact assessments on agriculture. However, real-time monitoring is not available due to a lack of current crop data and up-to-date disaster information. To address these challenges, this project aims to develop high-resolution crop data layers for 2023 and 2024 using AI (Artificial Intelligence)/ML(machine learning) models, create high-resolution fire and vegetation condition maps, and build a Google Earth Engine based online disaster monitoring and impact assessment platform. The platform will integrate multi-sensor Earth observation and geophysical data, enabling near real-time disaster monitoring and impact assessment. The project will collaborate with farmer-supporting organizations to provide outreach and train stakeholders to use the data and tools. Graduates and high-school or undergraduate students from the Hawaii State 4-H program will also actively participate in research activities. The data and tools developed will be integrated into university teaching and research. This project will enhance stakeholders' disaster response capabilities by providing analysis-ready data and real-time monitoring tools. It will aid in understanding vulnerabilities, mitigating risks, and fostering resilient agricultural practices. NASS and USDA will directly benefit from the improved disaster monitoring operation and decision support in disaster relief, and mitigation amid extreme weather and disasters.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
The overarching goal of this project is to develop rapid response and impact monitoring and assessment capabilities for disasters related to fire and agriculture in Hawaii. This will be achieved by) producing latest high spatialresolution maps of cropland and near real-time high-resolution maps of fires and vegetation conditions for the state of Hawaii and 2) developing web-based tools to easily and quickly disseminate the relevant information to farmers, resource managers, scientists, and decision makers in Hawaii and USDA NASS. Specifically, we have the following specific objectives:Objective 1: Develop 10-m-resolution Hawaii Cropland Data Layers (HCDL) for 2023 and 2024 using convolutional neural network (CNN)-based deep learning models and enable crop specific vegetation conditions and disaster impact monitoring and assessment.Objective 2: Create high spatial-temporal resolution fire and vegetation condition map products by fusing Sentinel-2 and VIIRS data using machine learning approach and enable near real time high resolution agricultural disaster monitoring and assessment.Objective 3: Build a comprehensive disaster monitoring and impact assessment platform using Google Earth Engine (GEE) and integrate multi-sensor Earth observation data and geophysical data to facilitate agricultural disaster monitoring and assessment.
Project Methods
We will use the following methods to achieve our goals and generate our expected products and outcomes:1) develop and incorporate latest technologies in AI, satellite remote sensing, cloud-computing, and web applications to quickly and accurately map crop types, fire, and vegetation conditions. For high resolution crop type mapping, we will use convolutional neural netowork (CNN)-based deep learning methods that have demonstrated successes in California and Texas and adapt these technologies in Hawaii. For high spatial-temporal mapping of fire and vegetation conditions, we will fuse satellite imagery of different saptial and temporal resolutions (Sentinel-2 and VIIRS) via an enhanced super-resolution generative-adversarial network that also has been demonstrated in previous studies. For the web app development, we will integrate Google Earth Engine (GEE) and other multi-sensor Earth observation and geophysical data to make our platform comprehensive, accessible, and user-friendly.2) maximize efficiency and quality in reference data collection by leveraging resources and networks from farmer-supporting organizations and coordinating efforts of a diverse team consisting of scientists, technicians, and students. The project directors, field technician, high-school and undergraduate students are from different islands, which allow us to harness knowledge from local experts and reduce travel costs.3) disseminate our products, platform (i.e., web app), technologies, and promote their wide use by organizating training workshops and field trips that are open to a variety of organizations,farmers, managers, students, stakeholders, and other potential users, sharing ourdata products and algorithms widely while following security policies and federal regulations, and presentating our technologies in national conferences and eventually publishing them in peer-reveiwed journals.