Invasive Grass Mapping
Description
USGS mapping of invasive exotic annual grasses is critical because these species, such as cheatgrass, alter ecosystems by increasing the frequency and intensity of wildfires and outcompeting native vegetation. Accurate, large-scale maps help land managers anticipate fire risk, prioritize restoration efforts, and guide grazing or herbicide treatments. Beyond fire management, these maps support wildlife habitat conservation, water resource protection, and long-term monitoring of ecosystem change, providing essential information for both local decision-making and national land management strategies.
Detailed example
Weekly maps depicting invasive exotic annual grass extent and coverage for the western United States, with weekly products from early April to early July.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
DNNs have greatly improved our ability to accurately map invasive annual exotic grasses. We now produce weekly estimates of invasive grasses for the western US from early April until July, providing fire and land managers with critical data.
Controls / human review
ATO: No; PIA: Not published
Data needed
Harmonized Landsat and Sentinel (HLS) data form the core dataset for mapping weekly exotic annual grasses (EAG). Bureau of Land Management’s Assessment, Inventory, and Monitoring (AIM) plots are used to train deep learning models on HLS data to produce EAG estimates.