Performing Department
Plant and Microbial Biology
Non Technical Summary
Invasive species are reshaping landscapes globally, causing harm to agricultural and ecological systems, and generating billions of dollars in economic losses annually. Among the many habitats threatened by invasive species, the untilled rangelands of the Northern Great Plains have been severely impacted. These habitats contribute substantially to agricultural production and also contribute valuable ecosystem services essential for healthy soil and clean water. Leafy spurge is a major weed of rangelands that cause enormous economic and environmental losses. It infests almost 2 million hectares that results in over $130 million dollars annually.The goal of our project is to leverage high resolution satellite imagery and deep learning models to detect leafy spurge populations, track population dynamics over the last decade, and assess the effects of environmental variation and biocontrol efforts on spatial patterns of invasion. This research will allow for cost-effective, rapid tracking of the spread and impact of this noxious weed. Our project will also reshape our understanding of the ecological and management factors that control invasion dynamics over large geographic scales. We will use extensive datasets of occurrences and management practices already collected by multiple government agencies over the last decade across the Northern Great Plains. This work has the ability to aid land management efforts with near real-time estimates of invasion and recommendations for likely success of biocontrol as a management tool. Further, demonstrating the use of remote sensing and deep learning in rangeland management will allow for more targeted and accurate management of leafy spurge and eventually other noxious invasive weeds
Animal Health Component
(N/A)
Research Effort Categories
Basic
100%
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
?We propose to leverage satellite imagery to detect and monitor leafy spurge populations across a large fraction of the Northern Great Plains. This research will allow for cost-effective, rapid tracking of the spread and impact of this noxious weed. Our project will also reshape our understanding of the ecological and management factors that control invasion dynamics over geographic scales. The main goals of this project are to use high-resolution satellite imagery and deep learning models to (1) detect leafy spurge populations, especially in emerging areas of invasion, (2) track population dynamics over the last decade to develop better predictive models of range expansion, and (3) to determine the causes of biocontrol success vs. failure to guide immediate management.Objectives:Objective 1: Detect leafy spurge populations across the Great Plains from satellite images using deep learning:1A. We will use existing data from agencies to assess the transferability of our Minnesota-based leafy spurge model and determine the strengths and pitfalls for transference of previously built models.1B. Develop new robust models to detect populations across geographic regions that differ in habitat characteristics.Objective 2: Track population dynamics over the last decade and predict range infilling and range expansion:2A. We will use satellite imagery to track population growth/decline over a decade across the Northern Great Plains.2B. Use time-series data of leafy spurge population growth to develop demographic species distribution models (SDMs). These SDMs will identify areas at risk of invasion or that require greater management.Objective 3: Identify environmental factors that modulate biocontrol success to inform management strategies:3A. We will determine where and under which environmental conditions biocontrol efforts have succeeded in reducing population growth across the range.3B. We will use the analyses of environmental determinants of biocontrol to provide agencies with guidance on where to best implement biocontrol versus other management strategies
Project Methods
Efforts: The project will be conducted in a number of stages:Dataset construction of high-resolution satellite imagery across multiple years.Build deep learning models that can detect leafy spurge populations and can track changes in population size over timeUse data on leafy spurge population demography to build species distribution models that determine what environmental factors influence establishment, growth, and persistence of populations.Determine environmental factors that modulate biocontrol success or failure in leafy spurge management.Evaluation: Evaluation will occur internally in the project via statistical validation of models prior to passing data to be used to accomplish the next objective in the project. Deep learning models and species distribution models should achieve leafy spurge detection rates > 80-85% in independent validation datasets to be used in subsequent objectives.Externally, validation will occur via peer-review of models, dataproducts, and results for journal publications and management recommendation documents