Detecting and characterizing disturbances and their effects on ecosystems will be increasingly important in light of climate change, land use and land cover change, pest and pathogens, and invasive species. We use remote sensing and other methods to study biotic and abiotic disturbances and their effect on vegetation. We tackle fundamental questions — for example, how do disturbances affect ecosystem processes such as carbon and nutrient cycling? — and are working to develop knowledge and methods that enable environmental stewardship.

Current projects:

Early disease detection in potato crops

Disease is a worldwide problem in crop production and could reduce crop yields by approximately 15-30% on a global scale annually. Early disease detection has the potential to save growers billions of dollars and increase food security. With the recent advancement with remote sensing, in particular hyperspectral imaging, we are able to characterize plant physiological status non-destructively and rapidly. This allows earlier and more targeted disease management interventions. PhD student Olee Hoi Ying Lam is investigating the capability of imaging spectroscopy in early detection of Alternaria solani (early blight), with the field experiments conducting at the University of Wisconsin Hancock Agricultural Research Station.

Plant trait compositions during California megadrought

PhD student Natalie Queally and postdoc Ting Zheng are using NASA AVIRIS-Classic imagery to map foliar functional traits and characterize the relationships between functional traits and drought impacts on natural vegetation. This work is supported by a NSF Graduate Research Fellowship to Natalie Queally and by funds from the NASA Jet Propulsion Laboratory in support of the Surface Biology and Geology (SBG) mission.  Previous funding was provided by the NASA Biodiversity program.

Disturbance legacies in plant trait phenology 

PhD student Natalie Queally is using NASA AVIRIS-Next Generation imagery from the SBG High Frequency Time series (SHIFT) project to investigate how functional trait compositions change throughout a growing season in disturbed landscapes in Santa Barbara County, California. This work is supported by a NASA FINESST Fellowship. 

Hyperspectral bioindicators

Environmental contamination is a growing concern across the globe as urbanization, industrialization, and intensive agriculture increases. Hyperspectral techniques offer unprecedented potential to quantify vegetative stress responses to such disturbances and potentially develop hyperspectral bioindicators. By identifying diagnostic hyperspectral signatures for metal-induced stress in plants, airborne sensors could be used for remote, high-frequency, non-destructive environmental monitoring and local vegetation could be used as passive, low-cost bioindicators of chemical leaks or spills. Kate Thompson is a PhD student exploring potential hyperspectral bioindicators using metal toxicity and tritium exposure.

Observing and modeling forest disturbance processes

Staff Scientist Matthew Garcia has developed a meteorology-driven aerobiology model of spruce budworm dispersal in U.S. and Canadian forests in collaboration with scientists at the Canadian Forest Service and the U.S. Forest Service. His current work, in collaboration with the U.S. Forest Service Northern Research Station, extends his dissertation (2018, UW–Madison) to examine forest disturbances and, potentially, detect invasive species using Landsat/Sentinel and weather/climate observations by modeling seasonal phenology across the northeastern U.S.

Past Projects

Past work funded by the National Science Foundation and NASA merged remote sensing and ground data to understand the impacts of insect disturbance on forest carbon and nutrient dynamics, as well as on water quality. We have also studied impacts of forest fires such as the unprecedented 2011 Pagami Creek Fire in Northern Minnesota on soil processes.

High-resolution detection of forest pests and diseases

PhD alumna Sarah Wegmueller used a range of remote sensing methods including imagery from Planet, Landsat and Sentinel, and airborne hyperspectral imagery to detect early signals of potential pest and pathogen outbreaks in forests. The project developed tools that on-the-ground resource managers can use to respond quickly to new threats. This work was supported by a U.S. Forest Service Environmental Monitoring grant and with funds from the University of Wisconsin–Madison Office of the Vice Chancellor for Research and Graduate Education and the Wisconsin Alumni Research Foundation.