I work on spatial-prediction and mapping problems in forests and agriculture using high-resolution remote sensing from the air and space. My research answers questions that are both basic and applied, and that have relevance outside the university.
- NASA
- National Science Foundation
- USDA
- U.S. Department of Defense
This research has been supported by NASA, the National Science Foundation, the USDA, and the United States Department of Defense. Outside the university, I’ve contributed to successful venture-capital fundraising through a private company founded by three former Brown undergraduates, and I’ve worked in partnership with state programs in Colorado and North Dakota.
We are currently focused on the development of statistical methods in support of the NASA Global Ecosystem Dynamics Investigation (GEDI). GEDI is a waveform lidar sensor on the International Space Station. The sensor generates measurements of vertical canopy from recorded laser waveforms. Our work produces the statistical models that convert calibrated geolocated laser waveforms into units of aboveground biomass density. Output from these models is publicly available as the GEDI level-4A data product. This work is supported by NASA in collaboration with the University of Maryland, College Park.
Strengthening GEDI algorithms through improved stratification and quality filtering
Quality filtering and stratification are two major challenges faced by GEDI lidar. Although the mission has collected tens of billions of footprints, most of them don’t pass quality filters. This happens for a variety of reasons, including measurement acquired under leaf-off conditions, or because cloud-cover or atmospheric haze degraded the quality of the measurement. Stratification refers to the necessity of applying waveform-processing algorithms and aboveground biomass density algorithms within categories dictated by geography and vegetation cover. Currently the mission uses geographic distinctions that roughly correspond to continents, and vegetation cover is based on categories derived from the NASA MODIS instrument at 1 kilometer resolution.
In work supported by NASA in collaboration with Boston University, we are investigating the application of high-resolution stratification derived from the NASA Landsat program. The Landsat program generates land-surface measurements worldwide at 30 meter resolution. Higher-resolution images allow us to more precisely control quality filtering and prediction than is currently possible at 1 kilometer resolution.
Development of machine-learning models for GEDI aboveground biomass density
The current approach to statistical model development for GEDI aboveground biomass density is based on 13 linear models applied to 35 combinations of vegetation cover and geographic world region. Each model uses 1 – 4 predictor variables. The simplicity of this approach makes the models easy to troubleshoot and limits the potential for artifacts. But it introduces complexity in design decisions, including which transformation and back-transformation corrections are applied, which input variables are considered, and how the models should be stratified.
With support from NASA, we are working to apply machine learning to GEDI aboveground biomass density. The objective is to produce a single, worldwide algorithm that does not require arbitrary transformation or back-transformation decisions, that is easy to train using off-the-shelf software, and that performs at least as well as current linear models.
Perennial is a private, venture-backed company founded by three former Brown University undergraduates with support from the Nelson Center for Entrepreneurship. The company’s mission is to make regenerative agriculture measurable, investable, and scalable by delivering registry-compliant measurement of soil carbon and ecosystem outcomes on agricultural land.
The core technology is a digital soil mapping platform driven by high-resolution satellite remote sensing and machine learning. I worked closely with the founders – Jack Roswell ’20, David Schurman ’20, and Alex Zhuk ’20 during their time as undergraduates at Brown University and now serve as Chief Scientist for Perennial through the Brown University Policy on Outside Professional Activities for Faculty.
Together we have published peer-reviewed academic journal articles demonstrating the potential of digital soil mapping technology in agriculture, generated new intellectual property and patent filings, developed a protocol for private sector carbon accounting, employed more than 50 people, and raised $35 million in venture capital. The company is supported by investments led by Temasek, a global investment company headquartered in Singapore, and Bloomberg, a global leader in business and financial information.
Low-altitude drone flight can generate images at scales clearly aligned with biological processes, like metabolism, natural selection, and resource allocation within and among individual plants. The quantitative improvement represented by this technology is significant, but the most important advance is conceptual. New measurements from low-altitude drones open the door to seeing things that have been beyond our grasp, and to properties related to organismal condition, like leaf chemistry, canopy temperature and solar induced fluorescence.
We have completed drone flights at sites in the Czech Republic, Switzerland, and the Atlantic lowlands of Costa Rica with very high density lidar sensors that can produce thousands of measurements per square meter, and with spectral sensors that are able to measure solar induced fluorescence. This research in being done in collaboration with Oregon State University, and has been funded by Brown University, NASA, and the National Science Foundation.
I encourage all Brown University undergraduates to consider joining the lab. Prospective doctoral students and graduate students seeking a postdoctoral position are also encouraged to apply. If you’re interested, send an email to James R. Kellner explaining why you want to join, including a description of your background and experience.
