zofin beech forest subset color

Drone remote sensing

Low altitude drone flight can produce observations 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 characterizing processes that have been beyond our grasp, and properties related to organismal condition, like leaf chemistry, canopy temperature and solar induced fluorescence.

Drone lidar in a temperate mountain forest in the southwest Czech Republic. The point density exceeds 4,000 per square meter and resolves individual trees. The tallest trees in this scene are about 40 meters.

The Kellner lab developed the Brown Platform for Autonomous Remote Sensing (BPAR) as a sensor package carried by a heavy-lift helicopter drone. The aircraft was designed and and is operated by Aeroscout GmbH of Hochdorf, Switzerland. The sensor package includes up to five remote sensing technologies: (i) a visible and near infrared (VNIR) imaging spectrometer, (ii) a shortwave infrared (SWIR) imaging spectrometer, (iii) a chlorophyll fluorescence imaging spectrometer that observes light within bands of 0.05 nm, (iv) a wide-angle scanning lidar sensor, and (v) a high-resolution digital camera. The BPAR has completed successful flight operations in Central America, Switzerland, and the Czech Republic, and has currently planned flight campaigns in Costa Rica and Kenya. These efforts support fundamental research in plant biology and the carbon cycle, conservation, and the calibration and validation activities of the NASA Global Ecosystem Dynamics Investigation.

The Brown Platform for Autonomous Remote Sensing at in the Atlantic lowlands of Costa Rica

Funding sources: NASA, NSF, Brown University


Demographic analysis of tree populations

Constellations of small sensors called cube-sats are working together in space to image the entire land surface of our planet every day at resolutions fine enough to resolve individual plants. These dense image time series provide the combination of high temporal frequency and fine spatial resolution to map individual trees and quantify population dynamics throughout whole continents. Dealing with these data requires statistical methods that can handle variable detection and missing observations. The Kellner lab is developing this framework and applying it to tree populations across thousands of miles of Amazonian forest.

flowering trees in the Colombian Amazon

Extracting the most important information from cube-sat time series requires the ability to identify and monitor objects, like individual plants, in contrast to pixel-based summaries. This new capability for object-based remote sensing at regional scales is fundamentally transformative. Developing the computational resources for object-based remote sensing at planetary scale requires advanced machine-learning and cloud-based computing to align millions of satellite images, and to identify and track objects through time. The promise of these high-density time series is the ability to determine how forests are changing over time with the granular resolution of individual plants, to test biological hypotheses across regions and continents, and to interpret high-resolution image time series from a biological point of view.

flowering trees and biological diversity in a Panamanian rain forest

Funding sources: National Science Foundation, Brown Data Science Initiative


NASA Global Ecosystem Dynamics Investigation

Estimates of aboveground carbon stocks are derived from a patchwork of methods that vary in quality and sampling density. Scientists lack consensus about the size of the forest carbon stock and the role of forests in the global carbon cycle. The NASA Global Ecosystem Dynamics Investigation (GEDI) was designed to overcome these challenges by placing a multibeam waveform lidar into low Earth orbit on the International Space Station. GEDI is a competitively selected NASA mission. It is producing globally representative measurements of the vertical distribution of vegetation in temperate and tropical forests, woodlands and savannas, and is the first spaceborne laser measurement system optimized for vegetation structure. The instrument successfully launched to the International Space Station in December, 2018 and began its nominal two-year mission life after in-orbit checkout early in 2019.

SpaceX CRS-16 launches from Cape Canaveral on December 5, 2018 with GEDI lidar.

Quantifying carbon stocks using remote sensing requires statistical relationships between measured variables and field estimates of aboveground carbon density. Our approach to developing these models is data driven. We develop candidate models that are stratified by plant functional types and regions, and we evaluate models with varying numbers of predictors and transformations to identify the subset with the best performance.

The first public release of GEDI aboveground biomass density data will be in August, 2021.

Collaborators: Dr. Laura Duncanson (University of Maryland, College Park), Dr. John Armston (University of Maryland, College Park), and the GEDI Science Team.

Funding source: NASA