We developed a series of studies aimed at unraveling how ecosystems respond to changes in the environment. By using time series data from satellite-remote sensing, these studies test hypotheses about biological invasion and changes in vegetation cover across large gradients in latitude and elevation.
Using measurements from the NASA Landsat program, we decomposed spectral reflectance into green vegetation cover within the historical range of mangroves near the northern range limit in Florida, and asked whether there was a change in vegetation cover over a 28-year time series. Our study found that the area of mangrove forests doubled at the northern end of the historic range. This expansion was caused by a threshold response to a decrease in the frequency of extreme cold, an interpretation we confirmed in a follow-up experimental study. Building on these efforts, we showed that realized velocities of vegetation were failing to keep pace with changes in temperature in mountain ranges of western North America. Using a 27-year time series from Landsat for mountain ranges spanning coastal California to interior deserts, and from subarctic Canada to tropical Mexico, we found that increases in vegetation cover were ubiquitous at the highest elevations in these ranges over the last three decades. But in three of five mountain ranges with long-term climate data, vegetation failed to keep pace with changes in temperature, a finding that challenges a cornerstone of conservation planning.
Collaborators: Dr. Gregory P. Asner (ASU), Dr. Kyle Cavanaugh (UCLA), Dr. Susan Cordell (USDA Forest Service) Dr. Dov Sax (Brown University).
Publications:
High-velocity upward shifts in vegetation are ubiquitous in mountains of western North America. Kellner, J. R., Kendrick, J., Sax, D. F. PLoS Climate. 10.1371/journal.pclm.0000071. [ Full Text ]
Integrating physiological threshold experiments with climate modeling to project mangrove species’ range expansion. Cavanaugh, K. C., Parker, J. D., Cook-Patton, S. C., Feller, I. C., Williams, A. P. and J. R. Kellner. Global Change Biology. 2015. [ Full Text ]
Reply to Giri and Long: Freeze-mediated expansion of mangroves does not depend on whether expansion is emergence or reemergence. Cavanaugh, K. C., J. R. Kellner, A. J. Forde, D. S. Gruner, J. D. Parker, W. Rodriguez and I. C. Feller. PNAS. 2014. [ Full Text ]
Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Cavanaugh, K. C., J. R. Kellner, A. J. Forde, D. S. Gruner, J. D. Parker, W. Rodriguez and I. C. Feller PNAS. 2014. [ Full Text ]
Remote analysis of biological invasion and the impact of enemy release. Kellner, J. R., G. P. Asner, K. M. Kinney, S. R. Loarie, D. E. Knapp, T. Kennedy-Bowdoin, E. J. Questad, S. Cordell, and J. M. Thaxton Ecological Applications. 2011. [ Full Text ]
https://kellnerlab.org/wp-content/uploads/2024/02/Sahale-Peak.jpg388636Jim Kellnerhttps://kellnerlab.org/wp-content/uploads/2023/10/blank.pngJim Kellner2024-02-26 23:27:342024-02-27 13:56:13Large-area analysis of ecosystems
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 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 collecting data 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 type and geographic world regions, and we evaluate models with varying numbers of predictors and transformations to identify the subset with the best performance. Ongoing work funded by NASA is evaluating the potential for machine learning to predict aboveground carbon density using GEDI data, and changes to stratification and quality filtering that will increase the amount of usable GEDI data.
Collaborators: Dr. Laura Duncanson (University of Maryland, College Park), Dr. John Armston (University of Maryland, College Park), Dr. Matthew Harrison (Brown University, Applied Mathematics), Dr. David Laidlaw (Brown University, Computer Science), Dr. James Tompkin (Brown University, Computer Science), Dr. Mark Friedl (Boston University), Dr., Paulo Arevalo (Boston University) and the GEDI Science Team.
Funding source: NASA GEDI Mission and Competed Science Teams
Publications:
GEDI L4B Gridded Aboveground Biomass Density, Version 2.1. Dubayah, R. O., Armston, J., Healey, S. P., Yang, Z., Patterson, P. l., Saarela, S., Stahl, G., Duncanson, L., Kellner, J. R., Bruening, J., Pascual, A. ORNL DAAC. Oak Ridge, Tennessee, USA. [ Full Text ]
Assessing the performance of NASA’s GEDI L4A footprint aboveground biomass density models using National Forest Inventory and airborne laser scanning data in Mediterranean forest ecosystems. Pascual, A., Guerra-Hernández, J., Armston, J., Duncanson, L., Minor, D. M., Kellner, J. R., Dubayah, R. O. Forest Ecology and Management. 538. [ Full Text ]
GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1. Dubayah, R. O., Armston, J., Kellner, J. R., Duncanson, L., Healey, S. P., Patterson, P. L., Hancock, S., Tang, H., Bruening, J., Hofton, M. A., Blair, J. B., Luthcke, S. B. ORNL DAAC. Oak Ridge, Tennessee, USA. [ Full Text ]
GEDI L4A Footprint Level Aboveground Biomass Density, Golden Weeks, Version 1. Dubayah, R. O., Armston, J., Kellner, J. R., Duncanson, L., Healey, S. P., Patterson, P. L., Hancock, S., Tang, H., Hofton, M. A., Blair, J. B., Luthcke, S. R. ORNL DAAC. Oak Ridge, Tennessee, USA. [ Full Text ]
GEDI L4B Gridded Aboveground Biomass Density, Version 2. Dubayah, R. O., Armston, J., Healey, S. P., Yang, Z., Patterson, P. l., Saarela, S., Stahl, G., Duncanson, L., Kellner, J. R. ORNL DAAC. Oak Ridge, Tennessee, USA. [ Full Text ]
Algorithm theoretical basis document for GEDI footprint aboveground biomass density. Kellner, J. R., Armston, J., Duncanson, L. Earth and Space Science. 10.1029/2022EA002516. [ Full Text ]
GEDI launches a new era of biomass inference from space. Dubayah, R. O., Armston, J., Healey, S. P., Bruening, J. M., Patterson, P. L., Kellner, J. R., Duncanson, L., Saarella, S., Ståhl, G., Yang, Z., Tang, H., Blair, J. B., Fatoyinbo, L., Goetz, S., Hancock, S., Hansen, M., Hofton, M., Hurtt G., Luthcke, S. Environmental Research Letters. 17: 095001. [ Full Text ]
Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment. Duncanson, L., Kellner, J.R., Armston, J., Dubayah, R., Minor, D.M., Hancock, S., Healey, S.P., Patterson, P.L., Saarela, S., Marselis, S., Silva, C.E., Bruening, J., Goetz, S.J., Tang, H., Hofton, M., Blair, B., Luthcke, S., Fatoyinbo, L., Abernethy, K., Alonso, A., Andersen, H.-E., Aplin, P., Baker, T.R., Barbier, N., Bastin, J.F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Boucher, P.B., Boyd, D.S., Burslem, D.F.R.P., Calvo-Rodriguez, S., Chave, J., Chazdon, R.L., Clark, D.B., Clark, D.A., Cohen, W.B., Coomes, D.A., Corona, P., Cushman, K.C., Cutler, M.E.J., Dalling, J.W., Dalponte, M., Dash, J., de-Miguel, S., Deng, S., Ellis, P.W., Erasmus, B., Fekety, P.A., Fernandez-Landa, A., Ferraz, A., Fischer, R., Fisher, A.G., García-Abril, A., Gobakken, T., Hacker, J.M., Heurich, M., Hill, R.A., Hopkinson, C., Huang, H., Hubbell, S.P., Hudak, A.T., Huth, A., Imbach, B., Jeffery, K.J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, N., Král, K., Krůček, M., Labrière, N., Lewis, S.L., Longo, M., Lucas, R.M., Main, R., Manzanera, J.A., Martínez, R.V., Mathieu, R., Memiaghe, H., Meyer, V., Mendoza, A.M., Monerris, A., Montesano, P., Morsdorf, F., Næsset, E., Naidoo, L., Nilus, R., O’Brien, M., Orwig, D.A., Papathanassiou, K., Parker, G., Philipson, C., Phillips, O.L., Pisek, J., Poulsen, J.R., Pretzsch, H., Rüdiger, C., Saatchi, S., Sanchez-Azofeifa, A., Sanchez-Lopez, N., Scholes, R., Silva, C.A., Simard, M., Skidmore, A., Stereńczak, K., Tanase, M., Torresan, C., Valbuena, R., Verbeeck, H., Vrska, T., Wessels, K., White, J.C., White, L.J.T., Zahabu, E., Zgraggen, C. Remote Sensing of Environment. 270: 112845. [ Full Text ]
GEDI L4A Footprint Level Aboveground Biomass Density, Version 2. Dubayah, R.O., Armston, J., Kellner, J. R., Duncanson, L., Healey, S.P. Patterson, P.L., Hancock, S., Tang, H., Bruening, J., Hofton, M. A., Blair, J. B., Luthcke, S. B. ORNL DAAC. Oak Ridge, Tennessee, USA. [ Full Text ]
GEDI L4A Footprint Level Aboveground Biomass Density, Version 1. Dubayah, R.O., J. Armston, J.R. Kellner, L. Duncanson, S.P. Healey, P.L. Patterson, S. Hancock, H. Tang, M.A. Hofton, J.B. Blair, Luthcke, S. B. ORNL DAAC. Oak Ridge, Tennessee, USA. [ Full Text ]
Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California.. Duncanson, L., Neuenschwander, A., Hancock, S. Thomas, N., Fatoyinbo, T., Simard, M. Silva, C. A., Armston, J. Luthcke, S. B., Hofton, M. Kellner, J. R., Dubayah, R. Remote Sensing of Environment. 242: 111779. [ Full Text ]
The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hurtt, G., Kellner, J. R., Luthcke, S. Armston, J. Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S. M., Patterson, P., Qi, W., and Silva, C. Science of Remote Sensing 1:100002. 2020. [ Full Text ]
Statistical properties of hybrid estimators proposed for GEDI – NASA’s Global Ecosystem Dynamics Investigation. Patterson, P. L., Healey, S. P., Stahl, G., Saarela, S., Holm, S., Andersen, H., Dubayah, R. O., Duncanson, L., Hancock, S., Armston, J., Kellner, J. R., Cohen, W. B., and Yang, Z. Environmental Research Letters. 2019. [ Full Text ]
The GEDI simulator: A large-footprint waveform lidar simulator for calibration and validation of spaceborne missions. Hancock, S., Armston, J., Hofton, M., Sun, X., Tang, H., Duncanson, L. I., Kellner, J. R., and R. O. Dubayah Earth and Space Science. 2019. [ Full Text ]
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.
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.
Funding sources: National Science Foundation, Brown Data Science Initiative
Collaborators: Dr. Gregory P. Asner (ASU), Dr. Brant Faircloth (LSU), Dr. Stephen P. Hubbell (UCLA)
Publications:
Individual-based remote sensing of canopy trees. Kellner, J. R., and J. T. Burley. In The First 100 Years of Research of Barro Colorado Island: Plant and Ecosystem Science, edited by Muller-Landau, H. C. and Wright, J. J. Smithsonian Institution Scholarly Press. 2024 in press.
Genome assemblies for two Neotropical trees: Jacaranda copaia and Handroanthus guayacan. Burley, J. T., Kellner, J. R., Hubbell, S. P., Faircloth, B. F. G3 Genes|Genomes|Genetics. jkab010. [ Full Text ]
The case for remote sensing of individual plants. Kellner, J. R. Albert, L. P., Burley, J. T., and Cushman, K. C. American Journal of Botany. 2019. [ Full Text ]
Density-dependent adult recruitment in a low-density tropical tree. Kellner, J. R. and S. P. Hubbell PNAS. 2018. [ Full Text ]
Adult mortality in a low-density tree population using high-resolution remote sensing. Kellner, J. R. and S. P. Hubbell Ecology. 2017. [ Full Text ]
https://kellnerlab.org/wp-content/uploads/2015/02/hp-6.jpg300300Jim Kellnerhttps://kellnerlab.org/wp-content/uploads/2023/10/blank.pngJim Kellner2024-02-26 22:42:412024-02-26 23:55:17Individual-based remote sensing of canopy trees
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. 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
Publications:
Impact of leaf phenology on estimates of aboveground biomass density in deciduous broadleaf forest from simulated GEDI lidar. Cushman, K. C., Armston, J., Dubayah, R. O., Duncanson, L., Hancock, S., Janik, D., Král, K., Krůček, M., Minor, D. M., Tang, H., Kellner, J. R. Environmental Research Letters. 18: 065009. [ Full Text ]
Impact of a tropical forest blowdown on aboveground carbon balance.. Cushman, K. C., Burley, J. T., Imbach, B., Saatchi, S. S., Silva, C. E., Vargas, O., Zgraggen, C., Kellner, J. R. Scientific Reports. 11: 11279.. [ Full Text ]
Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees. Krůček, M., Král, K., Cushman, KC, Missarov, A., Kellner, J.R. Remote Sensing. 2020 12(19): 3260. [ Full Text ]
The case for remote sensing of individual plants. Kellner, J. R. Albert, L. P., Burley, J. T., and Cushman, K. C. American Journal of Botany. 2019. [ Full Text ]
New opportunities for forest remote sensing through ultra-high-density drone lidar. Surveys in Geophysics. Kellner, J. R., Armston, J. D., Birrer, M., Cushman, K. C., Duncanson, L. I., Eck, C., Falleger, C., Imbach, B., Král, K., Krůček, M., Trochta, J., Vrška, T., Zgraggen, C. Surveys in Geophysics.. 2019. [ Full Text ]
