About

James Kellner is a professor of ecology, evolution and organismal biology, as well as environment and society at Brown University. Since joining Brown in 2013, he has been actively involved in various university committees, including the Advisory Committee on University Resources Management, the Academic Priorities Committee, and the Gifts and Grants Review Committee. Kellner's academic contributions include his work in the introductory biology course BIOL 0210: "Diversity of Life," for which he received the Dean’s Award for Excellence in Undergraduate Teaching, Advising and Mentoring in the Biological Sciences in 2019. His experience leading large, international research collaborations has equipped him with a deep understanding of identifying new opportunities to diversify sources of revenue. In 2026, Kellner was named Brown’s inaugural Associate Provost for Corporate Engagement, a role he assumed on July 1. In this capacity, he is committed to cultivating relationships that increase and diversify research funding, developing new student internship and career opportunities, and deepening academic-industry collaborations. He works strategically with the vice president for research, the Division of Advancement, and external corporate partners to establish a university-wide corporate engagement strategy, facilitating opportunities for faculty and the broader university community to develop relationships with industry partners.

Research topics

• Remote sensing
• Geology
• Geography
• Environmental science
• Oceanography
• Geodesy
• Biology
• Ecology
• Physical geography
• Meteorology
• Physics
• Engineering
• Archaeology
• Optics
• Astronomy

Selected publications

• The Global Canopy Atlas: analysis-ready maps of 3D structure for the world’s woody ecosystems

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-04 · 2 citations

  preprintOpen access

  Abstract Woody canopies regulate exchanges of energy, water and carbon, and their three-dimensional (3D) structure supports much of terrestrial biodiversity. Remote sensing technologies such as airborne laser scanning (ALS) now enable the 3D mapping of entire landscapes. However, we lack the large, harmonized and geographically representative ALS collections needed to build a global picture of woody ecosystem structure. To address this challenge, we developed the Global Canopy Atlas (GCA): 3,458 ALS acquisitions transformed into standardized and analysis-ready maps of canopy height and elevation at 1 m 2 resolution. The GCA covers 56,554 km 2 across all major biomes. 19% of this area has been scanned multiple times, and 87% of all GCA products are openly available, covering 95% of the total area. To showcase its wide range of applications, we applied the GCA in three case studies. First, we validated three global satellite-derived canopy height maps, finding poor performance at native resolution (1-30 m, R 2 < 0.38) and moderate performance at 250 m resolution (R 2 < 0.65). Second, analyzing global patterns in canopy gap size frequency we discovered an unexpectedly large variation of power law exponents from branch to stand level (α = 1.52 to 2.38), pointing to a fundamental scale-dependence of forest structure. Third, we developed a framework to standardize forest turnover quantification from multi-source, multi-temporal ALS. In a temperate forest in North America it revealed that 21% of canopy gaps closed within 12 years of opening and would thus be missed by infrequent monitoring. As demonstrated by these case studies, the GCA provides a novel data source for ecologists, foresters, remote sensing scientists and the ecosystem modelling community that substantially advances our ability to understand the structure and dynamics of woody ecosystems at global scales.

  PublisherOA PDFDOI

• Digital soil mapping in support of voluntary carbon market programs in agricultural land

  PLoS ONE · 2025-09-02 · 2 citations

  articleOpen access1st authorCorresponding

  Voluntary carbon market (VCM) programs in agriculture depend on accurate measurements of soil organic carbon (SOC) that can be deployed at scale efficiently, but barriers are preventing widespread adoption. To overcome these challenges, we developed a digital soil mapping (DSM) framework driven by machine-learning and numerous spatial covariates, including long-term climate proxies, short-term climate and weather-related variables, topographic and edaphic measurements, and remote sensing time-series summaries. We show that the model can predict SOC content in the top 30 cm of soil using 5,230 measurements of SOC in agricultural land within 47 states in the contiguous United States (CONUS). Model predictions closely matched independent measured values. The intercept and slope of the cross-validated relationship at the agricultural field level were -0.179 and 1.095. The coefficient of determination was R2 = 0.811, and the RMSE was 0.041. In contrast, comparison of independent field measurements to four publicly available SOC data products using 165 fields that contained 3,285 in-situ soil samples showed poor ability of existing public SOC maps to reproduce measured values, underscoring the importance of quantification technologies developed specifically for agricultural land and with recent soil measurements. Three prior SOC data products underestimated SOC content at small values and overestimated it at large ones, while one underestimated SOC content at all values examined. Analysis of feature importance showed that time series summaries from Sentinel-2 are the strongest predictors, followed by temperature variables and features related to surface hydrology. These findings underscore the value of geographically representative training and validation data for quantifying SOC content in agricultural land and demonstrate that feature engineering can increase the sensitivity of SOC quantification to optical remote sensing summaries. Data-driven algorithms can generate accurate estimates of field-level SOC content in agricultural land in CONUS that overcome barriers to scale in the VCM.

  PublisherOA PDFDOI

• A Closer Look at Uncertainties in Forest Ecosystem Surveys Using Remotely Sensed Data and Model-Based Inference

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Near-Complete Sampling of Forest Structure from High-Density Drone Lidar Demonstrated by Ray Tracing

  Remote Sensing · 2024-07-29 · 4 citations

  articleOpen accessSenior author

  Drone lidar has the potential to provide detailed measurements of vertical forest structure throughout large areas, but a systematic evaluation of unsampled forest structure in comparison to independent reference data has not been performed. Here, we used ray tracing on a high-resolution voxel grid to quantify sampling variation in a temperate mountain forest in the southwest Czech Republic. We decoupled the impact of pulse density and scan-angle range on the likelihood of generating a return using spatially and temporally coincident TLS data. We show three ways that a return can fail to be generated in the presence of vegetation: first, voxels could be searched without producing a return, even when vegetation is present; second, voxels could be shadowed (occluded) by other material in the beam path, preventing a pulse from searching a given voxel; and third, some voxels were unsearched because no pulse was fired in that direction. We found that all three types existed, and that the proportion of each of them varied with pulse density and scan-angle range throughout the canopy height profile. Across the entire data set, 98.1% of voxels known to contain vegetation from a combination of coincident drone lidar and TLS data were searched by high-density drone lidar, and 81.8% of voxels that were occupied by vegetation generated at least one return. By decoupling the impacts of pulse density and scan angle range, we found that sampling completeness was more sensitive to pulse density than to scan-angle range. There are important differences in the causes of sampling variation that change with pulse density, scan-angle range, and canopy height. Our findings demonstrate the value of ray tracing to quantifying sampling completeness in drone lidar.

  PublisherOA PDFDOI

• Radiocarbon ages of macroscopic charcoal fragments found in Hawaiian drylands

  Forest Service Research Data Archive · 2024-01-09

  datasetOpen accessSenior author

  This archive contains research data collected and/or funded by Forest Service Research and Development (FS R&D), U.S. Department of Agriculture. It is a resource for accessing both short and long-term FS R&D research data, which includes Experimental Forest and Range data. It is a way to both preserve and share the quality science of our researchers.

  PublisherOA PDFDOI

• Individual-Based Remote Sensing of Canopy Trees

  Smithsonian Institution Scholarly Press eBooks · 2024-11-22 · 2 citations

  book-chapterOpen access1st authorCorresponding

  Pioneering work over the past decade has demonstrated the potential to map and monitor individual canopy trees using a wide range of sensor data, including digital airborne images, high-density terrestrial laser scanning, drone lidar, airborne imaging spectroscopy and multispectral satellite observations. Studies on Barro Colorado Island have identified canopy individuals of 11 species of tropical trees in 7 families. Improvements in spatial resolution, spectral resolution, spectral range, temporal frequency, and radiometric accuracy will increase the number of species that can be mapped and monitored using remotely sensed data. Over the next decade, whole-tree segmentation algorithms using very-high-density point clouds from lidar and digital photogrammetry will augment ground-based tree inventories, expanding the size of field-inventory plots and the types of measurements that are collected. Constellations of small satellites will enable analysis of tree populations throughout entire species ranges. Critical to all of these efforts is the acquisition of high-quality and spatially representative training data, as well as investment in automated algorithms with clearly defined sources of uncertainty.<p></p>

  PublisherDOI

• Towards global spaceborne lidar biomass: Developing and applying boreal forest biomass models for ICESat-2 laser altimetry data

  Science of Remote Sensing · 2024-07-16 · 13 citations

  articleOpen access

  Space-based laser altimetry has revolutionized our capacity to characterize terrestrial ecosystems through the direct observation of vegetation structure and the terrain beneath it. Data from NASA's ICESat-2 mission provide the first comprehensive look at canopy structure for boreal forests from space-based lidar. The objective of this research was to create ICESat-2 aboveground biomass density (AGBD) models for the global entirety of boreal forests at a 30 m spatial resolution and apply those models to ICESat-2 data from the 2019–2021 period. Although limited in dense canopy, ICESat-2 is the only space-based laser altimeter capable of mapping vegetation in northern latitudes. Along each ICESat-2 orbit track, ground and vegetation height is captured with additional modeling required to characterize biomass. By implementing a similar methodology of estimating AGBD as GEDI, ICESat-2 AGBD estimates can complement GEDI's estimates for a full global accounting of aboveground carbon. Using a suite of field measurements with contemporaneous airborne lidar data over boreal forests, ICESat-2 photons were simulated over many field sites and the impact of two methods of computing relative height (RH) metrics on AGBD at a 30 m along-track spatial resolution were tested; with and without ground photons. AGBD models were developed specifically for ICESat-2 segments having land cover as either Evergreen Needleleaf or Deciduous Broadleaf Trees, whereas a generalized boreal-wide AGBD model was developed for ICESat-2 segments whose land cover was neither. Applying our AGBD models to a set of over 19 million ICESat-2 observations yielded a 30 m along-track AGBD product for the pan-boreal. The ability demonstrated herein to calculate ICESat-2 biomass estimates at a 30 m spatial resolution provides the scientific underpinning for a full, spatially explicit, global accounting of aboveground biomass.

  PublisherDOI

• Towards Global Spaceborne Lidar Biomass: Developing and Applying Boreal Forest Biomass Models for Icesat-2 Laser Altimetry Data

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning

  Remote Sensing · 2024-06-19 · 14 citations

  articleOpen accessSenior author

  Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS).

  PublisherOA PDFDOI

• High-velocity upward shifts in vegetation are ubiquitous in mountains of western North America

  PLOS Climate · 2023-02-15 · 20 citations

  articleOpen access1st authorCorresponding

  The velocity of climate change and its subsequent impact on vegetation has been well characterized at high elevations and latitudes, including the Arctic. But whether species and ecosystems are keeping pace with the velocity of temperature change is not as well documented. Some evidence indicates that species are less able to keep pace with the velocity of climate change along elevational gradients than latitudinal ones. If substantiated this finding could warrant reconsideration of a current cornerstone of conservation planning. Here we use 27 years of high-resolution satellite data to quantify changes in vegetation cover across elevation within nine mountain ranges in western North America, spanning tropical Mexico to subarctic Canada and from coastal California to interior deserts. Across these ranges we show a uniform pattern at the highest elevations in each range, where increases in vegetation have occurred ubiquitously over the past three decades. At these highest elevations, the realized velocity of vegetation varies among mountain ranges from 19.8–112.8 m · decade -1 (mean = 67.3 m · decade -1 ). This is equivalent, with respect to gradients in temperature, to a 14.4–104.3 km · decade -1 poleward shift (mean = 56.1 km · decade -1 ). This realized velocity is 4.4 times larger than previously reported for plants, and is among the fastest rates predicted for the velocity of climate change. However, in three of the five mountain ranges with long-term climate data, realized velocities fail to keep pace with changes in temperature, a finding with important implications for conservation of biological diversity.

  PublisherOA PDFDOI

Recent grants

• RAPID: Quantifying the impact of a large, infrequent disturbance on landscape carbon balance: strengthening conceptual understanding of ecosystem carbon fluxes

  NSF · $50k · 2018–2020

• Collaborative research: Remote sensing of foliar chemistry to reconstruct the genealogy of canopy trees in a Neotropical rain forest.

  NSF · $135k · 2013–2017

• Collaborative Research LTREB Renewal: Tropical rain forest ecosystem responses to climatic forcings

  NSF · $44k · 2014–2019

Frequent coauthors

• Laura Duncanson

  University of Maryland, College Park

  49 shared

• K. C. Cushman

  Oak Ridge National Laboratory

  44 shared

• John Armston

  43 shared

• R. Dubayah

  42 shared

• Steven Hancock

  33 shared

• Hao Tang

  National University of Singapore

  28 shared

• David B. Clark

  University of Missouri–St. Louis

  27 shared

• Gregory P. Asner

  University of Hawaii at Hilo

  27 shared