About

Ralph Dubayah is a Professor of Geographical Sciences at the University of Maryland, College Park. He received his B.A. in 1982 from the University of California, Berkeley, and his M.A. (1985) and Ph.D. (1990) degrees from the University of California, Santa Barbara. His main areas of interest include ecosystem characterization for carbon modeling, habitat and biodiversity studies, land surface energy and water balance modeling, spatial analysis, and remote sensing science. A common goal of his research is to develop and apply emerging technologies of spatial data acquisition and analysis to address environmental issues at policy-relevant scales. He has been an investigator for numerous NASA projects, including two Interdisciplinary Science Investigations on the use of remote sensing for hydrological and ecosystem modeling. He has recent awards as PI for NASA's Carbon Management System and the ICESAT2 mission. He was also principal investigator for the Vegetation Canopy Lidar (VCL), a NASA mission to measure the three-dimensional structure of Earth’s forests for carbon assessments. Dr. Dubayah has served in various national and international organizations, including roles as an Associate Editor for the Journal of Geophysical Research (Biogeosciences), and on the editorial boards of Remote Sensing of Environment and Remote Sensing. He was the Co-Lead Author for the CEOS Strategy for Carbon Observations from Space in 2014. Currently, he is the Science Definition Team Co-Leader for NASA’s NISAR mission and a Science Team member for CMS. In 2014, he was chosen as PI for the Global Ecosystems Dynamics Investigation Lidar (GEDI), which will deploy a multibeam lidar instrument onboard the International Space Station to measure forest vertical structure and biomass.

Research topics

• Environmental science
• Remote sensing
• Geography
• Geology
• Ecology
• Biology
• Cartography
• Oceanography
• Computer Science
• Meteorology
• Geodesy
• Astronomy
• Physics
• Optics
• Physical geography

Selected publications

• Spatial resolution for forest carbon maps

  Science · 2025-01-23 · 20 citations

  letter

  Forests are central to climate solutions, and transparent and accurate data on forest carbon stocks and fluxes are critical for scientists and decision-makers. Satellite-based forest carbon maps have recently proliferated from public agencies such as NASA and the private sector. These maps have tended toward ever-higher spatial resolutions. However, higher spatial resolutions increase the uncertainties of carbon maps, rendering products at very high spatial resolution largely meaningless for forest carbon monitoring.

  PublisherDOI

• Legacies of model initialization on predictions of future ecosystem dynamics in California's Sierra Nevada: insights from <scp>GEDI</scp>

  New Phytologist · 2025-11-11 · 1 citations

  articleOpen access

  Accurate estimates of aboveground vegetation structure are essential for making reliable predictions of terrestrial ecosystem responses to climate change. However, traditional small-scale ground-based inventory methods cannot easily be scaled up to comprehensive, large-scale estimates of ecosystem structure. We assimilate remotely-sensed Light Detection and Ranging measurements of vegetation structure and corresponding imaging-spectrometry-derived estimates of canopy composition into the ecosystem demography (ED2.2) terrestrial biosphere model across an elevational transect in California's Sierra Nevada. We then used the model to assess: how incorporating observed ecosystem structure and composition influences predictions of ecosystem change over the coming century as compared to simulations initialized with long-term potential vegetation; and how ecosystems are predicted to respond differently to future climate change. Our analyses show multi-decadal impacts of initialization on predictions of ecosystem composition and structure, emphasizing long-term legacies of climate and disturbance history in predictions of ecosystem responses to climate change that are not captured when models are initialized with outputs from long-term historical simulations. The remote sensing-initialized simulations predict increases in aboveground biomass and leaf area index, and pronounced elevation-dependent changes in canopy composition. The differences among initialization methods, climate scenarios, and elevational gradients have important implications for improving ecosystem modeling and informing land management strategies.

  PublisherOA PDFDOI

• Calibration of GEDI footprint aboveground biomass models in Mediterranean forests with NFI plots: A comparison of approaches

  Journal of Environmental Management · 2025-01-31 · 16 citations

  articleOpen accessSenior author

  Observations from the NASA Global Ecosystem Dynamics Investigation (GEDI) provide global information on forest structure and biomass. Footprint-level predictions of aboveground biomass density (AGBD) in the GEDI mission are based on training data sourced from sparsely distributed field plots coincident with airborne laser scanning surveys. National Forest Inventories (NFI) are rarely used to calibrate GEDI footprint biomass models because their sampling and positional accuracy prevent accurate colocation with GEDI or ALS. This omission can limit the harmonization of jurisdictional biomass estimates from NFI's and GEDI; however, there are methods available to improve the colocation of NFI plots with GEDI footprints. Focusing on Mediterranean forests in Spain, we compared different approaches to the collocation of NFI and GEDI data: (i) simulated waveforms from ALS; (ii) nearest-neighbor on-orbit GEDI waveforms; and (iii) imputed GEDI waveforms imputed to NFI plot locations using a novel geostatistical method. These methods are potential solutions to improve the local performance of biomass models and address potential local systematic deviations between GEDI and NFI estimates. We assess the advantages and limitations of these methods to locally calibrate GEDI biomass models and quantify the impact of geolocation errors in reference NFI plot data. The new biomass models from each method were used to predict footprint level AGBD, which were then gridded for a province in the North-West of Spain. It was found that the imputation approach is not sensitive to common errors in NFI plot geolocation, but it can outperform ALS-based simulation in some cases, highlighting the benefit of information from multiple GEDI footprints proximate to NFI plots for improving biomass predictions. This research provides users with benchmark of available techniques to locally-calibrate GEDI footprint biomass models. • Increasing need to harmonize GEDI estimates to more local conditions. • We tailored global biomass estimates to enhanced-geolocated in-situ biomass data. • Three methods to calibrate biomass using NFI data were tested in Spain. • Waveform imputation perform well despite not capturing geolocation offsets in training plots. • Properties of simulated and on-orbit GEDI and phenology flags affected biomass calibration.

  PublisherDOI

• Scalable deep fusion of spaceborne lidar and synthetic aperture radar for global forest structural complexity mapping

  ArXiv.org · 2025-10-07

  preprintOpen accessSenior author

  Forest structural complexity metrics integrate multiple canopy attributes into a single value that reflects habitat quality and ecosystem function. Spaceborne lidar from the Global Ecosystem Dynamics Investigation (GEDI) has enabled mapping of structural complexity in temperate and tropical forests, but its sparse sampling limits continuous high-resolution mapping. We present a scalable, deep learning framework fusing GEDI observations with multimodal Synthetic Aperture Radar (SAR) datasets to produce global, high-resolution (25 m) wall-to-wall maps of forest structural complexity. Our adapted EfficientNetV2 architecture, trained on over 130 million GEDI footprints, achieves high performance (global R2 = 0.82) with fewer than 400,000 parameters, making it an accessible tool that enables researchers to process datasets at any scale without requiring specialized computing infrastructure. The model produces accurate predictions with calibrated uncertainty estimates across biomes and time periods, preserving fine-scale spatial patterns. It has been used to generate a global, multi-temporal dataset of forest structural complexity from 2015 to 2022. Through transfer learning, this framework can be extended to predict additional forest structural variables with minimal computational cost. This approach supports continuous, multi-temporal monitoring of global forest structural dynamics and provides tools for biodiversity conservation and ecosystem management efforts in a changing climate.

  PublisherOA PDFDOI

• The importance of distinguishing between natural and managed tree cover gains in the moist tropics

  Nature Communications · 2025-07-02 · 3 citations

  articleOpen access

  Naturally regenerated forests and managed tree systems provide different levels of carbon, biodiversity, and livelihood benefits. Here, we show that tree cover gains in the moist tropics during 1982–2015 were 56% ± 3% naturally regenerated forests and 27% ± 2.6% managed tree systems, with these differences in forest type, not only natural conditions (climate, soil, and topography), driving observed carbon recovery rates. The remaining 17% ± 3% likely represents small, unmanaged tree patches within non-forest cover types. Achieving global forest restoration goals requires robust monitoring, reporting, and verification of forest types established by restoration initiatives. Tree cover gains in the moist tropics (1982–2015) were 56% naturally regenerated forests and 27% managed tree systems, with forest type influencing carbon recovery. Effective forest restoration requires robust tracking of forest types established by restoration efforts.

  PublisherOA PDFDOI

• Scalable deep fusion of spaceborne lidar and synthetic aperture radar for global forest structural complexity mapping

  Machine Learning Earth · 2025-12-12 · 2 citations

  articleOpen accessSenior author

  Forest structural complexity metrics integrate multiple canopy attributes into a single value that reflects habitat quality and ecosystem function. Spaceborne lidar from the Global Ecosystem Dynamics Investigation (GEDI) has enabled mapping of structural complexity in temperate and tropical forests, but its sparse sampling limits continuous high-resolution mapping. We present a scalable, deep learning framework fusing GEDI waveform structural complexity index estimates with multimodal synthetic aperture radar datasets from Sentinel-1, Advanced Land Observing Satellite PALSAR-2 and Copernicus DEM to produce global, high-resolution (25 m) wall-to-wall maps of forest structural complexity. Our adapted EfficientNetV2 architecture, trained on over 130 million GEDI footprints, achieves high performance (global R ^2 = 0.82) with fewer than 400 000 parameters, making it an accessible tool that enables researchers to process datasets at any scale without requiring specialized computing infrastructure. The model produces accurate predictions with calibrated uncertainty estimates across biomes and time periods, preserving fine-scale spatial patterns. It has been used to generate a global, multi-temporal dataset of forest structural complexity from 2015 to 2022. Through transfer learning, this framework can be extended to predict additional forest structural variables with minimal computational cost. This approach supports continuous, multi-temporal monitoring of global forest structural dynamics and provides tools for biodiversity conservation and ecosystem management efforts in a changing climate.

  PublisherDOI

• Spaceborne lidar observations reveal impacts of inundation on coastal forest structure across the U.S. Mid-Atlantic

  Estuarine Coastal and Shelf Science · 2025-06-01 · 2 citations

  articleOpen access

  The impacts of accelerated sea level rise (SLR) on coastal ecosystems due to climate change has yet to be fully realized. SLR, combined with an increasing intensity of storm surges, are driving significant regime shifts in vegetation across coastal landscapes, leading to marsh migration and upland forest mortality. However, the specific effects of tidal inundation, stemming from elevated water levels and soil salinity, on forest vertical structure remain poorly understood. In this study, we use spaceborne light detection and ranging (lidar) data from the Global Ecosystem Dynamics Investigation (GEDI) to explore the response of vertical forest structural dynamics in areas highly vulnerable to increased inundation across the U.S. Mid-Atlantic coastal region. We assessed the impact of inundation on three forest structural traits derived from GEDI data. We identified the threshold position where forest structure is no longer impacted and investigated the environmental factors influencing these positions across watersheds to determine the forest's vulnerability to transitioning into marshes. We discovered that watersheds with a high proportion of area below Mean Higher High Water (MHHW) tended to increase vulnerability to forest conversion into marshes whereas watersheds characterized by steeper slopes and drainage densities tended to have positions reflecting lower vulnerability, suggesting an overall increased resistance to marsh migration. These findings highlight the importance of monitoring forest structural dynamics for early detection of upland marsh expansion, with lidar technology offering a potentially valuable tool to enhance our understanding of ecological shifts in coastal environments. Such insights may be essential for evaluating ecosystem responses to SLR and may foster a more comprehensive understanding how SLR and other climate change-induced disturbances will affect the coastal carbon sink. • This study leverages GEDI lidar data to examine the impact of tidal inundation on vertical forest structure in vulnerable regions of the U.S. Mid-Atlantic coast. • We identified distinct thresholds in the relationship between forest structural traits and elevation across watersheds. • Watersheds with a greater proportion of area below Mean Higher High Water (MHHW) suggest watersheds that are more vulnerable to marsh conversion, while those with higher slopes and drainage densities may demonstrate increased resistance to this transition.

  PublisherDOI

• Definition criteria determine the success of old-growth mapping

  Ecological Indicators · 2024-02-01 · 8 citations

  articleOpen access

  Old-growth forests have been widely studied for decades. The extreme diversity of old forest characteristics has inspired an equally diverse set of old-growth definitions, and makes mapping old-growth difficult across large areas and different forest types. While the use of remote sensing in old-growth research is not new, there is a growing need for large scale mapping to improve understanding of old forest processes and to support old-growth conservation. Old-growth mapping requires definitions that are ecologically relevant to old forests while also transferable to remote sensing data. In this paper we develop a conceptual framework to evaluate three dimensions of old-growth—a temporal dimension related to tree ages, a physical dimension related to tree sizes, and a functional dimension related to forest processes. In the first part of our analysis, we classify forests throughout the eastern US as old or not with respect to each old-growth dimension using existing old-growth definitions and data from the US Forest Inventory and Analysis (FIA) program. We estimate the proportion of forest classified as old within a hexagon grid, resulting in a unique map of old forest proportion (OFP) for each dimension. Subsequently, we use spaceborne lidar data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to reproduce each OFP map in a modeling framework designed to 1) assess the extent to which each dimension of forest oldness can be mapped at large spatial scales, and 2) identify biophysical GEDI variables related to each dimension of forest oldness. We estimate that only 2% of forest classified as old in any dimension satisfied the old criteria in all three dimensions. We found substantial spatial variation in the mapped OFP estimates across the three dimensions, highlighting how definition criteria impacts old-growth maps. We also found that physically old forests were more effectively mapped using GEDI data than functionally or temporally old forests, and that physically old forests were more structurally similar to one another than temporally or functionally old forests. Our modeling results indicate that while remote sensing may be best suited to mapping physical old-growth characteristics, definitions that rely solely on physical characteristics do not adequately represent old forests throughout the eastern US. We propose that future efforts to map old-growth with spaceborne remote sensing data may maximize utility through collaboration between western and indigenous old-growth experts to determine broad yet nuanced approaches that are appropriately tailored to the target variable of old forests. These efforts should balance explicit and ecologically relevant old-growth definitions specifically for mapping that can be linked to remotely sensed data, 2) appropriate spatial resolutions, and 3) flexible quantitative frameworks that encompass the complexities and heterogeneity of old forests.

  PublisherDOI

• Mapping Large-Scale Pantropical Forest Canopy Height by Integrating Gedi Lidar and Tandem-X Insar Data

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Intergovernmental Panel on Climate Change (IPCC) Tier 1 forest biomass estimates from Earth Observation

  2024-03-04

  preprintOpen access

  Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration’s (NASA’s) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency’s (ESA’s) Climate Change Initiative (CCI). It also provides the underlying classification of forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (&gt;20 years)) as geospatial layers. The approaches leverage strengths of various EO-derived datasets, compiled in an open-science framework through the Multi-mission Algorithm and Analysis Platform (MAAP), enabling flexibility to adopt new datasets. EO-based AGBD estimates are expected to contribute to the IPCC Emission Factors Database in support of UNFCCC processes, and the forest classification expected to support the generation of other policy-relevant datasets while reflecting ongoing shifts in global forests with climate change.

  PublisherOA PDFDOI

Frequent coauthors

• John Armston

  102 shared

• Laura Duncanson

  University of Maryland, College Park

  90 shared

• G. C. Hurtt

  University of Maryland, College Park

  83 shared

• M. A. Hofton

  University of Maryland, College Park

  83 shared

• Hao Tang

  National University of Singapore

  70 shared

• J. B. Blair

  Goddard Space Flight Center

  60 shared

• Temilola Fatoyinbo

  51 shared

• S. J. Goetz

  Northern Arizona University

  45 shared

Education

• Ph.D., Geography

  University of Maryland

  1991

• M.S., Geography

  University of Maryland

  1986

• B.S., Geography

  University of Maryland

  1983

Awards & honors

• PI for NASA's Carbon Management System (CMS)
• PI for the Global Ecosystems Dynamics Investigation Lidar (G…