About

Laura Duncanson is an associate professor in the Department of Geographical Sciences at the University of Maryland. She is a remote sensing scientist with a focus on mapping and understanding forest 3D structure. Her research explores how forests occupy space and how this relates to carbon stocks and cycles, utilizing novel remote sensing technologies such as LiDAR, Landsat, SAR, and high-resolution optical datasets. Duncanson's work involves developing and applying algorithms for forest biomass mapping, monitoring forest structure, and studying forest ecology at a global scale. She is actively engaged in projects on the GEDI science team, NASA's ICESat-2, and ABoVE Science teams, contributing to the development of empirical biomass equations and algorithms for mapping boreal forest structure to support global forest carbon accounting. Additionally, she serves on NASA’s Carbon Monitoring System’s Science team and co-leads CEOS’s Land Product Validation Biomass sub-group, working toward international protocols for biomass product calibration and validation. Her research also extends to testing ecological theories with remote sensing data, particularly examining the linkages between forest structure and biodiversity in protected areas.

Research topics

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

Selected publications

• Spaceborne Lidar is for the Birds: Applications of GEDI lidar to improve species distribution and hotspot mapping in a National Park in Africa

  2026-03-10

  articleOpen accessSenior author

  In 2019, an alarming study of bird populations revealed that 3 billion birds have been lost since the 1970s. Bird populations are predicted to decrease further as a result of climate change and land conversion. Additionally, the usefulness of bird diversity as an indicator of overall biodiversity and ecosystem health has been well researched, as has the connection between forest structure and avian species. However, on-the-ground studies of forest structural diversity are often limited by time and financial resources. Remote sensing of forest structure, such as discrete return and aerial lidar, demonstrably improve species model performance in regional studies to predict bird species occurrence. In addition, the ability of spaceborne lidar to improve biodiversity predictions is still being explored and offers a publicly available method for measuring forest structure across large regions. Here, we will use the Global Ecosystem Dynamics Investigation (GEDI) lidar instrument to improve existing species habitat suitability models and predict biodiversity hotspots in a National Park in Africa. GEDI is a space-borne lidar instrument aboard the International Space Station that is capable of measuring the height and complexity of vegetation. In addition to sociocultural and ecological data, we will compile GEDI derived metrics of forest structure, including canopy height (RH98), foliage height diversity (FHD), plant area index (PAI), waveform structural complexity index (WSCI) to apply structural variables to an ensemble model of species distribution. Using species occupancy gathered from in-situ point count data from 2022-2023, along with the SSDM package in R, we will create stacked species distribution models and species richness predictions. Models with and without GEDI data will be compared to understand the impact of GEDI metrics on model accuracy. The results of this work will inform park management and bolster efforts to conserve species biodiversity in the park using remote sensing tools. Results of this work will also inform biodiversity hotspot mappings across larger regions of Africa where in situ data is sparse.

  PublisherDOI

• Meta-analysis of North American Arctic and boreal aboveground biomass datasets: assessing accuracy, dynamics, and similarities

  Environmental Research Letters · 2026-02-19 · 2 citations

  articleOpen access

  Abstract The North American arctic and boreal regions (ABRs) are rapidly warming and experiencing intensifying disturbances. Accurately quantifying aboveground biomass (AGB) is critical for understanding the impacts of these changes on the carbon cycle and for designing climate change mitigation strategies. Several AGB maps have been developed for the North American ABRs, including recent contributions from National Aeronautics and Space Administration’s Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. However, these maps differ widely in training data, methodology, and resulting AGB density estimates. Presently, a comprehensive comparative evaluation is lacking, making it difficult for users to select datasets suited to their research or management needs. Here, we conducted a comparative analysis of nine AGB density datasets across North American ABRs, specifically for Alaska and Canada. We (1) summarized AGB by ecoregion and Canadian provinces, (2) evaluated their accuracy against field-based measurements, (3) analyzed spatial and temporal similarities among datasets, and (4) assessed their ability to capture disturbance (fire and harvest) impacts on AGB. We found substantial variation in regional and local AGB estimates across datasets, with overall accuracy ranging from R 2 = 0.25–0.62 and Bias% from −47.8% to 69.9% when validated against field plots. Despite these differences, most datasets have comparatively consistent spatial patterns in AGB ( r > 0.8 for most cases). In contrast, agreement on the temporal patterns of AGB change is generally low. We found datasets with spatial resolutions ⩽300 m are capable of capturing disturbance impacts on AGB dynamics, though sensitivity varies across products. Our findings and dataset summary provide guidance for selecting appropriate AGB datasets for different applications within our study area. Our analysis also highlights the need to decrease map bias and increase capability to detect temporal change to decrease uncertainty of AGB datasets potentially by using training data which is representative of major plant functional types within the mapped area.

  PublisherDOI

• If a tree is “Protected”, is it? Using satellite-borne LiDAR to understand efficacy of protection status in West and Central African Protected Areas

  Research Square · 2026-02-10

  preprintOpen accessSenior author
  PublisherDOI

• 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

• Comparing the spatial effects and longevity of key fuel treatments in California using spaceborne lidar data

  Journal of Environmental Management · 2025-11-24

  articleOpen access

  Systematically comparing fuel treatment strategies' effects on vegetation and fuel structure is essential for mitigating wildfire risk, supporting forest management, and reducing climate-related impacts. While prescribed fire and mechanical activities have been compared using field data, large-scale comparisons across treatment types have been limited by inconsistent vegetation structure measurements. To address this gap, we integrate total of 29 million observations from NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar mission with 2870 records from two fuel treatment datasets for California. We assessed spatial and temporal effects of fuel treatments in California forests. We compared broadcast burns, mechanical fuel reduction, fuel breaks, right-of-way clearance, and forest stewardship to untreated zones using GEDI-derived metrics: aboveground biomass density (AGBD), canopy cover, ladder fuels, canopy height, and layering. Broadcast burns produced significant reductions across all metrics, with the largest decrease in AGBD and canopy height (13–18 %). Mechanical fuel reduction significantly reduced all metrics except canopy height, though with smaller magnitude (6–16%). Fuel breaks yielded the greatest reductions in canopy cover, layering, and ladder fuels (19–26 %) but showed limited effects on canopy height and no significant reduction in AGBD. Using a space-for-time substitution analysis, we found mechanical treatments maintain reductions for 9–15 years across crown fuel metrics, lasting 6–9 years longer than prescribed fire. Managed wildfires showed heterogeneous recovery, while forest stewardship exhibited gradual regrowth. These findings underscore the value of GEDI and future active remote sensing missions for monitoring fuel treatment outcomes. Understanding ecological responses to treatments supports optimizing forest management to reduce wildfire risk in fire-prone regions. • Broadcast burns significantly reduce all canopy and fuel structural metrics. • Mechanical treatments achieve longer fuel reduction, lasting about 9–15 years. • Forest stewardship reduces fuel structure short-term but shows recovery over time. • Fuel breaks substantially reduce canopy structure despite narrow implementation areas. • GEDI lidar and space-for-time methods quantify spatial and temporal reduction effects.

  PublisherDOI

• Advances in Lidar Remote Sensing of Forests

  Elsevier eBooks · 2025-08-15

  book-chapter1st authorCorresponding
  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

• A Scientific Community Vision for an Operational, Unified Greenhouse Gas Observing System to Support Earth System Science and Climate Intervention

  2025-05-05

  preprintOpen access

  Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources

  PublisherOA PDFDOI

• A U.S. Scientific Community Vision for Sustained Earth Observations of Greenhouse Gases to Support Local to Global Action

  AGU Advances · 2025-12-01 · 2 citations

  articleOpen access

  Abstract Managing carbon stocks in the land, ocean, and atmosphere under changing climate requires a globally‐integrated view of carbon cycle processes at local and regional scales. The growing Earth Observation (EO) record is the backbone of this multi‐scale system, providing local information with discrete coverage from surface measurements and regional information at global scale from satellites. Carbon flux information, anchored by inverse estimates from spaceborne Greenhouse Gas (GHG) concentrations, provides an important top‐down view of carbon emissions and sinks, but currently lacks global continuity at assessment and management scales (<100 km). Partial‐column data can help separate signals in the boundary layer from the overlying atmosphere, providing an opportunity to enhance surface sensitivity and bring flux resolution down from that of column‐integrated data (100–500 km). Based on a workshop held in September 2024, the carbon cycle community envisions a carbon observation system leveraging GHG partial columns in the lower and upper troposphere to weave together information across scales from surface and satellite EO data, and integration of top‐down/bottom‐up analyses to link process understanding to global assessment.

  PublisherOA PDFDOI

• Combining TanDEM-X Interferometry and GEDI Space LiDAR for Estimation of Forest Biomass Change in Tanzania

  Remote Sensing · 2025-07-28

  articleOpen access

  The background for this study is the limitations of the conventional approach of using deforestation area multiplied by biomass densities or emission factors. We demonstrated how TanDEM-X and GEDI data can be combined to estimate forest Above Ground Biomass (AGB) change at the national scale for Tanzania. The results can be further recalculated to estimate CO2 emissions and removals from the forest. We used repeated short wavelength, InSAR DEMs from TanDEM-X to derive changes in forest canopy height and combined this with GEDI data to convert such height changes to AGB changes. We estimated AGB change during 2012–2019 to be −2.96 ± 2.44 MT per year. This result cannot be validated, because the true value is unknown. However, we corroborated the results by comparing with other approaches, other datasets, and the results of other studies. In conclusion, TanDEM-X and GEDI can be combined to derive reliable temporal change in AGB at large scales such as a country. An important advantage of the method is that it is not required to have a representative field inventory plot network nor a full coverage DTM. A limitation for applying this method now is the lack of frequent and systematic InSAR elevation data.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Near Term Forecasts of Global Plant Distribution, Community Structure, and Ecosystem Function

  NSF · $296k · 2019–2022

Frequent coauthors

• R. Dubayah

  90 shared

• John Armston

  78 shared

• James R. Kellner

  Brown University

  49 shared

• Steven Hancock

  44 shared

• Hao Tang

  National University of Singapore

  31 shared

• Temilola Fatoyinbo

  30 shared

• Sean P. Healey

  US Forest Service

  29 shared

• M. A. Hofton

  University of Maryland, College Park

  28 shared

Education

• Ph.D., Geography

  University of Maryland

  2005

• M.S., Geography

  University of Maryland

  2001

• B.A., Geography

  University of California, Santa Barbara

  1998