Satellite data from NASA’s GEDI mission show that tropical forests on different continents respond differently to climate factors such as temperature and aridity, with effects further modified by soils, topography, and other local conditions.

In a 2026 Nature study (“Heterogeneous climatic controls on tropical-forest biomass”), researchers affiliated with the Smithsonian Tropical Research Institute (STRI), the University of Maryland, NASA, and a Brazilian university analyzed roughly 16 million 2020 GEDI spaceborne LiDAR estimates of aboveground biomass (AGB) in intact lowland forests of the Amazon, Congo Basin, and Southeast Asia.

GEDI (Global Ecosystem Dynamics Investigation) is a full-waveform LiDAR instrument developed by NASA Goddard Space Flight Center and the University of Maryland. It is mounted on the International Space Station (ISS) and provides high-resolution, dense sampling of Earth’s 3D surface structure—especially forest canopy height, vertical structure, and topography—between approximately 51.6° N and 51.6° S latitude.

GEDI is an active remote-sensing system that fires short pulses of near-infrared laser light (wavelength 1064 nm) toward Earth.

The light reflects off vegetation, the ground, and other surfaces. An onboard telescope collects the returning photons, which are converted into electronic signals and recorded as a function of time. This produces a full waveform—a continuous vertical profile of reflected energy rather than discrete points.

GEDI remains one of the highest-resolution and densest-sampling spaceborne LiDAR systems ever flown for vegetation and topography.

Key findings

Higher temperatures were generally linked to lower biomass, but sensitivity varied sharply by region: Congo Basin forests were the most sensitive (e.g., roughly 27.6 Mg/ha AGB decline associated with temperature increases in reported analyses), Amazonian forests moderately sensitive (~11 Mg/ha), and Southeast Asian forests relatively insensitive (~0.1 Mg/ha).

Water limitation/aridity had the strongest negative effects in Southeast Asia (strong biomass declines in more arid sites). Amazonian forests often showed peak biomass at intermediate aridity; African forests were relatively insensitive. Effects of temperature and drought anomalies intensified with overall aridity and interacted with soils and landscape features.

In the tallest forests (trees >70 m), storms (lightning and windthrow) emerged as a major negative driver of biomass.

These patterns help resolve earlier conflicting results from plot-based or regional studies: the differences largely reflect real biological and historical variation rather than just methodological inconsistencies. Africa’s drier evolutionary/climatic history, Southeast Asia’s wetter history, and the distinct trajectories of the Americas have shaped species composition and climate responses that persist today.

Co-author Helene Muller-Landau (STRI) noted that the study “definitively shows that tropical forests on different continents respond differently to climate,” consistent with enduring legacies of historical climates and evolutionary paths. Lead author Matheus Henrique Nunes emphasized that global analyses alone are insufficient because effects are highly context-dependent, underscoring the need for local expertise and data.

The results improve predictions of how tropical forests (which store ~40% of global forest carbon) may respond to ongoing climate change and highlight that resilience and vulnerability vary substantially even among intact forests. Networks of standardized ground plots (e.g., GEO-TREES) will remain important for validating satellite biomass estimates.

The paper is available via the DOI 10.1038/s41586-026-10880-2; press coverage from STRI/Smithsonian and the University of Maryland provides accessible summaries of the work.

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Heterogeneous climatic controls on tropical-forest biomass

Heterogeneous climatic controls on tropical-forest biomass is a 2026 Nature paper (published 12 August 2026; DOI: 10.1038/s41586-026-10880-2) led by Matheus Henrique Nunes (University of Maryland / NASA GEDI), with co-authors including Helene C. Muller-Landau (Smithsonian Tropical Research Institute), Eric Bastos Görgens, Adrián Pascual, and Ralph Dubayah.

The study analyzed ~16 million spaceborne LiDAR-derived estimates of aboveground biomass (AGB) for 2020 from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission. It focused on intact lowland tropical forests in three major regions: the Amazon, the Congo Basin, and Southeast Asia.

Key results show that climatic associations with AGB are heterogeneous and strongly context-dependent:

• Temperature effects dominate in drier forests. Congo Basin forests are the most sensitive to warming (largest AGB declines), Amazonian forests are moderately sensitive, and Southeast Asian forests are relatively insensitive.
• Water limitation / aridity has the strongest negative effects in Southeast Asia (strong AGB declines in more arid sites). Amazonian forests often peak at intermediate aridity; African forests are relatively insensitive to aridity.
• Effects of temperature and drought anomalies intensify with overall aridity and are further modified by soils and topography.
• In the tallest, carbon-dense forests (canopy height >70 m), storms (lightning and windthrow) emerge as a strong negative driver of biomass.

These patterns help reconcile previously conflicting findings from plot-based or regional studies. Differences largely reflect genuine biological and biogeographical variation (rooted in distinct evolutionary and climatic histories of Africa, Asia, and the Americas) rather than solely methodological differences.

Tropical forests store a major fraction of global forest carbon (~40%). Understanding how climate controls their biomass is critical for predicting climate–carbon feedbacks. The results demonstrate that simple global relationships are insufficient: accurate predictions require accounting for interactions among climate, disturbance, soils, topography, and regional biogeographical context. Local expertise and standardized ground networks (e.g., GEO-TREES) remain essential for validating satellite estimates and refining forecasts.

Data and code supporting the analyses are publicly available via Zenodo (DOI: 10.5281/zenodo.19474558). Press summaries from the Smithsonian Tropical Research Institute and the University of Maryland provide accessible overviews of the work.

Published:  Nature

DOI: 10.1038/s41586-026-10880-2, Zenodo (DOI: 10.5281/zenodo.19474558)

Provided: Smithsonian Tropical Research Institute

Authors: Matheus Henrique Nunes, 
Helene C. Muller-Landau, 
Eric Bastos Görgens, 
Adrian Pascual & 
Ralph Dubayah 

Abstract

Tropical-forest aboveground biomass (AGB) is a major component of the global carbon cycle and understanding its response to climate change is crucial for predicting future climate–carbon feedbacks. Yet debate continues as to whether differences between studies in observed associations with climate reflect true regional variation or methodological differences^1,2,3,4,5. Here we analyse around 16 million spaceborne-LiDAR-derived estimates of AGB for the year 2020 across intact lowland forests in the Amazon, the Congo Basin and Southeast Asia to investigate how climatic variables differentially relate to AGB on pantropical scales. We show that climatic associations with AGB are heterogeneous and depend on environmental context. Temperature dominates in drier forests, with AGB in the Congo Basin the most sensitive to warming, whereas Southeast Asian forests have the strongest declines in AGB under increasing water limitation. Across regions, the effects of temperature and drought anomalies intensify with aridity and are further modified by soils and topography. In the tallest (above 70 m), carbon-dense forests^6,7, storms (lightning and windthrow) emerge as a strong negative driver. These results reconcile previously conflicting findings and show that predicting tropical-forest carbon storage requires accounting for interactions among climate, disturbance, soil and topography in a variety of biogeographical contexts.