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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

(abstract) Monitoring Seasonal Change in Taiga Forests Using ERS-1 SAR Data

Sensitivity of radar backscatter to the dielectric and geometric character of forested regions suggests significant changes in backscatter are expected with season due to freezing temperatures, snow, wind, leaf fall, and drought. The first European Remote Sensing Satellite, ERS-1, offers a unique opportunity to monitor a complete seasonal cycle for the Alaskan taiga forest ecosystem with synthetic aperture radar. During the 3-day repeat Commissioning Phase of ERS-1, from August 1991to December 1991, ERS-1 SAR data were collected in the region of Manley Hot Springs, Alaska, along the Tanana River, west of Fairbanks. In parallel with the SAR data collection, meteorological data from three weather stations positioned in three forest stands were collected continuously along with in situ measurements of the dielectric and moisture properties of the canopy and of ground cover which were collected during each overflight. The in situ data were collected in floodplain forest stands dominated by balsam poplar, white spruce, and black spruce. These results from the Commissioning Phase as well as preliminary results from the 35-day Repeat Phase will be presented.

characteristics geometric characteristics moisture↗
NASA NTRS2014

Deciphering the Precision of Stereo IKONOS Canopy Height Models for U.S. Forests with G-LiHT Airborne LiDAR

Few studies have evaluated the precision of IKONOS stereo data for measuring forest canopy height. The high cost of airborne light detection and ranging (LiDAR) data collection for large area studies and the present lack of a spaceborne instrument lead to the need to explore other low cost options. The US Government currently has access to a large archive of commercial high-resolution imagery, which could be quite valuable to forest structure studies. At 1 m resolution, we here compared canopy height models (CHMs) and height data derived from Goddard's airborne LiDAR Hyper-spectral and Thermal Imager (G-LiHT) with three types of IKONOS stereo derived digital surface models (DSMs) that estimate CHMs by subtracting National Elevation Data (NED) digital terrain models (DTMs). We found the following in three different forested regions of the US after excluding heterogeneous and disturbed forest samples: (1) G-LiHT DTMs were highly correlated with NED DTMs with R (sup 2) greater than 0.98 and root mean square errors (RMSEs) less than 2.96 m; (2) when using one visually identifiable ground control point (GCP) from NED, G-LiHT DSMs and IKONOS DSMs had R (sup 2) greater than 0.84 and RMSEs of 2.7 to 4.1 m; and (3) one GCP CHMs for two study sites had R (sup 2) greater than 0.7 and RMSEs of 2.6 to 3 m where data were collected less than four years apart. Our results suggest that IKONOS stereo data are a useful LiDAR alternative where high-quality DTMs are available.

IKONOS↗
NASA NTRS2018

The Impacts of Recent Drought on Fire, Forest Loss, and Regional Smoke Emissions in Lowland Bolivia

In the southern Amazon relationships have been established among drought, human activities that cause forest loss, fire, and smoke emissions. We explore the impacts of recent drought on fire, forest loss, and atmospheric visibility in lowland Bolivia. To assess human influence on fire, we consider climate, fire, and vegetation dynamics in an area largely excluded from human activities since 1979, Noel Kempff Mercado National Park (NK) in northeastern Bolivia. We use data from five sources: the Moderate Resolution Imaging Spectroradiometer Collection 6 active fire product (2001-2015) (MODIS C6), Global Fire Weather Database (GFWED) data (1982-2015), MODIS land cover data (2001-2010), MODIS forest loss data (2000-2012), and the regional extinction coefficient for the southwestern Amazon (i.e., B(sub ext)), which is derived from horizontal visibility data from surface stations at the World Meteorological Organization (WMO) level (1973-2015). The B(sub ext) is affected by smoke and acts as a proxy for visibility and regional fire emissions. In lowland Bolivia from 2001 to 2015, interannual Drought Code (DC) variability was linked to fire activity, while from 1982 to 2015, interannual DC variability was linked to B(sub ext). From 2001 to 2015, the B(sub ext) and MODIS C6 active fire data for lowland Bolivia captured fire seasonality, and covaried between low- and high-fire years. Consistent with previous studies, our results suggest B(sub ext)t can be used as a longer-term proxy of regional fire emissions in southwestern Amazonia. Overall, our study found drought conditions were the dominant control on interannual fire variability in lowland Bolivia, and fires within NK were limited to the Cerrado and seasonally inundated wetland biomes. Our results suggest lowland Bolivian tropical forests were susceptible to human activities that may have amplified fire during drought. Human activities and drought need to be considered in future projections of southern Amazonian fire, in regard to carbon emissions and global climate.

Forest Loss↗
NASA NTRS2019

On Promoting the Use of Lidar Systems in Forest Ecosystem Research

Forest structure is an important driver of ecosystem dynamics, including the exchange of carbon, water and energy between canopies and the atmosphere. Structural descriptors are also used in numerous studies of eco-logical processes and ecosystem services. Over the last 20+ years, lidar technology has fundamentally changed the way we observe and describe forest structure, and it will continue to impact the ways in which we investigate and monitor the relations between forest structure and functions. Here we present the currently available lidar system types (ground, air, and space-based), we highlight opportunities and challenges associated with each system, as well as challenges associated with a wider use of lidar technology and wider availability of lidar derived products. We also suggest pathways for lidar to further contribute to addressing questions in forest ecosystem science and increase benefits to a wider community of researchers.

Lidar systems↗
NASA NTRS2021

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

coastal environment↗
NASA NTRSDate not supplied

Vegetation Heights for Landsat Land Cover Classes in the Interior Boreal Forest Region of Alaska

Accurate determination and mapping of vegetation height is a prerequisite for regional estimations of above ground biomass, carbon pool dynamics, and improved modeling of future climate-vegetation-soil interactions. This study combined land cover class maps (at 30-m pixel resolution) derived from Landsat image bands with canopy vegetation heights detected at 1-m resolution from NASA G-LiHT Lidar flights, both from the year 2014 for the Middle Tanana Valley near Fairbanks, Alaska. The statistical distributions and structural attributes from Lidar plant heights were derived and refined for all Landsat vegetation classes. Results showed that the tallest cover classes in this region were deciduous forests (dominated by birch and aspen), followed by evergreen spruce and wooded wetlands. Open shrubland and herbaceous cover areas were determined to be 2 - 5 m shorter, on average, than these boreal forest stands. Tall shrubland cover areas were slightly shorter than most spruce-dominated evergreen forest areas. Nearly 60% of all low shrubland and herbaceous wetland pixels were detected with at least one tree crown greater than 10 m tall. This was in contrast to evergreen and deciduous forest classes that showed 88% of stands with at least one tree crown greater than 10 m tall.

Vegetation↗
NASA NTRS2020

Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables

A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r2when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included.

site productivity↗
DOE OSTI2016

Forest Resources

Forest biomass is an abundant biomass feedstock that complements the conventional forest use of wood for paper and wood materials. It may be utilized for bioenergy production, such as heat and electricity, as well as for biofuels and a variety of bioproducts, such as industrial chemicals, textiles, and other renewable materials. The resources within the 2016 Billion-Ton Report include primary forest resources, which are taken directly from timberland-only forests, removed from the land, and taken to the roadside.

Forest Resources Factsheet↗
DOE OSTI2013

Forest Irrigation of Tritiated Water: A Proven Tritiated Water Management Tool - 13357

Tritium releases from the Old Radioactive Waste Burial Ground (ORWBG) at the SRS in South Carolina has impacted groundwater and surface water. Tritiated groundwater plumes discharge into Fourmile Branch which is a small tributary of the Savannah River, a regional water resource. Taking advantage of the groundwater flow paths and the local topography a water collection and irrigation system was constructed and has been used at the SRS for over a decade to reduce these tritiated water releases to Fourmile Branch. The tritiated water is transferred to the atmosphere by evaporation from the pond surface, and after irrigation, wetted surface evaporation and evapotranspiration through the forest vegetation. Over the last decade SRS has irrigated over 120,000,000 gallons of tritiated water, which diverted over 6000 curies away from Fourmile Branch and the Savannah River. The system has been effective in reducing the flux of tritiated groundwater by approximately 70%. Mass balance studies of tritium in the forest soils before operations and over the last decade indicate that approximately 90% of the tritiated water that is irrigated is transferred to the atmosphere. Dose studies indicate that exposure to site workers and offsite maximally exposed individual is very low, approximately 6 mrem/year and 0.004 mrem/year, respectively. To consistently meet the flux reduction goal of tritium into Fourmile Branch optimization activities are proposed. These efforts will increase irrigation capacity and area. An additional 17 acres are proposed for an expansion of the area to be irrigated and a planting of approximately 40 acres of pine forest plantations is underway to expand irrigation capacity. Co-mingled with the tritiated groundwater are low concentrations of chlorinated volatile organic compounds (cVOCs), and 1,4-dioxane. Research studies and SRS field data indicate the forest irrigation system may have an added benefit of reducing the mass of these co-contaminants via degradation. This semi-passive system makes use of natural processes of hydrology and evapotranspiration to manage tritium-contaminated water by reducing its entrance into site streams and the Savannah River, as well as treating low levels of co-mingled VOCs. SRS expects to operate the system until the tritium decays to levels that represent a minimal impact to Fourmile Branch and the Savannah River, and meets the stakeholder expectations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Sapling growth rates reveal conspecific negative density dependence in a temperate forest

Local tree species diversity is maintained in part by conspecific negative density dependence (CNDD). This pervasive mechanism occurs in a variety of forms and ecosystems, but research to date has been heavily skewed toward tree seedling survival in tropical forests. To evaluate CNDD more broadly, we investigated how sapling growth rates were affected by conspecific adult neighbors in a fully mapped 25.6 ha temperate deciduous forest. We examined growth rates as a function of the local adult tree neighborhood (via spatial autoregressive modeling) and compared the spatial positioning of faster-growing and slower-growing saplings with respect to adult conspecific and heterospecific trees (via bivariate point pattern analysis). In addition, to determine whether CNDD-driven variation in growth rates leaves a corresponding spatial signal, we extended our point pattern analysis to a static, growth-independent comparison of saplings and the next larger size class. We found that negative conspecific effects on sapling growth were most prevalent. Five of the nine species that were sufficiently abundant for analysis exhibited CNDD, while only one species showed evidence of a positive conspecific effect, and one or two species, depending on the analysis, displayed heterospecific effects. There was general agreement between the autoregressive models and the point pattern analyses based on sapling growth rates, but point pattern analyses based on single-point-in-time size classes yielded results that differed markedly from the other two approaches. Our work adds to the growing body of evidence that CNDD is an important force in temperate forests, and demonstrates that this process extends to sapling growth rates. Further, our findings indicate that point pattern analyses based solely on size classes may fail to detect the process of interest (e.g., neighborhood-driven variation in growth rates), in part due to the confounding of tree size and age.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Phosphorus sorption and its environmental predictors across pantropical forest soils sampled over the past decade

Tropical forest productivity is frequently constrained by soil phosphorus (P) availability, yet global Land Surface Model (LSM), which are used to simulate ecosystem processes, still represent P cycling in tropical regions only in a limited way, largely because of scarce observational data. Phosphorus adsorption and desorption of dissolved inorganic P to and from soil minerals (hereafter termed sorption), is an important process for predicting how much P is available to plants. This dataset was created to improve predictions of soil P sorption in tropical soils by identifying the isotherm equation that best describes pantropical soils. It includes raw measurements of environmental variables, such as soil properties and climate, together with P sorption data collected from 40 forest soil pits from 9 Forest Global Earth Observatory (ForestGEO) sites across 7 tropical countries during 2018-2022. The data are organized by site, country, and continent. Each site may include several soil pits. For each pit, P sorption was measured across a range of soil P concentrations to build sorption isotherm curves, typically with about 6 to 8 measurements per curve. While sorbed P varies across these concentration levels, the other environmental variables remain constant at the plot level.

Aluminum oxide↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2015

Complex terrain alters temperature and moisture limitations of forest soil respiration across a semiarid to subalpine gradient

Forest soil respiration is a major carbon (C) flux that is characterized by significant variability in space and time. In this paper, we quantified growing season soil respiration during both a drought year and a nondrought year across a complex landscape to identify how landscape and climate interact to control soil respiration. We asked the following questions: (1) How does soil respiration vary across the catchments due to terrain-induced variability in moisture availability and temperature? (2) Does the relative importance of moisture versus temperature limitation of respiration vary across space and time? And (3) what terrain elements are important for dictating the pattern of soil respiration and its controls? Moisture superseded temperature in explaining watershed respiration patterns, with wetter yet cooler areas higher up and on north facing slopes yielding greater soil respiration than lower and south facing areas. Wetter subalpine forests had reduced moisture limitation in favor of greater seasonal temperature limitation, and the reverse was true for low-elevation semiarid forests. Coincident climate poorly predicted soil respiration in the montane transition zone; however, antecedent precipitation from the prior 10 days provided additional explanatory power. A seasonal trend in respiration remained after accounting for microclimate effects, suggesting that local climate alone may not adequately predict seasonal variability in soil respiration in montane forests. Finally, soil respiration climate controls were more strongly related to topography during the drought year highlighting the importance of landscape complexity in ecosystem response to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Multi-spatial analysis of forest residue utilization for bioenergy

The alternative energy sector is expanding quickly in the USA since passage of the Energy Policy Act of 2005 and the Energy Independence and Security Act of 2007. Increased interest in wood-based bioenergy has led to the need for robust modeling methods to analyze woody biomass operations at landscape scales. However, analyzing woody biomass operations in regions like the US Inland Northwest is difficult due to highly variable terrain and wood characteristics. We developed the Forest Residue Economic Assessment Model (FREAM) to better integrate with Geographical Information Systems and overcome analytical modeling limitations. FREAM analyzes wood-based bioenergy logistics systems and provides a modeling platform that can be readily modified to analyze additional study locations. We evaluated three scenarios to test the FREAM's utility: a local-scale scenario in which a catalytic pyrolysis process produces gasoline from 181 437 Mg yr-1 of forest residues, a regional-scale scenario that assumes a biochemical process to create aviation fuel from 725 748 Mg yr-1 of forest residues, and an international scenario that assumes a pellet mill producing pellets for international markets from 272 155 Mg yr-1 of forest residues. The local scenario produced gasoline for a modeled cost of $22.33 GJ-1*, the regional scenario produced aviation fuel for a modeled cost of $35.83 GJ-1 and the international scenario produced pellets for a modeled cost of $10.51 GJ-1. Results show that incorporating input from knowledgeable stakeholders in the designing of a model yields positive results.

09 BIOMASS FUELS↗
DOE OSTI2026

A primer on forest structure measurement with lidar for ecologists

Light detection and ranging (lidar) technology has fundamentally advanced the way we measure forest structure, facilitating new insights into ecological processes. Lidar for forest ecology applications is deployed on multiple types of platforms that operate from the ground, air, or space, and each has associated strengths and limitations. Ideally, the choice of what kind of lidar to use in a particular study should be guided by the ecological question of interest; however, practical considerations of cost, data availability, and processing tools can be equally important. This synthesis is a practical introduction to how different lidar platforms characterize forest structure (e.g., tree size/location, wood volume, branching structure, aboveground biomass, leaf properties), designed for a general audience of ecologists (not remote sensing scientists) seeking an accessible introduction to the use of lidar. We also provide examples of novel ecological insights from recent lidar research and describe current limitations and areas of expected improvement. Last, we include an appendix of data collected from terrestrial, mobile, unoccupied aerial system, airplane, and satellite lidar platforms within a common temperate forest area, with associated code to allow new lidar users to visualize and manipulate data in R.

Cushman, KC [ORNL] (ORCID:0000000234641151)↗
DOE OSTI2025

When can we detect lianas from space? Toward a mechanistic understanding of liana‐infested forest optics

Abstract Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals—reflectance across wavelengths—from liana‐infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine‐scale airborne reflectance data from 999 liana‐infested canopies, and coarse‐scale satellite reflectance data covering 775 ha of liana‐infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short‐wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large‐scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy‐reflectance signal are not exclusive to lianas, accurate large‐scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross‐site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

Environmental Sciences & Ecology↗
DOE OSTI2016

Comparison of tree transpiration under wet and dry canopy conditions in a Costa Rican premontane tropical forest

Spatial and temporal variation in wet canopy conditions following precipitation events can influence processes such as transpiration and photosynthesis, which can be additionally enhanced as upper canopy leaves dry more rapidly than the understory following each event. As part of a larger study aimed at improving land surface modelling of evapotranspiration processes in wet tropical forests, we compared transpiration among trees with exposed and shaded crowns under both wet and dry canopy conditions in central Costa Rica, which has an average 420 mm annual rainfall. Transpiration was estimated for 5 months using 43 sap flux sensors in eight dominant, ten midstory and eight suppressed trees in a mature forest stand surrounding a 40-m tower equipped with micrometeorological sensors. Dominant trees were 13% of the plot's trees and contributed around 76% to total transpiration at this site, whereas midstory and suppressed trees contributed 18 and 5%, respectively. After accounting for vapour pressure deficit and solar radiation, leaf wetness was a significant driver of sap flux, reducing it by as much as 28%. Under dry conditions, sap flux rates (J s ) of dominant trees were similar to midstory trees and were almost double that of suppressed trees. On wet days, all trees had similarly low J s . As expected, semi-dry conditions (dry upper canopy) led to higher J s in dominant trees than midstory, which had wetter leaves, but semi-dry conditions only reduced total stand transpiration slightly and did not change the relative proportion of transpiration from dominant and midstory. Thus, models that better capture forest stand wet–dry canopy dynamics and individual tree water use strategies are needed to improve accuracy of predictions of water recycling over tropical forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Soil carbon stocks across tropical forests of Panama regulated by base cation effects on fine roots

We report that tropical forests are the most carbon (C)- rich ecosystems on Earth, containing 25–40% of global terrestrial C stocks. While large-scale quantifi- cation of aboveground biomass in tropical forests has improved recently, soil C dynamics remain one of the largest sources of uncertainty in Earth system models, which inhibits our ability to predict future climate. Globally, soil texture and climate predict B 30% of the variation in soil C stocks, so ecosystem models often predict soil C using measures of aboveground plant growth. However, this approach can underestimate tropical soil C stocks, and has proven inaccurate when compared with data for soil C in data-rich northern ecosystems. By quantifying soil organic C stocks to 1 m depth for 48 humid tropical forest plots across gradients of rainfall and soil fertility in Panama, we show that soil C does not correlate with common predictors used in models, such as plant biomass or litter production. Instead, a structural equation model including base cations, soil clay content, and rainfall as exogenous factors and root biomass as an endogenous factor predicted nearly 50% of the variation in tropical soil C stocks, indicating a strong indirect effect of base cation availability on tropical soil C storage. Including soil base cations in C cycle models, and thus emphasizing mechanistic links among nutrients, root biomass, and soil C stocks, will improve prediction of climate-soil feedbacks in tropical forests.

54 ENVIRONMENTAL SCIENCES↗