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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Informing forest carbon inventories under the Paris Agreement using ground-based forest monitoring data

Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Monitoring and Documentation of Forest Management Activities at Los Alamos National Laboratory

Forest management treatments at Los Alamos National Laboratory (LANL) are administered by the Emergency Management Division (EMD-DO) Wildland Fire Program within the Wildland Fire and Forest Management Program (WFFM Program or the Program). Open space forest inventory monitoring is conducted by Forest Health Program staff and students within the Environmental Stewardship Group (EPC-ES). The goals of the integrated Wildland Fire and Forest Management Program are to reduce impacts to Laboratory operations from climate driven events by implementing forest management and healthy forest initiatives such as forest thinning and erosion control. The LANL Wildland Fire Mitigation and Forest Health Plan (EMD PLAN-200, LA-UR-19-25122; LANL 2019) presents treatment standards for LANL property to meet the following goals: 1) Restore and maintain landscapes: LANL landscapes are resilient to disturbances; 2) Develop a fire-adapted community: LANL workforce, neighbors, and infrastructure can withstand a wildland fire without loss of life and property; 3) Ensure wildland fire mitigation implementation: All wildland fire mitigation working group organizations participate in making and implementing safe, effective, efficient risk-based wildland fire management decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tree-level carbon stock estimations across diverse species using multi-source remote sensing integration

Forests are critical carbon sinks, and remote sensing has been increasingly widely used for forest monitoring and biomass estimations. However, species-specific tree-level studies remain limited. In this study, we demonstrated the feasibility of integrating UAV-based LiDAR with high-resolution optical satellite imagery (0.5 m) to estimate biomass for individual trees across different species. The proposed method accurately estimated biomass for 53 trees (R² = 0.82, rRMSE = 0.44), with species-specific datasets, showing an average 25.2% increase in R² and a 14.8% reduction in rRMSE. A novel vegetation index combining forest structure parameters with vegetation indices (VIs) was developed using high-resolution multispectral satellite data (3 m) to explore its relationship with individual tree biomass. Combining forest structural parameters with VIs further improved estimation accuracy, achieving an R²of 0.89 and an rRMSE of 0.34. Species-specific datasets show an 11.6% increase in R²compared to methods without VIs, and a 22.2% improvement over methods using only VIs. SHapley Additive exPlanations (SHAP) analysis shows that the volume feature played a key role in model performance and remained stable throughout the training process. Altogether, the proposed approach enhances individual tree biomass and carbon sink estimations, showing great potential for large-scale precise forest carbon monitoring using multi-source remote sensing data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Context matters: Natural tree mortality can lead to neighbor growth release or suppression

Where competition suppresses tree growth, mortality of adjacent trees can release the surviving individuals, leading to a growth increase. However, primary forests are complicated systems, where trees interact in both competitive and facilitative ways mediated by their size, species, and the broad ecological context in which they grow. Thus, the magnitude and even direction of growth responses to the mortality of nearby trees may vary, which has implications for our understanding of community- and ecosystem-level dynamics following mortality events. Unfortunately, although many studies focus on the impacts of light availability and general crowding on tree growth, we know relatively little about the effects of naturally occurring mortality events on the growth of neighboring trees. To address this issue, we used 40 years of data from 15 permanent forest-monitoring plots in Mt Rainier old-growth forests, comparing observed to expected radial growth of individual trees following the death of their nearest neighbor. Although we found evidence of a general growth-release response, this was not universal among all trees, with small trees in particular exhibiting growth suppression (rather than release) following neighboring tree mortality. In addition to small size, growth-suppression was more likely if the dead neighbor was the same species, consistent with facilitative effects as mediated through belowground networks. At the stand level, the average growth release after nearest neighbor mortality was greatest in low-density stands with large trees, with elevation and community composition also playing a role. Decades more monitoring could reveal how long growth release (or suppression) is sustained by individual trees following neighboring mortality events, as well as potential response lags and the role of species identity in determining whether interactions with neighbors are competitive or facilitative. Nonetheless, our results suggest that although mature trees have competitive effects on their larger neighbors, they also have an important role in supporting the ingrowth of small trees. More broadly, we demonstrate that the nature of interactions between individual neighboring trees is highly context dependent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery would enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in Amazon forests. Images from medium-resolution optical satellites combined with extensive field data have allowed researchers to assess patterns of windthrow tree-mortality and to monitor forest recovery over decades of succession in different regions. Although satellites with high spatial-resolution have become available in the last decade, they have not yet been employed for the quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November 2015. Remote sensing estimates of windthrow tree-mortality were produced from Spectral Mixture Analysis and evaluated with forest inventory data (i.e., ground true) by using Generalized Linear Models. Field measured windthrow tree-mortality (3 transects and 30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Although the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3%, p < 0.001), Landsat 8 had the most accurate results and efficiently captured field-observed variations in windthrow tree-mortality across the entire gradient of disturbance (Sentinel 2 and WorldView 2 produced the second and third best results, respectively). As expected, mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of model fits showed the opposite pattern. We suggest that this reflects the influence of a relatively minor disturbance, such as defoliation and crown damage, and the fast growth of natural regeneration, which were not measured in the field nor can be captured by coarser resolution imagery. Our results validate the reliability of Landsat imagery for assessing plot-to-landscape patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Satellites with high spatial resolution can improve estimates of windthrow severity by allowing the quantification of crown damage and mortality of lower canopy and understory trees. However, this requires the validation of remote sensing metrics using field data at compatible scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The stable isotopes of natural waters at the Marcell Experimental Forest

Here, we present a new data set from the Marcell Experimental Forest (MEF) that compiles water isotope measurements from multiple research catchments, some of which have been studied since the 1960s. The MEF is located in northern Minnesota, USA, and is home to heavily studied and monitored forests, streams, bogs, and fens. Peat-forming systems (bogs and fens) are an important component of the MEF landscape and have a profound impact on the water cycle in these catchments. Within the last decade, analysis of stable isotopes of water (expressed as δD and δ 18 O) has been implemented to characterize the different components of the water budget, and to allow researchers to look at catchment and peatland-specific hydrologic effects in the watershed. This δD and δ 18 O data set of natural waters from MEF catchments is primarily composed of measurements from three peatlands (S1, S2, S6) during an 11-year period. More recently collection and analysis were expanded to also include samples from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) project in the S1 bog, peatlands S3, S4, S5, as well as nearby lakes. We establish a local meteoric water line by analyzing the isotopic composition of precipitation, which fills a void in regional meteoric water lines for Minnesota. Furthermore, we establish baseline isotopic composition for bog outlet streams, bog porewater, aquifer groundwater, overland flow, subsurface stormflow, and snowpack, as well as runoff from the SPRUCE experimental chambers. These data are publicly available and will be expanded upon in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivity and atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Characterizing the vertical structure of forests in the Brazilian Amazon

Little is known about the structure of tropical forests despite its critical role in the provisioning of ecosystem services. Here we assess the vertical structure of forests in the Brazilian Amazon with a large-scale airborne LiDAR dataset. We show that fire has greater impact in the lowest forest strata, differently from selective logging and windthrow. We also find that secondary forests quickly recover or even exceed reference areas at the 1-10 m height stratum but that full recovery for the 20-30 m height stratum has not been achieved even after 35 years. Our modeling results suggest that proximity to roads, elevation, precipitation, soil pH, and proportion of sand in the soil are the most important predictors of forest structure. Finally, we identify 5 forest structural types (FSTs) and use them to visualize the spatial distribution of forest structure. This study provides important information for forest monitoring, management, and conservation.

Biogeography↗
DOE OSTI2022

Tallo: A global tree allometry and crown architecture database

Abstract Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non‐forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC‐BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599 . To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Jucker, Tommaso↗
DOE OSTI2024

Remote Sensing Detection of Growing Season Freeze-Induced Defoliation of Montane Quaking Aspen (Populus tremuloides) in Southern Utah, USA

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

On the Investigation of Phase Fault Classification in Power Grid Signals: A Case Study for Support Vector Machines, Decision Tree and Random Forest

In monitoring the power grid, an ability to differentiate between fault types is essential to ensuring electrical safety. Accordingly, this study introduces a fault detection and classification method by considering different machine learning (ML) and feature extraction (FE) methods combinations. Specifically, the proposed method is established in two classification layers; the first layer determines the fault, and the second layer distinguishes the type of fault. Based on the proposed system model, this study seeks to determine the influential data attributes in a power grid signal using FE methods, including fast Fourier transform, power spectral density (PSD), auto-correlation, and wavelet transform (WT). A cross-comparison of the effectiveness of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) is also performed to accomplish the classification layers of the proposed method. The designed algorithm is analyzed under the various combinations of FE and ML methods, and outcomes are presented by considering the trade-off between computational complexity and prediction accuracy. The results reveal that the RF-based ML algorithm shows the most accurate classification performance with PSD, and the most time-saving of the models is the DT WT. Also, SVM emerges superior on a subsequent test of the simulated models on real-world signals.

Galbraith, Kelli↗
DOE OSTI2021

Characterizing Canopy Openness in Open Forests: Spherical Densiometer and Canopy Photography Are Equivalent but Less Sensitive than Direct Measurements of Solar Radiation

In forest ecosystems, canopy openness affects understory light availability, plant growth, and tree species recruitment, thus shaping future forest composition, structure, and functional diversity. Foresters must correctly and quickly measure canopy openness to meet their management objectives. To help guide the selection of an appropriate method for measuring canopy openness, we compared three common techniques that vary in cost, complexity, and time required for measurements and data processing: smartphone-based hemispherical photography, spherical densiometer measurements, and direct measurements of solar radiation (using AccuPAR ceptometer). We measured canopy openness using these three methods on 28 permanent forest health monitoring plots in pine-oak forests of the Central Pine Barrens of Long Island in New York State. By analysis of variance and regression analyses, we found the three methods (particularly densiometer and hemispherical photographs) yielded broadly equivalent and strongly positively correlated descriptions of canopy openness. The direct measurements of solar radiation seemed to have a greater potential to detect subtle variation in forest understory light. Forest managers may sufficiently characterize canopy openness using quick and cheap methods (e.g., spherical densiometers) and avoid larger costs of devices for direct light measurements (e.g., ceptometers) and the larger data processing times of hemispherical photography.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Ecological and genomic variation in ectomycorrhizal fungal exploration types

Ectomycorrhizal fungi (EMF) produce mycelia with variable extension and complexity, which can be classified according to soil ‘exploration types’ (ETs). ETs have received attention as one of the few mycorrhizal trait frameworks, but without an empirical classification of ET functional diversity and environmental preferences, understanding and interpreting EMF biogeographic patterns has been difficult. We conducted a synthesis combining: comparative EMF genomics to describe functional divergence in decomposition and nutrient cycling genes across ETs; and EMF trait distribution modeling across continental Europe, pairing soil and root EMF surveys to establish biogeographic ET niche profiles. We demonstrate a signature of ETs encoded in EMF genomes, which is independent from phylogeny and linked to biomass production strategies. EMF ET relative abundances were separated by soil, root, and dominant tree leaf type habitats and exhibited unique correlations with forest biotic (e.g. plant productivity and plant pathogen densities) and abiotic (e.g. nitrogen deposition and soil pH) conditions. These findings support a theory that EMF niche partitioning can be partially explained by extraradical mycelial traits, with underlying variation in ET biogeography likely arising from distinct decomposition and nutrient cycling potentials. We also identify important limitations to this trait framework and provide a guided outlook for future research.

biogeography↗
DOE OSTI2024

Seed classification with random forest models

Premise: To improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators. Methods and Results: Seeds from six North American conifer tree species were separated from leaf litter and imaged on a flatbed scanner. In the most successful species-classification approach, an ImageJ macro automatically extracted measurements for random forest classification in the software R. The method allows for good classification accuracy, and the same process can be used to train the model on other species. Conclusions: This protocol is an adaptable tool for efficient and consistent identification of seed species or potentially other objects. Automated seed classification is efficient and inexpensive, making it a practical solution that enhances the feasibility of large-scale monitoring projects in conservation biology.

59 BASIC BIOLOGICAL SCIENCES↗