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NASA NTRS1973

Inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Eucalyptus tree stands killed by low temperatures in December 1972 were outlined by image enhancement of two separate dates of ERTS-1 images (January 22, 1973-I.D. 1183-18175 and April 22, 1973-I.D. 1273-18183). Three stands larger than 500 meters in size were detected very accurately. In Colorado, range and grassland communities were analyzed by visual interpretation of color composite scene I.D. 1028-17135. It was found that mixtures of plant litter, amount and kind of bare soil, and plant foliage cover made classification of grasslands very difficult. Changes in forest land use were detected on areas as small as 5 acres when ERTS-1 color composite scene 1264-15445 (April 13, 1973) was compared with 1966 ASCS index mosaics (scale 1:60,000). Verification of the changes were made from RB-57 underflight CIR transparencies (scale 1:120,000).

Heller, R. C.↗

DOE OSTI2022

Multiple Stream Low-Cost Recycling Method

The global composite industry generates large quantities of waste and which mostly end as landfill due to lack of meaningful end-use applications for the multiple waste streams. In a recent report by the Electric Power Research Institute (EPRI), waste generated by the wind industry could reach 370,000 tons a year of composite wind energy blades being decommissioned and scrapped. Wind energy is just one major industry utilizing composite materials. The waste generated by industry includes End-of-Life (EoL) materials and manufacturing process scrap. GreenTex Solutions has developed a unique and innovative technology to recycle the composite waste streams in a range of forms made from production waste and EoL materials. This includes manufacturing waste materials such as dry chopped fiber tow, loose fibers, shredded fibers from reinforcement fabrics, cured/semi-cured prepregs, and it also includes fully-cured composite structure waste (such as edge trims from cured parts) from manufacturing aircraft, automobiles, wind blades, boats, and composite cylinders (tanks). Current recycling methods involve recovering the structural fiber by removing the matrix resin through methods such as pyrolysis. The resulting fibers are used in injection molding or wet laid nonwoven mats and other usable forms. The GreenTex technology bypasses these intermediate steps to create the lowest possible recycling processing costs and the lowest embodied energy/CO2 emissions. The end-product from the GreenTex technology is a finished industrial composite part/application versus intermediate fibers or fabrics. The GreenTex manufacturing process enables cross-industry reuse of recycled feedstock by taking waste from multiple industries (wind energy, aerospace, marine, etc.) and recycles the waste into a product used in other industries. The initial target market application is structural flooring for intermodal shipping containers and truck bodies. One of the team’s key partners is Wabash National which produced 29,000 truck bodies in 2019. The current flooring system is comprised of solid oak “butcher board” laminated panels. Additionally, Mediterranean Shipping Containers (MSC) transports over 1.8 million twenty-foot equivalent units per year in intermodal shipping containers. The current container floor is laminated hardwood that is harvested from the rain forests of Central and South America. The project is to develop a flooring system made from recycled composites that can be qualified for both companies (Wabash and MSC). Initial prototypes validated that the recycled composites panels are lighter and thinner with much higher mechanical strength. These results suggest a typical truck trailer would have 20% lower tare weight. The GreenTex technology is not limited to flooring and is widely applicable to other transportation elements such as walls, roof elements, cab areas and related structural components. Under this project different composite waste streams were evaluated and then combined to develop a formulation that would meet the targeted performance criteria for a flooring system. Wet compression molding was used to fabricate plaques at different tonnage using various composite waste streams. The plaques were tested for flexure and impact.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Performance Prediction of High‐Entropy Perovskites La 0.8 Sr 0.2 Mn x Co y Fe z O 3 with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition–structure–performance relationships of high-entropy La 0.8 Sr 0.2 Mn x Co y Fe z O 3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion—derived from spectral analysis of Raman-active modes—and enhanced performance.

high entropy oxides↗

NASA NTRS1993

Application of a two-stream radiative transfer model for leaf lignin and cellulose concentrations from spectral reflectance measurements, part 1

Lignin and nitrogen contents of leaves constitute the primary rate-limiting parameters for the decomposition of forest litter, and are determinants of nutrient- and carbon-cyclic rates in forest ecosystems (Melillo et al., 1982). Wessman et al. (1988a) developed empirical multivariate relationships between forest canopy lignin amount and the (first-difference) AIS spectral response in three bands spread over the wavelength interval 1256-1555 nm. Wessman et al. (1988b) and McLellan et al. (1991) developed similar regression relationships from laboratory reflectance measurements on dried samples prepared in a standard fashion. They used four to six infrared bands for analysis of nitrogen, lignin and cellulose content of foliage in forest and prairie species. In the present article (Parts 1 and 2) the feasibility of compositional determinations is explored using positions of composite absorption bands that originate from mixtures of lignin, cellulose, and possibly other chemical constituents in the spectral reflectance of green leaves. To carry out this program, we employ full-spectral-resolution single-leaf diffuse reflectance measurements made with a laboratory spectrometer and integrating sphere. The leaf and other chemical reflectance data compiled by Elvidge (1990) have also been utilized extensively.

Conel, James E.↗

DOE OSTI2023

Functional variability in specific root respiration translates to autotrophic differences in soil respiration in a temperate deciduous forest

CO 2 release from forest soils (R s ) is a prominent flux in the global carbon cycle. Rs is derived from roots (autotrophic respiration, R a ) and microbial (heterotrophic) respiration and is highly dynamic, as it depends on edaphic and environmental conditions as well as root functional traits and microbial community composition. It is unclear how root functional traits affect root and microbial respiration rates; however, their consideration may help parse out the relative contributions of root and microbial respiration to R s . At a temperate forest site, root systems of 3–4 functional root orders and their surrounding surface soil were carefully excavated and placed into custom trays designed to repeatedly measure R s in situ on eight temperate tree species that varied in their root functional strategies and mycorrhizal affinity. R s was measured bi-weekly to monthly for nearly one year using a custom chamber attached to a gas exchange system. R s varied over time, ranging from 0.3 to 12 µmol m -2 s -1 . Comparable root systems of the same species were excised from the soil and specific root respiration rates (R r ) were measured. Rr ranged from 2.5 to 9.0 nmol g -1 s -1 and was negatively correlated with root tissue density and positively related to root tissue nitrogen concentration. Using R r to estimate R a , we estimate that R a accounts for <10%, on average 2–3%, of R s for individual root systems (averaging 1.2 g dry biomass) housed in surrounding soil (average 1.3 kg dry mass) in situ; thus, Ra was roughly 20 times greater than Rh per unit mass. The contribution of R a peaked in the fall and coincided with leaf senescence of the forest canopy. A soil-sterilizing experimental treatment designed to help isolate R a in situ reduced bacterial biomass and shifted fungal community composition, but there was no reduction in Rs of the in-situ root-soil tray systems. The relative R a to R s ratio increased with root functional strategies characterized by greater specific root length and tip abundance, but also to greater root tissue density. The ratio of R a to R s also increased with warmer soil temperatures and decreased slightly with increasing soil moisture. We discuss how incorporating root functional traits as modulators of the autotrophic contribution to R s could be considered when modeling total soil CO 2 efflux from forests.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Diurnal rainfall response to the physiological and radiative effects of CO 2 in tropical forests in the Energy exascale earth system model v1

Understanding how the connection between rainfall and tropical forests will respond to increasing CO$_2$ concentrations is a key element in understanding how the tropical water cycle will respond to increasing CO$_2$. The plant physiological and radiative impacts of CO$_2$ on rainfall patterns over tropical forest regions are examined in the Energy Exascale Earth System Model version 1.1 (E3SMv1.1-BGC) biogeochemistry experiments. Composite analysis reveals a dampening of the diurnal cycle of rainfall over the Amazon, Congo, and Maritime Continent in response to rising CO$_2$ levels, regardless of the sign of total rainfall change. A full factorial model experiment confirms that the CO$_2$ radiative and CO$_2$ plant physiological effects can individually or jointly reduce the magnitude of the rainfall diurnal cycle, though the physical pathway giving rise to the reduction differs between the two effects. For the physiological response, stomatal closure reduces evapotranspiration, which dries the boundary layer and raises the lifting condensation level. These effects combine to reduce deep convective rainfall during its peak occurrence in the late daytime to early nighttime period. For the radiative response, a relative reduction in daytime CAPE (consistent with a reduction in the diurnal temperature range) leads to less frequent triggering of deep convection and a reduction of rainfall diurnal amplitude. These diurnal rainfall changes are structurally similar across seasons, and show little sensitivity to representation of nutrient coupling for the land biogeochemistry. In agreement with previous findings, the physiological response has only minor impact on extreme rainfall relative to the radiative response.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activity

Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales. Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed (n = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry (n = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development.

16S rRNA↗

DOE OSTI2025

SPRUCE Aboveground Vegetation Coverage in Root Ingrowth Core Plots, Marcell Experimental Forest, Minnesota, August 2022

This dataset contains vegetation survey measurements from root ingrowth core plots (Määttä et al. 2025) inside SPRUCE Experiment plots at the Marcell Experimental Forest in northern Minnesota. Vegetation surveys were conducted in 0.25 meter2 plots containing root ingrowth cores on August 8th and 9th, 2022 (2022-08-08 to 2022-08-09). The warming and elevated carbon dioxide (CO2) treatments in the dataset include the full treatment gradient: +0 degrees Celsius (C) (+0 and +500 parts per million (ppm) elevated CO2), +2.25 degrees C (+0 and +500 ppm), +4.5 degrees C (+0 and +500 ppm elevated CO2), +6.75 degrees C (+0 and +500 ppm) and +9 degrees C (+0 and +500 ppm elevated CO2) for both hummocks and hollows. This dataset includes measurements of the height and absolute coverage (%) for each vascular plant and moss species, as well as organic litter and dead overstory vascular plants, and the distance from the grid center to the nearest tree and the species of the nearest tree. These data were used as species-specific aboveground plant metadata for assessing the warming and elevated CO2 response of fine roots across different peatland microtopographical features (hummocks and hollows) and plant functional types (shrub, spruce and larch). This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗

NASA NTRS1995

Causal correlation of foliar biochemical concentrations with AVIRIS spectra using forced entry linear regression

A major goal of airborne imaging spectrometry is to estimate the biochemical composition of vegetation canopies from reflectance spectra. Remotely-sensed estimates of foliar biochemical concentrations of forests would provide valuable indicators of ecosystem function at regional and eventually global scales. Empirical research has shown a relationship exists between the amount of radiation reflected from absorption features and the concentration of given biochemicals in leaves and canopies (Matson et al., 1994, Johnson et al., 1994). A technique commonly used to determine which wavelengths have the strongest correlation with the biochemical of interest is unguided (stepwise) multiple regression. Wavelengths are entered into a multivariate regression equation, in their order of importance, each contributing to the reduction of the variance in the measured biochemical concentration. A significant problem with the use of stepwise regression for determining the correlation between biochemical concentration and spectra is that of 'overfitting' as there are significantly more wavebands than biochemical measurements. This could result in the selection of wavebands which may be more accurately attributable to noise or canopy effects. In addition, there is a real problem of collinearity in that the individual biochemical concentrations may covary. A strong correlation between the reflectance at a given wavelength and the concentration of a biochemical of interest, therefore, may be due to the effect of another biochemical which is closely related. Furthermore, it is not always possible to account for potentially suitable waveband omissions in the stepwise selection procedure. This concern about the suitability of stepwise regression has been identified and acknowledged in a number of recent studies (Wessman et al., 1988, Curran, 1989, Curran et al., 1992, Peterson and Hubbard, 1992, Martine and Aber, 1994, Kupiec, 1994). These studies have pointed to the lack of a physical link between wavelengths chosen by stepwise regression and the biochemical of interest, and this in turn has cast doubts on the use of imaging spectrometry for the estimation of foliar biochemical concentrations at sites distant from the training sites. To investigate this problem, an analysis was conducted on the variation in canopy biochemical concentrations and reflectance spectra using forced entry linear regression.

Dawson, Terence P.↗

NASA NTRS2021

Jobos Bay Water Resources: Using Earth Observations to Analyze Shoreline Changes and Understand the Effects of Sea Level Rise in Southern Puerto Rico

Jobos Bay is located on the southern coast of Puerto Rico, which is known for intense hurricane seasons and increased seasonal storm surge. Scientists at Jobos Bay National Estuarine Research Reserve (JBNERR) are concerned that sea level rise will exacerbate coastal damage from these weather events. Using NASA Earth observations, our team analyzed coastal change, land use land cover change (LULC), mangrove forest extent, and water quality of Jobos Bay. Using Google Earth Engine, we evaluated coastal change and mangrove forest habitat within the study region by classifying NASA Earth observation imagery. We created historic LULC composite images to observe how land use changes over time and improve understanding of urbanization in the watershed. Leveraging previous water quality studies, our team compared water quality datasets generated by the Optical Reef and Coastal Area Assessment (ORCAA) tool to in situ sensors provided by JBNERR partners to understand the overall quality of water in the study area with respect to turbidity, chlorophyll-a, sea surface temperature, and colored dissolved organic matter (CDOM) concentrations. We discovered that 17% of the reserve has shifted from land to water since 1997 and lost 4.85 square kilometers of mangrove habitat over the past decade. Results from this study will inform the scientists of JBNERR and community members of the regional impacts of sea level rise. Being the first comprehensive study done in the estuary in nearly a decade, this serves as a baseline for future conservation efforts and research in the estuary.

Olivia Spencer↗

DOE OSTI2025

Soil biogeochemical properties and metrics of tree-mycorrhizal dominance for a 25-Ha forest in South Central Indiana, USA.

This data package contains a dataset used in the papers “Seeing the forest for all the trees: Mycorrhizal-associated nutrient economies are modulated by stem density and the synchrony between overstory and understory communities” and “Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry”. Four csv files are included along with a dataset. The dataset features chemical soil properties for a single sampling campaign within the 25 Ha Lilly-Dickey Woods Smithsonian Forest Global Earth Observatory (ForestGEO) plot in South Central Indiana, USA (ldw_dat_raw.csv). Also included are separate files focused on pH (pH_data.csv), carbon and nitrogen (CN_data.csv), and nitrification rates (Nitrification_data.csv). These variables are commonly associated with the tree-mycorrhizal dominance of forest stands. In these data subsets, each soil variable was matched to a 10 meter radius neighborhood wherein metrics of tree-mycorrhizal dominance (basal area, stem count, importance value, etc.) were calculated. Models between these soil variables and dominance metrics were used to investigate how different assessments of mycorrhizal associated nutrient economies (MANE) capture these relationships. This research was performed as a part of the Smithsonian ForestGEO project. This data package can be used to explore spatial variability in soil chemistry within a mature hardwood forest, or it can be combined with the included tree data, other fine-scale spatial information, or other tree inventory data for the site to evaluate how soil chemistry varies with tree community composition or edaphic or topographic properties.

Craig, Matthew [ORNL] (ORCID:0000000288907920)↗

DOE OSTI2024

Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska

The Arctic is warming at over twice the rate of the rest of the Earth, resulting in significant changes in vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding the intricate interplay among climate, permafrost, and vegetation that generates high phenology variability across extensive tundra landscapes. This oversight has led to significant discrepancies in phenological patterns observed across warming experiments, long-term ecological observations, and satellite and modeling studies, undermining our ability to understand and forecast plant responses to climate change in the Arctic. To address this problem, we assessed plant phenology across three low-Arctic tundra landscapes on the Seward Peninsula, Alaska, using a combination of in-situ phenocam observations and high-resolution PlanetScope CubeSat data. We examined the patterns and drivers of phenological diversity across the landscape by (1) quantifying phenological diversity among dominant plant function types (PFTs) and (2) modeling the interrelations between plant phenology and fine-scale landscape features, such as topography, snowmelt, and vegetation. Our findings reveal that both spring and fall phenology varied significantly across Arctic PFTs, accounting for about 25%–44% and 34%–59% of the landscape-scale variation in the start of spring [SOS] and start of fall [SOF], respectively. Deciduous tall shrubs (e.g. alder and willow) had a later SOS (~7 d behind the mean of other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared to other PFTs. We modeled the landscape-scale variation in SOS and SOF using Random Forest, which showed that plant phenology can be accurately captured by a suite of variables related to vegetation composition, topographic characteristics, and snowmelt timing (variance explained: 53%–68% for SOS and 59%–82% for SOF). Notably, snowmelt timing was a crucial determinant of SOS, a factor often neglected in most spring phenology models. Our study highlights the impact of fine-scale vegetation composition, snow seasonality, and landscape features on tundra phenological heterogeneity. Improved understanding of such considerable intra-site phenological variability and associated proximate controls across extensive Arctic landscapes offers critical insights for representation of tundra phenology in process models and associated impact assessments with climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Species interactions amplify functional group responses to elevated CO 2 and N enrichment in a 24‐year grassland experiment

Abstract Plant functional groups (FGs) differ in their response to global changes, although species within those groups also vary in such responses. Both species and FG responses to global change are likely influenced by species interactions such as inter‐specific competition and facilitation, which are prevalent in species mixtures but not monocultures. As most studies focus on responses of plants growing in either monocultures or mixtures, but rarely both, it remains unclear how interspecific interactions in diverse ecological communities, especially among species in different FGs, modify FG responses to global changes. To address these issues, we leveraged data from a 16‐species, 24‐year perennial grassland experiment to examine plant FG biomass responses to atmospheric CO 2 , and N inputs at different planted diversity. FGs differed in their responses to N and CO 2 treatments in monocultures. Such differences were amplified in mixtures, where N enrichment strongly increased C3 grass success at ambient CO 2 and C4 grass success at elevated CO 2 . Legumes declined with N enrichment in mixtures at both CO 2 levels and increased with elevated CO 2 in the initial years of the experiment. Our results suggest that previous studies that considered responses to global changes in monocultures may underestimate biomass changes in diverse communities where interspecific interactions can amplify responses. Such effects of interspecific interactions on responses of FGs to global change may impact community composition over time and consequently influence ecosystem functions.

Mohanbabu, Neha↗

DOE OSTI2024

Chemical composition based machine learning model to predict defect formation in additive manufacturing

With a goal of exploiting additive manufacturing to improve the manufacturing of existing reactor materials, we developed a chemical composition-based machine learning model to predict the printability of any given alloy in laser powder bed fusion (L-PBF) using experimental data from peer-reviewed literature. We defined printability as the ability to avoid defects like cracking, balling, porosity, and lack of fusion, that are caused by thermal stresses (during solidification or liquation), molten pool disintegration into disconnected small beads or lack of heat input respectively. Our models predict the tendency of balling defect formation and porosity percentage for a given composition, under a given set of processing conditions. To predict the likelihood of balling defect, three models: a random forest classifier, a gradient boost regressor and a neural network were trained on a dataset containing both traditional alloys and high entropy alloys. The neural network model showed the highest accuracy of 92.3 % in predicting the balling defect formation. A random forest regressor, gradient boost regressor and neural network were trained and tested on a dataset of various alloys to predict porosity. The random forest regressor showed the best predictions with an R 2 score of 0.97. The models also revealed the relative importance of the input descriptors on defect-formation tendency. Of particular significance was the identification of carbon as an important element in determining the occurrence of balling and percent porosity in alloys like steel, as well as being moderately important to the percentage porosity in other alloys as well as steel. Manganese was also identified as a key descriptor for the percentage of porosity in steel and other alloys. Manganese’s low thermal conductivity and consistent presence in the dataset is the likely cause for its contribution. Carbon’s role is attributable to its relatively high specific heat and high melting temperature. In conclusion, our model serves as a swift, chemistry-based tool to design experiments and find modified compositions better suited for additive manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Small Mammal Prey Base for American Marten (Martes americana) within the Manistee National Forest of Michigan

American martens (Martes americana) are typically found in late-successional forests with closed canopy cover and high structural diversity. Reintroduced populations of martens in the Northern Lower Peninsula of Michigan inhabit areas that are devoid of many of these features, which may impact their prey base. The goal of our study was to evaluate the small mammal prey base available to martens in the Northern Lower Peninsula. To assess prey availability, diversity, and composition, as well as effects of trap type and habitat features on trapping success, we sampled 24 study plots within the Manistee National Forest for small mammals in 2013 (n=24) and 2014 (n=20). Study plots were situated in four habitat types: conifer, deciduous, mixed conifer-deciduous, and mixed oak. Total capture rates were significantly and positively associated with relative deciduous tree cover. Furthermore, this result highlights how managing tracts of land for small mammal prey base may overlap with goals set forth by researchers for marten habitat needs (e.g., resting site preferences). We found large Sherman traps had significantly higher total capture rates than other trap types (medium Sherman and pitfall traps), and we recommend that researchers use a variety of trap types to maximize detectability of small mammal species diversity and richness.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1990

Satellite measurements of large-scale air pollution - Measurements of forest fire smoke

The transport, optical properties, total mass, and removal of smoke produced by forest fires in western Canada during late July and early August 1982 are studied using NOAA 7 AVHRR data. Color composite imagery is produced to track the movement of the smoke over Canada and the U.S. as the smoke traveled thousands of km from the source region. Smoke optical thickness, particle size, and single scattering albedo are computed using radiances measured by AVHRR bands 1 and 2. Results show that smoke optical thickness ranged from less that 0.1 to greater than 3.7 and the geometric mean mass radii ranged from 300 to 900 nm. The smoke single scattering albedo ranged from 0.9 to nearly 1.0. The total smoke mass over the eastern U.S. ranged from 0.1 to 0.5 Tg, which is close to the 0.5 Tg estimated from the forest fuel content. The smoke lifetime is estimated to be between 15 and 20 days.

Ferrare, Richard A.↗

DOE OSTI2023

Climate, soil mineralogy and mycorrhizal fungi influence soil organic matter fractions in eastern US temperate forests

Identifying the primary controls of particulate (POM) and mineral-associated organic matter (MAOM) content in soils is critical for determining future stocks of soil carbon (C) and nitrogen (N) across the globe. However, drivers of these soil organic matter fractions are likely to vary among ecosystems in response to climate, soil type and the composition of local biological communities. We tested how soil factors, climate and plant–fungal associations influenced the distribution and concentrations of C and N in MAOM and POM in seven temperate forests in the National Ecological Observatory Network (NEON) across the eastern United States. Samples of upper mineral horizon soil within each forest were collected in plots representing a gradient of dominant tree–mycorrhizal association, allowing us to test how plant and microbial communities influenced POM and MAOM across sites differing in climate and soil conditions. We found that concentrations of C and N in soil organic matter were primarily driven by soil mineralogy, but the relative abundance of MAOM versus POM C was strongly linked to plot-level mycorrhizal dominance. Furthermore, the effect of dominant tree mycorrhizal type on the distribution of N among POM and MAOM fractions was sensitive to local climate: in cooler sites, an increasing proportion of ectomycorrhizal-associated trees was associated with lower proportions of N in MAOM, but in warmer sites, we found the reverse. As an indicator of soil carbon age, we measured radiocarbon in the MAOM fraction but found that within and across sites, Δ 14 C was unrelated to mycorrhizal dominance, climate, or soil factors, suggesting that additional site-specific factors may be primary determinants of long-term SOM persistence. Our results indicate that while soil mineralogy primarily controls SOM C and N concentrations, the distribution of SOM among density fractions depends on the composition of vegetation and microbial communities, with these effects varying across sites with distinct climates. Here we also suggest that within biomes, the age of mineral-associated soil carbon is not clearly linked to the factors that control concentrations of MAOM C and N.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery

In mountainous environments, topography strongly affects the reflectance due to illumination effects and cast shadows, which introduce errors in land cover classifications. However, topographic correction is not routinely implemented in standard data pre-processing chains (e.g., Landsat Analysis Ready Data), and there is a lack of consensus whether topographic correction is necessary, and if so, how to conduct it. Furthermore, methods that correct simultaneously for atmospheric and topographic effects are becoming available, but they have not been compared directly. Our objects were to investigate (1) the effectiveness of two topographic correction approaches that integrate atmospheric and topographic correction, (2) improvements in classification accuracy when analyzing topographically corrected single-date imagery (14 July 2016 and 2 October 2016), versus a full Landsat time series from 2014 to 2016, and 3) improvements in classification accuracy when including additional terrain information (i.e., topographic slope, elevation, and aspect). We developed a physical based model and compared it with an enhanced C-correction, both of which integrate atmospheric and topographic correction. We compared classification accuracies with and without topographic correction using combinations of single-date imagery, image composites and spectral-temporal metrics generated from the full Landsat time series, and additional terrain information in the Caucasus Mountains. We found that both the enhanced C-correction and the physical model performed very well and largely eliminated the correlation (Pearson’s correlation coefficient r ranges from 0.06 to 0.24) between surface reflectance and illumination condition, but the physical model performed best (r ranges from 0.05 to 0.11). Both image composites, and spectral-temporal metrics generated from corrected imagery, resulted in significantly (p ≤ 0.05) higher classification accuracies and better forest classifications, especially for the mixed forests. Adding terrain information reduced classification error significantly, but not as much as topographic correction. In summary, topographic correction remains necessary, even when analyzing a full Landsat time series and including a digital elevation model in the classification. We recommend that topographic correction should be applied when analyzing Landsat satellite imagery in mountainous region for forest cover classification.

Atmospheric correction↗